From d858a0d16eaedbd7ab0a196e7cf27aeb3a829f06 Mon Sep 17 00:00:00 2001 From: Eslam Khaled Korany Date: Sun, 19 Mar 2023 23:38:30 +0000 Subject: [PATCH 01/85] Fix serialize_example() example description --- site/en/tutorials/load_data/tfrecord.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/site/en/tutorials/load_data/tfrecord.ipynb b/site/en/tutorials/load_data/tfrecord.ipynb index 8c6ec7f7bac..c4ecaf5ecfc 100644 --- a/site/en/tutorials/load_data/tfrecord.ipynb +++ b/site/en/tutorials/load_data/tfrecord.ipynb @@ -350,12 +350,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "XftzX9CN_uGT" }, "source": [ - "For example, suppose you have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. You can create and print the `tf.train.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.train.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message:" + "For example, suppose you have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. You can create and print the `tf.train.Example` message for this observation using `serialize_example()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.train.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message:" ] }, { @@ -368,9 +369,8 @@ "source": [ "# This is an example observation from the dataset.\n", "\n", - "example_observation = []\n", - "\n", - "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", + "example_observation = [False, 4, b'goat', 0.9876]\n", + "serialized_example = serialize_example(*example_observation)\n", "serialized_example" ] }, From 4f2ff12ea1a9467392c75181fb738dd99bbab57f Mon Sep 17 00:00:00 2001 From: Eslam Khaled Korany Date: Mon, 20 Mar 2023 00:05:21 +0000 Subject: [PATCH 02/85] Update buttons urls to match my github, colab urls --- site/en/tutorials/load_data/tfrecord.ipynb | 195 ++++++++++++++++----- 1 file changed, 155 insertions(+), 40 deletions(-) diff --git a/site/en/tutorials/load_data/tfrecord.ipynb b/site/en/tutorials/load_data/tfrecord.ipynb index c4ecaf5ecfc..0ed05770ea4 100644 --- a/site/en/tutorials/load_data/tfrecord.ipynb +++ b/site/en/tutorials/load_data/tfrecord.ipynb @@ -14,7 +14,10 @@ "execution_count": null, "metadata": { "cellView": "form", - "id": "uBDvXpYzYnGj" + "id": "uBDvXpYzYnGj", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -32,6 +35,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "HQzaEQuJiW_d" @@ -44,10 +48,10 @@ " View on TensorFlow.org\n", " \n", " \n", - " Run in Google Colab\n", + " Run in Google Colab\n", " \n", " \n", - " View source on GitHub\n", + " View source on GitHub\n", " \n", " \n", " Download notebook\n", @@ -96,7 +100,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Ja7sezsmnXph" + "id": "Ja7sezsmnXph", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -167,7 +174,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "mbsPOUpVtYxA" + "id": "mbsPOUpVtYxA", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -211,7 +221,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "hZzyLGr0u73y" + "id": "hZzyLGr0u73y", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -237,7 +250,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "5afZkORT5pjm" + "id": "5afZkORT5pjm", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -292,7 +308,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "CnrguFAy3YQv" + "id": "CnrguFAy3YQv", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -326,7 +345,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "RTCS49Ij_kUw" + "id": "RTCS49Ij_kUw", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -363,7 +385,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "N8BtSx2RjYcb" + "id": "N8BtSx2RjYcb", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -387,7 +412,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "dGim-mEm6vit" + "id": "dGim-mEm6vit", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -465,7 +493,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "mXeaukvwu5_-" + "id": "mXeaukvwu5_-", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -485,7 +516,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "H5sWyu1kxnvg" + "id": "H5sWyu1kxnvg", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -497,7 +531,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "m1C-t71Nywze" + "id": "m1C-t71Nywze", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -526,7 +563,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "apB5KYrJzjPI" + "id": "apB5KYrJzjPI", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -542,7 +582,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "lHFjW4u4Npz9" + "id": "lHFjW4u4Npz9", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -562,7 +605,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "VDeqYVbW3ww9" + "id": "VDeqYVbW3ww9", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -574,7 +620,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "DlDfuh46bRf6" + "id": "DlDfuh46bRf6", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -587,7 +636,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "iv9oXKrcbhvX" + "id": "iv9oXKrcbhvX", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -599,7 +651,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Dqz8C4D5cIj9" + "id": "Dqz8C4D5cIj9", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -619,7 +674,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "vP1VgTO44UIE" + "id": "vP1VgTO44UIE", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -654,7 +712,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "6OjX6UZl-bHC" + "id": "6OjX6UZl-bHC", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -680,7 +741,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "hxVXpLz_AJlm" + "id": "hxVXpLz_AJlm", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -701,7 +765,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "zQjbIR1nleiy" + "id": "zQjbIR1nleiy", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -731,7 +798,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "6Ob7D-zmBm1w" + "id": "6Ob7D-zmBm1w", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -752,7 +822,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "x2LT2JCqhoD_" + "id": "x2LT2JCqhoD_", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -809,7 +882,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "MKPHzoGv7q44" + "id": "MKPHzoGv7q44", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -824,7 +900,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "EjdFHHJMpUUo" + "id": "EjdFHHJMpUUo", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -846,7 +925,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "U3tnd3LerOtV" + "id": "U3tnd3LerOtV", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -859,7 +941,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "nsEAACHcnm3f" + "id": "nsEAACHcnm3f", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -891,7 +976,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Ziv9tiNE1l6J" + "id": "Ziv9tiNE1l6J", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -942,7 +1030,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "3a0fmwg8lHdF" + "id": "3a0fmwg8lHdF", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -959,7 +1050,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7aJJh7vENeE4" + "id": "7aJJh7vENeE4", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -971,7 +1065,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "KkW0uuhcXZqA" + "id": "KkW0uuhcXZqA", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1001,7 +1098,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "kC4TS1ZEONHr" + "id": "kC4TS1ZEONHr", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1015,7 +1115,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "c5njMSYNEhNZ" + "id": "c5njMSYNEhNZ", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1056,7 +1159,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "qcw06lQCOCZU" + "id": "qcw06lQCOCZU", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1075,7 +1181,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yJrTe6tHPCfs" + "id": "yJrTe6tHPCfs", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1097,7 +1206,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "M6Cnfd3cTKHN" + "id": "M6Cnfd3cTKHN", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1133,7 +1245,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yZf8jOyEIjSF" + "id": "yZf8jOyEIjSF", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ From db9bb2f7de361c5be959ea22dc77a11163541c3a Mon Sep 17 00:00:00 2001 From: Eslam Khaled Korany Date: Tue, 28 Mar 2023 06:48:55 +0200 Subject: [PATCH 03/85] Remove Vscode metadata & fix urls --- site/en/tutorials/load_data/tfrecord.ipynb | 198 +++++---------------- 1 file changed, 42 insertions(+), 156 deletions(-) diff --git a/site/en/tutorials/load_data/tfrecord.ipynb b/site/en/tutorials/load_data/tfrecord.ipynb index 0ed05770ea4..cfb98244fb5 100644 --- a/site/en/tutorials/load_data/tfrecord.ipynb +++ b/site/en/tutorials/load_data/tfrecord.ipynb @@ -14,10 +14,7 @@ "execution_count": null, "metadata": { "cellView": "form", - "id": "uBDvXpYzYnGj", - "vscode": { - "languageId": "python" - } + "id": "uBDvXpYzYnGj" }, "outputs": [], "source": [ @@ -35,7 +32,7 @@ ] }, { - "attachments": {}, + "cell_type": "markdown", "metadata": { "id": "HQzaEQuJiW_d" @@ -48,10 +45,10 @@ " View on TensorFlow.org\n", " \n", " \n", - " Run in Google Colab\n", + " Run in Google Colab\n", " \n", " \n", - " View source on GitHub\n", + " View source on GitHub\n", " \n", " \n", " Download notebook\n", @@ -100,10 +97,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Ja7sezsmnXph", - "vscode": { - "languageId": "python" - } + "id": "Ja7sezsmnXph" }, "outputs": [], "source": [ @@ -174,10 +168,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "mbsPOUpVtYxA", - "vscode": { - "languageId": "python" - } + "id": "mbsPOUpVtYxA" }, "outputs": [], "source": [ @@ -221,10 +212,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "hZzyLGr0u73y", - "vscode": { - "languageId": "python" - } + "id": "hZzyLGr0u73y" }, "outputs": [], "source": [ @@ -250,10 +238,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "5afZkORT5pjm", - "vscode": { - "languageId": "python" - } + "id": "5afZkORT5pjm" }, "outputs": [], "source": [ @@ -308,10 +293,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "CnrguFAy3YQv", - "vscode": { - "languageId": "python" - } + "id": "CnrguFAy3YQv" }, "outputs": [], "source": [ @@ -345,10 +327,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "RTCS49Ij_kUw", - "vscode": { - "languageId": "python" - } + "id": "RTCS49Ij_kUw" }, "outputs": [], "source": [ @@ -372,7 +351,7 @@ ] }, { - "attachments": {}, + "cell_type": "markdown", "metadata": { "id": "XftzX9CN_uGT" @@ -385,10 +364,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "N8BtSx2RjYcb", - "vscode": { - "languageId": "python" - } + "id": "N8BtSx2RjYcb" }, "outputs": [], "source": [ @@ -412,10 +388,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "dGim-mEm6vit", - "vscode": { - "languageId": "python" - } + "id": "dGim-mEm6vit" }, "outputs": [], "source": [ @@ -493,10 +466,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "mXeaukvwu5_-", - "vscode": { - "languageId": "python" - } + "id": "mXeaukvwu5_-" }, "outputs": [], "source": [ @@ -516,10 +486,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "H5sWyu1kxnvg", - "vscode": { - "languageId": "python" - } + "id": "H5sWyu1kxnvg" }, "outputs": [], "source": [ @@ -531,10 +498,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "m1C-t71Nywze", - "vscode": { - "languageId": "python" - } + "id": "m1C-t71Nywze" }, "outputs": [], "source": [ @@ -563,10 +527,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "apB5KYrJzjPI", - "vscode": { - "languageId": "python" - } + "id": "apB5KYrJzjPI" }, "outputs": [], "source": [ @@ -582,10 +543,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "lHFjW4u4Npz9", - "vscode": { - "languageId": "python" - } + "id": "lHFjW4u4Npz9" }, "outputs": [], "source": [ @@ -605,10 +563,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "VDeqYVbW3ww9", - "vscode": { - "languageId": "python" - } + "id": "VDeqYVbW3ww9" }, "outputs": [], "source": [ @@ -620,10 +575,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "DlDfuh46bRf6", - "vscode": { - "languageId": "python" - } + "id": "DlDfuh46bRf6" }, "outputs": [], "source": [ @@ -636,10 +588,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "iv9oXKrcbhvX", - "vscode": { - "languageId": "python" - } + "id": "iv9oXKrcbhvX" }, "outputs": [], "source": [ @@ -651,10 +600,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Dqz8C4D5cIj9", - "vscode": { - "languageId": "python" - } + "id": "Dqz8C4D5cIj9" }, "outputs": [], "source": [ @@ -674,10 +620,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "vP1VgTO44UIE", - "vscode": { - "languageId": "python" - } + "id": "vP1VgTO44UIE" }, "outputs": [], "source": [ @@ -712,10 +655,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "6OjX6UZl-bHC", - "vscode": { - "languageId": "python" - } + "id": "6OjX6UZl-bHC" }, "outputs": [], "source": [ @@ -741,10 +681,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "hxVXpLz_AJlm", - "vscode": { - "languageId": "python" - } + "id": "hxVXpLz_AJlm" }, "outputs": [], "source": [ @@ -765,10 +702,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "zQjbIR1nleiy", - "vscode": { - "languageId": "python" - } + "id": "zQjbIR1nleiy" }, "outputs": [], "source": [ @@ -798,10 +732,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "6Ob7D-zmBm1w", - "vscode": { - "languageId": "python" - } + "id": "6Ob7D-zmBm1w" }, "outputs": [], "source": [ @@ -822,10 +753,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "x2LT2JCqhoD_", - "vscode": { - "languageId": "python" - } + "id": "x2LT2JCqhoD_" }, "outputs": [], "source": [ @@ -882,10 +810,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "MKPHzoGv7q44", - "vscode": { - "languageId": "python" - } + "id": "MKPHzoGv7q44" }, "outputs": [], "source": [ @@ -900,10 +825,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "EjdFHHJMpUUo", - "vscode": { - "languageId": "python" - } + "id": "EjdFHHJMpUUo" }, "outputs": [], "source": [ @@ -925,10 +847,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "U3tnd3LerOtV", - "vscode": { - "languageId": "python" - } + "id": "U3tnd3LerOtV" }, "outputs": [], "source": [ @@ -941,10 +860,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "nsEAACHcnm3f", - "vscode": { - "languageId": "python" - } + "id": "nsEAACHcnm3f" }, "outputs": [], "source": [ @@ -976,10 +892,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "Ziv9tiNE1l6J", - "vscode": { - "languageId": "python" - } + "id": "Ziv9tiNE1l6J" }, "outputs": [], "source": [ @@ -1030,10 +943,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "3a0fmwg8lHdF", - "vscode": { - "languageId": "python" - } + "id": "3a0fmwg8lHdF" }, "outputs": [], "source": [ @@ -1050,10 +960,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7aJJh7vENeE4", - "vscode": { - "languageId": "python" - } + "id": "7aJJh7vENeE4" }, "outputs": [], "source": [ @@ -1065,10 +972,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "KkW0uuhcXZqA", - "vscode": { - "languageId": "python" - } + "id": "KkW0uuhcXZqA" }, "outputs": [], "source": [ @@ -1098,10 +1002,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "kC4TS1ZEONHr", - "vscode": { - "languageId": "python" - } + "id": "kC4TS1ZEONHr" }, "outputs": [], "source": [ @@ -1115,10 +1016,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "c5njMSYNEhNZ", - "vscode": { - "languageId": "python" - } + "id": "c5njMSYNEhNZ" }, "outputs": [], "source": [ @@ -1159,10 +1057,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "qcw06lQCOCZU", - "vscode": { - "languageId": "python" - } + "id": "qcw06lQCOCZU" }, "outputs": [], "source": [ @@ -1181,10 +1076,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yJrTe6tHPCfs", - "vscode": { - "languageId": "python" - } + "id": "yJrTe6tHPCfs" }, "outputs": [], "source": [ @@ -1206,10 +1098,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "M6Cnfd3cTKHN", - "vscode": { - "languageId": "python" - } + "id": "M6Cnfd3cTKHN" }, "outputs": [], "source": [ @@ -1245,10 +1134,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yZf8jOyEIjSF", - "vscode": { - "languageId": "python" - } + "id": "yZf8jOyEIjSF" }, "outputs": [], "source": [ From 89030082a2ecb2d516fcb8f546502c76be4fe943 Mon Sep 17 00:00:00 2001 From: Eslam Khaled Korany Date: Tue, 28 Mar 2023 07:23:11 +0200 Subject: [PATCH 04/85] Format notebook --- site/en/tutorials/load_data/tfrecord.ipynb | 2 -- 1 file changed, 2 deletions(-) diff --git a/site/en/tutorials/load_data/tfrecord.ipynb b/site/en/tutorials/load_data/tfrecord.ipynb index cfb98244fb5..7d8b8fbbfca 100644 --- a/site/en/tutorials/load_data/tfrecord.ipynb +++ b/site/en/tutorials/load_data/tfrecord.ipynb @@ -32,7 +32,6 @@ ] }, { - "cell_type": "markdown", "metadata": { "id": "HQzaEQuJiW_d" @@ -351,7 +350,6 @@ ] }, { - "cell_type": "markdown", "metadata": { "id": "XftzX9CN_uGT" From 53f3748426001e4be9923a25f9f3394707f4e206 Mon Sep 17 00:00:00 2001 From: Susheel Thapa Date: Fri, 20 Oct 2023 06:35:15 +0545 Subject: [PATCH 05/85] Chore: Fix the typo in multiple files --- site/en/community/contribute/docs_style.md | 2 +- site/en/guide/migrate/evaluator.ipynb | 2 +- site/en/guide/sparse_tensor.ipynb | 7 ++++++- site/en/guide/tf_numpy_type_promotion.ipynb | 5 +++-- site/en/hub/tutorials/boundless.ipynb | 2 +- site/en/hub/tutorials/s3gan_generation_with_tf_hub.ipynb | 2 +- site/en/hub/tutorials/tf2_object_detection.ipynb | 2 +- site/en/hub/tutorials/wiki40b_lm.ipynb | 2 +- site/en/r1/guide/autograph.ipynb | 2 +- site/en/r1/guide/distribute_strategy.ipynb | 6 +++--- site/en/r1/tutorials/representation/unicode.ipynb | 4 ++-- 11 files changed, 21 insertions(+), 15 deletions(-) diff --git a/site/en/community/contribute/docs_style.md b/site/en/community/contribute/docs_style.md index eba78afa896..d4e42cb5235 100644 --- a/site/en/community/contribute/docs_style.md +++ b/site/en/community/contribute/docs_style.md @@ -63,7 +63,7 @@ repository like this: * \[Basics\]\(../../guide/basics.ipynb\) produces [Basics](../../guide/basics.ipynb). -This is the prefered approach because this way the links on +This is the preferred approach because this way the links on [tensorflow.org](https://www.tensorflow.org), [GitHub](https://github.com/tensorflow/docs){:.external} and [Colab](https://github.com/tensorflow/docs/tree/master/site/en/guide/bazics.ipynb){:.external} diff --git a/site/en/guide/migrate/evaluator.ipynb b/site/en/guide/migrate/evaluator.ipynb index fd8bd12d1e1..c8f848e4406 100644 --- a/site/en/guide/migrate/evaluator.ipynb +++ b/site/en/guide/migrate/evaluator.ipynb @@ -122,7 +122,7 @@ "\n", "In TensorFlow 1, you can configure a `tf.estimator` to evaluate the estimator using `tf.estimator.train_and_evaluate`.\n", "\n", - "In this example, start by defining the `tf.estimator.Estimator` and speciyfing training and evaluation specifications:" + "In this example, start by defining the `tf.estimator.Estimator` and specifying training and evaluation specifications:" ] }, { diff --git a/site/en/guide/sparse_tensor.ipynb b/site/en/guide/sparse_tensor.ipynb index cd38fdf55ab..407561ec6f5 100644 --- a/site/en/guide/sparse_tensor.ipynb +++ b/site/en/guide/sparse_tensor.ipynb @@ -31,6 +31,11 @@ "# limitations under the License." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": { @@ -620,7 +625,7 @@ "\n", "However, there are a few cases where it can be useful to distinguish zero values from missing values. In particular, this allows for one way to encode missing/unknown data in your training data. For example, consider a use case where you have a tensor of scores (that can have any floating point value from -Inf to +Inf), with some missing scores. You can encode this tensor using a sparse tensor where the explicit zeros are known zero scores but the implicit zero values actually represent missing data and not zero. \n", "\n", - "Note: This is generally not the intended usage of `tf.sparse.SparseTensor`s; and you might want to also consier other techniques for encoding this such as for example using a separate mask tensor that identifies the locations of known/unknown values. However, exercise caution while using this approach, since most sparse operations will treat explicit and implicit zero values identically." + "Note: This is generally not the intended usage of `tf.sparse.SparseTensor`s; and you might want to also consider other techniques for encoding this such as for example using a separate mask tensor that identifies the locations of known/unknown values. However, exercise caution while using this approach, since most sparse operations will treat explicit and implicit zero values identically." ] }, { diff --git a/site/en/guide/tf_numpy_type_promotion.ipynb b/site/en/guide/tf_numpy_type_promotion.ipynb index a9e176c5db6..51bea78914f 100644 --- a/site/en/guide/tf_numpy_type_promotion.ipynb +++ b/site/en/guide/tf_numpy_type_promotion.ipynb @@ -178,7 +178,8 @@ "* `f32*` means Python `float` or weakly-typed `f32`\n", "* `c128*` means Python `complex` or weakly-typed `c128`\n", "\n", - "The asterik (*) denotes that the corresponding type is “weak” - such a dtype is temporarily inferred by the system, and could defer to other dtypes. This concept is explained more in detail [here](#weak_tensor)." + "The asterisk\n", + " (*) denotes that the corresponding type is “weak” - such a dtype is temporarily inferred by the system, and could defer to other dtypes. This concept is explained more in detail [here](#weak_tensor)." ] }, { @@ -449,7 +450,7 @@ "source": [ "### WeakTensor Construction\n", "\n", - "WeakTensors are created if you create a tensor without specifing a dtype the result is a WeakTensor. You can check whether a Tensor is \"weak\" or not by checking the weak attribute at the end of the Tensor's string representation." + "WeakTensors are created if you create a tensor without specifying a dtype the result is a WeakTensor. You can check whether a Tensor is \"weak\" or not by checking the weak attribute at the end of the Tensor's string representation." ] }, { diff --git a/site/en/hub/tutorials/boundless.ipynb b/site/en/hub/tutorials/boundless.ipynb index 570e9413362..4697a810bb8 100644 --- a/site/en/hub/tutorials/boundless.ipynb +++ b/site/en/hub/tutorials/boundless.ipynb @@ -271,7 +271,7 @@ "* The input image with a mask applied\n", "* The masked image with the extrapolation to complete it\n", "\n", - "we can use these two images to show a comparisson visualization." + "we can use these two images to show a comparison visualization." ] }, { diff --git a/site/en/hub/tutorials/s3gan_generation_with_tf_hub.ipynb b/site/en/hub/tutorials/s3gan_generation_with_tf_hub.ipynb index d8efd802ae0..bd73cffebdf 100644 --- a/site/en/hub/tutorials/s3gan_generation_with_tf_hub.ipynb +++ b/site/en/hub/tutorials/s3gan_generation_with_tf_hub.ipynb @@ -86,7 +86,7 @@ "2. Click **Runtime > Run all** to run each cell in order.\n", " * Afterwards, the interactive visualizations should update automatically when you modify the settings using the sliders and dropdown menus.\n", "\n", - "Note: if you run into any issues, youn can try restarting the runtime and rerunning all cells from scratch by clicking **Runtime > Restart and run all...**.\n", + "Note: if you run into any issues, you can try restarting the runtime and rerunning all cells from scratch by clicking **Runtime > Restart and run all...**.\n", "\n", "[1] Mario Lucic\\*, Michael Tschannen\\*, Marvin Ritter\\*, Xiaohua Zhai, Olivier\n", " Bachem, Sylvain Gelly, [High-Fidelity Image Generation With Fewer Labels](https://arxiv.org/abs/1903.02271), ICML 2019." diff --git a/site/en/hub/tutorials/tf2_object_detection.ipynb b/site/en/hub/tutorials/tf2_object_detection.ipynb index 38b162068d9..3793ad20485 100644 --- a/site/en/hub/tutorials/tf2_object_detection.ipynb +++ b/site/en/hub/tutorials/tf2_object_detection.ipynb @@ -291,7 +291,7 @@ "id": "yX3pb_pXDjYA" }, "source": [ - "Intalling the Object Detection API" + "Installing the Object Detection API" ] }, { diff --git a/site/en/hub/tutorials/wiki40b_lm.ipynb b/site/en/hub/tutorials/wiki40b_lm.ipynb index e696160faca..ad94ce0aab8 100644 --- a/site/en/hub/tutorials/wiki40b_lm.ipynb +++ b/site/en/hub/tutorials/wiki40b_lm.ipynb @@ -214,7 +214,7 @@ " # Generate the tokens from the language model\n", " generation_outputs = module(generation_input_dict, signature=\"prediction\", as_dict=True)\n", "\n", - " # Get the probablities and the inputs for the next steps\n", + " # Get the probabilities and the inputs for the next steps\n", " probs = generation_outputs[\"probs\"]\n", " new_mems = [generation_outputs[\"new_mem_{}\".format(i)] for i in range(n_layer)]\n", "\n", diff --git a/site/en/r1/guide/autograph.ipynb b/site/en/r1/guide/autograph.ipynb index f028b33ce9f..790dbb49df1 100644 --- a/site/en/r1/guide/autograph.ipynb +++ b/site/en/r1/guide/autograph.ipynb @@ -241,7 +241,7 @@ "id": "m-jWmsCmByyw" }, "source": [ - "AutoGraph supports common Python statements like `while`, `for`, `if`, `break`, and `return`, with support for nesting. Compare this function with the complicated graph verson displayed in the following code blocks:" + "AutoGraph supports common Python statements like `while`, `for`, `if`, `break`, and `return`, with support for nesting. Compare this function with the complicated graph version displayed in the following code blocks:" ] }, { diff --git a/site/en/r1/guide/distribute_strategy.ipynb b/site/en/r1/guide/distribute_strategy.ipynb index 79d6293eba7..cc51259b78e 100644 --- a/site/en/r1/guide/distribute_strategy.ipynb +++ b/site/en/r1/guide/distribute_strategy.ipynb @@ -118,7 +118,7 @@ "## Types of strategies\n", "`tf.distribute.Strategy` intends to cover a number of use cases along different axes. Some of these combinations are currently supported and others will be added in the future. Some of these axes are:\n", "\n", - "* Syncronous vs asynchronous training: These are two common ways of distributing training with data parallelism. In sync training, all workers train over different slices of input data in sync, and aggregating gradients at each step. In async training, all workers are independently training over the input data and updating variables asynchronously. Typically sync training is supported via all-reduce and async through parameter server architecture.\n", + "* Synchronous vs asynchronous training: These are two common ways of distributing training with data parallelism. In sync training, all workers train over different slices of input data in sync, and aggregating gradients at each step. In async training, all workers are independently training over the input data and updating variables asynchronously. Typically sync training is supported via all-reduce and async through parameter server architecture.\n", "* Hardware platform: Users may want to scale their training onto multiple GPUs on one machine, or multiple machines in a network (with 0 or more GPUs each), or on Cloud TPUs.\n", "\n", "In order to support these use cases, we have 4 strategies available. In the next section we will talk about which of these are supported in which scenarios in TF." @@ -371,7 +371,7 @@ "id": "hQv1lm9UPDFy" }, "source": [ - "So far we've talked about what are the different stategies available and how you can instantiate them. In the next few sections, we will talk about the different ways in which you can use them to distribute your training. We will show short code snippets in this guide and link off to full tutorials which you can run end to end." + "So far we've talked about what are the different strategies available and how you can instantiate them. In the next few sections, we will talk about the different ways in which you can use them to distribute your training. We will show short code snippets in this guide and link off to full tutorials which you can run end to end." ] }, { @@ -595,7 +595,7 @@ "### Examples and Tutorials\n", "Here are some examples that show end to end usage of various strategies with Estimator:\n", "\n", - "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/master/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kuberentes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n", + "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/master/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kubernetes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n", "2. Official [ResNet50](https://github.com/tensorflow/models/blob/master/official/r1/resnet/imagenet_main.py) model, which can be trained using either `MirroredStrategy` or `MultiWorkerMirroredStrategy`.\n", "3. [ResNet50](https://github.com/tensorflow/tpu/blob/master/models/experimental/distribution_strategy/resnet_estimator.py) example with TPUStrategy." ] diff --git a/site/en/r1/tutorials/representation/unicode.ipynb b/site/en/r1/tutorials/representation/unicode.ipynb index 98aaacff5b9..301a64d72fc 100644 --- a/site/en/r1/tutorials/representation/unicode.ipynb +++ b/site/en/r1/tutorials/representation/unicode.ipynb @@ -136,7 +136,7 @@ "id": "jsMPnjb6UDJ1" }, "source": [ - "Note: When using python to construct strings, the handling of unicode differs betweeen v2 and v3. In v2, unicode strings are indicated by the \"u\" prefix, as above. In v3, strings are unicode-encoded by default." + "Note: When using python to construct strings, the handling of unicode differs between v2 and v3. In v2, unicode strings are indicated by the \"u\" prefix, as above. In v3, strings are unicode-encoded by default." ] }, { @@ -587,7 +587,7 @@ "id": "CapnbShuGU8i" }, "source": [ - "First, we decode the sentences into character codepoints, and find the script identifeir for each character." + "First, we decode the sentences into character codepoints, and find the script identifier for each character." ] }, { From 6bb1b145f87171d20e70095a1a74ba8844fed368 Mon Sep 17 00:00:00 2001 From: Emmanuel Ferdman Date: Mon, 25 Sep 2023 20:21:26 +0300 Subject: [PATCH 06/85] fix: update g3doc links --- site/en/hub/common_saved_model_apis/images.md | 2 +- site/en/hub/common_saved_model_apis/text.md | 2 +- site/en/hub/installation.md | 4 ++-- site/en/hub/migration_tf2.md | 4 ++-- site/en/hub/tf2_saved_model.md | 4 ++-- site/en/hub/tutorials/text_cookbook.md | 14 +++++++------- 6 files changed, 15 insertions(+), 15 deletions(-) diff --git a/site/en/hub/common_saved_model_apis/images.md b/site/en/hub/common_saved_model_apis/images.md index 9754d52feed..5413f0adc07 100644 --- a/site/en/hub/common_saved_model_apis/images.md +++ b/site/en/hub/common_saved_model_apis/images.md @@ -70,7 +70,7 @@ consumer. The SavedModel itself should not perform dropout on the actual outputs Reusable SavedModels for image feature vectors are used in * the Colab tutorial - [Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb), + [Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb), diff --git a/site/en/hub/common_saved_model_apis/text.md b/site/en/hub/common_saved_model_apis/text.md index 1c45b8ea026..d64938a6677 100644 --- a/site/en/hub/common_saved_model_apis/text.md +++ b/site/en/hub/common_saved_model_apis/text.md @@ -94,7 +94,7 @@ distributed way. For example ### Examples * Colab tutorial - [Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb). + [Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb). diff --git a/site/en/hub/installation.md b/site/en/hub/installation.md index 33594cd3079..2381fbea614 100644 --- a/site/en/hub/installation.md +++ b/site/en/hub/installation.md @@ -50,8 +50,8 @@ $ pip install --upgrade tf-hub-nightly - [Library overview](lib_overview.md) - Tutorials: - - [Text classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb) - - [Image classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb) + - [Text classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb) + - [Image classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb) - Additional examples [on GitHub](https://github.com/tensorflow/hub/blob/master/examples/README.md) - Find models on [tfhub.dev](https://tfhub.dev). \ No newline at end of file diff --git a/site/en/hub/migration_tf2.md b/site/en/hub/migration_tf2.md index 24c1bf14c4d..0ed60225893 100644 --- a/site/en/hub/migration_tf2.md +++ b/site/en/hub/migration_tf2.md @@ -48,8 +48,8 @@ model = tf.keras.Sequential([ Many tutorials show these APIs in action. See in particular -* [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb) -* [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb) +* [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb) +* [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb) ### Using the new API in Estimator training diff --git a/site/en/hub/tf2_saved_model.md b/site/en/hub/tf2_saved_model.md index 7a7220d0a2e..81b90471cbc 100644 --- a/site/en/hub/tf2_saved_model.md +++ b/site/en/hub/tf2_saved_model.md @@ -51,7 +51,7 @@ model = tf.keras.Sequential([ ``` The [Text classification -colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb) +colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb) is a complete example how to train and evaluate such a classifier. The model weights in a `hub.KerasLayer` are set to non-trainable by default. @@ -244,7 +244,7 @@ to the Keras model, and runs the SavedModel's computation in training mode (think of dropout etc.). The [image classification -colab](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb) +colab](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb) contains an end-to-end example with optional fine-tuning. #### Re-exporting the fine-tuning result diff --git a/site/en/hub/tutorials/text_cookbook.md b/site/en/hub/tutorials/text_cookbook.md index 0ac9c6d6df3..dee9c1cf466 100644 --- a/site/en/hub/tutorials/text_cookbook.md +++ b/site/en/hub/tutorials/text_cookbook.md @@ -34,7 +34,7 @@ library for tokenization and preprocessing. ### Kaggle -[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) - +[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) - shows how to easily interact with a Kaggle competition from a Colab, including downloading the data and submitting the results. @@ -43,14 +43,14 @@ downloading the data and submitting the results. [Text classification](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | [Text classification with Keras](https://www.tensorflow.org/tutorials/keras/text_classification_with_hub) | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | [Predicting Movie Review Sentiment with BERT on TF Hub](https://github.com/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | -[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) +[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) ### Bangla task with FastText embeddings TensorFlow Hub does not currently offer a module in every language. The following tutorial shows how to leverage TensorFlow Hub for fast experimentation and modular ML development. -[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/bangla_article_classifier.ipynb) - +[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/bangla_article_classifier.ipynb) - demonstrates how to create a reusable TensorFlow Hub text embedding, and use it to train a Keras classifier for [BARD Bangla Article dataset](https://github.com/tanvirfahim15/BARD-Bangla-Article-Classifier). @@ -64,24 +64,24 @@ setup (no training examples). ### Basic -[Semantic similarity](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.ipynb) - +[Semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.ipynb) - shows how to use the sentence encoder module to compute sentence similarity. ### Cross-lingual -[Cross-lingual semantic similarity](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) - +[Cross-lingual semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) - shows how to use one of the cross-lingual sentence encoders to compute sentence similarity across languages. ### Semantic retrieval -[Semantic retrieval](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb) - +[Semantic retrieval](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb) - shows how to use Q/A sentence encoder to index a collection of documents for retrieval based on semantic similarity. ### SentencePiece input -[Semantic similarity with universal encoder lite](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) - +[Semantic similarity with universal encoder lite](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) - shows how to use sentence encoder modules that accept [SentencePiece](https://github.com/google/sentencepiece) ids on input instead of text. From dadf8c3b56e7b650a7396b251995caad5122d96f Mon Sep 17 00:00:00 2001 From: Hussnain <36568694+husszaf@users.noreply.github.com> Date: Sun, 22 Oct 2023 23:20:31 +0100 Subject: [PATCH 07/85] Typo fix in boundless.ipynb In the boundless.ipynb file in the section: "Loading an image" there is a typo in the description: "We will load a sample image but fell free to upload your own image to the colab and try with it." but it should be "We will load a sample image but feel free to upload your own image to the colab and try with it." the change is from "fell" to the corrected "feel" --- site/en/hub/tutorials/boundless.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/hub/tutorials/boundless.ipynb b/site/en/hub/tutorials/boundless.ipynb index 570e9413362..7aec68190de 100644 --- a/site/en/hub/tutorials/boundless.ipynb +++ b/site/en/hub/tutorials/boundless.ipynb @@ -185,7 +185,7 @@ "source": [ "## Loading an Image\n", "\n", - "We will load a sample image but fell free to upload your own image to the colab and try with it. Remember that the model have some limitations regarding human images." + "We will load a sample image but feel free to upload your own image to the colab and try with it. Remember that the model have some limitations regarding human images." ] }, { From 4d108a9ddb0ac6ffcc7cc2b1d558be6ae92abf3a Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Wed, 25 Oct 2023 15:09:21 +0000 Subject: [PATCH 08/85] Update site/en/hub/tutorials/boundless.ipynb --- site/en/hub/tutorials/boundless.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/hub/tutorials/boundless.ipynb b/site/en/hub/tutorials/boundless.ipynb index 7aec68190de..50ae2a5cfec 100644 --- a/site/en/hub/tutorials/boundless.ipynb +++ b/site/en/hub/tutorials/boundless.ipynb @@ -185,7 +185,7 @@ "source": [ "## Loading an Image\n", "\n", - "We will load a sample image but feel free to upload your own image to the colab and try with it. Remember that the model have some limitations regarding human images." + "You will load a sample image but feel free to upload your own image to the Colab notebook. Remember that the model may have some limitations regarding human images." ] }, { From 57e7adb5be0b912a6efa4b9f1e81694e616d0c87 Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Wed, 25 Oct 2023 15:12:40 +0000 Subject: [PATCH 09/85] Update site/en/guide/tf_numpy_type_promotion.ipynb --- site/en/guide/tf_numpy_type_promotion.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/site/en/guide/tf_numpy_type_promotion.ipynb b/site/en/guide/tf_numpy_type_promotion.ipynb index 51bea78914f..1b0f6d116c8 100644 --- a/site/en/guide/tf_numpy_type_promotion.ipynb +++ b/site/en/guide/tf_numpy_type_promotion.ipynb @@ -178,8 +178,7 @@ "* `f32*` means Python `float` or weakly-typed `f32`\n", "* `c128*` means Python `complex` or weakly-typed `c128`\n", "\n", - "The asterisk\n", - " (*) denotes that the corresponding type is “weak” - such a dtype is temporarily inferred by the system, and could defer to other dtypes. This concept is explained more in detail [here](#weak_tensor)." + "The asterisk (*) denotes that the corresponding type is “weak” - such a dtype is temporarily inferred by the system, and could defer to other dtypes. This concept is explained more in detail [here](#weak_tensor)." ] }, { From f3bc5209dd2d2e6981c3e5a2ba413f865170d95d Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Mon, 30 Oct 2023 15:07:54 +0000 Subject: [PATCH 10/85] Lint and update Boundless notebook --- site/en/hub/tutorials/boundless.ipynb | 32 +++++++++++++-------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/site/en/hub/tutorials/boundless.ipynb b/site/en/hub/tutorials/boundless.ipynb index 50ae2a5cfec..f53fc5bb004 100644 --- a/site/en/hub/tutorials/boundless.ipynb +++ b/site/en/hub/tutorials/boundless.ipynb @@ -82,9 +82,9 @@ "id": "hDKbpAEZf8Lt" }, "source": [ - "## Imports and Setup\n", + "## Imports and setup\n", "\n", - "Lets start with the base imports." + "Start with the base imports:" ] }, { @@ -110,9 +110,9 @@ "id": "pigUDIXtciQO" }, "source": [ - "## Reading image for input\n", + "## Create a function for reading an image\n", "\n", - "Lets create a util method to help load the image and format it for the model (257x257x3). This method will also crop the image to a square to avoid distortion and you can use with local images or from the internet." + "Create a utility function to help load an image and format it for the model (257x257x3). This method will also crop the image to a square to avoid distortion and you can use it with local images or from the internet." ] }, { @@ -147,9 +147,9 @@ "id": "lonrLxuKcsL0" }, "source": [ - "## Visualization method\n", + "## Create a visualization function\n", "\n", - "We will also create a visuzalization method to show the original image side by side with the masked version and the \"filled\" version, both generated by the model." + "Create a visualization function to show the original image side-by-side with the masked version and the \"filled\" version, both generated by the model." ] }, { @@ -183,9 +183,9 @@ "id": "8rwaCWmxdJGH" }, "source": [ - "## Loading an Image\n", + "## Load an image\n", "\n", - "You will load a sample image but feel free to upload your own image to the Colab notebook. Remember that the model may have some limitations regarding human images." + "Now you can load a sample image. Feel free to use your own image by uploading it to the Colab notebook. Remember that the model may have some limitations regarding human images." ] }, { @@ -210,10 +210,10 @@ "id": "4lIkmZL_dtyX" }, "source": [ - "## Selecting a model from TensorFlow Hub\n", + "## Select a model from TensorFlow Hub\n", "\n", - "On TensorFlow Hub we have 3 versions of the Boundless model: Half, Quarter and Three Quarters.\n", - "In the following cell you can chose any of them and try on your image. If you want to try with another one, just chose it and execute the following cells." + "On TensorFlow Hub there are three versions of the Boundless model: Half, Quarter and Three Quarters.\n", + "In the following cell you can choose any of the models and apply them on your image. If you want to pick another model, select it below and then run the following cells." ] }, { @@ -241,9 +241,9 @@ "id": "aSJFeNNSeOn8" }, "source": [ - "Now that we've chosen the model we want, lets load it from TensorFlow Hub.\n", + "After choosing your model, you can load it from TensorFlow Hub.\n", "\n", - "**Note**: You can point your browser to the model handle to read the model's documentation." + "**Note**: You can point to a model handle to read the model's documentation." ] }, { @@ -264,14 +264,14 @@ "id": "L4G7CPOaeuQb" }, "source": [ - "## Doing Inference\n", + "## Perform inference\n", "\n", - "The boundless model have two outputs:\n", + "The boundless model has two outputs:\n", "\n", "* The input image with a mask applied\n", "* The masked image with the extrapolation to complete it\n", "\n", - "we can use these two images to show a comparisson visualization." + "You can compare these two images with a visualization as follows:" ] }, { From 5f3d2732f20e0a5d3b003f2bff952574d552fccf Mon Sep 17 00:00:00 2001 From: Yury Mikhaylov <44315225+Mikhaylov-yv@users.noreply.github.com> Date: Sun, 12 Nov 2023 16:54:19 +0300 Subject: [PATCH 11/85] Update preprocessing_layers.ipynb bug fix ValueError: Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead. --- site/en/tutorials/structured_data/preprocessing_layers.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/tutorials/structured_data/preprocessing_layers.ipynb b/site/en/tutorials/structured_data/preprocessing_layers.ipynb index 928a56eb8bc..b9afe29a710 100644 --- a/site/en/tutorials/structured_data/preprocessing_layers.ipynb +++ b/site/en/tutorials/structured_data/preprocessing_layers.ipynb @@ -297,7 +297,7 @@ "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n", " df = dataframe.copy()\n", " labels = df.pop('target')\n", - " df = {key: value[:,tf.newaxis] for key, value in dataframe.items()}\n", + " df = {key: value.to_numpy()[:,tf.newaxis] for key, value in dataframe.items()}\n", " ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n", " if shuffle:\n", " ds = ds.shuffle(buffer_size=len(dataframe))\n", From af33301a434ea70e104865b9d2e93e230494c1cb Mon Sep 17 00:00:00 2001 From: David Huntsperger Date: Mon, 27 Nov 2023 15:27:59 -0800 Subject: [PATCH 12/85] Update the `tf.function` docs to align better with the actual implementation, and fix other issues in the docs as well. PiperOrigin-RevId: 585777041 --- site/en/guide/function.ipynb | 175 +++++++++++++++++++--------- site/en/guide/intro_to_graphs.ipynb | 53 +++++---- 2 files changed, 149 insertions(+), 79 deletions(-) diff --git a/site/en/guide/function.ipynb b/site/en/guide/function.ipynb index 9f3d93db057..f4677f21eb8 100644 --- a/site/en/guide/function.ipynb +++ b/site/en/guide/function.ipynb @@ -146,7 +146,7 @@ "source": [ "### Usage\n", "\n", - "A `Function` you define (for example by applying the `@tf.function` decorator) is just like a core TensorFlow operation: You can execute it eagerly; you can compute gradients; and so on." + "A `tf.function` that you define (for example by applying the `@tf.function` decorator) is just like a core TensorFlow operation: You can execute it eagerly; you can compute gradients; and so on." ] }, { @@ -157,7 +157,7 @@ }, "outputs": [], "source": [ - "@tf.function # The decorator converts `add` into a `Function`.\n", + "@tf.function # The decorator converts `add` into a `PolymorphicFunction`.\n", "def add(a, b):\n", " return a + b\n", "\n", @@ -184,7 +184,7 @@ "id": "ocWZvqrmHnmX" }, "source": [ - "You can use `Function`s inside other `Function`s." + "You can use `tf.function`s inside other `tf.function`s." ] }, { @@ -208,7 +208,7 @@ "id": "piBhz7gYsHqU" }, "source": [ - "`Function`s can be faster than eager code, especially for graphs with many small ops. But for graphs with a few expensive ops (like convolutions), you may not see much speedup.\n" + "`tf.function`s can be faster than eager code, especially for graphs with many small ops. But for graphs with a few expensive ops (like convolutions), you may not see much speedup.\n" ] }, { @@ -242,7 +242,7 @@ "source": [ "### Tracing\n", "\n", - "This section exposes how `Function` works under the hood, including implementation details *which may change in the future*. However, once you understand why and when tracing happens, it's much easier to use `tf.function` effectively!" + "This section exposes how `tf.function` works under the hood, including implementation details *which may change in the future*. However, once you understand why and when tracing happens, it's much easier to use `tf.function` effectively!" ] }, { @@ -253,17 +253,17 @@ "source": [ "#### What is \"tracing\"?\n", "\n", - "A `Function` runs your program in a [TensorFlow Graph](https://www.tensorflow.org/guide/intro_to_graphs#what_are_graphs). However, a `tf.Graph` cannot represent all the things that you'd write in an eager TensorFlow program. For instance, Python supports polymorphism, but `tf.Graph` requires its inputs to have a specified data type and dimension. Or you may perform side tasks like reading command-line arguments, raising an error, or working with a more complex Python object; none of these things can run in a `tf.Graph`.\n", + "A `tf.function` runs your program in a [TensorFlow Graph](https://www.tensorflow.org/guide/intro_to_graphs#what_are_graphs). However, a `tf.Graph` cannot represent all the things that you'd write in an eager TensorFlow program. For instance, Python supports polymorphism, but `tf.Graph` requires its inputs to have a specified data type and dimension. Or you may perform side tasks like reading command-line arguments, raising an error, or working with a more complex Python object; none of these things can run in a `tf.Graph`.\n", "\n", - "`Function` bridges this gap by separating your code in two stages:\n", + "`tf.function` bridges this gap by separating your code in two stages:\n", "\n", - " 1) In the first stage, referred to as \"**tracing**\", `Function` creates a new `tf.Graph`. Python code runs normally, but all TensorFlow operations (like adding two Tensors) are *deferred*: they are captured by the `tf.Graph` and not run.\n", + " 1) In the first stage, referred to as \"**tracing**\", `tf.function` creates a new `tf.Graph`. Python code runs normally, but all TensorFlow operations (like adding two Tensors) are *deferred*: they are captured by the `tf.Graph` and not run.\n", "\n", " 2) In the second stage, a `tf.Graph` which contains everything that was deferred in the first stage is run. This stage is much faster than the tracing stage.\n", "\n", - "Depending on its inputs, `Function` will not always run the first stage when it is called. See [\"Rules of tracing\"](#rules_of_tracing) below to get a better sense of how it makes that determination. Skipping the first stage and only executing the second stage is what gives you TensorFlow's high performance.\n", + "Depending on its inputs, `tf.function` will not always run the first stage when it is called. See [\"Rules of tracing\"](#rules_of_tracing) below to get a better sense of how it makes that determination. Skipping the first stage and only executing the second stage is what gives you TensorFlow's high performance.\n", "\n", - "When `Function` does decide to trace, the tracing stage is immediately followed by the second stage, so calling the `Function` both creates and runs the `tf.Graph`. Later you will see how you can run only the tracing stage with [`get_concrete_function`](#obtaining_concrete_functions)." + "When `tf.function` does decide to trace, the tracing stage is immediately followed by the second stage, so calling the `tf.function` both creates and runs the `tf.Graph`. Later you will see how you can run only the tracing stage with [`get_concrete_function`](#obtaining_concrete_functions)." ] }, { @@ -272,7 +272,7 @@ "id": "K7scSzLx662f" }, "source": [ - "When you pass arguments of different types into a `Function`, both stages are run:\n" + "When you pass arguments of different types into a `tf.function`, both stages are run:\n" ] }, { @@ -302,7 +302,7 @@ "id": "QPfouGUQrcNb" }, "source": [ - "Note that if you repeatedly call a `Function` with the same argument type, TensorFlow will skip the tracing stage and reuse a previously traced graph, as the generated graph would be identical." + "Note that if you repeatedly call a `tf.function` with the same argument type, TensorFlow will skip the tracing stage and reuse a previously traced graph, as the generated graph would be identical." ] }, { @@ -346,10 +346,11 @@ "So far, you've seen that `tf.function` creates a cached, dynamic dispatch layer over TensorFlow's graph tracing logic. To be more specific about the terminology:\n", "\n", "- A `tf.Graph` is the raw, language-agnostic, portable representation of a TensorFlow computation.\n", - "- A `ConcreteFunction` wraps a `tf.Graph`.\n", - "- A `Function` manages a cache of `ConcreteFunction`s and picks the right one for your inputs.\n", - "- `tf.function` wraps a Python function, returning a `Function` object.\n", - "- **Tracing** creates a `tf.Graph` and wraps it in a `ConcreteFunction`, also known as a **trace.**\n" + "- Tracing is the process through which new `tf.Graph`s are generated from Python code.\n", + "- An instance of `tf.Graph` is specialized to the specific input types it was traced with. Differing types require retracing.\n", + "- Each traced `tf.Graph` has a corresponding `ConcreteFunction`.\n", + "- A `tf.function` manages a cache of `ConcreteFunction`s and picks the right one for your inputs.\n", + "- `tf.function` wraps the Python function that will be traced, returning a `tf.types.experimental.PolymorphicFunction` object.\n" ] }, { @@ -360,7 +361,7 @@ "source": [ "#### Rules of tracing\n", "\n", - "When called, a `Function` matches the call arguments to existing `ConcreteFunction`s using `tf.types.experimental.TraceType` of each argument. If a matching `ConcreteFunction` is found, the call is dispatched to it. If no match is found, a new `ConcreteFunction` is traced.\n", + "When called, a `tf.function` first evaluates the type of each input argument using the `tf.types.experimental.TraceType` of each argument. This is used to construct a `tf.types.experimental.FunctionType` describing the signature of the desired `ConcreteFunction`. We compare this `FunctionType` to the `FunctionType`s of existing `ConcreteFunction`s. If a matching `ConcreteFunction` is found, the call is dispatched to it. If no match is found, a new `ConcreteFunction` is traced for the desired `FunctionType`.\n", "\n", "If multiple matches are found, the most specific signature is chosen. Matching is done by [subtyping](https://en.wikipedia.org/wiki/Subtyping), much like normal function calls in C++ or Java, for instance. For example, `TensorShape([1, 2])` is a subtype of `TensorShape([None, None])` and so a call to the tf.function with `TensorShape([1, 2])` can be dispatched to the `ConcreteFunction` produced with `TensorShape([None, None])` but if a `ConcreteFunction` with `TensorShape([1, None])` also exists then it will be prioritized since it is more specific.\n", "\n", @@ -369,13 +370,13 @@ "* For `Variable`, the type is similar to `Tensor`, but also includes a unique resource ID of the variable, necessary to correctly wire control dependencies\n", "* For Python primitive values, the type corresponds to the **value** itself. For example, the `TraceType` of the value `3` is `LiteralTraceType<3>`, not `int`.\n", "* For Python ordered containers such as `list` and `tuple`, etc., the type is parameterized by the types of their elements; for example, the type of `[1, 2]` is `ListTraceType, LiteralTraceType<2>>` and the type for `[2, 1]` is `ListTraceType, LiteralTraceType<1>>` which is different.\n", - "* For Python mappings such as `dict`, the type is also a mapping from the same keys but to the types of values instead the actual values. For example, the type of `{1: 2, 3: 4}`, is `MappingTraceType<>>, >>>`. However, unlike ordered containers, `{1: 2, 3: 4}` and `{3: 4, 1: 2}` have equivalent types.\n", - "* For Python objects which implement the `__tf_tracing_type__` method, the type is whatever that method returns\n", - "* For any other Python objects, the type is a generic `TraceType`, its matching precedure is:\n", - " * First it checks if the object is the same object used in the previous trace (using python `id()` or `is`). Note that this will still match if the object has changed, so if you use python objects as `tf.function` arguments it's best to use *immutable* ones.\n", - " * Next it checks if the object is equal to the object used in the previous trace (using python `==`).\n", + "* For Python mappings such as `dict`, the type is also a mapping from the same keys but to the types of values instead of the actual values. For example, the type of `{1: 2, 3: 4}`, is `MappingTraceType<>>, >>>`. However, unlike ordered containers, `{1: 2, 3: 4}` and `{3: 4, 1: 2}` have equivalent types.\n", + "* For Python objects which implement the `__tf_tracing_type__` method, the type is whatever that method returns.\n", + "* For any other Python objects, the type is a generic `TraceType`, and the matching precedure is:\n", + " * First it checks if the object is the same object used in the previous trace (using Python `id()` or `is`). Note that this will still match if the object has changed, so if you use Python objects as `tf.function` arguments it's best to use *immutable* ones.\n", + " * Next it checks if the object is equal to the object used in the previous trace (using Python `==`).\n", " \n", - " Note that this procedure only keeps a [weakref](https://docs.python.org/3/library/weakref.html) to the object and hence only works as long as the object is in scope/not deleted.)\n" + " Note that this procedure only keeps a [weakref](https://docs.python.org/3/library/weakref.html) to the object and hence only works as long as the object is in scope/not deleted.\n" ] }, { @@ -384,7 +385,7 @@ "id": "GNNN4lgRzpIs" }, "source": [ - "Note: `TraceType` is based on the `Function` input parameters so changes to global and [free variables](https://docs.python.org/3/reference/executionmodel.html#binding-of-names) alone will not create a new trace. See [this section](#depending_on_python_global_and_free_variables) for recommended practices when dealing with Python global and free variables." + "Note: `TraceType` is based on the `tf.function` input parameters so changes to global and [free variables](https://docs.python.org/3/reference/executionmodel.html#binding-of-names) alone will not create a new trace. See [this section](#depending_on_python_global_and_free_variables) for recommended practices when dealing with Python global and free variables." ] }, { @@ -395,7 +396,7 @@ "source": [ "### Controlling retracing\n", "\n", - "Retracing, which is when your `Function` creates more than one trace, helps ensure that TensorFlow generates correct graphs for each set of inputs. However, tracing is an expensive operation! If your `Function` retraces a new graph for every call, you'll find that your code executes more slowly than if you didn't use `tf.function`.\n", + "Retracing, which is when your `tf.function` creates more than one trace, helps ensure that TensorFlow generates correct graphs for each set of inputs. However, tracing is an expensive operation! If your `tf.function` retraces a new graph for every call, you'll find that your code executes more slowly than if you didn't use `tf.function`.\n", "\n", "To control the tracing behavior, you can use the following techniques:" ] @@ -406,7 +407,9 @@ "id": "EUtycWJa34TT" }, "source": [ - "#### Pass a fixed `input_signature` to `tf.function`" + "#### Pass a fixed `input_signature` to `tf.function`\n", + "\n", + "This forces `tf.function` to constrain itself to only one `tf.types.experimental.FunctionType` composed of the types enumerated by the `input_signature`. Calls that cannot be dispatched to this `FunctionType` will throw an error." ] }, { @@ -440,7 +443,7 @@ "source": [ "#### Use unknown dimensions for flexibility\n", "\n", - " Since TensorFlow matches tensors based on their shape, using a `None` dimension as a wildcard will allow `Function`s to reuse traces for variably-sized input. Variably-sized input can occur if you have sequences of different length, or images of different sizes for each batch. You can check out the [Transformer](https://www.tensorflow.org/text/tutorials/transformer) and [Deep Dream](../tutorials/generative/deepdream.ipynb) tutorials for examples." + " Since TensorFlow matches tensors based on their shape, using a `None` dimension as a wildcard will allow `tf.function`s to reuse traces for variably-sized input. Variably-sized input can occur if you have sequences of different length, or images of different sizes for each batch. You can check out the [Transformer](https://www.tensorflow.org/text/tutorials/transformer) and [Deep Dream](../tutorials/generative/deepdream.ipynb) tutorials for examples." ] }, { @@ -461,6 +464,41 @@ "print(g(tf.constant([1, 2, 3, 4, 5])))\n" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "37cc12f93cbd" + }, + "source": [ + "#### Use `reduce_retracing` for automatic flexibility\n", + "\n", + "When `reduce_retracing` is enabled, `tf.function` automatically identifies supertypes of the input types it is observing and chooses to trace more generalized graphs automatically. It is less efficient than setting the `input_signature` directly but useful when many types need to be supported." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0403fae03a1f" + }, + "outputs": [], + "source": [ + "@tf.function(reduce_retracing=True)\n", + "def g(x):\n", + " print('Tracing with', x)\n", + " return x\n", + "\n", + "# Traces once.\n", + "print(g(tf.constant([1, 2, 3])))\n", + "\n", + "# Traces again, but more generalized this time.\n", + "print(g(tf.constant([1, 2, 3, 4, 5])))\n", + "\n", + "# No more tracing!\n", + "print(g(tf.constant([1, 2, 3, 4, 5, 6, 7])))\n", + "print(g(tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9])))" + ] + }, { "cell_type": "markdown", "metadata": { @@ -508,7 +546,7 @@ "id": "4pJqkDR_Q2wz" }, "source": [ - "If you need to force retracing, create a new `Function`. Separate `Function` objects are guaranteed not to share traces." + "If you need to force retracing, create a new `tf.function`. Separate `tf.function` objects are guaranteed not to share traces." ] }, { @@ -537,7 +575,7 @@ "\n", "Where possible, you should prefer converting the Python type into a `tf.experimental.ExtensionType` instead. Moreover, the `TraceType` of an `ExtensionType` is the `tf.TypeSpec` associated with it. Therefore, if needed, you can simply override the default `tf.TypeSpec` to take control of an `ExtensionType`'s `Tracing Protocol`. Refer to the _Customizing the ExtensionType's TypeSpec_ section in the [Extension types](extension_type.ipynb) guide for details.\n", "\n", - "Otherwise, for direct control over when `Function` should retrace in regards to a particular Python type, you can implement the `Tracing Protocol` for it yourself." + "Otherwise, for direct control over when `tf.function` should retrace in regards to a particular Python type, you can implement the `Tracing Protocol` for it yourself." ] }, { @@ -689,8 +727,7 @@ }, "outputs": [], "source": [ - "print(double_strings.structured_input_signature)\n", - "print(double_strings.structured_outputs)" + "print(double_strings.function_type)" ] }, { @@ -761,7 +798,7 @@ "source": [ "### Obtaining graphs\n", "\n", - "Each concrete function is a callable wrapper around a `tf.Graph`. Although retrieving the actual `tf.Graph` object is not something you'll normally need to do, you can obtain it easily from any concrete function." + "Although retrieving the actual `tf.Graph` object is not something you'll normally need to do, you can obtain it easily from any concrete function." ] }, { @@ -777,6 +814,36 @@ " print(f'{node.input} -> {node.name}')\n" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "2d49c486ccd4" + }, + "source": [ + "In reality, `tf.Graph`s are not directly callable. We actually use an `tf.types.experimental.AtomicFunction` to perform the computations described by the `tf.Graph`. You can access the `AtomicFunction` describing the traced `tf.Graph` and call it directly instead of the `ConcreteFunction`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4c3879aa0be0" + }, + "outputs": [], + "source": [ + "atomic_fn = double_strings.inference_fn\n", + "atomic_fn(tf.constant(\"a\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c3bd1036c18c" + }, + "source": [ + "This has the advantage of having lower Python overhead for high-performance scenarios. But it should only be used for forward inference (no gradient support), and captured tensor values (if any) would need to be explicitly supplied." + ] + }, { "cell_type": "markdown", "metadata": { @@ -833,7 +900,7 @@ "id": "KxwJ8znPI0Cg" }, "source": [ - "If you're curious you can inspect the code autograph generates." + "If you're curious you can inspect the code AutoGraph generates." ] }, { @@ -1029,7 +1096,7 @@ "source": [ "## Limitations\n", "\n", - "TensorFlow `Function` has a few limitations by design that you should be aware of when converting a Python function to a `Function`." + "`tf.function` has a few limitations by design that you should be aware of when converting a Python function to a `tf.function`." ] }, { @@ -1040,7 +1107,7 @@ "source": [ "### Executing Python side effects\n", "\n", - "Side effects, like printing, appending to lists, and mutating globals, can behave unexpectedly inside a `Function`, sometimes executing twice or not all. They only happen the first time you call a `Function` with a set of inputs. Afterwards, the traced `tf.Graph` is reexecuted, without executing the Python code.\n", + "Side effects, like printing, appending to lists, and mutating globals, can behave unexpectedly inside a `tf.function`, sometimes executing twice or not all. They only happen the first time you call a `tf.function` with a set of inputs. Afterwards, the traced `tf.Graph` is reexecuted, without executing the Python code.\n", "\n", "The general rule of thumb is to avoid relying on Python side effects in your logic and only use them to debug your traces. Otherwise, TensorFlow APIs like `tf.data`, `tf.print`, `tf.summary`, `tf.Variable.assign`, and `tf.TensorArray` are the best way to ensure your code will be executed by the TensorFlow runtime with each call." ] @@ -1069,7 +1136,7 @@ "id": "e1I0dPiqTV8H" }, "source": [ - "If you would like to execute Python code during each invocation of a `Function`, `tf. py_function` is an exit hatch. The drawbacks of `tf.py_function` are that it's not portable or particularly performant, cannot be saved with SavedModel, and does not work well in distributed (multi-GPU, TPU) setups. Also, since `tf.py_function` has to be wired into the graph, it casts all inputs/outputs to tensors." + "If you would like to execute Python code during each invocation of a `tf.function`, `tf. py_function` is an exit hatch. The drawbacks of `tf.py_function` are that it's not portable or particularly performant, cannot be saved with `SavedModel`, and does not work well in distributed (multi-GPU, TPU) setups. Also, since `tf.py_function` has to be wired into the graph, it casts all inputs/outputs to tensors." ] }, { @@ -1170,7 +1237,7 @@ "id": "5eZTFRv_k_nR" }, "source": [ - "Sometimes unexpected behaviors are very hard to notice. In the example below, the `counter` is intended to safeguard the increment of a variable. However because it is a python integer and not a TensorFlow object, it's value is captured during the first trace. When the `tf.function` is used, the `assign_add` will be recorded unconditionally in the underlying graph. Therefore `v` will increase by 1, every time the `tf.function` is called. This issue is common among users that try to migrate their Grpah-mode Tensorflow code to Tensorflow 2 using `tf.function` decorators, when python side-effects (the `counter` in the example) are used to determine what ops to run (`assign_add` in the example). Usually, users realize this only after seeing suspicious numerical results, or significantly lower performance than expected (e.g. if the guarded operation is very costly)." + "Sometimes unexpected behaviors are very hard to notice. In the example below, the `counter` is intended to safeguard the increment of a variable. However because it is a python integer and not a TensorFlow object, it's value is captured during the first trace. When the `tf.function` is used, the `assign_add` will be recorded unconditionally in the underlying graph. Therefore `v` will increase by 1, every time the `tf.function` is called. This issue is common among users that try to migrate their Graph-mode Tensorflow code to Tensorflow 2 using `tf.function` decorators, when python side-effects (the `counter` in the example) are used to determine what ops to run (`assign_add` in the example). Usually, users realize this only after seeing suspicious numerical results, or significantly lower performance than expected (e.g. if the guarded operation is very costly)." ] }, { @@ -1243,7 +1310,7 @@ "id": "pbFG5CX4LwQA" }, "source": [ - "In summary, as a rule of thumb, you should avoid mutating python objects such as integers or containers like lists that live outside the `Function`. Instead, use arguments and TF objects. For example, the section [\"Accumulating values in a loop\"](#accumulating_values_in_a_loop) has one example of how list-like operations can be implemented.\n", + "In summary, as a rule of thumb, you should avoid mutating python objects such as integers or containers like lists that live outside the `tf.function`. Instead, use arguments and TF objects. For example, the section [\"Accumulating values in a loop\"](#accumulating_values_in_a_loop) has one example of how list-like operations can be implemented.\n", "\n", "You can, in some cases, capture and manipulate state if it is a [`tf.Variable`](https://www.tensorflow.org/guide/variable). This is how the weights of Keras models are updated with repeated calls to the same `ConcreteFunction`." ] @@ -1437,7 +1504,7 @@ "source": [ "### Recursive tf.functions are not supported\n", "\n", - "Recursive `Function`s are not supported and could cause infinite loops. For example," + "Recursive `tf.function`s are not supported and could cause infinite loops. For example," ] }, { @@ -1465,7 +1532,7 @@ "id": "LyRyooKGUxNV" }, "source": [ - "Even if a recursive `Function` seems to work, the python function will be traced multiple times and could have performance implication. For example," + "Even if a recursive `tf.function` seems to work, the Python function will be traced multiple times and could have performance implications. For example," ] }, { @@ -1495,7 +1562,7 @@ "source": [ "## Known Issues\n", "\n", - "If your `Function` is not evaluating correctly, the error may be explained by these known issues which are planned to be fixed in the future." + "If your `tf.function` is not evaluating correctly, the error may be explained by these known issues which are planned to be fixed in the future." ] }, { @@ -1506,7 +1573,7 @@ "source": [ "### Depending on Python global and free variables\n", "\n", - "`Function` creates a new `ConcreteFunction` when called with a new value of a Python argument. However, it does not do that for the Python closure, globals, or nonlocals of that `Function`. If their value changes in between calls to the `Function`, the `Function` will still use the values they had when it was traced. This is different from how regular Python functions work.\n", + "`tf.function` creates a new `ConcreteFunction` when called with a new value of a Python argument. However, it does not do that for the Python closure, globals, or nonlocals of that `tf.function`. If their value changes in between calls to the `tf.function`, the `tf.function` will still use the values they had when it was traced. This is different from how regular Python functions work.\n", "\n", "For that reason, you should follow a functional programming style that uses arguments instead of closing over outer names." ] @@ -1552,7 +1619,7 @@ "id": "ZoPg5w1Pjqnb" }, "source": [ - "Another way to update a global value, is to make it a `tf.Variable` and use the `Variable.assign` method instead.\n" + "Another way to update a global value is to make it a `tf.Variable` and use the `Variable.assign` method instead.\n" ] }, { @@ -1648,11 +1715,11 @@ "id": "Ytcgg2qFWaBF" }, "source": [ - "Using the same `Function` to evaluate the modified instance of the model will be buggy since it still has the [same instance-based TraceType](#rules_of_tracing) as the original model.\n", + "Using the same `tf.function` to evaluate the modified instance of the model will be buggy since it still has the [same instance-based TraceType](#rules_of_tracing) as the original model.\n", "\n", - "For that reason, you're recommended to write your `Function` to avoid depending on mutable object attributes or implement the [Tracing Protocol](#use_the_tracing_protocol) for the objects to inform `Function` about such attributes.\n", + "For that reason, you're recommended to write your `tf.function` to avoid depending on mutable object attributes or implement the [Tracing Protocol](#use_the_tracing_protocol) for the objects to inform `tf.function` about such attributes.\n", "\n", - "If that is not possible, one workaround is to make new `Function`s each time you modify your object to force retracing:" + "If that is not possible, one workaround is to make new `tf.function`s each time you modify your object to force retracing:" ] }, { @@ -1668,7 +1735,7 @@ "\n", "new_model = SimpleModel()\n", "evaluate_no_bias = tf.function(evaluate).get_concrete_function(new_model, x)\n", - "# Don't pass in `new_model`, `Function` already captured its state during tracing.\n", + "# Don't pass in `new_model`. `tf.function` already captured its state during tracing.\n", "print(evaluate_no_bias(x))" ] }, @@ -1682,7 +1749,7 @@ "source": [ "print(\"Adding bias!\")\n", "new_model.bias += 5.0\n", - "# Create new Function and ConcreteFunction since you modified new_model.\n", + "# Create new `tf.function` and `ConcreteFunction` since you modified `new_model`.\n", "evaluate_with_bias = tf.function(evaluate).get_concrete_function(new_model, x)\n", "print(evaluate_with_bias(x)) # Don't pass in `new_model`." ] @@ -1739,7 +1806,7 @@ "source": [ "### Creating tf.Variables\n", "\n", - "`Function` only supports singleton `tf.Variable`s created once on the first call, and reused across subsequent function calls. The code snippet below would create a new `tf.Variable` in every function call, which results in a `ValueError` exception.\n", + "`tf.function` only supports singleton `tf.Variable`s created once on the first call, and reused across subsequent function calls. The code snippet below would create a new `tf.Variable` in every function call, which results in a `ValueError` exception.\n", "\n", "Example:" ] @@ -1800,7 +1867,7 @@ }, "source": [ "#### Using with multiple Keras optimizers\n", - "You may encounter `ValueError: tf.function only supports singleton tf.Variables created on the first call.` when using more than one Keras optimizer with a `tf.function`. This error occurs because optimizers internally create `tf.Variables` when they apply gradients for the first time." + "You may encounter `ValueError: tf.function only supports singleton tf.Variables created on the first call.` when using more than one Keras optimizer with a `tf.function`. This error occurs because optimizers internally create `tf.Variable`s when they apply gradients for the first time." ] }, { @@ -1901,7 +1968,7 @@ "x = tf.constant([-1.])\n", "y = tf.constant([2.])\n", "\n", - "# Make a new Function and ConcreteFunction for each optimizer.\n", + "# Make a new tf.function and ConcreteFunction for each optimizer.\n", "train_step_1 = tf.function(train_step)\n", "train_step_2 = tf.function(train_step)\n", "for i in range(10):\n", @@ -1919,9 +1986,9 @@ "source": [ "#### Using with multiple Keras models\n", "\n", - "You may also encounter `ValueError: tf.function only supports singleton tf.Variables created on the first call.` when passing different model instances to the same `Function`.\n", + "You may also encounter `ValueError: tf.function only supports singleton tf.Variables created on the first call.` when passing different model instances to the same `tf.function`.\n", "\n", - "This error occurs because Keras models (which [do not have their input shape defined](https://www.tensorflow.org/guide/keras/custom_layers_and_models#best_practice_deferring_weight_creation_until_the_shape_of_the_inputs_is_known)) and Keras layers create `tf.Variables`s when they are first called. You may be attempting to initialize those variables inside a `Function`, which has already been called. To avoid this error, try calling `model.build(input_shape)` to initialize all the weights before training the model.\n" + "This error occurs because Keras models (which [do not have their input shape defined](https://www.tensorflow.org/guide/keras/custom_layers_and_models#best_practice_deferring_weight_creation_until_the_shape_of_the_inputs_is_known)) and Keras layers create `tf.Variable`s when they are first called. You may be attempting to initialize those variables inside a `tf.function`, which has already been called. To avoid this error, try calling `model.build(input_shape)` to initialize all the weights before training the model.\n" ] }, { @@ -1932,7 +1999,7 @@ "source": [ "## Further reading\n", "\n", - "To learn about how to export and load a `Function`, see the [SavedModel guide](../../guide/saved_model). To learn more about graph optimizations that are performed after tracing, see the [Grappler guide](../../guide/graph_optimization). To learn how to optimize your data pipeline and profile your model, see the [Profiler guide](../../guide/profiler.md)." + "To learn about how to export and load a `tf.function`, see the [SavedModel guide](../../guide/saved_model). To learn more about graph optimizations that are performed after tracing, see the [Grappler guide](../../guide/graph_optimization). To learn how to optimize your data pipeline and profile your model, see the [Profiler guide](../../guide/profiler.md)." ] } ], diff --git a/site/en/guide/intro_to_graphs.ipynb b/site/en/guide/intro_to_graphs.ipynb index 0392a160d55..4fe442632ba 100644 --- a/site/en/guide/intro_to_graphs.ipynb +++ b/site/en/guide/intro_to_graphs.ipynb @@ -87,7 +87,7 @@ "source": [ "### What are graphs?\n", "\n", - "In the previous three guides, you ran TensorFlow **eagerly**. This means TensorFlow operations are executed by Python, operation by operation, and returning results back to Python.\n", + "In the previous three guides, you ran TensorFlow **eagerly**. This means TensorFlow operations are executed by Python, operation by operation, and return results back to Python.\n", "\n", "While eager execution has several unique advantages, graph execution enables portability outside Python and tends to offer better performance. **Graph execution** means that tensor computations are executed as a *TensorFlow graph*, sometimes referred to as a `tf.Graph` or simply a \"graph.\"\n", "\n", @@ -174,7 +174,7 @@ "source": [ "## Taking advantage of graphs\n", "\n", - "You create and run a graph in TensorFlow by using `tf.function`, either as a direct call or as a decorator. `tf.function` takes a regular function as input and returns a `Function`. **A `Function` is a Python callable that builds TensorFlow graphs from the Python function. You use a `Function` in the same way as its Python equivalent.**\n" + "You create and run a graph in TensorFlow by using `tf.function`, either as a direct call or as a decorator. `tf.function` takes a regular function as input and returns a `tf.types.experimental.PolymorphicFunction`. **A `PolymorphicFunction` is a Python callable that builds TensorFlow graphs from the Python function. You use a `tf.function` in the same way as its Python equivalent.**\n" ] }, { @@ -191,7 +191,8 @@ " x = x + b\n", " return x\n", "\n", - "# `a_function_that_uses_a_graph` is a TensorFlow `Function`.\n", + "# The Python type of `a_function_that_uses_a_graph` will now be a\n", + "# `PolymorphicFunction`.\n", "a_function_that_uses_a_graph = tf.function(a_regular_function)\n", "\n", "# Make some tensors.\n", @@ -200,7 +201,7 @@ "b1 = tf.constant(4.0)\n", "\n", "orig_value = a_regular_function(x1, y1, b1).numpy()\n", - "# Call a `Function` like a Python function.\n", + "# Call a `tf.function` like a Python function.\n", "tf_function_value = a_function_that_uses_a_graph(x1, y1, b1).numpy()\n", "assert(orig_value == tf_function_value)" ] @@ -211,7 +212,7 @@ "id": "PNvuAYpdrTOf" }, "source": [ - "On the outside, a `Function` looks like a regular function you write using TensorFlow operations. [Underneath](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/eager/def_function.py), however, it is *very different*. A `Function` **encapsulates several `tf.Graph`s behind one API** (learn more in the _Polymorphism_ section). That is how a `Function` is able to give you the benefits of graph execution, like speed and deployability (refer to _The benefits of graphs_ above)." + "On the outside, a `tf.function` looks like a regular function you write using TensorFlow operations. [Underneath](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/eager/polymorphic_function/polymorphic_function.py), however, it is *very different*. The underlying `PolymorphicFunction` **encapsulates several `tf.Graph`s behind one API** (learn more in the _Polymorphism_ section). That is how a `tf.function` is able to give you the benefits of graph execution, like speed and deployability (refer to _The benefits of graphs_ above)." ] }, { @@ -236,7 +237,8 @@ " x = x + b\n", " return x\n", "\n", - "# Use the decorator to make `outer_function` a `Function`.\n", + "# Using the `tf.function` decorator makes `outer_function` into a\n", + "# `PolymorphicFunction`.\n", "@tf.function\n", "def outer_function(x):\n", " y = tf.constant([[2.0], [3.0]])\n", @@ -283,7 +285,8 @@ " else:\n", " return 0\n", "\n", - "# `tf_simple_relu` is a TensorFlow `Function` that wraps `simple_relu`.\n", + "# Using `tf.function` makes `tf_simple_relu` a `PolymorphicFunction` that wraps\n", + "# `simple_relu`.\n", "tf_simple_relu = tf.function(simple_relu)\n", "\n", "print(\"First branch, with graph:\", tf_simple_relu(tf.constant(1)).numpy())\n", @@ -338,13 +341,13 @@ "id": "sIpc_jfjEZEg" }, "source": [ - "### Polymorphism: one `Function`, many graphs\n", + "### Polymorphism: one `tf.function`, many graphs\n", "\n", "A `tf.Graph` is specialized to a specific type of inputs (for example, tensors with a specific [`dtype`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) or objects with the same [`id()`](https://docs.python.org/3/library/functions.html#id)).\n", "\n", - "Each time you invoke a `Function` with a set of arguments that can't be handled by any of its existing graphs (such as arguments with new `dtypes` or incompatible shapes), `Function` creates a new `tf.Graph` specialized to those new arguments. The type specification of a `tf.Graph`'s inputs is known as its **input signature** or just a **signature**. For more information regarding when a new `tf.Graph` is generated and how that can be controlled, go to the _Rules of tracing_ section of the [Better performance with `tf.function`](./function.ipynb) guide.\n", + "Each time you invoke a `tf.function` with a set of arguments that can't be handled by any of its existing graphs (such as arguments with new `dtypes` or incompatible shapes), it creates a new `tf.Graph` specialized to those new arguments. The type specification of a `tf.Graph`'s inputs is represented by `tf.types.experimental.FunctionType`, also referred to as the **signature**. For more information regarding when a new `tf.Graph` is generated, how that can be controlled, and how `FunctionType` can be useful, go to the _Rules of tracing_ section of the [Better performance with `tf.function`](./function.ipynb) guide.\n", "\n", - "The `Function` stores the `tf.Graph` corresponding to that signature in a `ConcreteFunction`. **A `ConcreteFunction` is a wrapper around a `tf.Graph`.**\n" + "The `tf.function` stores the `tf.Graph` corresponding to that signature in a `ConcreteFunction`. **A `ConcreteFunction` can be thought of as a wrapper around a `tf.Graph`.**\n" ] }, { @@ -359,7 +362,7 @@ "def my_relu(x):\n", " return tf.maximum(0., x)\n", "\n", - "# `my_relu` creates new graphs as it observes more signatures.\n", + "# `my_relu` creates new graphs as it observes different input types.\n", "print(my_relu(tf.constant(5.5)))\n", "print(my_relu([1, -1]))\n", "print(my_relu(tf.constant([3., -3.])))" @@ -371,7 +374,7 @@ "id": "1qRtw7R4KL9X" }, "source": [ - "If the `Function` has already been called with that signature, `Function` does not create a new `tf.Graph`." + "If the `tf.function` has already been called with the same input types, it does not create a new `tf.Graph`." ] }, { @@ -383,8 +386,8 @@ "outputs": [], "source": [ "# These two calls do *not* create new graphs.\n", - "print(my_relu(tf.constant(-2.5))) # Signature matches `tf.constant(5.5)`.\n", - "print(my_relu(tf.constant([-1., 1.]))) # Signature matches `tf.constant([3., -3.])`." + "print(my_relu(tf.constant(-2.5))) # Input type matches `tf.constant(5.5)`.\n", + "print(my_relu(tf.constant([-1., 1.]))) # Input type matches `tf.constant([3., -3.])`." ] }, { @@ -393,7 +396,7 @@ "id": "UohRmexhIpvQ" }, "source": [ - "Because it's backed by multiple graphs, a `Function` is **polymorphic**. That enables it to support more input types than a single `tf.Graph` could represent, and to optimize each `tf.Graph` for better performance." + "Because it's backed by multiple graphs, a `tf.function` is (as the name \"PolymorphicFunction\" suggests) **polymorphic**. That enables it to support more input types than a single `tf.Graph` could represent, and to optimize each `tf.Graph` for better performance." ] }, { @@ -428,7 +431,7 @@ "source": [ "### Graph execution vs. eager execution\n", "\n", - "The code in a `Function` can be executed both eagerly and as a graph. By default, `Function` executes its code as a graph:\n" + "The code in a `tf.function` can be executed both eagerly and as a graph. By default, `tf.function` executes its code as a graph:\n" ] }, { @@ -476,7 +479,7 @@ "id": "cyZNCRcQorGO" }, "source": [ - "To verify that your `Function`'s graph is doing the same computation as its equivalent Python function, you can make it execute eagerly with `tf.config.run_functions_eagerly(True)`. This is a switch that **turns off `Function`'s ability to create and run graphs**, instead of executing the code normally." + "To verify that your `tf.function`'s graph is doing the same computation as its equivalent Python function, you can make it execute eagerly with `tf.config.run_functions_eagerly(True)`. This is a switch that **turns off `tf.function`'s ability to create and run graphs**, instead of executing the code normally." ] }, { @@ -519,7 +522,7 @@ "id": "DKT3YBsqy0x4" }, "source": [ - "However, `Function` can behave differently under graph and eager execution. The Python [`print`](https://docs.python.org/3/library/functions.html#print) function is one example of how these two modes differ. Let's check out what happens when you insert a `print` statement to your function and call it repeatedly." + "However, `tf.function` can behave differently under graph and eager execution. The Python [`print`](https://docs.python.org/3/library/functions.html#print) function is one example of how these two modes differ. Let's check out what happens when you insert a `print` statement to your function and call it repeatedly." ] }, { @@ -567,7 +570,7 @@ "source": [ "Is the output surprising? **`get_MSE` only printed once even though it was called *three* times.**\n", "\n", - "To explain, the `print` statement is executed when `Function` runs the original code in order to create the graph in a process known as \"tracing\" (refer to the _Tracing_ section of the [`tf.function` guide](./function.ipynb). **Tracing captures the TensorFlow operations into a graph, and `print` is not captured in the graph.** That graph is then executed for all three calls **without ever running the Python code again**.\n", + "To explain, the `print` statement is executed when `tf.function` runs the original code in order to create the graph in a process known as \"tracing\" (refer to the _Tracing_ section of the [`tf.function` guide](./function.ipynb). **Tracing captures the TensorFlow operations into a graph, and `print` is not captured in the graph.** That graph is then executed for all three calls **without ever running the Python code again**.\n", "\n", "As a sanity check, let's turn off graph execution to compare:" ] @@ -615,7 +618,7 @@ "id": "PUR7qC_bquCn" }, "source": [ - "`print` is a *Python side effect*, and there are other differences that you should be aware of when converting a function into a `Function`. Learn more in the _Limitations_ section of the [Better performance with `tf.function`](./function.ipynb) guide." + "`print` is a *Python side effect*, and there are other differences that you should be aware of when converting a function into a `tf.function`. Learn more in the _Limitations_ section of the [Better performance with `tf.function`](./function.ipynb) guide." ] }, { @@ -637,7 +640,7 @@ "\n", "\n", "\n", - "Graph execution only executes the operations necessary to produce the observable effects, which includes:\n", + "Graph execution only executes the operations necessary to produce the observable effects, which include:\n", "\n", "- The return value of the function\n", "- Documented well-known side-effects such as:\n", @@ -697,7 +700,7 @@ "source": [ "### `tf.function` best practices\n", "\n", - "It may take some time to get used to the behavior of `Function`. To get started quickly, first-time users should play around with decorating toy functions with `@tf.function` to get experience with going from eager to graph execution.\n", + "It may take some time to get used to the behavior of `tf.function`. To get started quickly, first-time users should play around with decorating toy functions with `@tf.function` to get experience with going from eager to graph execution.\n", "\n", "*Designing for `tf.function`* may be your best bet for writing graph-compatible TensorFlow programs. Here are some tips:\n", "- Toggle between eager and graph execution early and often with `tf.config.run_functions_eagerly` to pinpoint if/ when the two modes diverge.\n", @@ -787,7 +790,7 @@ "\n", "Graphs can speed up your code, but the process of creating them has some overhead. For some functions, the creation of the graph takes more time than the execution of the graph. **This investment is usually quickly paid back with the performance boost of subsequent executions, but it's important to be aware that the first few steps of any large model training can be slower due to tracing.**\n", "\n", - "No matter how large your model, you want to avoid tracing frequently. The [`tf.function` guide](./function.ipynb) discusses how to set input specifications and use tensor arguments to avoid retracing in the _Controlling retracing_ section. If you find you are getting unusually poor performance, it's a good idea to check if you are retracing accidentally." + "No matter how large your model, you want to avoid tracing frequently. In the _Controlling retracing_ section, the [`tf.function` guide](./function.ipynb) discusses how to set input specifications and use tensor arguments to avoid retracing. If you find you are getting unusually poor performance, it's a good idea to check if you are retracing accidentally." ] }, { @@ -796,9 +799,9 @@ "id": "F4InDaTjwmBA" }, "source": [ - "## When is a `Function` tracing?\n", + "## When is a `tf.function` tracing?\n", "\n", - "To figure out when your `Function` is tracing, add a `print` statement to its code. As a rule of thumb, `Function` will execute the `print` statement every time it traces." + "To figure out when your `tf.function` is tracing, add a `print` statement to its code. As a rule of thumb, `tf.function` will execute the `print` statement every time it traces." ] }, { From 48fd03d5991220717f45b12620bf8091d042531b Mon Sep 17 00:00:00 2001 From: Ramesh Sampath Date: Thu, 30 Nov 2023 16:20:40 -0800 Subject: [PATCH 13/85] Add `--extra-index-url` to pip install command to get nvidia libraries when installing TensorFlow with cuda support. PiperOrigin-RevId: 586819519 --- site/en/install/pip.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/site/en/install/pip.md b/site/en/install/pip.md index 4add60b11d7..578b6a3cf54 100644 --- a/site/en/install/pip.md +++ b/site/en/install/pip.md @@ -26,7 +26,8 @@ step-by-step instructions. for more information about this collaboration. ```bash - python3 -m pip install tensorflow[and-cuda] + python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 + python3 -m pip install -U tensorflow[and-cuda] # Verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ``` @@ -70,7 +71,8 @@ step-by-step instructions. for CUDA in WSL. ```bash - python3 -m pip install tensorflow[and-cuda] + python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 + python3 -m pip install -U tensorflow[and-cuda] # Verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ``` @@ -206,7 +208,8 @@ The following NVIDIA® software are only required for GPU support. ```bash # For GPU users - pip install tensorflow[and-cuda] + pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 + pip install -U tensorflow[and-cuda] # For CPU users pip install tensorflow ``` @@ -446,7 +449,8 @@ The following NVIDIA® software are only required for GPU support. ```bash # For GPU users - pip install tensorflow[and-cuda] + pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 + pip install -U tensorflow[and-cuda] # For CPU users pip install tensorflow ``` From 8b7b6f1da4231d15527a851911a43cdf8e5e7b8c Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Wed, 6 Dec 2023 11:14:48 -0800 Subject: [PATCH 14/85] Internal change. PiperOrigin-RevId: 588486739 --- tools/tensorflow_docs/vis/webp_animation.py | 156 -------------------- tools/tensorflow_docs/vis/webp_test.py | 41 ----- 2 files changed, 197 deletions(-) delete mode 100644 tools/tensorflow_docs/vis/webp_animation.py delete mode 100644 tools/tensorflow_docs/vis/webp_test.py diff --git a/tools/tensorflow_docs/vis/webp_animation.py b/tools/tensorflow_docs/vis/webp_animation.py deleted file mode 100644 index ae6a8713d4f..00000000000 --- a/tools/tensorflow_docs/vis/webp_animation.py +++ /dev/null @@ -1,156 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Easy notebook embedded webp animations. - -``` -import tensorflow_docs.vis.webp_animation as webp_animation - -env = gym.make('SpaceInvaders-v0') -obs = env.reset() -done = False -n = 0 - -anim = webp_animation.Webp() - -while not done: - img = env.render(mode = 'rgb_array') - anim.append(img) - act = env.action_space.sample() # take a random action - obs, reward, done, info = env.step(act) - n += 1 - -anim.save("test.webp") -anim -``` -""" - -import numpy as np -import PIL.Image - -from tensorflow_docs.vis import embed -import webp - - -class Webp(object): - """Builds a webp animation. - - Attributes: - frame_rate: The default frame rate for appended images. - shape: The shape of the animation frames. Will default to the size of the - first image if not set. - result: The binary image data string. Once the animation has been used, it - can no longer updated. And the result field contains the webp encoded - data. - """ - - def __init__(self, shape=None, frame_rate=60.0, **options): - """A notebook-embedable webp animation. - - Args: - shape: Optional. The image_shape of the animation. Defaults to the shape - of the first image if unset. - frame_rate: The default frame rate for the animation. - **options: Additional arguments passed to `WebPAnimEncoderOptions.new`. - """ - self.frame_rate = frame_rate - self._timestamp_ms = 0 - self._empty = True - - if options is None: - options = {} - - self._options = webp.WebPAnimEncoderOptions.new(**options) - self._encoder = None - self._shape = shape - self._result = None - - def append(self, img, dt_ms=None): - """Append an image to the animation. - - Args: - img: The image to add. - dt_ms: override the animation frame rate for this frame with a frame - length in ms. - - Raises: - ValueError: - * if the video has already been "assembled" (used). - * if `img` does not match the shape of the animation. - """ - if self._result is not None: - raise ValueError( - "Can't append to an animation after it has been \"assembled\" (used)." - ) - self._empty = False - - if not isinstance(img, PIL.Image.Image): - img = np.asarray(img) - img = PIL.Image.fromarray(img) - - if self._shape is None: - self._shape = img.size - - if self._encoder is None: - self._encoder = webp.WebPAnimEncoder.new(self.shape[0], self.shape[1], - self._options) - - if img.size != self.shape: - raise ValueError("Image shape does not match video shape") - - img = webp.WebPPicture.from_pil(img) - - self._encoder.encode_frame(img, int(self._timestamp_ms)) - - if dt_ms is None: - self._timestamp_ms += 1000 * (1.0 / self.frame_rate) - else: - self._timestamp_ms += dt_ms - - def extend(self, imgs, dt_ms=None): - """Extend tha animation with an iterable if images. - - Args: - imgs: An iterable of images, to pass to `.append`. - dt_ms: Override the animation frame rate for these frames with a frame - length in ms. - """ - for img in imgs: - self.append(img, dt_ms=dt_ms) - - @property - def result(self): - result = self._result - if result is None: - anim_data = self._encoder.assemble(int(self._timestamp_ms)) - result = anim_data.buffer() - self._result = result - return result - - @property - def shape(self): - """The shape of the animation. Read only once set.""" - return self._shape - - def _repr_html_(self): - """Notebook display hook, embed the image in an tag.""" - if self._empty: - return "Empty Animation" - - return embed.embed_data("image/webp", self.result)._repr_html_() # pylint: disable=protected-access, - - def save(self, filename): - """Write the webp data to a file.""" - with open(filename, "wb") as f: - f.write(self.result) diff --git a/tools/tensorflow_docs/vis/webp_test.py b/tools/tensorflow_docs/vis/webp_test.py deleted file mode 100644 index 0bc1dd28aed..00000000000 --- a/tools/tensorflow_docs/vis/webp_test.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for tensorflow_docs.vis.webp.""" - -import os - -from absl.testing import absltest - -import numpy as np -import PIL.Image - -from tensorflow_docs.vis import webp_animation - - -class WebpTest(absltest.TestCase): - - def test_smoke(self): - workdir = self.create_tempdir().full_path - - img = PIL.Image.fromarray(np.zeros([10, 12, 3], dtype=np.uint8)) - anim = webp_animation.Webp() - - anim.append(img) - anim.extend([img]) - anim.save(os.path.join(workdir, 'test.webp')) - - -if __name__ == '__main__': - absltest.main() From 70174a87eb9c39b6f71d948801e140e9436e66e8 Mon Sep 17 00:00:00 2001 From: Austin Anderson Date: Wed, 6 Dec 2023 15:53:45 -0800 Subject: [PATCH 15/85] Automated rollback of commit 48fd03d5991220717f45b12620bf8091d042531b PiperOrigin-RevId: 588568875 --- site/en/install/pip.md | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/site/en/install/pip.md b/site/en/install/pip.md index 578b6a3cf54..4add60b11d7 100644 --- a/site/en/install/pip.md +++ b/site/en/install/pip.md @@ -26,8 +26,7 @@ step-by-step instructions. for more information about this collaboration. ```bash - python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 - python3 -m pip install -U tensorflow[and-cuda] + python3 -m pip install tensorflow[and-cuda] # Verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ``` @@ -71,8 +70,7 @@ step-by-step instructions. for CUDA in WSL. ```bash - python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 - python3 -m pip install -U tensorflow[and-cuda] + python3 -m pip install tensorflow[and-cuda] # Verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ``` @@ -208,8 +206,7 @@ The following NVIDIA® software are only required for GPU support. ```bash # For GPU users - pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 - pip install -U tensorflow[and-cuda] + pip install tensorflow[and-cuda] # For CPU users pip install tensorflow ``` @@ -449,8 +446,7 @@ The following NVIDIA® software are only required for GPU support. ```bash # For GPU users - pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1 - pip install -U tensorflow[and-cuda] + pip install tensorflow[and-cuda] # For CPU users pip install tensorflow ``` From 0fc1acda98cc4e04b68032257a071c831d8ccbe8 Mon Sep 17 00:00:00 2001 From: Surya <116063290+SuryanarayanaY@users.noreply.github.com> Date: Wed, 13 Dec 2023 16:28:54 +0530 Subject: [PATCH 16/85] Update cudnn version of TF2.15v The documentation of source.md mentioned cudnn version 8.8 is needed for Tf2.15v. However, after installing that, and running TensorFlow, it complains: 2023-12-12 16:48:40.306928: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:447] Loaded runtime CuDNN library: 8.8.0 but source was compiled with: 8.9.4. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile . Hence updating it 8.9 version. --- site/en/install/source.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/install/source.md b/site/en/install/source.md index 8d250f51149..b70acdb93ea 100644 --- a/site/en/install/source.md +++ b/site/en/install/source.md @@ -462,7 +462,7 @@ Success: TensorFlow is now installed. - + From fa06aeec62124aa76f4cf7e80172be3e925ef252 Mon Sep 17 00:00:00 2001 From: Jim Lin Date: Fri, 15 Dec 2023 09:37:44 -0800 Subject: [PATCH 17/85] #tensorflow fix breakage caused from PR #62362 PiperOrigin-RevId: 591279603 --- site/en/guide/extension_type.ipynb | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/site/en/guide/extension_type.ipynb b/site/en/guide/extension_type.ipynb index 76e20e8d283..7e8edeea7c9 100644 --- a/site/en/guide/extension_type.ipynb +++ b/site/en/guide/extension_type.ipynb @@ -1822,13 +1822,17 @@ " transpose_a=False, transpose_b=False,\n", " adjoint_a=False, adjoint_b=False,\n", " a_is_sparse=False, b_is_sparse=False,\n", - " output_type=None):\n", + " output_type=None,\n", + " grad_a=False, grad_b=False,\n", + " name=None,\n", + " ):\n", " if isinstance(a, MaskedTensor):\n", " a = a.with_default(0)\n", " if isinstance(b, MaskedTensor):\n", " b = b.with_default(0)\n", " return tf.matmul(a, b, transpose_a, transpose_b, adjoint_a,\n", - " adjoint_b, a_is_sparse, b_is_sparse, output_type)" + " adjoint_b, a_is_sparse, b_is_sparse,\n", + " output_type)" ] }, { From 8156353da0046a2f2db92dec94327f0144ce45f5 Mon Sep 17 00:00:00 2001 From: sharkfisher Date: Sun, 17 Dec 2023 16:37:45 -0800 Subject: [PATCH 18/85] Update transfer_learning.ipynb - fix initial_epoch for fine tuning initial_epoch for the fine tuning phase should be 1 more than history.epoch[-1], so that the history_fine.epoch would be [10, 11, ...19], a total of 10 fine tune epochs. Without the '+1', history.epoch is [0, 1, ...9], and history_fine.epoch is [9, 10, ... 19], the epoch index overlaps at 9, fine tune actually trained for 11 epochs, not 10, and the model is actually trained for 21 epochs in total (confirmed in the x axis of the combined training history curve - 21 data points) --- site/en/tutorials/images/transfer_learning.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index 6406ccdce74..3342c6eb838 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -930,7 +930,7 @@ "\n", "history_fine = model.fit(train_dataset,\n", " epochs=total_epochs,\n", - " initial_epoch=history.epoch[-1],\n", + " initial_epoch=history.epoch[-1]+1,\n", " validation_data=validation_dataset)" ] }, From e96ecf10bf0b39abe50722b1cc28e747091001bc Mon Sep 17 00:00:00 2001 From: M Liang Date: Mon, 18 Dec 2023 12:15:21 -0800 Subject: [PATCH 19/85] Alternative fix for initial_epoch (better conceptualization). Formatting check. --- site/en/tutorials/images/transfer_learning.ipynb | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index 3342c6eb838..dd9b97cabe2 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -930,7 +930,7 @@ "\n", "history_fine = model.fit(train_dataset,\n", " epochs=total_epochs,\n", - " initial_epoch=history.epoch[-1]+1,\n", + " initial_epoch=len(history.epoch),\n", " validation_data=validation_dataset)" ] }, @@ -1081,22 +1081,12 @@ "\n", "To learn more, visit the [Transfer learning guide](https://www.tensorflow.org/guide/keras/transfer_learning).\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uKIByL01da8c" - }, - "outputs": [], - "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "transfer_learning.ipynb", - "private_outputs": true, "toc_visible": true }, "kernelspec": { From 0993ccc2cf4564ce121febbf870ff9578c51d2ca Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Dec 2023 14:20:41 -0800 Subject: [PATCH 20/85] Cleaner Clang install instructions for Debian/Ubuntu. PiperOrigin-RevId: 592342788 --- site/en/install/source.md | 42 ++++++++++++++++++++++----------------- 1 file changed, 24 insertions(+), 18 deletions(-) diff --git a/site/en/install/source.md b/site/en/install/source.md index b70acdb93ea..0c556810fad 100644 --- a/site/en/install/source.md +++ b/site/en/install/source.md @@ -74,36 +74,42 @@ package sources: Alternatively, you can download and unpack the pre-built [Clang + LLVM 16](https://github.com/llvm/llvm-project/releases/tag/llvmorg-16.0.0). -Below is an example of steps you can take to set up the downloaded -Clang + LLVM 16 binaries: +Below is an example of steps you can take to set up the downloaded Clang + LLVM +16 binaries on Debian/Ubuntu operating systems: -1. Change to the desired destination directory: - ```cd ``` +1. Change to the desired destination directory: `cd ` -2. Load and extract an archive file...(suitable to your architecture): +1. Load and extract an archive file...(suitable to your architecture): 
-    
-    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-16.0.0/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz
+    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-16.0.0/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz
     
     tar -xvf clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz
-3. Check the obtained Clang + LLVM 16 binaries version: +1. Copy the extracted contents (directories and files) to `/usr` (you may need + sudo permissions, and the correct directory may vary by distribution). This + effectively installs Clang and LLVM, and adds it to the path. You should not + have to replace anything, unless you have a previous installation, in which + case you should replace the files: 
-    
-    ./clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/clang-16 --version 
+    cp -r clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/* /usr
-4. Directory `/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/clang-16` is - the actual path to your new clang. You can run the `./configure` script or - manually set environment variables `CC` and `BAZEL_COMPILER` to this path. +1. Check the obtained Clang + LLVM 16 binaries version: + 
+    clang --version
+
+ +1. Now that `/usr/bin/clang` is the actual path to your new clang. You can run + the `./configure` script or manually set environment variables `CC` and + `BAZEL_COMPILER` to this path. ### Install GPU support (optional, Linux only) There is *no* GPU support for macOS. -Read the [GPU support](./gpu.md) guide to install the drivers and additional +Read the [GPU support](./pip.md) guide to install the drivers and additional software required to run TensorFlow on a GPU. Note: It is easier to set up one of TensorFlow's GPU-enabled [Docker images](#docker_linux_builds). @@ -204,7 +210,7 @@ Preconfigured Bazel build configs to DISABLE default on features: #### GPU support -For [GPU support](./gpu.md), set `cuda=Y` during configuration and specify the +For [GPU support](./pip.md), set `cuda=Y` during configuration and specify the versions of CUDA and cuDNN. If your system has multiple versions of CUDA or cuDNN installed, explicitly set the version instead of relying on the default. `./configure` creates symbolic links to your system's CUDA libraries—so if you @@ -336,8 +342,8 @@ docker run -it -w /tensorflow -v /path/to/tensorflow:/tensorflow -v $ With the source tree set up, build the TensorFlow package within the container's virtual environment: -1. Optional: Configure the build—this prompts the user to answer build configuration - questions. +1. Optional: Configure the build—this prompts the user to answer build + configuration questions. 2. Build the tool used to create the *pip* package. 3. Run the tool to create the *pip* package. 4. Adjust the ownership permissions of the file for outside the container. @@ -374,7 +380,7 @@ Docker is the easiest way to build GPU support for TensorFlow since the *host* machine only requires the [NVIDIA® driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver){:.external} (the *NVIDIA® CUDA® Toolkit* doesn't have to be installed). Refer to the -[GPU support guide](./gpu.md) and the TensorFlow [Docker guide](./docker.md) to +[GPU support guide](./pip.md) and the TensorFlow [Docker guide](./docker.md) to set up [nvidia-docker](https://github.com/NVIDIA/nvidia-docker){:.external} (Linux only). From 2c5c356edfe1f80070ee8bc1d9956cad612cecb6 Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Tue, 19 Dec 2023 23:36:54 +0000 Subject: [PATCH 21/85] Update site/en/guide/sparse_tensor.ipynb --- site/en/guide/sparse_tensor.ipynb | 5 ----- 1 file changed, 5 deletions(-) diff --git a/site/en/guide/sparse_tensor.ipynb b/site/en/guide/sparse_tensor.ipynb index 407561ec6f5..45f1e3fd3c3 100644 --- a/site/en/guide/sparse_tensor.ipynb +++ b/site/en/guide/sparse_tensor.ipynb @@ -31,11 +31,6 @@ "# limitations under the License." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": { From 8246c1cba26bb07d9c02c165d83936c5b50825ca Mon Sep 17 00:00:00 2001 From: Fergus Henderson Date: Wed, 20 Dec 2023 10:52:39 -0800 Subject: [PATCH 22/85] Fix formatting errors in links. This makes the link on line 214-215 consistent with the link on line 205-206. PiperOrigin-RevId: 592603752 --- site/en/guide/versions.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/site/en/guide/versions.md b/site/en/guide/versions.md index df0d75114ef..58f9f3848fb 100644 --- a/site/en/guide/versions.md +++ b/site/en/guide/versions.md @@ -203,7 +203,7 @@ These include: such as: - [C++](../install/lang_c.ipynb) (exposed through header files in - [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)). + [`tensorflow/cc/`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)). - [Java](../install/lang_java_legacy.md), - [Go](https://github.com/tensorflow/build/blob/master/golang_install_guide/README.md) - [JavaScript](https://www.tensorflow.org/js) @@ -212,7 +212,7 @@ These include: Objective-C, and Swift, in particular - **C++** (exposed through header files in - [`tensorflow/lite/`]\(https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/\)) + [`tensorflow/lite/`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/)) * **Details of composite ops:** Many public functions in Python expand to several primitive ops in the graph, and these details will be part of any From 5ed8fbe64df2a05c6532919591219b545e61ef5f Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 10 Jan 2024 16:11:11 -0800 Subject: [PATCH 23/85] Fix doc-gen scripts for TF2.15+ - filter_builtin_modules was not compatible with keras lazy loaders. - but it's redundant now, totally covered by the base_dir filters. - Add logging. - Remove the path-checks in generate2, these mostly give false-positive errors > not worth it. PiperOrigin-RevId: 597382362 --- .../api_generator/doc_generator_visitor.py | 3 +++ .../api_generator/generate_lib.py | 12 ++++++---- .../api_generator/public_api.py | 24 ------------------- 3 files changed, 10 insertions(+), 29 deletions(-) diff --git a/tools/tensorflow_docs/api_generator/doc_generator_visitor.py b/tools/tensorflow_docs/api_generator/doc_generator_visitor.py index 8776644878c..ce6fe68105f 100644 --- a/tools/tensorflow_docs/api_generator/doc_generator_visitor.py +++ b/tools/tensorflow_docs/api_generator/doc_generator_visitor.py @@ -421,6 +421,9 @@ def build(self): duplicates = {} for path, node in self.path_tree.items(): + _LOGGER.debug('DocGeneratorVisitor.build') + _LOGGER.debug(' path: %s', path) + if not path: continue full_name = node.full_name diff --git a/tools/tensorflow_docs/api_generator/generate_lib.py b/tools/tensorflow_docs/api_generator/generate_lib.py index cb0e3916927..fdeb0f60601 100644 --- a/tools/tensorflow_docs/api_generator/generate_lib.py +++ b/tools/tensorflow_docs/api_generator/generate_lib.py @@ -15,11 +15,11 @@ """Generate tensorflow.org style API Reference docs for a Python module.""" import collections +import logging import os import pathlib import shutil import tempfile - from typing import Any, Optional, Sequence, Type, Union from tensorflow_docs.api_generator import config @@ -29,11 +29,8 @@ from tensorflow_docs.api_generator import reference_resolver as reference_resolver_lib from tensorflow_docs.api_generator import toc as toc_lib from tensorflow_docs.api_generator import traverse - from tensorflow_docs.api_generator.pretty_docs import docs_for_object - from tensorflow_docs.api_generator.report import utils - import yaml # Used to add a collections.OrderedDict representer to yaml so that the @@ -42,6 +39,9 @@ # Using a normal dict doesn't preserve the order of the input dictionary. _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG +# To see the logs pass: --logger_levels=tensorflow_docs:DEBUG --alsologtostderr +_LOGGER = logging.getLogger(__name__) + def dict_representer(dumper, data): return dumper.represent_dict(data.items()) @@ -121,6 +121,9 @@ def write_docs( # Parse and write Markdown pages, resolving cross-links (`tf.symbol`). num_docs_output = 0 for api_node in parser_config.api_tree.iter_nodes(): + _LOGGER.debug('generate_lib.write_docs') + _LOGGER.debug(' full_name: %s', api_node.full_name) + full_name = api_node.full_name if api_node.output_type() is api_node.OutputType.FRAGMENT: @@ -391,7 +394,6 @@ def make_default_filters(self) -> list[public_api.ApiFilter]: public_api.FailIfNestedTooDeep(10), public_api.filter_module_all, public_api.add_proto_fields, - public_api.filter_builtin_modules, public_api.filter_private_symbols, public_api.FilterBaseDirs(self._base_dir), public_api.FilterPrivateMap(self._private_map), diff --git a/tools/tensorflow_docs/api_generator/public_api.py b/tools/tensorflow_docs/api_generator/public_api.py index c9803ee04e3..e6a994bff5b 100644 --- a/tools/tensorflow_docs/api_generator/public_api.py +++ b/tools/tensorflow_docs/api_generator/public_api.py @@ -489,27 +489,3 @@ def add_proto_fields(path: Sequence[str], parent: Any, children = sorted(children.items(), key=lambda item: item[0]) return children - - -def filter_builtin_modules(path: Sequence[str], parent: Any, - children: Children) -> Children: - """Filters module children to remove builtin modules. - - Args: - path: API to this symbol - parent: The object - children: A list of (name, object) pairs. - - Returns: - `children` with all builtin modules removed. - """ - del path - del parent - # filter out 'builtin' modules - filtered_children = [] - for name, child in children: - # Do not descend into built-in modules - if inspect.ismodule(child) and child.__name__ in sys.builtin_module_names: - continue - filtered_children.append((name, child)) - return filtered_children From ad55dbd374f3d48c2260f2a8a41da4b2c171cb32 Mon Sep 17 00:00:00 2001 From: Brian Wieder Date: Thu, 11 Jan 2024 14:56:42 -0800 Subject: [PATCH 24/85] Update `preprocessing_layers` tutorial to support pandas 2.0 PiperOrigin-RevId: 597662203 --- site/en/tutorials/structured_data/preprocessing_layers.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/tutorials/structured_data/preprocessing_layers.ipynb b/site/en/tutorials/structured_data/preprocessing_layers.ipynb index 928a56eb8bc..ead524ca13c 100644 --- a/site/en/tutorials/structured_data/preprocessing_layers.ipynb +++ b/site/en/tutorials/structured_data/preprocessing_layers.ipynb @@ -297,7 +297,7 @@ "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n", " df = dataframe.copy()\n", " labels = df.pop('target')\n", - " df = {key: value[:,tf.newaxis] for key, value in dataframe.items()}\n", + " df = {key: value.values[:,tf.newaxis] for key, value in dataframe.items()}\n", " ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n", " if shuffle:\n", " ds = ds.shuffle(buffer_size=len(dataframe))\n", From 15f45f8ef3e9307fc980a4c6e2c2d690cf4b4130 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 16 Jan 2024 17:19:57 -0800 Subject: [PATCH 25/85] Fix Typos. PiperOrigin-RevId: 599005387 --- site/en/community/contribute/docs_style.md | 2 +- site/en/guide/data.ipynb | 2 +- site/en/guide/dtensor_overview.ipynb | 4 ++-- .../en/guide/migrate/migrating_feature_columns.ipynb | 12 ++++++------ site/en/guide/migrate/migration_debugging.ipynb | 2 +- site/en/guide/profiler.md | 4 ++-- site/en/guide/random_numbers.ipynb | 2 +- site/en/guide/tf_numpy_type_promotion.ipynb | 2 +- site/en/hub/common_saved_model_apis/text.md | 8 ++++---- site/en/hub/tf2_saved_model.md | 2 +- .../tutorials/action_recognition_with_tf_hub.ipynb | 2 +- site/en/hub/tutorials/cropnet_cassava.ipynb | 2 +- ..._with_tf_hub_multilingual_universal_encoder.ipynb | 4 ++-- site/en/hub/tutorials/image_enhancing.ipynb | 2 +- site/en/hub/tutorials/image_feature_vector.ipynb | 2 +- site/en/hub/tutorials/movenet.ipynb | 4 ++-- site/en/hub/tutorials/movinet.ipynb | 4 ++-- ...enteval_for_universal_sentence_encoder_cmlm.ipynb | 2 +- site/en/hub/tutorials/spice.ipynb | 2 +- site/en/hub/tutorials/tf2_object_detection.ipynb | 4 ++-- .../tutorials/tf_hub_generative_image_module.ipynb | 2 +- site/en/install/source_windows.md | 4 ++-- site/en/r1/guide/datasets.md | 4 ++-- site/en/r1/guide/distribute_strategy.ipynb | 2 +- site/en/r1/guide/graph_viz.md | 2 +- site/en/r1/guide/performance/overview.md | 4 ++-- site/en/r1/tutorials/distribute/keras.ipynb | 2 +- site/en/r1/tutorials/images/deep_cnn.md | 4 ++-- site/en/r1/tutorials/images/image_recognition.md | 2 +- site/en/r1/tutorials/representation/unicode.ipynb | 2 +- site/en/r1/tutorials/representation/word2vec.md | 12 ++++++------ site/en/r1/tutorials/sequences/audio_recognition.md | 4 ++-- .../distribute/multi_worker_with_estimator.ipynb | 2 +- site/en/tutorials/generative/cyclegan.ipynb | 2 +- site/en/tutorials/generative/data_compression.ipynb | 2 +- site/en/tutorials/generative/pix2pix.ipynb | 2 +- .../interpretability/integrated_gradients.ipynb | 2 +- site/en/tutorials/keras/save_and_load.ipynb | 2 +- site/en/tutorials/load_data/pandas_dataframe.ipynb | 6 +++--- .../tutorials/structured_data/imbalanced_data.ipynb | 2 +- .../api_generator/doc_generator_visitor.py | 2 +- tools/tensorflow_docs/api_generator/parser_test.py | 3 ++- tools/tensorflow_docs/api_generator/toc.py | 2 +- tools/tensorflow_docs/tools/nblint/decorator.py | 2 +- 44 files changed, 71 insertions(+), 70 deletions(-) diff --git a/site/en/community/contribute/docs_style.md b/site/en/community/contribute/docs_style.md index eba78afa896..d4e42cb5235 100644 --- a/site/en/community/contribute/docs_style.md +++ b/site/en/community/contribute/docs_style.md @@ -63,7 +63,7 @@ repository like this: * \[Basics\]\(../../guide/basics.ipynb\) produces [Basics](../../guide/basics.ipynb). -This is the prefered approach because this way the links on +This is the preferred approach because this way the links on [tensorflow.org](https://www.tensorflow.org), [GitHub](https://github.com/tensorflow/docs){:.external} and [Colab](https://github.com/tensorflow/docs/tree/master/site/en/guide/bazics.ipynb){:.external} diff --git a/site/en/guide/data.ipynb b/site/en/guide/data.ipynb index d9c8fff8982..739ef131005 100644 --- a/site/en/guide/data.ipynb +++ b/site/en/guide/data.ipynb @@ -1385,7 +1385,7 @@ "The simplest form of batching stacks `n` consecutive elements of a dataset into\n", "a single element. The `Dataset.batch()` transformation does exactly this, with\n", "the same constraints as the `tf.stack()` operator, applied to each component\n", - "of the elements: i.e. for each component *i*, all elements must have a tensor\n", + "of the elements: i.e., for each component *i*, all elements must have a tensor\n", "of the exact same shape." ] }, diff --git a/site/en/guide/dtensor_overview.ipynb b/site/en/guide/dtensor_overview.ipynb index 95a50f3465f..1b55ee0283f 100644 --- a/site/en/guide/dtensor_overview.ipynb +++ b/site/en/guide/dtensor_overview.ipynb @@ -281,7 +281,7 @@ "id": "Eyp_qOSyvieo" }, "source": [ - "\"A\n" + "\"A\n" ] }, { @@ -303,7 +303,7 @@ "source": [ "For the same `mesh_2d`, the layout `Layout([\"x\", dtensor.UNSHARDED], mesh_2d)` is a layout for a rank-2 `Tensor` that is replicated across `\"y\"`, and whose first axis is sharded on mesh dimension `x`.\n", "\n", - "\"A\n" + "\"A\n" ] }, { diff --git a/site/en/guide/migrate/migrating_feature_columns.ipynb b/site/en/guide/migrate/migrating_feature_columns.ipynb index ea12a5ef391..b2dbc5fe7c0 100644 --- a/site/en/guide/migrate/migrating_feature_columns.ipynb +++ b/site/en/guide/migrate/migrating_feature_columns.ipynb @@ -654,17 +654,17 @@ "source": [ "categorical_col = tf1.feature_column.categorical_column_with_identity(\n", " 'type', num_buckets=one_hot_dims)\n", - "# Convert index to one-hot; e.g. [2] -> [0,0,1].\n", + "# Convert index to one-hot; e.g., [2] -> [0,0,1].\n", "indicator_col = tf1.feature_column.indicator_column(categorical_col)\n", "\n", - "# Convert strings to indices; e.g. ['small'] -> [1].\n", + "# Convert strings to indices; e.g., ['small'] -> [1].\n", "vocab_col = tf1.feature_column.categorical_column_with_vocabulary_list(\n", " 'size', vocabulary_list=vocab, num_oov_buckets=1)\n", "# Embed the indices.\n", "embedding_col = tf1.feature_column.embedding_column(vocab_col, embedding_dims)\n", "\n", "normalizer_fn = lambda x: (x - weight_mean) / math.sqrt(weight_variance)\n", - "# Normalize the numeric inputs; e.g. [2.0] -> [0.0].\n", + "# Normalize the numeric inputs; e.g., [2.0] -> [0.0].\n", "numeric_col = tf1.feature_column.numeric_column(\n", " 'weight', normalizer_fn=normalizer_fn)\n", "\n", @@ -727,12 +727,12 @@ " 'size': tf.keras.Input(shape=(), dtype='string'),\n", " 'weight': tf.keras.Input(shape=(), dtype='float32'),\n", "}\n", - "# Convert index to one-hot; e.g. [2] -> [0,0,1].\n", + "# Convert index to one-hot; e.g., [2] -> [0,0,1].\n", "type_output = tf.keras.layers.CategoryEncoding(\n", " one_hot_dims, output_mode='one_hot')(inputs['type'])\n", - "# Convert size strings to indices; e.g. ['small'] -> [1].\n", + "# Convert size strings to indices; e.g., ['small'] -> [1].\n", "size_output = tf.keras.layers.StringLookup(vocabulary=vocab)(inputs['size'])\n", - "# Normalize the numeric inputs; e.g. [2.0] -> [0.0].\n", + "# Normalize the numeric inputs; e.g., [2.0] -> [0.0].\n", "weight_output = tf.keras.layers.Normalization(\n", " axis=None, mean=weight_mean, variance=weight_variance)(inputs['weight'])\n", "outputs = {\n", diff --git a/site/en/guide/migrate/migration_debugging.ipynb b/site/en/guide/migrate/migration_debugging.ipynb index 86c86680dc9..25cb7f9065f 100644 --- a/site/en/guide/migrate/migration_debugging.ipynb +++ b/site/en/guide/migrate/migration_debugging.ipynb @@ -128,7 +128,7 @@ "\n", " a. Check training behaviors with TensorBoard\n", "\n", - " * use simple optimizers e.g. SGD and simple distribution strategies e.g.\n", + " * use simple optimizers e.g., SGD and simple distribution strategies e.g.\n", " `tf.distribute.OneDeviceStrategy` first\n", " * training metrics\n", " * evaluation metrics\n", diff --git a/site/en/guide/profiler.md b/site/en/guide/profiler.md index 1cd19c109fe..e92d1b9eae4 100644 --- a/site/en/guide/profiler.md +++ b/site/en/guide/profiler.md @@ -694,7 +694,7 @@ first few batches to avoid inaccuracies due to initialization overhead. An example for profiling multiple workers: ```python - # E.g. your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you + # E.g., your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you # would like to profile for a duration of 2 seconds. tf.profiler.experimental.client.trace( 'grpc://10.0.0.2:8466,grpc://10.0.0.3:8466,grpc://10.0.0.4:8466', @@ -845,7 +845,7 @@ more efficient by casting to different data types after applying spatial transformations, such as flipping, cropping, rotating, etc. Note: Some ops like `tf.image.resize` transparently change the `dtype` to -`fp32`. Make sure you normalize your data to lie between `0` and `1` if its not +`fp32`. Make sure you normalize your data to lie between `0` and `1` if it's not done automatically. Skipping this step could lead to `NaN` errors if you have enabled [AMP](https://developer.nvidia.com/automatic-mixed-precision). diff --git a/site/en/guide/random_numbers.ipynb b/site/en/guide/random_numbers.ipynb index 5212a10a49a..f8b824ad906 100644 --- a/site/en/guide/random_numbers.ipynb +++ b/site/en/guide/random_numbers.ipynb @@ -166,7 +166,7 @@ "source": [ "See the *Algorithms* section below for more information about it.\n", "\n", - "Another way to create a generator is with `Generator.from_non_deterministic_state`. A generator created this way will start from a non-deterministic state, depending on e.g. time and OS." + "Another way to create a generator is with `Generator.from_non_deterministic_state`. A generator created this way will start from a non-deterministic state, depending on e.g., time and OS." ] }, { diff --git a/site/en/guide/tf_numpy_type_promotion.ipynb b/site/en/guide/tf_numpy_type_promotion.ipynb index a9e176c5db6..703f481e5cf 100644 --- a/site/en/guide/tf_numpy_type_promotion.ipynb +++ b/site/en/guide/tf_numpy_type_promotion.ipynb @@ -455,7 +455,7 @@ { "cell_type": "markdown", "metadata": { - "id": "7UmunnJ8Tru3" + "id": "7UmunnJ8True3" }, "source": [ "**First Case**: When `tf.constant` is called with an input with no user-specified dtype." diff --git a/site/en/hub/common_saved_model_apis/text.md b/site/en/hub/common_saved_model_apis/text.md index 1c45b8ea026..c618b02d9f1 100644 --- a/site/en/hub/common_saved_model_apis/text.md +++ b/site/en/hub/common_saved_model_apis/text.md @@ -132,8 +132,8 @@ preprocessor = hub.load("path/to/preprocessor") # Must match `encoder`. encoder_inputs = preprocessor(text_input) encoder = hub.load("path/to/encoder") -enocder_outputs = encoder(encoder_inputs) -embeddings = enocder_outputs["default"] +encoder_outputs = encoder(encoder_inputs) +embeddings = encoder_outputs["default"] ``` Recall from the [Reusable SavedModel API](../reusable_saved_models.md) that @@ -304,8 +304,8 @@ provisions from the [Reusable SavedModel API](../reusable_saved_models.md). #### Usage synopsis ```python -enocder = hub.load("path/to/encoder") -enocder_outputs = encoder(encoder_inputs) +encoder = hub.load("path/to/encoder") +encoder_outputs = encoder(encoder_inputs) ``` or equivalently in Keras: diff --git a/site/en/hub/tf2_saved_model.md b/site/en/hub/tf2_saved_model.md index 7a7220d0a2e..641f9b3517b 100644 --- a/site/en/hub/tf2_saved_model.md +++ b/site/en/hub/tf2_saved_model.md @@ -82,7 +82,7 @@ and uncompressed SavedModels. For details, see [Caching](caching.md). SavedModels can be loaded from a specified `handle`, where the `handle` is a filesystem path, valid TFhub.dev model URL (e.g. "https://tfhub.dev/..."). Kaggle Models URLs mirror TFhub.dev handles in accordance with our Terms and the -license associated with the model assets, e.g. "https://www.kaggle.com/...". +license associated with the model assets, e.g., "https://www.kaggle.com/...". Handles from Kaggle Models are equivalent to their corresponding TFhub.dev handle. diff --git a/site/en/hub/tutorials/action_recognition_with_tf_hub.ipynb b/site/en/hub/tutorials/action_recognition_with_tf_hub.ipynb index b4a1e439621..3f586991ba9 100644 --- a/site/en/hub/tutorials/action_recognition_with_tf_hub.ipynb +++ b/site/en/hub/tutorials/action_recognition_with_tf_hub.ipynb @@ -184,7 +184,7 @@ " return list(_VIDEO_LIST)\n", "\n", "def fetch_ucf_video(video):\n", - " \"\"\"Fetchs a video and cache into local filesystem.\"\"\"\n", + " \"\"\"Fetches a video and cache into local filesystem.\"\"\"\n", " cache_path = os.path.join(_CACHE_DIR, video)\n", " if not os.path.exists(cache_path):\n", " urlpath = request.urljoin(UCF_ROOT, video)\n", diff --git a/site/en/hub/tutorials/cropnet_cassava.ipynb b/site/en/hub/tutorials/cropnet_cassava.ipynb index 18f41c00da1..926b5395e41 100644 --- a/site/en/hub/tutorials/cropnet_cassava.ipynb +++ b/site/en/hub/tutorials/cropnet_cassava.ipynb @@ -199,7 +199,7 @@ "id": "QT3XWAtR6BRy" }, "source": [ - "The *cassava* dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. The model can predict all of these classes as well as sixth class for \"unknown\" when the model is not confident in it's prediction." + "The *cassava* dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. The model can predict all of these classes as well as sixth class for \"unknown\" when the model is not confident in its prediction." ] }, { diff --git a/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb b/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb index 31fc037dfe7..920d197811e 100644 --- a/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb +++ b/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb @@ -271,7 +271,7 @@ "spanish_sentences = ['perro', 'Los cachorros son agradables.', 'Disfruto de dar largos paseos por la playa con mi perro.']\n", "\n", "# Multilingual example\n", - "multilingual_example = [\"Willkommen zu einfachen, aber\", \"verrassend krachtige\", \"multilingüe\", \"compréhension du langage naturel\", \"модели.\", \"大家是什么意思\" , \"보다 중요한\", \".اللغة التي يتحدثونها\"]\n", + "multilingual_example = [\"Willkommen zu einfachen, aber\", \"verrassend krachtige\", \"multilingüe\", \"compréhension du language naturel\", \"модели.\", \"大家是什么意思\" , \"보다 중요한\", \".اللغة التي يتحدثونها\"]\n", "multilingual_example_in_en = [\"Welcome to simple yet\", \"surprisingly powerful\", \"multilingual\", \"natural language understanding\", \"models.\", \"What people mean\", \"matters more than\", \"the language they speak.\"]\n" ] }, @@ -4174,7 +4174,7 @@ "id": "Dxu66S8wJIG9" }, "source": [ - "### Semantic-search crosss-lingual capabilities\n", + "### Semantic-search cross-lingual capabilities\n", "\n", "In this section we show how to retrieve sentences related to a set of sample English sentences. Things to try:\n", "\n", diff --git a/site/en/hub/tutorials/image_enhancing.ipynb b/site/en/hub/tutorials/image_enhancing.ipynb index 4c9496b79ae..3710ebd6d66 100644 --- a/site/en/hub/tutorials/image_enhancing.ipynb +++ b/site/en/hub/tutorials/image_enhancing.ipynb @@ -346,7 +346,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "r_dautO6qbTV" + "id": "r_defaultO6qbTV" }, "outputs": [], "source": [ diff --git a/site/en/hub/tutorials/image_feature_vector.ipynb b/site/en/hub/tutorials/image_feature_vector.ipynb index 29ac0c97ddd..b5283c45b3d 100644 --- a/site/en/hub/tutorials/image_feature_vector.ipynb +++ b/site/en/hub/tutorials/image_feature_vector.ipynb @@ -357,7 +357,7 @@ "source": [ "## Train the network\n", "\n", - "Now that our model is built, let's train it and see how it perfoms on our test set." + "Now that our model is built, let's train it and see how it performs on our test set." ] }, { diff --git a/site/en/hub/tutorials/movenet.ipynb b/site/en/hub/tutorials/movenet.ipynb index 2b6ffc6eb54..f7955a5253b 100644 --- a/site/en/hub/tutorials/movenet.ipynb +++ b/site/en/hub/tutorials/movenet.ipynb @@ -450,7 +450,7 @@ "id": "ymTVR2I9x22I" }, "source": [ - "This session demonstrates the minumum working example of running the model on a **single image** to predict the 17 human keypoints." + "This session demonstrates the minimum working example of running the model on a **single image** to predict the 17 human keypoints." ] }, { @@ -697,7 +697,7 @@ " return output_image\n", "\n", "def run_inference(movenet, image, crop_region, crop_size):\n", - " \"\"\"Runs model inferece on the cropped region.\n", + " \"\"\"Runs model inference on the cropped region.\n", "\n", " The function runs the model inference on the cropped region and updates the\n", " model output to the original image coordinate system.\n", diff --git a/site/en/hub/tutorials/movinet.ipynb b/site/en/hub/tutorials/movinet.ipynb index 61609dbf72a..24600256cf9 100644 --- a/site/en/hub/tutorials/movinet.ipynb +++ b/site/en/hub/tutorials/movinet.ipynb @@ -890,7 +890,7 @@ " steps = video.shape[0]\n", " # estimate duration of the video (in seconds)\n", " duration = steps / video_fps\n", - " # estiamte top_k probabilities and corresponding labels\n", + " # estimate top_k probabilities and corresponding labels\n", " top_probs, top_labels, _ = get_top_k_streaming_labels(probs, k=top_k)\n", "\n", " images = []\n", @@ -950,7 +950,7 @@ " logits, states = model({**states, 'image': image})\n", " all_logits.append(logits)\n", "\n", - "# concatinating all the logits\n", + "# concatenating all the logits\n", "logits = tf.concat(all_logits, 0)\n", "# estimating probabilities\n", "probs = tf.nn.softmax(logits, axis=-1)" diff --git a/site/en/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb b/site/en/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb index b152d3deee8..c33dce64c92 100644 --- a/site/en/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb +++ b/site/en/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb @@ -117,7 +117,7 @@ "id": "7a2ohPn8vMe2" }, "source": [ - "#Execute a SentEval evaulation task\n", + "#Execute a SentEval evaluation task\n", "The following code block executes a SentEval task and output the results, choose one of the following tasks to evaluate the USE CMLM model:\n", "\n", "```\n", diff --git a/site/en/hub/tutorials/spice.ipynb b/site/en/hub/tutorials/spice.ipynb index b58d07e46da..9ff6cd3bd62 100644 --- a/site/en/hub/tutorials/spice.ipynb +++ b/site/en/hub/tutorials/spice.ipynb @@ -658,7 +658,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "eMUTI4L52ZHA" + "id": "eMULTI4L52ZHA" }, "outputs": [], "source": [ diff --git a/site/en/hub/tutorials/tf2_object_detection.ipynb b/site/en/hub/tutorials/tf2_object_detection.ipynb index 38b162068d9..d06ad401824 100644 --- a/site/en/hub/tutorials/tf2_object_detection.ipynb +++ b/site/en/hub/tutorials/tf2_object_detection.ipynb @@ -291,7 +291,7 @@ "id": "yX3pb_pXDjYA" }, "source": [ - "Intalling the Object Detection API" + "Installing the Object Detection API" ] }, { @@ -554,7 +554,7 @@ "\n", "Among the available object detection models there's Mask R-CNN and the output of this model allows instance segmentation.\n", "\n", - "To visualize it we will use the same method we did before but adding an aditional parameter: `instance_masks=output_dict.get('detection_masks_reframed', None)`\n" + "To visualize it we will use the same method we did before but adding an additional parameter: `instance_masks=output_dict.get('detection_masks_reframed', None)`\n" ] }, { diff --git a/site/en/hub/tutorials/tf_hub_generative_image_module.ipynb b/site/en/hub/tutorials/tf_hub_generative_image_module.ipynb index 4669f3b2dc3..4937bc2eb22 100644 --- a/site/en/hub/tutorials/tf_hub_generative_image_module.ipynb +++ b/site/en/hub/tutorials/tf_hub_generative_image_module.ipynb @@ -421,7 +421,7 @@ "If image is from the module space, the descent is quick and converges to a reasonable sample. Try out descending to an image that is **not from the module space**. The descent will only converge if the image is reasonably close to the space of training images.\n", "\n", "How to make it descend faster and to a more realistic image? One can try:\n", - "* using different loss on the image difference, e.g. quadratic,\n", + "* using different loss on the image difference, e.g., quadratic,\n", "* using different regularizer on the latent vector,\n", "* initializing from a random vector in multiple runs,\n", "* etc.\n" diff --git a/site/en/install/source_windows.md b/site/en/install/source_windows.md index 9cf33d0458b..758e5dbea45 100644 --- a/site/en/install/source_windows.md +++ b/site/en/install/source_windows.md @@ -95,7 +95,7 @@ a release branch that is known to work. ## Optional: Environmental Variable Set Up Run following commands before running build command to avoid issue with package creation: -(If the below commands were set up while installing the packages, please ignore them). Run `set` check if all the paths were set correctly, run `echo %Environmental Variable%` e.g. `echo %BAZEL_VC%` to check path set up for a specific Environmental Variable +(If the below commands were set up while installing the packages, please ignore them). Run `set` check if all the paths were set correctly, run `echo %Environmental Variable%` e.g., `echo %BAZEL_VC%` to check path set up for a specific Environmental Variable Python path set up issue [tensorflow:issue#59943](https://github.com/tensorflow/tensorflow/issues/59943),[tensorflow:issue#9436](https://github.com/tensorflow/tensorflow/issues/9436),[tensorflow:issue#60083](https://github.com/tensorflow/tensorflow/issues/60083) @@ -257,7 +257,7 @@ your platform. Use `pip3 install` to install the package, for example: 
 pip3 install C:/tmp/tensorflow_pkg/tensorflow-version-tags.whl
 
-e.g. pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
+e.g., pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
Success: TensorFlow is now installed. diff --git a/site/en/r1/guide/datasets.md b/site/en/r1/guide/datasets.md index b1ed1b6e113..d7c38bf2f92 100644 --- a/site/en/r1/guide/datasets.md +++ b/site/en/r1/guide/datasets.md @@ -437,7 +437,7 @@ dataset = dataset.batch(32) iterator = dataset.make_initializable_iterator() # You can feed the initializer with the appropriate filenames for the current -# phase of execution, e.g. training vs. validation. +# phase of execution, e.g., training vs. validation. # Initialize `iterator` with training data. training_filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"] @@ -639,7 +639,7 @@ TODO(mrry): Add this section. The simplest form of batching stacks `n` consecutive elements of a dataset into a single element. The `Dataset.batch()` transformation does exactly this, with the same constraints as the `tf.stack()` operator, applied to each component -of the elements: i.e. for each component *i*, all elements must have a tensor +of the elements: i.e., for each component *i*, all elements must have a tensor of the exact same shape. ```python diff --git a/site/en/r1/guide/distribute_strategy.ipynb b/site/en/r1/guide/distribute_strategy.ipynb index 79d6293eba7..3c0b453a278 100644 --- a/site/en/r1/guide/distribute_strategy.ipynb +++ b/site/en/r1/guide/distribute_strategy.ipynb @@ -607,7 +607,7 @@ }, "source": [ "## Using `tf.distribute.Strategy` with custom training loops\n", - "As you've seen, using `tf.distrbute.Strategy` with high level APIs is only a couple lines of code change. With a little more effort, `tf.distrbute.Strategy` can also be used by other users who are not using these frameworks.\n", + "As you've seen, using `tf.distribute.Strategy` with high level APIs is only a couple lines of code change. With a little more effort, `tf.distribute.Strategy` can also be used by other users who are not using these frameworks.\n", "\n", "TensorFlow is used for a wide variety of use cases and some users (such as researchers) require more flexibility and control over their training loops. This makes it hard for them to use the high level frameworks such as Estimator or Keras. For instance, someone using a GAN may want to take a different number of generator or discriminator steps each round. Similarly, the high level frameworks are not very suitable for Reinforcement Learning training. So these users will usually write their own training loops.\n", "\n", diff --git a/site/en/r1/guide/graph_viz.md b/site/en/r1/guide/graph_viz.md index 1965378e03e..1e3780e7928 100644 --- a/site/en/r1/guide/graph_viz.md +++ b/site/en/r1/guide/graph_viz.md @@ -251,7 +251,7 @@ is a snippet from the train and test section of a modification of the [Estimators MNIST tutorial](../tutorials/estimators/cnn.md), in which we have recorded summaries and runtime statistics. See the -[Tensorboard](https://tensorflow.org/tensorboard) +[TensorBoard](https://tensorflow.org/tensorboard) for details on how to record summaries. Full source is [here](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py). diff --git a/site/en/r1/guide/performance/overview.md b/site/en/r1/guide/performance/overview.md index af74f0f28c6..461fa4feb58 100644 --- a/site/en/r1/guide/performance/overview.md +++ b/site/en/r1/guide/performance/overview.md @@ -122,7 +122,7 @@ tf.Session(config=config) Intel® has added optimizations to TensorFlow for Intel® Xeon® and Intel® Xeon Phi™ through the use of the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) optimized primitives. The optimizations also provide speedups -for the consumer line of processors, e.g. i5 and i7 Intel processors. The Intel +for the consumer line of processors, e.g., i5 and i7 Intel processors. The Intel published paper [TensorFlow* Optimizations on Modern Intel® Architecture](https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture) contains additional details on the implementation. @@ -255,7 +255,7 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi a docker container, the data is not cached and the penalty is paid each time TensorFlow starts. The best practice is to include the [compute capabilities](http://developer.nvidia.com/cuda-gpus) - of the GPUs that will be used, e.g. P100: 6.0, Titan X (Pascal): 6.1, + of the GPUs that will be used, e.g., P100: 6.0, Titan X (Pascal): 6.1, Titan X (Maxwell): 5.2, and K80: 3.7. * Use a version of `gcc` that supports all of the optimizations of the target CPU. The recommended minimum gcc version is 4.8.3. On macOS, upgrade to the diff --git a/site/en/r1/tutorials/distribute/keras.ipynb b/site/en/r1/tutorials/distribute/keras.ipynb index 059b8c2d66f..14e8bf739a9 100644 --- a/site/en/r1/tutorials/distribute/keras.ipynb +++ b/site/en/r1/tutorials/distribute/keras.ipynb @@ -86,7 +86,7 @@ "Essentially, it copies all of the model's variables to each processor.\n", "Then, it uses [all-reduce](http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/) to combine the gradients from all processors and applies the combined value to all copies of the model.\n", "\n", - "`MirroredStategy` is one of several distribution strategy available in TensorFlow core. You can read about more strategies at [distribution strategy guide](../../guide/distribute_strategy.ipynb).\n" + "`MirroredStrategy` is one of several distribution strategy available in TensorFlow core. You can read about more strategies at [distribution strategy guide](../../guide/distribute_strategy.ipynb).\n" ] }, { diff --git a/site/en/r1/tutorials/images/deep_cnn.md b/site/en/r1/tutorials/images/deep_cnn.md index 00a914d8976..ef259516952 100644 --- a/site/en/r1/tutorials/images/deep_cnn.md +++ b/site/en/r1/tutorials/images/deep_cnn.md @@ -108,7 +108,7 @@ reusable by constructing the graph with the following modules: operations that read and preprocess CIFAR images for evaluation and training, respectively. 1. [**Model prediction:**](#model-prediction) `inference()` -adds operations that perform inference, i.e. classification, on supplied images. +adds operations that perform inference, i.e., classification, on supplied images. 1. [**Model training:**](#model-training) `loss()` and `train()` add operations that compute the loss, gradients, variable updates and visualization summaries. @@ -405,7 +405,7 @@ a "tower". We must set two attributes for each tower: * A unique name for all operations within a tower. `tf.name_scope` provides this unique name by prepending a scope. For instance, all operations in -the first tower are prepended with `tower_0`, e.g. `tower_0/conv1/Conv2D`. +the first tower are prepended with `tower_0`, e.g., `tower_0/conv1/Conv2D`. * A preferred hardware device to run the operation within a tower. `tf.device` specifies this. For diff --git a/site/en/r1/tutorials/images/image_recognition.md b/site/en/r1/tutorials/images/image_recognition.md index 0be884de403..2cbf9eee378 100644 --- a/site/en/r1/tutorials/images/image_recognition.md +++ b/site/en/r1/tutorials/images/image_recognition.md @@ -140,7 +140,7 @@ score of 0.8. -Next, try it out on your own images by supplying the --image= argument, e.g. +Next, try it out on your own images by supplying the --image= argument, e.g., ```bash bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png diff --git a/site/en/r1/tutorials/representation/unicode.ipynb b/site/en/r1/tutorials/representation/unicode.ipynb index 98aaacff5b9..a128724d31e 100644 --- a/site/en/r1/tutorials/representation/unicode.ipynb +++ b/site/en/r1/tutorials/representation/unicode.ipynb @@ -425,7 +425,7 @@ "source": [ "### Character substrings\n", "\n", - "Similarly, the `tf.strings.substr` operation accepts the \"`unit`\" parameter, and uses it to determine what kind of offsets the \"`pos`\" and \"`len`\" paremeters contain." + "Similarly, the `tf.strings.substr` operation accepts the \"`unit`\" parameter, and uses it to determine what kind of offsets the \"`pos`\" and \"`len`\" parameters contain." ] }, { diff --git a/site/en/r1/tutorials/representation/word2vec.md b/site/en/r1/tutorials/representation/word2vec.md index f6a27c68f3c..c76db7ab108 100644 --- a/site/en/r1/tutorials/representation/word2vec.md +++ b/site/en/r1/tutorials/representation/word2vec.md @@ -36,7 +36,7 @@ like to get your hands dirty with the details. Image and audio processing systems work with rich, high-dimensional datasets encoded as vectors of the individual raw pixel-intensities for image data, or -e.g. power spectral density coefficients for audio data. For tasks like object +e.g., power spectral density coefficients for audio data. For tasks like object or speech recognition we know that all the information required to successfully perform the task is encoded in the data (because humans can perform these tasks from the raw data). However, natural language processing systems traditionally @@ -109,7 +109,7 @@ $$ where \\(\text{score}(w_t, h)\\) computes the compatibility of word \\(w_t\\) with the context \\(h\\) (a dot product is commonly used). We train this model by maximizing its [log-likelihood](https://en.wikipedia.org/wiki/Likelihood_function) -on the training set, i.e. by maximizing +on the training set, i.e., by maximizing $$ \begin{align} @@ -176,7 +176,7 @@ As an example, let's consider the dataset We first form a dataset of words and the contexts in which they appear. We could define 'context' in any way that makes sense, and in fact people have looked at syntactic contexts (i.e. the syntactic dependents of the current -target word, see e.g. +target word, see e.g., [Levy et al.](https://levyomer.files.wordpress.com/2014/04/dependency-based-word-embeddings-acl-2014.pdf)), words-to-the-left of the target, words-to-the-right of the target, etc. For now, let's stick to the vanilla definition and define 'context' as the window @@ -204,7 +204,7 @@ where the goal is to predict `the` from `quick`. We select `num_noise` number of noisy (contrastive) examples by drawing from some noise distribution, typically the unigram distribution, \\(P(w)\\). For simplicity let's say `num_noise=1` and we select `sheep` as a noisy example. Next we compute the -loss for this pair of observed and noisy examples, i.e. the objective at time +loss for this pair of observed and noisy examples, i.e., the objective at time step \\(t\\) becomes $$J^{(t)}_\text{NEG} = \log Q_\theta(D=1 | \text{the, quick}) + @@ -212,7 +212,7 @@ $$J^{(t)}_\text{NEG} = \log Q_\theta(D=1 | \text{the, quick}) + The goal is to make an update to the embedding parameters \\(\theta\\) to improve (in this case, maximize) this objective function. We do this by deriving the -gradient of the loss with respect to the embedding parameters \\(\theta\\), i.e. +gradient of the loss with respect to the embedding parameters \\(\theta\\), i.e., \\(\frac{\partial}{\partial \theta} J_\text{NEG}\\) (luckily TensorFlow provides easy helper functions for doing this!). We then perform an update to the embeddings by taking a small step in the direction of the gradient. When this @@ -227,7 +227,7 @@ When we inspect these visualizations it becomes apparent that the vectors capture some general, and in fact quite useful, semantic information about words and their relationships to one another. It was very interesting when we first discovered that certain directions in the induced vector space specialize -towards certain semantic relationships, e.g. *male-female*, *verb tense* and +towards certain semantic relationships, e.g., *male-female*, *verb tense* and even *country-capital* relationships between words, as illustrated in the figure below (see also for example [Mikolov et al., 2013](https://www.aclweb.org/anthology/N13-1090)). diff --git a/site/en/r1/tutorials/sequences/audio_recognition.md b/site/en/r1/tutorials/sequences/audio_recognition.md index 8ad71b88a3c..0388514ec92 100644 --- a/site/en/r1/tutorials/sequences/audio_recognition.md +++ b/site/en/r1/tutorials/sequences/audio_recognition.md @@ -159,9 +159,9 @@ accuracy. If the training accuracy increases but the validation doesn't, that's a sign that overfitting is occurring, and your model is only learning things about the training clips, not broader patterns that generalize. -## Tensorboard +## TensorBoard -A good way to visualize how the training is progressing is using Tensorboard. By +A good way to visualize how the training is progressing is using TensorBoard. By default, the script saves out events to /tmp/retrain_logs, and you can load these by running: diff --git a/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb b/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb index 2abf05aa9f8..fcee0618854 100644 --- a/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb +++ b/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb @@ -186,7 +186,7 @@ "\n", "There are two components of `TF_CONFIG`: `cluster` and `task`. `cluster` provides information about the entire cluster, namely the workers and parameter servers in the cluster. `task` provides information about the current task. The first component `cluster` is the same for all workers and parameter servers in the cluster, and the second component `task` is different on each worker and parameter server and specifies its own `type` and `index`. In this example, the task `type` is `worker` and the task `index` is `0`.\n", "\n", - "For illustration purposes, this tutorial shows how to set a `TF_CONFIG` with 2 workers on `localhost`. In practice, you would create multiple workers on an external IP address and port, and set `TF_CONFIG` on each worker appropriately, i.e. modify the task `index`.\n", + "For illustration purposes, this tutorial shows how to set a `TF_CONFIG` with 2 workers on `localhost`. In practice, you would create multiple workers on an external IP address and port, and set `TF_CONFIG` on each worker appropriately, i.e., modify the task `index`.\n", "\n", "Warning: *Do not execute the following code in Colab.* TensorFlow's runtime will attempt to create a gRPC server at the specified IP address and port, which will likely fail. See the [keras version](multi_worker_with_keras.ipynb) of this tutorial for an example of how you can test run multiple workers on a single machine.\n", "\n", diff --git a/site/en/tutorials/generative/cyclegan.ipynb b/site/en/tutorials/generative/cyclegan.ipynb index 4c2b3ba8777..313be519591 100644 --- a/site/en/tutorials/generative/cyclegan.ipynb +++ b/site/en/tutorials/generative/cyclegan.ipynb @@ -154,7 +154,7 @@ "This is similar to what was done in [pix2pix](https://www.tensorflow.org/tutorials/generative/pix2pix#load_the_dataset)\n", "\n", "* In random jittering, the image is resized to `286 x 286` and then randomly cropped to `256 x 256`.\n", - "* In random mirroring, the image is randomly flipped horizontally i.e. left to right." + "* In random mirroring, the image is randomly flipped horizontally i.e., left to right." ] }, { diff --git a/site/en/tutorials/generative/data_compression.ipynb b/site/en/tutorials/generative/data_compression.ipynb index b6c043c0598..f756f088acd 100644 --- a/site/en/tutorials/generative/data_compression.ipynb +++ b/site/en/tutorials/generative/data_compression.ipynb @@ -821,7 +821,7 @@ { "cell_type": "markdown", "metadata": { - "id": "3ELLMAN1OwMQ" + "id": "3ELLMANN1OwMQ" }, "source": [ "The strings begin to get much shorter now, on the order of one byte per digit. However, this comes at a cost. More digits are becoming unrecognizable.\n", diff --git a/site/en/tutorials/generative/pix2pix.ipynb b/site/en/tutorials/generative/pix2pix.ipynb index 5912fab9be3..e45950dd923 100644 --- a/site/en/tutorials/generative/pix2pix.ipynb +++ b/site/en/tutorials/generative/pix2pix.ipynb @@ -280,7 +280,7 @@ "\n", "1. Resize each `256 x 256` image to a larger height and width—`286 x 286`.\n", "2. Randomly crop it back to `256 x 256`.\n", - "3. Randomly flip the image horizontally i.e. left to right (random mirroring).\n", + "3. Randomly flip the image horizontally i.e., left to right (random mirroring).\n", "4. Normalize the images to the `[-1, 1]` range." ] }, diff --git a/site/en/tutorials/interpretability/integrated_gradients.ipynb b/site/en/tutorials/interpretability/integrated_gradients.ipynb index 2ee792aa4e2..e63c8cdb7a2 100644 --- a/site/en/tutorials/interpretability/integrated_gradients.ipynb +++ b/site/en/tutorials/interpretability/integrated_gradients.ipynb @@ -724,7 +724,7 @@ "ax2 = plt.subplot(1, 2, 2)\n", "# Average across interpolation steps\n", "average_grads = tf.reduce_mean(path_gradients, axis=[1, 2, 3])\n", - "# Normalize gradients to 0 to 1 scale. E.g. (x - min(x))/(max(x)-min(x))\n", + "# Normalize gradients to 0 to 1 scale. E.g., (x - min(x))/(max(x)-min(x))\n", "average_grads_norm = (average_grads-tf.math.reduce_min(average_grads))/(tf.math.reduce_max(average_grads)-tf.reduce_min(average_grads))\n", "ax2.plot(alphas, average_grads_norm)\n", "ax2.set_title('Average pixel gradients (normalized) over alpha')\n", diff --git a/site/en/tutorials/keras/save_and_load.ipynb b/site/en/tutorials/keras/save_and_load.ipynb index 02c8af3a71d..404fa1ee8be 100644 --- a/site/en/tutorials/keras/save_and_load.ipynb +++ b/site/en/tutorials/keras/save_and_load.ipynb @@ -854,7 +854,7 @@ " * `from_config(cls, config)` uses the returned config from `get_config` to create a new object. By default, this function will use the config as initialization kwargs (`return cls(**config)`).\n", "2. Pass the custom objects to the model in one of three ways:\n", " - Register the custom object with the `@tf.keras.utils.register_keras_serializable` decorator. **(recommended)**\n", - " - Directly pass the object to the `custom_objects` argument when loading the model. The argument must be a dictionary mapping the string class name to the Python class. E.g. `tf.keras.models.load_model(path, custom_objects={'CustomLayer': CustomLayer})`\n", + " - Directly pass the object to the `custom_objects` argument when loading the model. The argument must be a dictionary mapping the string class name to the Python class. E.g., `tf.keras.models.load_model(path, custom_objects={'CustomLayer': CustomLayer})`\n", " - Use a `tf.keras.utils.custom_object_scope` with the object included in the `custom_objects` dictionary argument, and place a `tf.keras.models.load_model(path)` call within the scope.\n", "\n", "Refer to the [Writing layers and models from scratch](https://www.tensorflow.org/guide/keras/custom_layers_and_models) tutorial for examples of custom objects and `get_config`.\n" diff --git a/site/en/tutorials/load_data/pandas_dataframe.ipynb b/site/en/tutorials/load_data/pandas_dataframe.ipynb index cee2483a350..66bace1ff87 100644 --- a/site/en/tutorials/load_data/pandas_dataframe.ipynb +++ b/site/en/tutorials/load_data/pandas_dataframe.ipynb @@ -1036,8 +1036,8 @@ }, "outputs": [], "source": [ - "preprocesssed_result = tf.concat(preprocessed, axis=-1)\n", - "preprocesssed_result" + "preprocessed_result = tf.concat(preprocessed, axis=-1)\n", + "preprocessed_result" ] }, { @@ -1057,7 +1057,7 @@ }, "outputs": [], "source": [ - "preprocessor = tf.keras.Model(inputs, preprocesssed_result)" + "preprocessor = tf.keras.Model(inputs, preprocessed_result)" ] }, { diff --git a/site/en/tutorials/structured_data/imbalanced_data.ipynb b/site/en/tutorials/structured_data/imbalanced_data.ipynb index 0d9578b30dc..16d08e53385 100644 --- a/site/en/tutorials/structured_data/imbalanced_data.ipynb +++ b/site/en/tutorials/structured_data/imbalanced_data.ipynb @@ -445,7 +445,7 @@ "\n", "#### Metrics for probability predictions\n", "\n", - "As we train our network with the cross entropy as a loss function, it is fully capable of predicting class probabilities, i.e. it is a probabilistic classifier.\n", + "As we train our network with the cross entropy as a loss function, it is fully capable of predicting class probabilities, i.e., it is a probabilistic classifier.\n", "Good metrics to assess probabilistic predictions are, in fact, **proper scoring rules**. Their key property is that predicting the true probability is optimal. We give two well-known examples:\n", "\n", "* **cross entropy** also known as log loss\n", diff --git a/tools/tensorflow_docs/api_generator/doc_generator_visitor.py b/tools/tensorflow_docs/api_generator/doc_generator_visitor.py index ce6fe68105f..0467f74b153 100644 --- a/tools/tensorflow_docs/api_generator/doc_generator_visitor.py +++ b/tools/tensorflow_docs/api_generator/doc_generator_visitor.py @@ -596,7 +596,7 @@ def _get_physical_path(self, py_object): @classmethod def from_path_tree(cls, path_tree: PathTree, score_name_fn) -> ApiTree: - """Create an ApiTree from an PathTree. + """Create an ApiTree from a PathTree. Args: path_tree: The `PathTree` to convert. diff --git a/tools/tensorflow_docs/api_generator/parser_test.py b/tools/tensorflow_docs/api_generator/parser_test.py index ee8a55f707f..0bfffeded92 100644 --- a/tools/tensorflow_docs/api_generator/parser_test.py +++ b/tools/tensorflow_docs/api_generator/parser_test.py @@ -799,7 +799,7 @@ class A(): self.assertEqual('Instance of `m.A`', result) - def testIsClasssAttr(self): + def testIsClassAttr(self): result = parser.is_class_attr('test_module.test_function', {'test_module': test_module}) self.assertFalse(result) @@ -808,6 +808,7 @@ def testIsClasssAttr(self): {'TestClass': TestClass}) self.assertTrue(result) + RELU_DOC = """Computes rectified linear: `max(features, 0)` RELU is an activation diff --git a/tools/tensorflow_docs/api_generator/toc.py b/tools/tensorflow_docs/api_generator/toc.py index 1e72bcda75c..feaa15b8bda 100644 --- a/tools/tensorflow_docs/api_generator/toc.py +++ b/tools/tensorflow_docs/api_generator/toc.py @@ -273,7 +273,7 @@ def _is_deprecated(self, api_node: doc_generator_visitor.ApiTreeNode): api_node: The node to evaluate. Returns: - True if depreacted else False. + True if deprecated else False. """ if doc_controls.is_deprecated(api_node.py_object): return True diff --git a/tools/tensorflow_docs/tools/nblint/decorator.py b/tools/tensorflow_docs/tools/nblint/decorator.py index 408fef3d969..d74045c7ca7 100644 --- a/tools/tensorflow_docs/tools/nblint/decorator.py +++ b/tools/tensorflow_docs/tools/nblint/decorator.py @@ -161,7 +161,7 @@ def fail(message: Optional[str] = None, Failure messages come in two flavors: - conditional: (Default) While this test may fail here, it may succeed - elsewhere, and thus, the larger condition passes and do not dislay this + elsewhere, and thus, the larger condition passes and do not display this message. - non-conditional (always show): Regardless if the larger condition is met, display this error message in the status report. For example, a From f0b36d0c2e18e661fa72d423cf3c0703ce6e753d Mon Sep 17 00:00:00 2001 From: Surya <116063290+SuryanarayanaY@users.noreply.github.com> Date: Fri, 19 Jan 2024 10:12:42 +0530 Subject: [PATCH 26/85] Update broken link CONTRIBUTING.md The hyperlink for docs@tensorflow.org not working. Updating it to https://discuss.tensorflow.org/ as suggested by doc-Infra team. --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 1559b721f51..a117c5582fc 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -7,7 +7,7 @@ This guide shows how to make contributions to [tensorflow.org](https://www.tenso See the [TensorFlow docs contributor guide](https://www.tensorflow.org/community/contribute/docs) for guidance. For questions, the -[docs@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs) +[docs@tensorflow.org](https://discuss.tensorflow.org/) mailing list is available. Questions about TensorFlow usage are better addressed on From 37e6318bb7f24b7e987df7f7cb9105deb525529d Mon Sep 17 00:00:00 2001 From: Surya <116063290+SuryanarayanaY@users.noreply.github.com> Date: Fri, 19 Jan 2024 10:15:34 +0530 Subject: [PATCH 27/85] Updated broken link of README.md Updated broken link of docs@tensorflow.org mailing list to https://discuss.tensorflow.org/ . --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b94ce5f90f..bf508b6c80c 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ To file a docs issue, use the issue tracker in the [tensorflow/tensorflow](https://github.com/tensorflow/tensorflow/issues/new?template=20-documentation-issue.md) repo. And join the TensorFlow documentation contributors on the -[docs@tensorflow.org mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs). +[docs@tensorflow.org mailing list](https://discuss.tensorflow.org/). ## Community translations From 41115aabc39bebf330e66a6785b25dcd12267dec Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Fri, 19 Jan 2024 16:59:40 +0000 Subject: [PATCH 28/85] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bf508b6c80c..66b6d3fb065 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ To file a docs issue, use the issue tracker in the [tensorflow/tensorflow](https://github.com/tensorflow/tensorflow/issues/new?template=20-documentation-issue.md) repo. And join the TensorFlow documentation contributors on the -[docs@tensorflow.org mailing list](https://discuss.tensorflow.org/). +[TensorFlow Forum](https://discuss.tensorflow.org/). ## Community translations From a7c7f4f0ca9bbe58dd6af37de2bccc0bc0fef09a Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Fri, 19 Jan 2024 17:00:31 +0000 Subject: [PATCH 29/85] Update CONTRIBUTING.md --- CONTRIBUTING.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a117c5582fc..6f301eab782 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -6,9 +6,7 @@ This guide shows how to make contributions to [tensorflow.org](https://www.tenso See the [TensorFlow docs contributor guide](https://www.tensorflow.org/community/contribute/docs) -for guidance. For questions, the -[docs@tensorflow.org](https://discuss.tensorflow.org/) -mailing list is available. +for guidance. For questions, check out [TensorFlow Forum](https://discuss.tensorflow.org/). Questions about TensorFlow usage are better addressed on [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow) or the From 5b8ed9affd9fd675cb9f8a1a11c2f16f73db2550 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 23 Jan 2024 10:28:39 -0800 Subject: [PATCH 30/85] Update estimator warnings. PiperOrigin-RevId: 600833489 --- site/en/guide/estimator.ipynb | 2 +- site/en/tutorials/estimator/keras_model_to_estimator.ipynb | 2 +- site/en/tutorials/estimator/linear.ipynb | 2 +- site/en/tutorials/estimator/premade.ipynb | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/site/en/guide/estimator.ipynb b/site/en/guide/estimator.ipynb index e58ef46cf86..05e8fb4012a 100644 --- a/site/en/guide/estimator.ipynb +++ b/site/en/guide/estimator.ipynb @@ -68,7 +68,7 @@ "id": "rILQuAiiRlI7" }, "source": [ - "> Warning: Estimators are not recommended for new code. Estimators run `v1.Session`-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under our [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details." + "> Warning: TensorFlow 2.15 included the final release of the `tf-estimator` package. Estimators will not be available in TensorFlow 2.16 or after. See the [migration guide](https://www.tensorflow.org/guide/migrate/migrating_estimator) for more information about how to convert off of Estimators." ] }, { diff --git a/site/en/tutorials/estimator/keras_model_to_estimator.ipynb b/site/en/tutorials/estimator/keras_model_to_estimator.ipynb index 7b34e283ef3..be97a38b6eb 100644 --- a/site/en/tutorials/estimator/keras_model_to_estimator.ipynb +++ b/site/en/tutorials/estimator/keras_model_to_estimator.ipynb @@ -68,7 +68,7 @@ "id": "Dhcq8Ds4mCtm" }, "source": [ - "> Warning: Estimators are not recommended for new code. Estimators run `v1.Session`-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under our [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details." + "> Warning: TensorFlow 2.15 included the final release of the `tf-estimator` package. Estimators will not be available in TensorFlow 2.16 or after. See the [migration guide](https://tensorflow.org/guide/migrate/migrating_estimator) for more information about how to convert off of Estimators." ] }, { diff --git a/site/en/tutorials/estimator/linear.ipynb b/site/en/tutorials/estimator/linear.ipynb index 7732ebe3b9e..a26ffe2df4f 100644 --- a/site/en/tutorials/estimator/linear.ipynb +++ b/site/en/tutorials/estimator/linear.ipynb @@ -61,7 +61,7 @@ "id": "JOccPOFMm5Tc" }, "source": [ - "> Warning: Estimators are not recommended for new code. Estimators run `v1.Session`-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under our [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details." + "> Warning: TensorFlow 2.15 included the final release of the `tf-estimator` package. Estimators will not be available in TensorFlow 2.16 or after. See the [migration guide](https://tensorflow.org/guide/migrate/migrating_estimator) for more information about how to convert off of Estimators." ] }, { diff --git a/site/en/tutorials/estimator/premade.ipynb b/site/en/tutorials/estimator/premade.ipynb index a34096ea2b8..dc81847c7cd 100644 --- a/site/en/tutorials/estimator/premade.ipynb +++ b/site/en/tutorials/estimator/premade.ipynb @@ -68,7 +68,7 @@ "id": "stQiPWL6ni6_" }, "source": [ - "> Warning: Estimators are not recommended for new code. Estimators run `v1.Session`-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details." + "> Warning: TensorFlow 2.15 included the final release of the `tf-estimator` package. Estimators will not be available in TensorFlow 2.16 or after. See the [migration guide](https://tensorflow.org/guide/migrate/migrating_estimator) for more information about how to convert off of Estimators." ] }, { From d13c500b2e552ce04095287a99c575d2685a2160 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 30 Jan 2024 06:11:54 -0800 Subject: [PATCH 31/85] Point v1 links at 1.15 tags instead of master since files move. Remove tensorfow.org/code links. PiperOrigin-RevId: 602703415 --- site/en/community/contribute/docs.md | 8 +-- site/en/community/contribute/docs_ref.md | 4 +- site/en/guide/create_op.md | 68 +++++++++--------- site/en/guide/versions.md | 2 +- site/en/r1/guide/autograph.ipynb | 2 +- site/en/r1/guide/custom_estimators.md | 4 +- site/en/r1/guide/debugger.md | 18 ++--- site/en/r1/guide/distribute_strategy.ipynb | 12 ++-- site/en/r1/guide/eager.ipynb | 6 +- site/en/r1/guide/extend/architecture.md | 18 ++--- site/en/r1/guide/extend/bindings.md | 8 +-- site/en/r1/guide/extend/filesystem.md | 18 ++--- site/en/r1/guide/extend/formats.md | 10 +-- site/en/r1/guide/extend/model_files.md | 12 ++-- site/en/r1/guide/extend/op.md | 70 +++++++++---------- site/en/r1/guide/feature_columns.md | 2 +- site/en/r1/guide/performance/benchmarks.md | 2 +- site/en/r1/guide/performance/overview.md | 4 +- site/en/r1/guide/saved_model.md | 32 ++++----- site/en/r1/guide/using_tpu.md | 10 +-- site/en/r1/guide/version_compat.md | 32 ++++----- site/en/r1/tutorials/README.md | 2 +- site/en/r1/tutorials/images/deep_cnn.md | 14 ++-- .../r1/tutorials/images/image_recognition.md | 6 +- .../keras/save_and_restore_models.ipynb | 2 +- .../r1/tutorials/load_data/tf_records.ipynb | 6 +- .../representation/kernel_methods.md | 4 +- site/en/r1/tutorials/representation/linear.md | 6 +- .../r1/tutorials/representation/word2vec.md | 6 +- .../sequences/recurrent_quickdraw.md | 4 +- 30 files changed, 196 insertions(+), 196 deletions(-) diff --git a/site/en/community/contribute/docs.md b/site/en/community/contribute/docs.md index 29b2b5c9550..34b1619ca5d 100644 --- a/site/en/community/contribute/docs.md +++ b/site/en/community/contribute/docs.md @@ -32,7 +32,7 @@ To participate in the TensorFlow docs community: For details, use the [TensorFlow API docs contributor guide](docs_ref.md). This shows you how to find the -[source file](https://www.tensorflow.org/code/tensorflow/python/) +[source file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/) and edit the symbol's docstring. Many API reference pages on tensorflow.org include a link to the source file @@ -53,9 +53,9 @@ main tensorflow/tensorflow repo. The reference documentation is generated from code comments and docstrings in the source code for -Python, -C++, and -Java. +Python, +C++, and +Java. Previous versions of the TensorFlow documentation are available as [rX.x branches](https://github.com/tensorflow/docs/branches) in the TensorFlow diff --git a/site/en/community/contribute/docs_ref.md b/site/en/community/contribute/docs_ref.md index fbf207a47f1..41fce4dde40 100644 --- a/site/en/community/contribute/docs_ref.md +++ b/site/en/community/contribute/docs_ref.md @@ -8,7 +8,7 @@ TensorFlow uses [DocTest](https://docs.python.org/3/library/doctest.html) to test code snippets in Python docstrings. The snippet must be executable Python code. To enable testing, prepend the line with `>>>` (three left-angle brackets). For example, here's a excerpt from the `tf.concat` function in the -[array_ops.py](https://www.tensorflow.org/code/tensorflow/python/ops/array_ops.py) +[array_ops.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/array_ops.py) source file: ``` @@ -178,7 +178,7 @@ There are two ways to test the code in the docstring locally: * If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to - [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py). + [tf_doctest.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docs/tf_doctest.py). For example: 
diff --git a/site/en/guide/create_op.md b/site/en/guide/create_op.md
index 3c84204844c..fa4f573fa32 100644
--- a/site/en/guide/create_op.md
+++ b/site/en/guide/create_op.md
@@ -152,17 +152,17 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 >   Important: Instances of your OpKernel may be accessed concurrently.
 >   Your `Compute` method must be thread-safe. Guard any access to class
 >   members with a mutex. Or better yet, don't share state via class members!
->   Consider using a [`ResourceMgr`](https://www.tensorflow.org/code/tensorflow/core/framework/resource_mgr.h)
+>   Consider using a [`ResourceMgr`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/resource_mgr.h)
 >   to keep track of op state.
 
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
+[`work_sharder.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
-[`config.proto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)).
+[`config.proto`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)).
 
 ### GPU kernels
 
@@ -519,13 +519,13 @@ This asserts that the input is a vector, and returns having set the
 
 *   The `context`, which can either be an `OpKernelContext` or
     `OpKernelConstruction` pointer (see
-    [`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)),
+    [`tensorflow/core/framework/op_kernel.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_kernel.h)),
     for its `SetStatus()` method.
 *   The condition.  For example, there are functions for validating the shape
     of a tensor in
-    [`tensorflow/core/framework/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.h)
+    [`tensorflow/core/framework/tensor_shape.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.h)
 *   The error itself, which is represented by a `Status` object, see
-    [`tensorflow/core/platform/status.h`](https://www.tensorflow.org/code/tensorflow/core/platform/status.h). A
+    [`tensorflow/core/platform/status.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/status.h). A
     `Status` has both a type (frequently `InvalidArgument`, but see the list of
     types) and a message.  Functions for constructing an error may be found in
     [`tensorflow/core/platform/errors.h`][validation-macros].
@@ -668,7 +668,7 @@ There are shortcuts for common type constraints:
 
 The specific lists of types allowed by these are defined by the functions (like
 `NumberTypes()`) in
-[`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h).
+[`tensorflow/core/framework/types.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.h).
 In this example the attr `t` must be one of the numeric types:
 
 ```c++
@@ -1226,7 +1226,7 @@ There are several ways to preserve backwards-compatibility.
     type into a list of varying types).
 
 The full list of safe and unsafe changes can be found in
-[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc).
+[`tensorflow/core/framework/op_compatibility_test.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_compatibility_test.cc).
 If you cannot make your change to an operation backwards compatible, then create
 a new operation with a new name with the new semantics.
 
@@ -1243,16 +1243,16 @@ made when TensorFlow changes major versions, and must conform to the
 You can implement different OpKernels and register one for CPU and another for
 GPU, just like you can [register kernels for different types](#polymorphism).
 There are several examples of kernels with GPU support in
-[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/).
+[`tensorflow/core/kernels/`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/).
 Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
 ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
 
 For example, the `tf.pad` has
 everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
 The GPU kernel is in
-[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc),
+[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op_gpu.cu.cc),
 and the shared code is a templated class defined in
-[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h).
+[`tensorflow/core/kernels/pad_op.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.h).
 We organize the code this way for two reasons: it allows you to share common
 code among the CPU and GPU implementations, and it puts the GPU implementation
 into a separate file so that it can be compiled only by the GPU compiler.
@@ -1273,16 +1273,16 @@ kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.:
 #### Compiling the kernel for the GPU device
 
 Look at
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 for an example that uses a CUDA kernel to implement an op. The
 `tf_custom_op_library` accepts a `gpu_srcs` argument in which the list of source
 files containing the CUDA kernels (`*.cu.cc` files) can be specified. For use
 with a binary installation of TensorFlow, the CUDA kernels have to be compiled
 with NVIDIA's `nvcc` compiler. Here is the sequence of commands you can use to
 compile the
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 and
-[cuda_op_kernel.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
+[cuda_op_kernel.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
 into a single dynamically loadable library:
 
 ```bash
@@ -1412,7 +1412,7 @@ be set to the first input's shape. If the output is selected by its index as in
 
 There are a number of common shape functions
 that apply to many ops, such as `shape_inference::UnchangedShape` which can be
-found in [common_shape_fns.h](https://www.tensorflow.org/code/tensorflow/core/framework/common_shape_fns.h) and used as follows:
+found in [common_shape_fns.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/common_shape_fns.h) and used as follows:
 
 ```c++
 REGISTER_OP("ZeroOut")
@@ -1459,7 +1459,7 @@ provides access to the attributes of the op).
 
 Since shape inference is an optional feature, and the shapes of tensors may vary
 dynamically, shape functions must be robust to incomplete shape information for
-any of the inputs. The `Merge` method in [`InferenceContext`](https://www.tensorflow.org/code/tensorflow/core/framework/shape_inference.h)
+any of the inputs. The `Merge` method in [`InferenceContext`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/shape_inference.h)
 allows the caller to assert that two shapes are the same, even if either
 or both of them do not have complete information. Shape functions are defined
 for all of the core TensorFlow ops and provide many different usage examples.
@@ -1484,7 +1484,7 @@ If you have a complicated shape function, you should consider adding a test for
 validating that various input shape combinations produce the expected output
 shape combinations.  You can see examples of how to write these tests in some
 our
-[core ops tests](https://www.tensorflow.org/code/tensorflow/core/ops/array_ops_test.cc).
+[core ops tests](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops_test.cc).
 (The syntax of `INFER_OK` and `INFER_ERROR` are a little cryptic, but try to be
 compact in representing input and output shape specifications in tests.  For
 now, see the surrounding comments in those tests to get a sense of the shape
@@ -1497,20 +1497,20 @@ To build a `pip` package for your op, see the
 guide shows how to build custom ops from the TensorFlow pip package instead
 of building TensorFlow from source.
 
-[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc
-[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py
-[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/
-[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/
-[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc
-[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py
-[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/platform/errors.h
-[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h
-[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h
-[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc
-[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto
-[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto
-[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto
+[core-array_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops.cc
+[python-user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/user_ops/user_ops.py
+[tf-kernels]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/
+[user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/user_ops/
+[pad_op]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.cc
+[standard_ops-py]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/standard_ops.py
+[standard_ops-cc]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/ops/standard_ops.h
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
+[validation-macros]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/errors.h
+[op_def_builder]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.h
+[register_types]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/register_types.h
+[FinalizeAttr]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.cc
+[DataTypeString]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.cc
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
+[types-proto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto
+[TensorShapeProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto
+[TensorProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
diff --git a/site/en/guide/versions.md b/site/en/guide/versions.md
index 58f9f3848fb..5e660892b6d 100644
--- a/site/en/guide/versions.md
+++ b/site/en/guide/versions.md
@@ -471,7 +471,7 @@ existing producer scripts will not suddenly use the new functionality.
 1.  Add a new similar op named `SomethingV2` or similar and go through the
     process of adding it and switching existing Python wrappers to use it.
     To ensure forward compatibility use the checks suggested in
-    [compat.py](https://www.tensorflow.org/code/tensorflow/python/compat/compat.py)
+    [compat.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/compat/compat.py)
     when changing the Python wrappers.
 2.  Remove the old op (Can only take place with a major version change due to
     backward compatibility).
diff --git a/site/en/r1/guide/autograph.ipynb b/site/en/r1/guide/autograph.ipynb
index f028b33ce9f..6c169066c03 100644
--- a/site/en/r1/guide/autograph.ipynb
+++ b/site/en/r1/guide/autograph.ipynb
@@ -78,7 +78,7 @@
         "id": "CydFK2CL7ZHA"
       },
       "source": [
-        "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md)."
+        "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/g3doc/reference/limitations.md)."
       ]
     },
     {
diff --git a/site/en/r1/guide/custom_estimators.md b/site/en/r1/guide/custom_estimators.md
index 87dce26a0dc..7bbf3573909 100644
--- a/site/en/r1/guide/custom_estimators.md
+++ b/site/en/r1/guide/custom_estimators.md
@@ -592,10 +592,10 @@ function for custom Estimators; everything else is the same.
 For more details, be sure to check out:
 
 * The
-  [official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/master/official/r1/mnist),
+  [official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/r1.15/official/r1/mnist),
   which uses a custom estimator.
 * The TensorFlow
-  [official models repository](https://github.com/tensorflow/models/tree/master/official),
+  [official models repository](https://github.com/tensorflow/models/tree/r1.15/official),
   which contains more curated examples using custom estimators.
 * This [TensorBoard video](https://youtu.be/eBbEDRsCmv4), which introduces
   TensorBoard.
diff --git a/site/en/r1/guide/debugger.md b/site/en/r1/guide/debugger.md
index 2b4b6497ec4..963765b97db 100644
--- a/site/en/r1/guide/debugger.md
+++ b/site/en/r1/guide/debugger.md
@@ -10,7 +10,7 @@ due to TensorFlow's computation-graph paradigm.
 This guide focuses on the command-line interface (CLI) of `tfdbg`. For guide on
 how to use the graphical user interface (GUI) of tfdbg, i.e., the
 **TensorBoard Debugger Plugin**, please visit
-[its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
+[its README](https://github.com/tensorflow/tensorboard/blob/r1.15/tensorboard/plugins/debugger/README.md).
 
 Note: The TensorFlow debugger uses a
 [curses](https://en.wikipedia.org/wiki/Curses_\(programming_library\))-based text
@@ -35,7 +35,7 @@ TensorFlow. Later sections of this document describe how to use **tfdbg** with
 higher-level APIs of TensorFlow, including `tf.estimator`, `tf.keras` / `keras`
 and `tf.contrib.slim`. To *observe* such an issue, run the following command
 without the debugger (the source code can be found
-[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/v1/debug_mnist.py)):
+[here](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py)):
 
 
 python -m tensorflow.python.debug.examples.v1.debug_mnist
@@ -64,7 +64,7 @@ numeric problem first surfaced.
 To add support for tfdbg in our example, all that is needed is to add the
 following lines of code and wrap the Session object with a debugger wrapper.
 This code is already added in
-[debug_mnist.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/v1/debug_mnist.py),
+[debug_mnist.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py),
 so you can activate tfdbg CLI with the `--debug` flag at the command line.
 
 ```python
@@ -370,7 +370,7 @@ traceback of the node's construction.
 
 From the traceback, you can see that the op is constructed at the following
 line:
-[`debug_mnist.py`](https://www.tensorflow.org/code/tensorflow/python/debug/examples/v1/debug_mnist.py):
+[`debug_mnist.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_mnist.py):
 
 ```python
 diff = y_ * tf.log(y)
@@ -457,7 +457,7 @@ accuracy_score = classifier.evaluate(eval_input_fn,
 predict_results = classifier.predict(predict_input_fn, hooks=hooks)
 ```
 
-[debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/v1/debug_tflearn_iris.py),
+[debug_tflearn_iris.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/debug/examples/v1/debug_tflearn_iris.py),
 contains a full example of how to use the tfdbg with `Estimator`s. To run this
 example, do:
 
@@ -501,7 +501,7 @@ TensorFlow backend. You just need to replace `tf.keras.backend` with
 ## Debugging tf-slim with TFDBG
 
 TFDBG supports debugging of training and evaluation with
-[tf-slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
+[tf-slim](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/slim).
 As detailed below, training and evaluation require slightly different debugging
 workflows.
 
@@ -605,7 +605,7 @@ The `watch_fn` argument accepts a `Callable` that allows you to configure what
 If your model code is written in C++ or other languages, you can also
 modify the `debug_options` field of `RunOptions` to generate debug dumps that
 can be inspected offline. See
-[the proto definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/debug.proto)
+[the proto definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/debug.proto)
 for more details.
 
 ### Debugging Remotely-Running Estimators
@@ -648,7 +648,7 @@ python -m tensorflow.python.debug.cli.offline_analyzer \
        model, check out
 
    1. The profiling mode of tfdbg: `tfdbg> run -p`.
-   2. [tfprof](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler)
+   2. [tfprof](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/core/profiler)
       and other profiling tools for TensorFlow.
 
 **Q**: _How do I link tfdbg against my `Session` in Bazel? Why do I see an
@@ -808,4 +808,4 @@ tensor dumps.
        and conditional breakpoints, and tying tensors to their
        graph-construction source code, all in the browser environment.
        To get started, please visit
-       [its README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md).
+       [its README](https://github.com/tensorflow/tensorboard/blob/r1.15/tensorboard/plugins/debugger/README.md).
diff --git a/site/en/r1/guide/distribute_strategy.ipynb b/site/en/r1/guide/distribute_strategy.ipynb
index 3c0b453a278..af50683c845 100644
--- a/site/en/r1/guide/distribute_strategy.ipynb
+++ b/site/en/r1/guide/distribute_strategy.ipynb
@@ -245,7 +245,7 @@
         "\n",
         "`tf.distribute.experimental.MultiWorkerMirroredStrategy` is very similar to `MirroredStrategy`. It implements synchronous distributed training across multiple workers, each with potentially multiple GPUs. Similar to `MirroredStrategy`, it creates copies of all variables in the model on each device across all workers.\n",
         "\n",
-        "It uses [CollectiveOps](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/collective_ops.py) as the multi-worker all-reduce communication method used to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.\n",
+        "It uses [CollectiveOps](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/collective_ops.py) as the multi-worker all-reduce communication method used to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.\n",
         "\n",
         "It also implements additional performance optimizations. For example, it includes a static optimization that converts multiple all-reductions on small tensors into fewer all-reductions on larger tensors. In addition, we are designing it to have a plugin architecture - so that in the future, users will be able to plugin algorithms that are better tuned for their hardware. Note that collective ops also implement other collective operations such as broadcast and all-gather.\n",
         "\n",
@@ -490,8 +490,8 @@
         "Here is a list of tutorials and examples that illustrate the above integration end to end with Keras:\n",
         "\n",
         "1. [Tutorial](../tutorials/distribute/keras.ipynb) to train MNIST with `MirroredStrategy`.\n",
-        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/master/official/vision/image_classification/resnet_imagenet_main.py) training with ImageNet data using `MirroredStrategy`.\n",
-        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/master/models/experimental/resnet50_keras/resnet50.py) trained with Imagenet data on Cloud TPus with `TPUStrategy`."
+        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/r1.15/official/vision/image_classification/resnet_imagenet_main.py) training with ImageNet data using `MirroredStrategy`.\n",
+        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/1.15/models/experimental/resnet50_keras/resnet50.py) trained with Imagenet data on Cloud TPus with `TPUStrategy`."
       ]
     },
     {
@@ -595,9 +595,9 @@
         "### Examples and Tutorials\n",
         "Here are some examples that show end to end usage of various strategies with Estimator:\n",
         "\n",
-        "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/master/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kuberentes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n",
-        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/master/official/r1/resnet/imagenet_main.py) model, which can be trained using either `MirroredStrategy` or `MultiWorkerMirroredStrategy`.\n",
-        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/master/models/experimental/distribution_strategy/resnet_estimator.py) example with TPUStrategy."
+        "1. [End to end example](https://github.com/tensorflow/ecosystem/tree/r1.15/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kuberentes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n",
+        "2. Official [ResNet50](https://github.com/tensorflow/models/blob/r1.15/official/r1/resnet/imagenet_main.py) model, which can be trained using either `MirroredStrategy` or `MultiWorkerMirroredStrategy`.\n",
+        "3. [ResNet50](https://github.com/tensorflow/tpu/blob/1.15/models/experimental/distribution_strategy/resnet_estimator.py) example with TPUStrategy."
       ]
     },
     {
diff --git a/site/en/r1/guide/eager.ipynb b/site/en/r1/guide/eager.ipynb
index 6a0a78c2443..f76acb4b702 100644
--- a/site/en/r1/guide/eager.ipynb
+++ b/site/en/r1/guide/eager.ipynb
@@ -95,7 +95,7 @@
         "\n",
         "Eager execution supports most TensorFlow operations and GPU acceleration. For a\n",
         "collection of examples running in eager execution, see:\n",
-        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples).\n",
+        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples).\n",
         "\n",
         "Note: Some models may experience increased overhead with eager execution\n",
         "enabled. Performance improvements are ongoing, but please\n",
@@ -1160,7 +1160,7 @@
         "### Benchmarks\n",
         "\n",
         "For compute-heavy models, such as\n",
-        "[ResNet50](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/resnet50)\n",
+        "[ResNet50](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples/resnet50)\n",
         "training on a GPU, eager execution performance is comparable to graph execution.\n",
         "But this gap grows larger for models with less computation and there is work to\n",
         "be done for optimizing hot code paths for models with lots of small operations."
@@ -1225,7 +1225,7 @@
         "production deployment. Use `tf.train.Checkpoint` to save and restore model\n",
         "variables, this allows movement between eager and graph execution environments.\n",
         "See the examples in:\n",
-        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples).\n"
+        "[tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/eager/python/examples).\n"
       ]
     },
     {
diff --git a/site/en/r1/guide/extend/architecture.md b/site/en/r1/guide/extend/architecture.md
index 1f2ac53066f..0753824e15e 100644
--- a/site/en/r1/guide/extend/architecture.md
+++ b/site/en/r1/guide/extend/architecture.md
@@ -34,7 +34,7 @@ This document focuses on the following layers:
 *  **Client**:
    *  Defines the computation as a dataflow graph.
    *  Initiates graph execution using a [**session**](
-      https://www.tensorflow.org/code/tensorflow/python/client/session.py).
+      https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/client/session.py).
 *  **Distributed Master**
    *  Prunes a specific subgraph from the graph, as defined by the arguments
       to Session.run().
@@ -144,8 +144,8 @@ The distributed master then ships the graph pieces to the distributed tasks.
 
 ### Code
 
-*  [MasterService API definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/master_service.proto)
-*  [Master interface](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/master_interface.h)
+*  [MasterService API definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/master_service.proto)
+*  [Master interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/master_interface.h)
 
 ## Worker Service
 
@@ -178,7 +178,7 @@ For transfers between tasks, TensorFlow uses multiple protocols, including:
 
 We also have preliminary support for NVIDIA's NCCL library for multi-GPU
 communication, see:
-[`tf.contrib.nccl`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nccl_ops.py).
+[`tf.contrib.nccl`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/nccl_ops.py).
 
 Partitioned Graph
 
@@ -186,9 +186,9 @@ communication, see:
 
 ### Code
 
-*   [WorkerService API definition](https://www.tensorflow.org/code/tensorflow/core/protobuf/worker_service.proto)
-*   [Worker interface](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/worker_interface.h)
-*   [Remote rendezvous (for Send and Recv implementations)](https://www.tensorflow.org/code/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h)
+*   [WorkerService API definition](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/worker_service.proto)
+*   [Worker interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/worker_interface.h)
+*   [Remote rendezvous (for Send and Recv implementations)](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h)
 
 ## Kernel Implementations
 
@@ -199,7 +199,7 @@ Many of the operation kernels are implemented using Eigen::Tensor, which uses
 C++ templates to generate efficient parallel code for multicore CPUs and GPUs;
 however, we liberally use libraries like cuDNN where a more efficient kernel
 implementation is possible. We have also implemented
-[quantization](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_quantization.md), which enables
+[quantization](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/lite/g3doc/performance/post_training_quantization.md), which enables
 faster inference in environments such as mobile devices and high-throughput
 datacenter applications, and use the
 [gemmlowp](https://github.com/google/gemmlowp) low-precision matrix library to
@@ -215,4 +215,4 @@ experimental implementation of automatic kernel fusion.
 
 ### Code
 
-*   [`OpKernel` interface](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)
+*   [`OpKernel` interface](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_kernel.h)
diff --git a/site/en/r1/guide/extend/bindings.md b/site/en/r1/guide/extend/bindings.md
index 9c10e90840f..7daa2212106 100644
--- a/site/en/r1/guide/extend/bindings.md
+++ b/site/en/r1/guide/extend/bindings.md
@@ -112,11 +112,11 @@ There are a few ways to get a list of the `OpDef`s for the registered ops:
     to interpret the `OpDef` messages.
 -   The C++ function `OpRegistry::Global()->GetRegisteredOps()` returns the same
     list of all registered `OpDef`s (defined in
-    [`tensorflow/core/framework/op.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op.h)). This can be used to write the generator
+    [`tensorflow/core/framework/op.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op.h)). This can be used to write the generator
     in C++ (particularly useful for languages that do not have protocol buffer
     support).
 -   The ASCII-serialized version of that list is periodically checked in to
-    [`tensorflow/core/ops/ops.pbtxt`](https://www.tensorflow.org/code/tensorflow/core/ops/ops.pbtxt) by an automated process.
+    [`tensorflow/core/ops/ops.pbtxt`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/ops.pbtxt) by an automated process.
 
 The `OpDef` specifies the following:
 
@@ -159,7 +159,7 @@ between the generated code and the `OpDef`s checked into the repository, but is
 useful for languages where code is expected to be generated ahead of time like
 `go get` for Go and `cargo ops` for Rust. At the other end of the spectrum, for
 some languages the code could be generated dynamically from
-[`tensorflow/core/ops/ops.pbtxt`](https://www.tensorflow.org/code/tensorflow/core/ops/ops.pbtxt).
+[`tensorflow/core/ops/ops.pbtxt`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/ops.pbtxt).
 
 #### Handling Constants
 
@@ -228,4 +228,4 @@ At this time, support for gradients, functions and control flow operations ("if"
 and "while") is not available in languages other than Python. This will be
 updated when the [C API] provides necessary support.
 
-[C API]: https://www.tensorflow.org/code/tensorflow/c/c_api.h
+[C API]: https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/c/c_api.h
diff --git a/site/en/r1/guide/extend/filesystem.md b/site/en/r1/guide/extend/filesystem.md
index 4d34c07102e..2d6ea0c4645 100644
--- a/site/en/r1/guide/extend/filesystem.md
+++ b/site/en/r1/guide/extend/filesystem.md
@@ -54,7 +54,7 @@ To implement a custom filesystem plugin, you must do the following:
 ### The FileSystem interface
 
 The `FileSystem` interface is an abstract C++ interface defined in
-[file_system.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/file_system.h).
+[file_system.h](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/file_system.h).
 An implementation of the `FileSystem` interface should implement all relevant
 the methods defined by the interface. Implementing the interface requires
 defining operations such as creating `RandomAccessFile`, `WritableFile`, and
@@ -70,26 +70,26 @@ involves calling `stat()` on the file and then returns the filesize as reported
 by the return of the stat object. Similarly, for the `HDFSFileSystem`
 implementation, these calls simply delegate to the `libHDFS` implementation of
 similar functionality, such as `hdfsDelete` for
-[DeleteFile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/hadoop/hadoop_file_system.cc#L386).
+[DeleteFile](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/hadoop/hadoop_file_system.cc#L386).
 
 We suggest looking through these code examples to get an idea of how different
 filesystem implementations call their existing libraries. Examples include:
 
 *   [POSIX
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/posix/posix_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/posix/posix_file_system.h)
 *   [HDFS
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/hadoop/hadoop_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/hadoop/hadoop_file_system.h)
 *   [GCS
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/cloud/gcs_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/cloud/gcs_file_system.h)
 *   [S3
-    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/s3/s3_file_system.h)
+    plugin](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/s3/s3_file_system.h)
 
 #### The File interfaces
 
 Beyond operations that allow you to query and manipulate files and directories
 in a filesystem, the `FileSystem` interface requires you to implement factories
 that return implementations of abstract objects such as the
-[RandomAccessFile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/file_system.h#L223),
+[RandomAccessFile](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/platform/file_system.h#L223),
 the `WritableFile`, so that TensorFlow code and read and write to files in that
 `FileSystem` implementation.
 
@@ -224,7 +224,7 @@ it will use the `FooBarFileSystem` implementation.
 
 Next, you must build a shared object containing this implementation. An example
 of doing so using bazel's `cc_binary` rule can be found
-[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD#L244),
+[here](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD#L244),
 but you may use any build system to do so. See the section on [building the op library](../extend/op.md#build_the_op_library) for similar
 instructions.
 
@@ -236,7 +236,7 @@ passing the path to the shared object. Calling this in your client program loads
 the shared object in the process, thus registering your implementation as
 available for any file operations going through the `FileSystem` interface. You
 can see
-[test_file_system.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/file_system_test.py)
+[test_file_system.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/framework/file_system_test.py)
 for an example.
 
 ## What goes through this interface?
diff --git a/site/en/r1/guide/extend/formats.md b/site/en/r1/guide/extend/formats.md
index 3b7b4aafbd6..bdebee5487d 100644
--- a/site/en/r1/guide/extend/formats.md
+++ b/site/en/r1/guide/extend/formats.md
@@ -28,11 +28,11 @@ individual records in a file. There are several examples of "reader" datasets
 that are already built into TensorFlow:
 
 *   `tf.data.TFRecordDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 *   `tf.data.FixedLengthRecordDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 *   `tf.data.TextLineDataset`
-    ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc))
+    ([source in `kernels/data/reader_dataset_ops.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/data/reader_dataset_ops.cc))
 
 Each of these implementations comprises three related classes:
 
@@ -279,7 +279,7 @@ if __name__ == "__main__":
 ```
 
 You can see some examples of `Dataset` wrapper classes in
-[`tensorflow/python/data/ops/dataset_ops.py`](https://www.tensorflow.org/code/tensorflow/python/data/ops/dataset_ops.py).
+[`tensorflow/python/data/ops/dataset_ops.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/data/ops/dataset_ops.py).
 
 ## Writing an Op for a record format
 
@@ -297,7 +297,7 @@ Examples of Ops useful for decoding records:
 
 Note that it can be useful to use multiple Ops to decode a particular record
 format.  For example, you may have an image saved as a string in
-[a `tf.train.Example` protocol buffer](https://www.tensorflow.org/code/tensorflow/core/example/example.proto).
+[a `tf.train.Example` protocol buffer](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto).
 Depending on the format of that image, you might take the corresponding output
 from a `tf.parse_single_example` op and call `tf.image.decode_jpeg`,
 `tf.image.decode_png`, or `tf.decode_raw`.  It is common to take the output
diff --git a/site/en/r1/guide/extend/model_files.md b/site/en/r1/guide/extend/model_files.md
index 30e73a5169e..e590fcf1f27 100644
--- a/site/en/r1/guide/extend/model_files.md
+++ b/site/en/r1/guide/extend/model_files.md
@@ -28,7 +28,7 @@ by calling `as_graph_def()`, which returns a `GraphDef` object.
 
 The GraphDef class is an object created by the ProtoBuf library from the
 definition in
-[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto). The protobuf tools parse
+[tensorflow/core/framework/graph.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/graph.proto). The protobuf tools parse
 this text file, and generate the code to load, store, and manipulate graph
 definitions. If you see a standalone TensorFlow file representing a model, it's
 likely to contain a serialized version of one of these `GraphDef` objects
@@ -87,7 +87,7 @@ for node in graph_def.node
 ```
 
 Each node is a `NodeDef` object, defined in
-[tensorflow/core/framework/node_def.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/node_def.proto). These
+[tensorflow/core/framework/node_def.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/node_def.proto). These
 are the fundamental building blocks of TensorFlow graphs, with each one defining
 a single operation along with its input connections. Here are the members of a
 `NodeDef`, and what they mean.
@@ -107,7 +107,7 @@ This defines what operation to run, for example `"Add"`, `"MatMul"`, or
 `"Conv2D"`. When a graph is run, this op name is looked up in a registry to
 find an implementation. The registry is populated by calls to the
 `REGISTER_OP()` macro, like those in
-[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/nn_ops.cc).
+[tensorflow/core/ops/nn_ops.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/nn_ops.cc).
 
 ### `input`
 
@@ -133,7 +133,7 @@ size of filters for convolutions, or the values of constant ops. Because there
 can be so many different types of attribute values, from strings, to ints, to
 arrays of tensor values, there's a separate protobuf file defining the data
 structure that holds them, in
-[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto).
+[tensorflow/core/framework/attr_value.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/attr_value.proto).
 
 Each attribute has a unique name string, and the expected attributes are listed
 when the operation is defined. If an attribute isn't present in a node, but it
@@ -151,7 +151,7 @@ the file format during training. Instead, they're held in separate checkpoint
 files, and there are `Variable` ops in the graph that load the latest values
 when they're initialized. It's often not very convenient to have separate files
 when you're deploying to production, so there's the
-[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) script that takes a graph definition and a set
+[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/freeze_graph.py) script that takes a graph definition and a set
 of checkpoints and freezes them together into a single file.
 
 What this does is load the `GraphDef`, pull in the values for all the variables
@@ -167,7 +167,7 @@ the most common problems is extracting and interpreting the weight values. A
 common way to store them, for example in graphs created by the freeze_graph
 script, is as `Const` ops containing the weights as `Tensors`. These are
 defined in
-[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto), and contain information
+[tensorflow/core/framework/tensor.proto](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto), and contain information
 about the size and type of the data, as well as the values themselves. In
 Python, you get a `TensorProto` object from a `NodeDef` representing a `Const`
 op by calling something like `some_node_def.attr['value'].tensor`.
diff --git a/site/en/r1/guide/extend/op.md b/site/en/r1/guide/extend/op.md
index dc2d9fbe678..186d9c28c04 100644
--- a/site/en/r1/guide/extend/op.md
+++ b/site/en/r1/guide/extend/op.md
@@ -47,7 +47,7 @@ To incorporate your custom op you'll need to:
     test the op in C++. If you define gradients, you can verify them with the
     Python `tf.test.compute_gradient_error`.
     See
-    [`relu_op_test.py`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/relu_op_test.py) as
+    [`relu_op_test.py`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/kernel_tests/relu_op_test.py) as
     an example that tests the forward functions of Relu-like operators and
     their gradients.
 
@@ -155,17 +155,17 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 >   Important: Instances of your OpKernel may be accessed concurrently.
 >   Your `Compute` method must be thread-safe. Guard any access to class
 >   members with a mutex. Or better yet, don't share state via class members!
->   Consider using a [`ResourceMgr`](https://www.tensorflow.org/code/tensorflow/core/framework/resource_mgr.h)
+>   Consider using a [`ResourceMgr`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/resource_mgr.h)
 >   to keep track of op state.
 
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
+[`work_sharder.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in
-[`config.proto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)).
+[`config.proto`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/config.proto)).
 
 ### GPU kernels
 
@@ -486,13 +486,13 @@ This asserts that the input is a vector, and returns having set the
 
 *   The `context`, which can either be an `OpKernelContext` or
     `OpKernelConstruction` pointer (see
-    [`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)),
+    [`tensorflow/core/framework/op_kernel.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_kernel.h)),
     for its `SetStatus()` method.
 *   The condition.  For example, there are functions for validating the shape
     of a tensor in
-    [`tensorflow/core/framework/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.h)
+    [`tensorflow/core/framework/tensor_shape.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.h)
 *   The error itself, which is represented by a `Status` object, see
-    [`tensorflow/core/lib/core/status.h`](https://www.tensorflow.org/code/tensorflow/core/lib/core/status.h). A
+    [`tensorflow/core/lib/core/status.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/lib/core/status.h). A
     `Status` has both a type (frequently `InvalidArgument`, but see the list of
     types) and a message.  Functions for constructing an error may be found in
     [`tensorflow/core/lib/core/errors.h`][validation-macros].
@@ -633,7 +633,7 @@ define an attr with constraints, you can use the following ``s:
 
     The specific lists of types allowed by these are defined by the functions
     (like `NumberTypes()`) in
-    [`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h).
+    [`tensorflow/core/framework/types.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.h).
     In this example the attr `t` must be one of the numeric types:
 
     ```c++
@@ -1180,7 +1180,7 @@ There are several ways to preserve backwards-compatibility.
    type into a list of varying types).
 
 The full list of safe and unsafe changes can be found in
-[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc).
+[`tensorflow/core/framework/op_compatibility_test.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_compatibility_test.cc).
 If you cannot make your change to an operation backwards compatible, then create
 a new operation with a new name with the new semantics.
 
@@ -1197,16 +1197,16 @@ made when TensorFlow changes major versions, and must conform to the
 You can implement different OpKernels and register one for CPU and another for
 GPU, just like you can [register kernels for different types](#polymorphism).
 There are several examples of kernels with GPU support in
-[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/).
+[`tensorflow/core/kernels/`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/).
 Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
 ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
 
 For example, the `tf.pad` has
 everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
 The GPU kernel is in
-[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc),
+[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op_gpu.cu.cc),
 and the shared code is a templated class defined in
-[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h).
+[`tensorflow/core/kernels/pad_op.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op.h).
 We organize the code this way for two reasons: it allows you to share common
 code among the CPU and GPU implementations, and it puts the GPU implementation
 into a separate file so that it can be compiled only by the GPU compiler.
@@ -1227,16 +1227,16 @@ kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.:
 #### Compiling the kernel for the GPU device
 
 Look at
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 for an example that uses a CUDA kernel to implement an op. The
 `tf_custom_op_library` accepts a `gpu_srcs` argument in which the list of source
 files containing the CUDA kernels (`*.cu.cc` files) can be specified. For use
 with a binary installation of TensorFlow, the CUDA kernels have to be compiled
 with NVIDIA's `nvcc` compiler. Here is the sequence of commands you can use to
 compile the
-[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
+[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
 and
-[cuda_op_kernel.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
+[cuda_op_kernel.cc](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
 into a single dynamically loadable library:
 
 ```bash
@@ -1361,7 +1361,7 @@ be set to the first input's shape. If the output is selected by its index as in
 
 There are a number of common shape functions
 that apply to many ops, such as `shape_inference::UnchangedShape` which can be
-found in [common_shape_fns.h](https://www.tensorflow.org/code/tensorflow/core/framework/common_shape_fns.h) and used as follows:
+found in [common_shape_fns.h](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/common_shape_fns.h) and used as follows:
 
 ```c++
 REGISTER_OP("ZeroOut")
@@ -1408,7 +1408,7 @@ provides access to the attributes of the op).
 
 Since shape inference is an optional feature, and the shapes of tensors may vary
 dynamically, shape functions must be robust to incomplete shape information for
-any of the inputs. The `Merge` method in [`InferenceContext`](https://www.tensorflow.org/code/tensorflow/core/framework/shape_inference.h)
+any of the inputs. The `Merge` method in [`InferenceContext`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/shape_inference.h)
 allows the caller to assert that two shapes are the same, even if either
 or both of them do not have complete information. Shape functions are defined
 for all of the core TensorFlow ops and provide many different usage examples.
@@ -1433,7 +1433,7 @@ If you have a complicated shape function, you should consider adding a test for
 validating that various input shape combinations produce the expected output
 shape combinations.  You can see examples of how to write these tests in some
 our
-[core ops tests](https://www.tensorflow.org/code/tensorflow/core/ops/array_ops_test.cc).
+[core ops tests](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/array_ops_test.cc).
 (The syntax of `INFER_OK` and `INFER_ERROR` are a little cryptic, but try to be
 compact in representing input and output shape specifications in tests.  For
 now, see the surrounding comments in those tests to get a sense of the shape
@@ -1446,20 +1446,20 @@ To build a `pip` package for your op, see the
 guide shows how to build custom ops from the TensorFlow pip package instead
 of building TensorFlow from source.
 
-[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc
-[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py
-[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/
-[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/
-[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc
-[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py
-[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/lib/core/errors.h
-[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h
-[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h
-[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc
-[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc
-[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
-[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto
-[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto
-[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto
+[core-array_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/ops/array_ops.cc
+[python-user_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/user_ops/user_ops.py
+[tf-kernels]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/
+[user_ops]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/user_ops/
+[pad_op]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/kernels/pad_op.cc
+[standard_ops-py]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/ops/standard_ops.py
+[standard_ops-cc]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/ops/standard_ops.h
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD
+[validation-macros]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/lib/core/errors.h
+[op_def_builder]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def_builder.h
+[register_types]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/register_types.h
+[FinalizeAttr]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def_builder.cc
+[DataTypeString]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.cc
+[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/BUILD
+[types-proto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.proto
+[TensorShapeProto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.proto
+[TensorProto]:https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto
diff --git a/site/en/r1/guide/feature_columns.md b/site/en/r1/guide/feature_columns.md
index 5a4dfbbf46d..e4259f85e9f 100644
--- a/site/en/r1/guide/feature_columns.md
+++ b/site/en/r1/guide/feature_columns.md
@@ -562,7 +562,7 @@ For more examples on feature columns, view the following:
 
 * The [Low Level Introduction](../guide/low_level_intro.md#feature_columns) demonstrates how
   experiment directly with `feature_columns` using TensorFlow's low level APIs.
-* The [Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+* The [Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
   solves a binary classification problem using `feature_columns` on a variety of
   input data types.
 
diff --git a/site/en/r1/guide/performance/benchmarks.md b/site/en/r1/guide/performance/benchmarks.md
index 8998c0723db..a56959ea416 100644
--- a/site/en/r1/guide/performance/benchmarks.md
+++ b/site/en/r1/guide/performance/benchmarks.md
@@ -401,7 +401,7 @@ GPUs | InceptionV3 (batch size 32) | ResNet-50 (batch size 32)
 ## Methodology
 
 This
-[script](https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks)
+[script](https://github.com/tensorflow/benchmarks/tree/r1.15/scripts/tf_cnn_benchmarks)
 was run on the various platforms to generate the above results.
 
 In order to create results that are as repeatable as possible, each test was run
diff --git a/site/en/r1/guide/performance/overview.md b/site/en/r1/guide/performance/overview.md
index 461fa4feb58..be7217f4b99 100644
--- a/site/en/r1/guide/performance/overview.md
+++ b/site/en/r1/guide/performance/overview.md
@@ -19,9 +19,9 @@ Reading large numbers of small files significantly impacts I/O performance.
 One approach to get maximum I/O throughput is to preprocess input data into
 larger (~100MB) `TFRecord` files. For smaller data sets (200MB-1GB), the best
 approach is often to load the entire data set into memory. The document
-[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/master/research/slim#downloading-and-converting-to-tfrecord-format)
+[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/r1.15/research/slim#downloading-and-converting-to-tfrecord-format)
 includes information and scripts for creating `TFRecord`s, and this
-[script](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py)
+[script](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py)
 converts the CIFAR-10 dataset into `TFRecord`s.
 
 While feeding data using a `feed_dict` offers a high level of flexibility, in
diff --git a/site/en/r1/guide/saved_model.md b/site/en/r1/guide/saved_model.md
index 623863a9df9..34447ffe861 100644
--- a/site/en/r1/guide/saved_model.md
+++ b/site/en/r1/guide/saved_model.md
@@ -23,7 +23,7 @@ TensorFlow saves variables in binary *checkpoint files* that map variable
 names to tensor values.
 
 Caution: TensorFlow model files are code. Be careful with untrusted code.
-See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md)
+See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/r1.15/SECURITY.md)
 for details.
 
 ### Save variables
@@ -148,7 +148,7 @@ Notes:
    `tf.variables_initializer` for more information.
 
 *  To inspect the variables in a checkpoint, you can use the
-   [`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py)
+   [`inspect_checkpoint`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/inspect_checkpoint.py)
    library, particularly the `print_tensors_in_checkpoint_file` function.
 
 *  By default, `Saver` uses the value of the `tf.Variable.name` property
@@ -159,7 +159,7 @@ Notes:
 ### Inspect variables in a checkpoint
 
 We can quickly inspect variables in a checkpoint with the
-[`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library.
+[`inspect_checkpoint`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/tools/inspect_checkpoint.py) library.
 
 Continuing from the save/restore examples shown earlier:
 
@@ -216,7 +216,7 @@ simple_save(session,
 
 This configures the `SavedModel` so it can be loaded by
 [TensorFlow serving](https://www.tensorflow.org/tfx/tutorials/serving/rest_simple) and supports the
-[Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
+[Predict API](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/predict.proto).
 To access the classify, regress, or multi-inference APIs, use the manual
 `SavedModel` builder APIs or an `tf.estimator.Estimator`.
 
@@ -328,7 +328,7 @@ with tf.Session(graph=tf.Graph()) as sess:
 ### Load a SavedModel in C++
 
 The C++ version of the SavedModel
-[loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h)
+[loader](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/loader.h)
 provides an API to load a SavedModel from a path, while allowing
 `SessionOptions` and `RunOptions`.
 You have to specify the tags associated with the graph to be loaded.
@@ -383,20 +383,20 @@ reuse and share across tools consistently.
 You may use sets of tags to uniquely identify a `MetaGraphDef` saved in a
 SavedModel. A subset of commonly used tags is specified in:
 
-* [Python](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/tag_constants.py)
-* [C++](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h)
+* [Python](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/tag_constants.py)
+* [C++](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/tag_constants.h)
 
 
 #### Standard SignatureDef constants
 
-A [**SignatureDef**](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto)
+A [**SignatureDef**](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/meta_graph.proto)
 is a protocol buffer that defines the signature of a computation
 supported by a graph.
 Commonly used input keys, output keys, and method names are
 defined in:
 
-* [Python](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/signature_constants.py)
-* [C++](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/signature_constants.h)
+* [Python](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/signature_constants.py)
+* [C++](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/cc/saved_model/signature_constants.h)
 
 ## Using SavedModel with Estimators
 
@@ -408,7 +408,7 @@ To prepare a trained Estimator for serving, you must export it in the standard
 SavedModel format. This section explains how to:
 
 * Specify the output nodes and the corresponding
-  [APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto)
+  [APIs](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto)
   that can be served (Classify, Regress, or Predict).
 * Export your model to the SavedModel format.
 * Serve the model from a local server and request predictions.
@@ -506,7 +506,7 @@ Each `output` value must be an `ExportOutput` object  such as
 `tf.estimator.export.PredictOutput`.
 
 These output types map straightforwardly to the
-[TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto),
+[TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto),
 and so determine which request types will be honored.
 
 Note: In the multi-headed case, a `SignatureDef` will be generated for each
@@ -515,7 +515,7 @@ the same keys.  These `SignatureDef`s differ only in their outputs, as
 provided by the corresponding `ExportOutput` entry.  The inputs are always
 those provided by the `serving_input_receiver_fn`.
 An inference request may specify the head by name.  One head must be named
-using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/tensorflow/python/saved_model/signature_constants.py)
+using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/saved_model/signature_constants.py)
 indicating which `SignatureDef` will be served when an inference request
 does not specify one.
 
@@ -566,9 +566,9 @@ Now you have a server listening for inference requests via gRPC on port 9000!
 ### Request predictions from a local server
 
 The server responds to gRPC requests according to the
-[PredictionService](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto#L15)
+[PredictionService](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis/prediction_service.proto#L15)
 gRPC API service definition.  (The nested protocol buffers are defined in
-various [neighboring files](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis)).
+various [neighboring files](https://github.com/tensorflow/serving/blob/r1.15/tensorflow_serving/apis)).
 
 From the API service definition, the gRPC framework generates client libraries
 in various languages providing remote access to the API.  In a project using the
@@ -620,7 +620,7 @@ The returned result in this example is a `ClassificationResponse` protocol
 buffer.
 
 This is a skeletal example; please see the [Tensorflow Serving](../deploy/index.md)
-documentation and [examples](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example)
+documentation and [examples](https://github.com/tensorflow/serving/tree/r1.15/tensorflow_serving/example)
 for more details.
 
 > Note: `ClassificationRequest` and `RegressionRequest` contain a
diff --git a/site/en/r1/guide/using_tpu.md b/site/en/r1/guide/using_tpu.md
index 74169092189..e3e338adf49 100644
--- a/site/en/r1/guide/using_tpu.md
+++ b/site/en/r1/guide/using_tpu.md
@@ -7,8 +7,8 @@ changing the *hardware accelerator* in your notebook settings:
 TPU-enabled Colab notebooks are available to test:
 
   1. [A quick test, just to measure FLOPS](https://colab.research.google.com/notebooks/tpu.ipynb).
-  2. [A CNN image classifier with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/fashion_mnist.ipynb).
-  3. [An LSTM markov chain text generator with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/shakespeare_with_tpu_and_keras.ipynb)
+  2. [A CNN image classifier with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/r1.15/tools/colab/fashion_mnist.ipynb).
+  3. [An LSTM markov chain text generator with `tf.keras`](https://colab.research.google.com/github/tensorflow/tpu/blob/r1.15/tools/colab/shakespeare_with_tpu_and_keras.ipynb)
 
 ## TPUEstimator
 
@@ -25,7 +25,7 @@ Cloud TPU is to define the model's inference phase (from inputs to predictions)
 outside of the `model_fn`. Then maintain separate implementations of the
 `Estimator` setup and `model_fn`, both wrapping this inference step. For an
 example of this pattern compare the `mnist.py` and `mnist_tpu.py` implementation in
-[tensorflow/models](https://github.com/tensorflow/models/tree/master/official/r1/mnist).
+[tensorflow/models](https://github.com/tensorflow/models/tree/r1.15/official/r1/mnist).
 
 ### Run a TPUEstimator locally
 
@@ -350,10 +350,10 @@ in bytes. A minimum of a few MB (`buffer_size=8*1024*1024`) is recommended so
 that data is available when needed.
 
 The TPU-demos repo includes
-[a script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
+[a script](https://github.com/tensorflow/tpu/blob/1.15/tools/datasets/imagenet_to_gcs.py)
 for downloading the imagenet dataset and converting it to an appropriate format.
 This together with the imagenet
-[models](https://github.com/tensorflow/tpu/tree/master/models)
+[models](https://github.com/tensorflow/tpu/tree/r1.15/models)
 included in the repo demonstrate all of these best-practices.
 
 ## Next steps
diff --git a/site/en/r1/guide/version_compat.md b/site/en/r1/guide/version_compat.md
index 6702f6e0819..a765620518d 100644
--- a/site/en/r1/guide/version_compat.md
+++ b/site/en/r1/guide/version_compat.md
@@ -49,19 +49,19 @@ patch versions.  The public APIs consist of
   submodules, but is not documented, then it is **not** considered part of the
   public API.
 
-* The [C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h).
+* The [C API](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/c/c_api.h).
 
 * The following protocol buffer files:
-    * [`attr_value`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/attr_value.proto)
-    * [`config`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)
-    * [`event`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/event.proto)
-    * [`graph`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto)
-    * [`op_def`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def.proto)
-    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/reader_base.proto)
-    * [`summary`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto)
-    * [`tensor`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto)
-    * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
-    * [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto)
+    * [`attr_value`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/attr_value.proto)
+    * [`config`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/protobuf/config.proto)
+    * [`event`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/util/event.proto)
+    * [`graph`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/graph.proto)
+    * [`op_def`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/op_def.proto)
+    * [`reader_base`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/reader_base.proto)
+    * [`summary`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/summary.proto)
+    * [`tensor`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor.proto)
+    * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/tensor_shape.proto)
+    * [`types`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/types.proto)
 
 
 ## What is *not* covered
@@ -79,7 +79,7 @@ backward incompatible ways between minor releases. These include:
     such as:
 
   - [C++](./extend/cc.md) (exposed through header files in
-    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)).
+    [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/cc)).
   - [Java](../api_docs/java/reference/org/tensorflow/package-summary),
   - [Go](https://pkg.go.dev/github.com/tensorflow/tensorflow/tensorflow/go)
   - [JavaScript](https://js.tensorflow.org)
@@ -209,7 +209,7 @@ guidelines for evolving `GraphDef` versions.
 There are different data versions for graphs and checkpoints. The two data
 formats evolve at different rates from each other and also at different rates
 from TensorFlow. Both versioning systems are defined in
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h).
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/public/version.h).
 Whenever a new version is added, a note is added to the header detailing what
 changed and the date.
 
@@ -224,7 +224,7 @@ We distinguish between the following kinds of data version information:
   (`min_producer`).
 
 Each piece of versioned data has a [`VersionDef
-versions`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/versions.proto)
+versions`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/framework/versions.proto)
 field which records the `producer` that made the data, the `min_consumer`
 that it is compatible with, and a list of `bad_consumers` versions that are
 disallowed.
@@ -239,7 +239,7 @@ accept a piece of data if the following are all true:
 *   `consumer` not in data's `bad_consumers`
 
 Since both producers and consumers come from the same TensorFlow code base,
-[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/version.h)
+[`core/public/version.h`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/public/version.h)
 contains a main data version which is treated as either `producer` or
 `consumer` depending on context and both `min_consumer` and `min_producer`
 (needed by producers and consumers, respectively). Specifically,
@@ -309,7 +309,7 @@ existing producer scripts will not suddenly use the new functionality.
 1.  Add a new similar op named `SomethingV2` or similar and go through the
     process of adding it and switching existing Python wrappers to use it.
     To ensure forward compatibility use the checks suggested in
-    [compat.py](https://www.tensorflow.org/code/tensorflow/python/compat/compat.py)
+    [compat.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/compat/compat.py)
     when changing the Python wrappers.
 2.  Remove the old op (Can only take place with a major version change due to
     backward compatibility).
diff --git a/site/en/r1/tutorials/README.md b/site/en/r1/tutorials/README.md
index 5094e645e6e..9ff164ad77c 100644
--- a/site/en/r1/tutorials/README.md
+++ b/site/en/r1/tutorials/README.md
@@ -68,4 +68,4 @@ implement common ML algorithms. See the
 * [Boosted trees](./estimators/boosted_trees.ipynb)
 * [Gradient Boosted Trees: Model understanding](./estimators/boosted_trees_model_understanding.ipynb)
 * [Build a Convolutional Neural Network using Estimators](./estimators/cnn.ipynb)
-* [Wide and deep learning with Estimators](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+* [Wide and deep learning with Estimators](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
diff --git a/site/en/r1/tutorials/images/deep_cnn.md b/site/en/r1/tutorials/images/deep_cnn.md
index ef259516952..885f3907aa7 100644
--- a/site/en/r1/tutorials/images/deep_cnn.md
+++ b/site/en/r1/tutorials/images/deep_cnn.md
@@ -80,15 +80,15 @@ for details.  It consists of 1,068,298 learnable parameters and requires about
 ## Code Organization
 
 The code for this tutorial resides in
-[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/).
+[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/).
 
 File | Purpose
 --- | ---
-[`cifar10_input.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_input.py) | Loads CIFAR-10 dataset using [tensorflow-datasets library](https://github.com/tensorflow/datasets).
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
-[`cifar10_train.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
-[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
-[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
+[`cifar10_input.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_input.py) | Loads CIFAR-10 dataset using [tensorflow-datasets library](https://github.com/tensorflow/datasets).
+[`cifar10.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
+[`cifar10_train.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
+[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
+[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
 
 To run this tutorial, you will need to:
 
@@ -99,7 +99,7 @@ pip install tensorflow-datasets
 ## CIFAR-10 Model
 
 The CIFAR-10 network is largely contained in
-[`cifar10.py`](https://github.com/tensorflow/models/tree/master/research/tutorials/image/cifar10/cifar10.py).
+[`cifar10.py`](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/image/cifar10/cifar10.py).
 The complete training
 graph contains roughly 765 operations. We find that we can make the code most
 reusable by constructing the graph with the following modules:
diff --git a/site/en/r1/tutorials/images/image_recognition.md b/site/en/r1/tutorials/images/image_recognition.md
index 2cbf9eee378..cb66e594629 100644
--- a/site/en/r1/tutorials/images/image_recognition.md
+++ b/site/en/r1/tutorials/images/image_recognition.md
@@ -146,7 +146,7 @@ Next, try it out on your own images by supplying the --image= argument, e.g.,
 bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
 ```
 
-If you look inside the [`tensorflow/examples/label_image/main.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc)
+If you look inside the [`tensorflow/examples/label_image/main.cc`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc)
 file, you can find out
 how it works. We hope this code will help you integrate TensorFlow into
 your own applications, so we will walk step by step through the main functions:
@@ -164,7 +164,7 @@ training. If you have a graph that you've trained yourself, you'll just need
 to adjust the values to match whatever you used during your training process.
 
 You can see how they're applied to an image in the
-[`ReadTensorFromImageFile()`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc#L88)
+[`ReadTensorFromImageFile()`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc#L88)
 function.
 
 ```C++
@@ -334,7 +334,7 @@ The `PrintTopLabels()` function takes those sorted results, and prints them out
 friendly way. The `CheckTopLabel()` function is very similar, but just makes sure that
 the top label is the one we expect, for debugging purposes.
 
-At the end, [`main()`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc#L252)
+At the end, [`main()`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/label_image/main.cc#L252)
 ties together all of these calls.
 
 ```C++
diff --git a/site/en/r1/tutorials/keras/save_and_restore_models.ipynb b/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
index e9d112bd3f3..04cc94417a9 100644
--- a/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
+++ b/site/en/r1/tutorials/keras/save_and_restore_models.ipynb
@@ -115,7 +115,7 @@
         "\n",
         "Sharing this data helps others understand how the model works and try it themselves with new data.\n",
         "\n",
-        "Caution: Be careful with untrusted code—TensorFlow models are code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for details.\n",
+        "Caution: Be careful with untrusted code—TensorFlow models are code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/r1.15/SECURITY.md) for details.\n",
         "\n",
         "### Options\n",
         "\n",
diff --git a/site/en/r1/tutorials/load_data/tf_records.ipynb b/site/en/r1/tutorials/load_data/tf_records.ipynb
index fa7bf83c8bb..45635034c69 100644
--- a/site/en/r1/tutorials/load_data/tf_records.ipynb
+++ b/site/en/r1/tutorials/load_data/tf_records.ipynb
@@ -141,7 +141,7 @@
       "source": [
         "Fundamentally a `tf.Example` is a `{\"string\": tf.train.Feature}` mapping.\n",
         "\n",
-        "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these.\n",
+        "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these.\n",
         "\n",
         "1. `tf.train.BytesList` (the following types can be coerced)\n",
         "\n",
@@ -276,7 +276,7 @@
         "\n",
         "1. We create a map (dictionary) from the feature name string to the encoded feature value produced in #1.\n",
         "\n",
-        "1. The map produced in #2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)."
+        "1. The map produced in #2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/feature.proto#L85)."
       ]
     },
     {
@@ -365,7 +365,7 @@
         "id": "XftzX9CN_uGT"
       },
       "source": [
-        "For example, suppose we have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. We can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message."
+        "For example, suppose we have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. We can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message."
       ]
     },
     {
diff --git a/site/en/r1/tutorials/representation/kernel_methods.md b/site/en/r1/tutorials/representation/kernel_methods.md
index 67adc4951c6..227fe81d515 100644
--- a/site/en/r1/tutorials/representation/kernel_methods.md
+++ b/site/en/r1/tutorials/representation/kernel_methods.md
@@ -24,7 +24,7 @@ following sources for an introduction:
 Currently, TensorFlow supports explicit kernel mappings for dense features only;
 TensorFlow will provide support for sparse features at a later release.
 
-This tutorial uses [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn)
+This tutorial uses [tf.contrib.learn](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/contrib/learn/python/learn)
 (TensorFlow's high-level Machine Learning API) Estimators for our ML models.
 If you are not familiar with this API, The [Estimator guide](../../guide/estimators.md)
 is a good place to start. We will use the MNIST dataset. The tutorial consists
@@ -131,7 +131,7 @@ In addition to experimenting with the (training) batch size and the number of
 training steps, there are a couple other parameters that can be tuned as well.
 For instance, you can change the optimization method used to minimize the loss
 by explicitly selecting another optimizer from the collection of
-[available optimizers](https://www.tensorflow.org/code/tensorflow/python/training).
+[available optimizers](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/training).
 As an example, the following code constructs a LinearClassifier estimator that
 uses the Follow-The-Regularized-Leader (FTRL) optimization strategy with a
 specific learning rate and L2-regularization.
diff --git a/site/en/r1/tutorials/representation/linear.md b/site/en/r1/tutorials/representation/linear.md
index 5516672b34a..d996a13bc1f 100644
--- a/site/en/r1/tutorials/representation/linear.md
+++ b/site/en/r1/tutorials/representation/linear.md
@@ -12,7 +12,7 @@ those tools. It explains:
 
 Read this overview to decide whether the Estimator's linear model tools  might
 be useful to you. Then work through the
-[Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+[Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
 to give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 
@@ -177,7 +177,7 @@ the name of a `FeatureColumn`. Each key's value is a tensor containing the
 values of that feature for all data instances. See
 [Premade Estimators](../../guide/premade_estimators.md#input_fn) for a
 more comprehensive look at input functions, and `input_fn` in the
-[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep)
+[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep)
 for an example implementation of an input function.
 
 The input function is passed to the `train()` and `evaluate()` calls that
@@ -236,4 +236,4 @@ e = tf.estimator.DNNLinearCombinedClassifier(
     dnn_hidden_units=[100, 50])
 ```
 For more information, see the
-[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/r1/wide_deep).
+[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/r1.15/official/r1/wide_deep).
diff --git a/site/en/r1/tutorials/representation/word2vec.md b/site/en/r1/tutorials/representation/word2vec.md
index c76db7ab108..517a5dbc5c5 100644
--- a/site/en/r1/tutorials/representation/word2vec.md
+++ b/site/en/r1/tutorials/representation/word2vec.md
@@ -327,7 +327,7 @@ for inputs, labels in generate_batch(...):
 ```
 
 See the full example code in
-[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/word2vec/word2vec_basic.py).
+[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/examples/tutorials/word2vec/word2vec_basic.py).
 
 ## Visualizing the learned embeddings
 
@@ -341,7 +341,7 @@ t-SNE.
 Et voila! As expected, words that are similar end up clustering nearby each
 other. For a more heavyweight implementation of word2vec that showcases more of
 the advanced features of TensorFlow, see the implementation in
-[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/research/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/embedding/word2vec.py).
 
 ## Evaluating embeddings: analogical reasoning
 
@@ -357,7 +357,7 @@ Download the dataset for this task from
 
 To see how we do this evaluation, have a look at the `build_eval_graph()` and
 `eval()` functions in
-[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/research/tutorials/embedding/word2vec.py).
+[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/r1.15/research/tutorials/embedding/word2vec.py).
 
 The choice of hyperparameters can strongly influence the accuracy on this task.
 To achieve state-of-the-art performance on this task requires training over a
diff --git a/site/en/r1/tutorials/sequences/recurrent_quickdraw.md b/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
index 435076f629c..d6a85377d17 100644
--- a/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
+++ b/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
@@ -109,7 +109,7 @@ This download will take a while and download a bit more than 23GB of data.
 
 To convert the `ndjson` files to
 [TFRecord](../../api_guides/python/python_io.md#TFRecords_Format_Details) files containing
-[`tf.train.Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
+[`tf.train.Example`](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto)
 protos run the following command.
 
 ```shell
@@ -213,7 +213,7 @@ screen coordinates and normalize the size such that the drawing has unit height.
 
 Finally, we compute the differences between consecutive points and store these
 as a `VarLenFeature` in a
-[tensorflow.Example](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
+[tensorflow.Example](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/core/example/example.proto)
 under the key `ink`. In addition we store the `class_index` as a single entry
 `FixedLengthFeature` and the `shape` of the `ink` as a `FixedLengthFeature` of
 length 2.

From 12082b13c70b0b2373c22a580633a8218def25ae Mon Sep 17 00:00:00 2001
From: Mark McDonald 
Date: Mon, 29 Jan 2024 14:36:28 +0800
Subject: [PATCH 32/85] Add support for `google` in notebook metadata.

These fields will be used to support document-specific metadata that
needs to be pushed to the publishing system.
---
 tools/tensorflow_docs/tools/nbfmt/__main__.py |  9 ++-
 .../tools/nbfmt/nbfmtmain_test.py             | 74 +++++++++++++++++++
 2 files changed, 81 insertions(+), 2 deletions(-)
 create mode 100644 tools/tensorflow_docs/tools/nbfmt/nbfmtmain_test.py

diff --git a/tools/tensorflow_docs/tools/nbfmt/__main__.py b/tools/tensorflow_docs/tools/nbfmt/__main__.py
index 9426e6fd690..004fd8e9248 100644
--- a/tools/tensorflow_docs/tools/nbfmt/__main__.py
+++ b/tools/tensorflow_docs/tools/nbfmt/__main__.py
@@ -99,16 +99,16 @@ def clean_root(data: Dict[str, Any], filepath: pathlib.Path) -> None:
       data, keep=["cells", "metadata", "nbformat_minor", "nbformat"])
   # All metadata is optional according to spec, but we use some of it.
   notebook_utils.del_entries_except(
-      data["metadata"], keep=["accelerator", "colab", "kernelspec"])
+      data["metadata"], keep=["accelerator", "colab", "kernelspec", "google"])
 
   metadata = data.get("metadata", {})
-  colab = metadata.get("colab", {})
 
   # Set top-level notebook defaults.
   data["nbformat"] = 4
   data["nbformat_minor"] = 0
 
   # Colab metadata
+  colab = metadata.get("colab", {})
   notebook_utils.del_entries_except(
       colab, keep=["collapsed_sections", "name", "toc_visible"])
   colab["name"] = os.path.basename(filepath)
@@ -128,6 +128,11 @@ def clean_root(data: Dict[str, Any], filepath: pathlib.Path) -> None:
   kernelspec["display_name"] = supported_kernels[kernel_name]
   metadata["kernelspec"] = kernelspec
 
+  # Google metadata
+  google = metadata.get("google", {})
+  notebook_utils.del_entries_except(google, keep=["keywords", "image_path"])
+  metadata["google"] = google
+
   data["metadata"] = metadata
 
 
diff --git a/tools/tensorflow_docs/tools/nbfmt/nbfmtmain_test.py b/tools/tensorflow_docs/tools/nbfmt/nbfmtmain_test.py
new file mode 100644
index 00000000000..e7a8849b05f
--- /dev/null
+++ b/tools/tensorflow_docs/tools/nbfmt/nbfmtmain_test.py
@@ -0,0 +1,74 @@
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Unit tests for nbfmt."""
+import pathlib
+import unittest
+from nbformat import notebooknode
+from tensorflow_docs.tools.nbfmt import __main__ as nbfmt
+
+
+class NotebookFormatTest(unittest.TestCase):
+
+  def test_metadata_cleansing(self):
+    subject_notebook = notebooknode.NotebookNode({
+        "cells": [],
+        "metadata": {
+            "unknown": ["delete", "me"],
+            "accelerator": "GPU",
+            "colab": {
+                "name": "/this/is/clobbered.ipynb",
+                "collapsed_sections": [],
+                "deleteme": "pls",
+            },
+            "kernelspec": {
+                "display_name": "Python 2 foreverrrr",
+                "name": "python2",
+                "deleteme": "deldeldel",
+            },
+            "google": {
+                "keywords": ["one", "two"],
+                "image_path": "/foo/img.png",
+                "more_stuff": "delete me",
+            }
+        }
+    })
+
+    expected_notebook = notebooknode.NotebookNode({
+        "cells": [],
+        "metadata": {
+            "accelerator": "GPU",
+            "colab": {
+                "name": "test.ipynb",
+                "collapsed_sections": [],
+                "toc_visible": True,
+            },
+            "kernelspec": {
+                "display_name": "Python 3",
+                "name": "python3",
+            },
+            "google": {
+                "keywords": ["one", "two"],
+                "image_path": "/foo/img.png",
+            }
+        },
+        'nbformat': 4,
+        'nbformat_minor': 0,
+    })
+
+    nbfmt.clean_root(subject_notebook, pathlib.Path('/path/test.ipynb'))
+    self.assertEqual(subject_notebook, expected_notebook)
+
+
+if __name__ == '__main__':
+  unittest.main()

From 0e209714638e31b261ab23adee58f72f5db16ce7 Mon Sep 17 00:00:00 2001
From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com>
Date: Mon, 12 Feb 2024 20:23:35 +0000
Subject: [PATCH 33/85] Update site/en/hub/migration_tf2.md

---
 site/en/hub/migration_tf2.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/site/en/hub/migration_tf2.md b/site/en/hub/migration_tf2.md
index 0ed60225893..c2cc4b50759 100644
--- a/site/en/hub/migration_tf2.md
+++ b/site/en/hub/migration_tf2.md
@@ -46,7 +46,7 @@ model = tf.keras.Sequential([
     ...])
 ```
 
-Many tutorials show these APIs in action. See in particular
+Many tutorials show these APIs in action. Here are some examples:
 
 *   [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
 *   [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)

From 8b36191001b53bfce4fe15b77e243fbd7f382e41 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 13 Feb 2024 22:56:26 -0800
Subject: [PATCH 34/85] Generate just one seed, rather than generating two
 seeds and then taking the first half of each seed.

rng.make_seeds(n) generates a tensor with shape (2,n): each seed is a 2-tuple. The previous code took the first half of each of two seed tuples. Instead, we generate just one 2-tuple.

PiperOrigin-RevId: 606866194
---
 site/en/tutorials/images/data_augmentation.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/site/en/tutorials/images/data_augmentation.ipynb b/site/en/tutorials/images/data_augmentation.ipynb
index bdc7ae0c56a..8a1eaaabec4 100644
--- a/site/en/tutorials/images/data_augmentation.ipynb
+++ b/site/en/tutorials/images/data_augmentation.ipynb
@@ -1273,7 +1273,7 @@
       "source": [
         "# Create a wrapper function for updating seeds.\n",
         "def f(x, y):\n",
-        "  seed = rng.make_seeds(2)[0]\n",
+        "  seed = rng.make_seeds(1)[:, 0]\n",
         "  image, label = augment((x, y), seed)\n",
         "  return image, label"
       ]

From 065658214f9878e8e1f3c61bed3e61a6381379fc Mon Sep 17 00:00:00 2001
From: Fergus Henderson 
Date: Thu, 29 Feb 2024 11:06:01 -0800
Subject: [PATCH 35/85] Fix formatting error in versions.md.

PiperOrigin-RevId: 611531167
---
 site/en/guide/versions.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/site/en/guide/versions.md b/site/en/guide/versions.md
index 5e660892b6d..0b089885552 100644
--- a/site/en/guide/versions.md
+++ b/site/en/guide/versions.md
@@ -171,12 +171,10 @@ incrementing the major version number for TensorFlow Lite, or vice versa.
 The API surface that is covered by the TensorFlow Lite Extension APIs version
 number is comprised of the following public APIs:
 
-```
 *   [tensorflow/lite/c/c_api_opaque.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/c_api_opaque.h)
 *   [tensorflow/lite/c/common.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/common.h)
 *   [tensorflow/lite/c/builtin_op_data.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/builtin_op_data.h)
 *   [tensorflow/lite/builtin_ops.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/builtin_ops.h)
-```
 
 Again, experimental symbols are not covered; see [below](#not_covered) for
 details.

From 350b22d2bbb8d9aab686a6f6c8a066ce1ddad2f7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" 
Date: Tue, 5 Mar 2024 16:22:33 -0800
Subject: [PATCH 36/85] Add Tensorflow 2.16 tested build configurations

PiperOrigin-RevId: 613006193
---
 site/en/install/source.md         | 21 +++++++++++++--------
 site/en/install/source_windows.md |  1 +
 2 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/site/en/install/source.md b/site/en/install/source.md
index 0c556810fad..765be347f8a 100644
--- a/site/en/install/source.md
+++ b/site/en/install/source.md
@@ -60,7 +60,7 @@ file.
 
 Clang is a C/C++/Objective-C compiler that is compiled in C++ based on LLVM. It
 is the default compiler to build TensorFlow starting with TensorFlow 2.13. The
-current supported version is LLVM/Clang 16.
+current supported version is LLVM/Clang 17.
 
 [LLVM Debian/Ubuntu nightly packages](https://apt.llvm.org) provide an automatic
 installation script and packages for manual installation on Linux. Make sure you
@@ -68,22 +68,24 @@ run the following command if you manually add llvm apt repository to your
 package sources:
 
 
-sudo apt-get update && sudo apt-get install -y llvm-16 clang-16
+sudo apt-get update && sudo apt-get install -y llvm-17 clang-17
 

 
+Now that `/usr/lib/llvm-17/bin/clang` is the actual path to clang in this case.
+
 Alternatively, you can download and unpack the pre-built
-[Clang + LLVM 16](https://github.com/llvm/llvm-project/releases/tag/llvmorg-16.0.0).
+[Clang + LLVM 17](https://github.com/llvm/llvm-project/releases/tag/llvmorg-17.0.2).
 
 Below is an example of steps you can take to set up the downloaded Clang + LLVM
-16 binaries on Debian/Ubuntu operating systems:
+17 binaries on Debian/Ubuntu operating systems:
 
 1.  Change to the desired destination directory: `cd `
 
 1.  Load and extract an archive file...(suitable to your architecture):
     
-    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-16.0.0/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz
+    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-17.0.2/clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
     
-    tar -xvf clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz
+    tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
     
     

 
@@ -93,10 +95,10 @@ Below is an example of steps you can take to set up the downloaded Clang + LLVM
     have to replace anything, unless you have a previous installation, in which
     case you should replace the files:
     
-    cp -r clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/* /usr
+    cp -r clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04/* /usr
     

 
-1.  Check the obtained Clang + LLVM 16 binaries version:
+1.  Check the obtained Clang + LLVM 17 binaries version:
     
     clang --version
     

@@ -430,6 +432,7 @@ Success: TensorFlow is now installed.
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.812.2
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.912.2
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.08.611.8
+ @@ -468,6 +471,7 @@ Success: TensorFlow is now installed.
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang 17.0.1Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.0
+ @@ -508,6 +512,7 @@ Success: TensorFlow is now installed.
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow-2.16.13.9-3.12Clang 17.0.1Bazel 6.5.08.912.3
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.912.2
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
+ diff --git a/site/en/install/source_windows.md b/site/en/install/source_windows.md index 758e5dbea45..7a600947ad4 100644 --- a/site/en/install/source_windows.md +++ b/site/en/install/source_windows.md @@ -309,6 +309,7 @@ Note: Starting in TF 2.11, CUDA build is not supported for Windows. For using Te
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang from xcode 13.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
+ From 57a0d991e684cd01cec3ac152112a7df3a18a26a Mon Sep 17 00:00:00 2001 From: Kanglan Tang Date: Wed, 6 Mar 2024 09:33:21 -0800 Subject: [PATCH 37/85] Update Clang version to 17.0.6 for TF 2.16 in tested build configurations PiperOrigin-RevId: 613244520 --- site/en/install/source.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/site/en/install/source.md b/site/en/install/source.md index 765be347f8a..d841b8ff9b4 100644 --- a/site/en/install/source.md +++ b/site/en/install/source.md @@ -432,7 +432,7 @@ Success: TensorFlow is now installed.
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12MSVC 2019Bazel 6.5.0
tensorflow-2.15.03.9-3.11MSVC 2019Bazel 6.1.0
tensorflow-2.14.03.9-3.11MSVC 2019Bazel 6.1.0
tensorflow-2.12.03.8-3.11MSVC 2019Bazel 5.3.0
- + @@ -471,7 +471,7 @@ Success: TensorFlow is now installed.
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang 17.0.1Bazel 6.5.0
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.0
- + From 3688f3cff2685cfeab307c13435f77d2c96cf434 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 8 Mar 2024 10:48:15 -0800 Subject: [PATCH 38/85] Update docs for building from source PiperOrigin-RevId: 613981262 --- site/en/install/source.md | 70 +++++++++++++++++---------------------- 1 file changed, 30 insertions(+), 40 deletions(-) diff --git a/site/en/install/source.md b/site/en/install/source.md index d841b8ff9b4..6a0aa08ed4b 100644 --- a/site/en/install/source.md +++ b/site/en/install/source.md @@ -34,8 +34,7 @@ Install the TensorFlow *pip* package dependencies (if using a virtual environment, omit the `--user` argument): 
-pip install -U --user pip numpy wheel packaging requests opt_einsum
-pip install -U --user keras_preprocessing --no-deps
+pip install -U --user pip
Note: A `pip` version >19.0 is required to install the TensorFlow 2 `.whl` @@ -242,19 +241,6 @@ There are some preconfigured build configs available that can be added to the ## Build and install the pip package -The pip package is build in two steps. A `bazel build` commands creates a -"package-builder" program. You then run the package-builder to create the -package. - -### Build the package-builder -Note: GPU support can be enabled with `cuda=Y` during the `./configure` stage. - -Use `bazel build` to create the TensorFlow 2.x package-builder: - -
-bazel build [--config=option] //tensorflow/tools/pip_package:build_pip_package
-
- #### Bazel build options Refer to the Bazel @@ -270,25 +256,34 @@ that complies with the manylinux2014 package standard. ### Build the package -The `bazel build` command creates an executable named `build_pip_package`—this -is the program that builds the `pip` package. Run the executable as shown -below to build a `.whl` package in the `/tmp/tensorflow_pkg` directory. +To build pip package, you need to specify `--repo_env=WHEEL_NAME` flag. +depending on the provided name, package will be created, e.g: -To build from a release branch: +To build tensorflow CPU package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
+
+To build tensorflow GPU package: 
-./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda
-To build from master, use `--nightly_flag` to get the right dependencies: +To build tensorflow TPU package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_tpu --config=tpu
+
+To build nightly package, set `tf_nightly` instead of `tensorflow`, e.g. +to build CPU nightly package: 
-./bazel-bin/tensorflow/tools/pip_package/build_pip_package --nightly_flag /tmp/tensorflow_pkg
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly_cpu
-Although it is possible to build both CUDA and non-CUDA configurations under the -same source tree, it's recommended to run `bazel clean` when switching between -these two configurations in the same source tree. +As a result, generated wheel will be located in +
+bazel-bin/tensorflow/tools/pip_package/wheel_house/
+
### Install the package @@ -296,7 +291,7 @@ The filename of the generated `.whl` file depends on the TensorFlow version and your platform. Use `pip install` to install the package, for example: 
-pip install /tmp/tensorflow_pkg/tensorflow-version-tags.whl
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
Success: TensorFlow is now installed. @@ -346,18 +341,15 @@ virtual environment: 1. Optional: Configure the build—this prompts the user to answer build configuration questions. -2. Build the tool used to create the *pip* package. -3. Run the tool to create the *pip* package. -4. Adjust the ownership permissions of the file for outside the container. +2. Build the *pip* package. +3. Adjust the ownership permissions of the file for outside the container. 
 ./configure  # if necessary
 
-bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
-
-./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt  # create package
-
-chown $HOST_PERMS /mnt/tensorflow-version-tags.whl
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu --config=opt
+`
+chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
Install and verify the package within the container: @@ -365,7 +357,7 @@ Install and verify the package within the container: 
 pip uninstall tensorflow  # remove current version
 
-pip install /mnt/tensorflow-version-tags.whl
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
 cd /tmp  # don't import from source directory
 python -c "import tensorflow as tf; print(tf.__version__)"
@@ -403,11 +395,9 @@ with GPU support: 
 ./configure  # if necessary
 
-bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
-
-./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt  # create package
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=opt
 
-chown $HOST_PERMS /mnt/tensorflow-version-tags.whl
+chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
Install and verify the package within the container and check for a GPU: @@ -415,7 +405,7 @@ Install and verify the package within the container and check for a GPU: 
 pip uninstall tensorflow  # remove current version
 
-pip install /mnt/tensorflow-version-tags.whl
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
 cd /tmp  # don't import from source directory
 python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
From b64768499123da8b2253a534277d62e20de3ec73 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 12 Mar 2024 15:21:47 -0700 Subject: [PATCH 39/85] Fix notebook failure with Keras 3. PiperOrigin-RevId: 615189902 --- .../tutorials/images/transfer_learning.ipynb | 23 +-- .../tutorials/keras/text_classification.ipynb | 44 ++--- site/en/tutorials/quickstart/advanced.ipynb | 20 +- .../structured_data/time_series.ipynb | 186 ++++++++++-------- 4 files changed, 142 insertions(+), 131 deletions(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index 6406ccdce74..30353697208 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -585,7 +585,7 @@ }, "outputs": [], "source": [ - "prediction_layer = tf.keras.layers.Dense(1)\n", + "prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid')\n", "prediction_batch = prediction_layer(feature_batch_average)\n", "print(prediction_batch.shape)" ] @@ -667,7 +667,7 @@ "source": [ "### Compile the model\n", "\n", - "Compile the model before training it. Since there are two classes, use the `tf.keras.losses.BinaryCrossentropy` loss with `from_logits=True` since the model provides a linear output." + "Compile the model before training it. Since there are two classes and a sigmoid oputput, use the `BinaryAccuracy`." ] }, { @@ -680,8 +680,8 @@ "source": [ "base_learning_rate = 0.0001\n", "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n", - " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", - " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0, name='accuracy')])" + " loss=tf.keras.losses.BinaryCrossentropy(),\n", + " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy')])" ] }, { @@ -872,9 +872,9 @@ }, "outputs": [], "source": [ - "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", + "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n", " optimizer = tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n", - " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0, name='accuracy')])" + " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy')])" ] }, { @@ -1081,22 +1081,13 @@ "\n", "To learn more, visit the [Transfer learning guide](https://www.tensorflow.org/guide/keras/transfer_learning).\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uKIByL01da8c" - }, - "outputs": [], - "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "transfer_learning.ipynb", - "private_outputs": true, + "provenance": [], "toc_visible": true }, "kernelspec": { diff --git a/site/en/tutorials/keras/text_classification.ipynb b/site/en/tutorials/keras/text_classification.ipynb index f14964207ff..c66d0fce0d3 100644 --- a/site/en/tutorials/keras/text_classification.ipynb +++ b/site/en/tutorials/keras/text_classification.ipynb @@ -267,9 +267,9 @@ "id": "95kkUdRoaeMw" }, "source": [ - "Next, you will use the `text_dataset_from_directory` utility to create a labeled `tf.data.Dataset`. [tf.data](https://www.tensorflow.org/guide/data) is a powerful collection of tools for working with data. \n", + "Next, you will use the `text_dataset_from_directory` utility to create a labeled `tf.data.Dataset`. [tf.data](https://www.tensorflow.org/guide/data) is a powerful collection of tools for working with data.\n", "\n", - "When running a machine learning experiment, it is a best practice to divide your dataset into three splits: [train](https://developers.google.com/machine-learning/glossary#training_set), [validation](https://developers.google.com/machine-learning/glossary#validation_set), and [test](https://developers.google.com/machine-learning/glossary#test-set). \n", + "When running a machine learning experiment, it is a best practice to divide your dataset into three splits: [train](https://developers.google.com/machine-learning/glossary#training_set), [validation](https://developers.google.com/machine-learning/glossary#validation_set), and [test](https://developers.google.com/machine-learning/glossary#test-set).\n", "\n", "The IMDB dataset has already been divided into train and test, but it lacks a validation set. Let's create a validation set using an 80:20 split of the training data by using the `validation_split` argument below." ] @@ -286,10 +286,10 @@ "seed = 42\n", "\n", "raw_train_ds = tf.keras.utils.text_dataset_from_directory(\n", - " 'aclImdb/train', \n", - " batch_size=batch_size, \n", - " validation_split=0.2, \n", - " subset='training', \n", + " 'aclImdb/train',\n", + " batch_size=batch_size,\n", + " validation_split=0.2,\n", + " subset='training',\n", " seed=seed)" ] }, @@ -322,7 +322,7 @@ "id": "JWq1SUIrp1a-" }, "source": [ - "Notice the reviews contain raw text (with punctuation and occasional HTML tags like `
`). You will show how to handle these in the following section. \n", + "Notice the reviews contain raw text (with punctuation and occasional HTML tags like `
`). You will show how to handle these in the following section.\n", "\n", "The labels are 0 or 1. To see which of these correspond to positive and negative movie reviews, you can check the `class_names` property on the dataset.\n" ] @@ -366,10 +366,10 @@ "outputs": [], "source": [ "raw_val_ds = tf.keras.utils.text_dataset_from_directory(\n", - " 'aclImdb/train', \n", - " batch_size=batch_size, \n", - " validation_split=0.2, \n", - " subset='validation', \n", + " 'aclImdb/train',\n", + " batch_size=batch_size,\n", + " validation_split=0.2,\n", + " subset='validation',\n", " seed=seed)" ] }, @@ -382,7 +382,7 @@ "outputs": [], "source": [ "raw_test_ds = tf.keras.utils.text_dataset_from_directory(\n", - " 'aclImdb/test', \n", + " 'aclImdb/test',\n", " batch_size=batch_size)" ] }, @@ -394,7 +394,7 @@ "source": [ "### Prepare the dataset for training\n", "\n", - "Next, you will standardize, tokenize, and vectorize the data using the helpful `tf.keras.layers.TextVectorization` layer. \n", + "Next, you will standardize, tokenize, and vectorize the data using the helpful `tf.keras.layers.TextVectorization` layer.\n", "\n", "Standardization refers to preprocessing the text, typically to remove punctuation or HTML elements to simplify the dataset. Tokenization refers to splitting strings into tokens (for example, splitting a sentence into individual words, by splitting on whitespace). Vectorization refers to converting tokens into numbers so they can be fed into a neural network. All of these tasks can be accomplished with this layer.\n", "\n", @@ -580,7 +580,7 @@ "\n", "`.cache()` keeps data in memory after it's loaded off disk. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache, which is more efficient to read than many small files.\n", "\n", - "`.prefetch()` overlaps data preprocessing and model execution while training. \n", + "`.prefetch()` overlaps data preprocessing and model execution while training.\n", "\n", "You can learn more about both methods, as well as how to cache data to disk in the [data performance guide](https://www.tensorflow.org/guide/data_performance)." ] @@ -635,7 +635,7 @@ " layers.Dropout(0.2),\n", " layers.GlobalAveragePooling1D(),\n", " layers.Dropout(0.2),\n", - " layers.Dense(1)])\n", + " layers.Dense(1, activation='sigmoid')])\n", "\n", "model.summary()" ] @@ -674,9 +674,9 @@ }, "outputs": [], "source": [ - "model.compile(loss=losses.BinaryCrossentropy(from_logits=True),\n", + "model.compile(loss=losses.BinaryCrossentropy(),\n", " optimizer='adam',\n", - " metrics=tf.metrics.BinaryAccuracy(threshold=0.0))" + " metrics=[tf.metrics.BinaryAccuracy(threshold=0.5)])" ] }, { @@ -884,11 +884,11 @@ }, "outputs": [], "source": [ - "examples = [\n", + "examples = tf.constant([\n", " \"The movie was great!\",\n", " \"The movie was okay.\",\n", " \"The movie was terrible...\"\n", - "]\n", + "])\n", "\n", "export_model.predict(examples)" ] @@ -916,7 +916,7 @@ "\n", "This tutorial showed how to train a binary classifier from scratch on the IMDB dataset. As an exercise, you can modify this notebook to train a multi-class classifier to predict the tag of a programming question on [Stack Overflow](http://stackoverflow.com/).\n", "\n", - "A [dataset](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz) has been prepared for you to use containing the body of several thousand programming questions (for example, \"How can I sort a dictionary by value in Python?\") posted to Stack Overflow. Each of these is labeled with exactly one tag (either Python, CSharp, JavaScript, or Java). Your task is to take a question as input, and predict the appropriate tag, in this case, Python. \n", + "A [dataset](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz) has been prepared for you to use containing the body of several thousand programming questions (for example, \"How can I sort a dictionary by value in Python?\") posted to Stack Overflow. Each of these is labeled with exactly one tag (either Python, CSharp, JavaScript, or Java). Your task is to take a question as input, and predict the appropriate tag, in this case, Python.\n", "\n", "The dataset you will work with contains several thousand questions extracted from the much larger public Stack Overflow dataset on [BigQuery](https://console.cloud.google.com/marketplace/details/stack-exchange/stack-overflow), which contains more than 17 million posts.\n", "\n", @@ -950,7 +950,7 @@ "\n", "1. When plotting accuracy over time, change `binary_accuracy` and `val_binary_accuracy` to `accuracy` and `val_accuracy`, respectively.\n", "\n", - "1. Once these changes are complete, you will be able to train a multi-class classifier. " + "1. Once these changes are complete, you will be able to train a multi-class classifier." ] }, { @@ -968,8 +968,8 @@ "metadata": { "accelerator": "GPU", "colab": { - "collapsed_sections": [], "name": "text_classification.ipynb", + "provenance": [], "toc_visible": true }, "kernelspec": { diff --git a/site/en/tutorials/quickstart/advanced.ipynb b/site/en/tutorials/quickstart/advanced.ipynb index 2fe0ce85773..7cc134b2613 100644 --- a/site/en/tutorials/quickstart/advanced.ipynb +++ b/site/en/tutorials/quickstart/advanced.ipynb @@ -200,7 +200,7 @@ "id": "uGih-c2LgbJu" }, "source": [ - "Choose an optimizer and loss function for training: " + "Choose an optimizer and loss function for training:" ] }, { @@ -311,10 +311,10 @@ "\n", "for epoch in range(EPOCHS):\n", " # Reset the metrics at the start of the next epoch\n", - " train_loss.reset_states()\n", - " train_accuracy.reset_states()\n", - " test_loss.reset_states()\n", - " test_accuracy.reset_states()\n", + " train_loss.reset_state()\n", + " train_accuracy.reset_state()\n", + " test_loss.reset_state()\n", + " test_accuracy.reset_state()\n", "\n", " for images, labels in train_ds:\n", " train_step(images, labels)\n", @@ -324,10 +324,10 @@ "\n", " print(\n", " f'Epoch {epoch + 1}, '\n", - " f'Loss: {train_loss.result()}, '\n", - " f'Accuracy: {train_accuracy.result() * 100}, '\n", - " f'Test Loss: {test_loss.result()}, '\n", - " f'Test Accuracy: {test_accuracy.result() * 100}'\n", + " f'Loss: {train_loss.result():0.2f}, '\n", + " f'Accuracy: {train_accuracy.result() * 100:0.2f}, '\n", + " f'Test Loss: {test_loss.result():0.2f}, '\n", + " f'Test Accuracy: {test_accuracy.result() * 100:0.2f}'\n", " )" ] }, @@ -344,8 +344,8 @@ "metadata": { "accelerator": "GPU", "colab": { - "collapsed_sections": [], "name": "advanced.ipynb", + "provenance": [], "toc_visible": true }, "kernelspec": { diff --git a/site/en/tutorials/structured_data/time_series.ipynb b/site/en/tutorials/structured_data/time_series.ipynb index 0b0eb55bce3..31aab384859 100644 --- a/site/en/tutorials/structured_data/time_series.ipynb +++ b/site/en/tutorials/structured_data/time_series.ipynb @@ -70,7 +70,7 @@ "source": [ "This tutorial is an introduction to time series forecasting using TensorFlow. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs).\n", "\n", - "This is covered in two main parts, with subsections: \n", + "This is covered in two main parts, with subsections:\n", "\n", "* Forecast for a single time step:\n", " * A single feature.\n", @@ -452,7 +452,7 @@ "id": "HiurzTGQgf_D" }, "source": [ - "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important. \n", + "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important.\n", "\n", "If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. To check the assumptions, here is the `tf.signal.rfft` of the temperature over time. Note the obvious peaks at frequencies near `1/year` and `1/day`:\n" ] @@ -590,13 +590,13 @@ "source": [ "## Data windowing\n", "\n", - "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data. \n", + "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data.\n", "\n", "The main features of the input windows are:\n", "\n", "- The width (number of time steps) of the input and label windows.\n", "- The time offset between them.\n", - "- Which features are used as inputs, labels, or both. \n", + "- Which features are used as inputs, labels, or both.\n", "\n", "This tutorial builds a variety of models (including Linear, DNN, CNN and RNN models), and uses them for both:\n", "\n", @@ -616,11 +616,11 @@ "\n", "1. For example, to make a single prediction 24 hours into the future, given 24 hours of history, you might define a window like this:\n", "\n", - " ![One prediction 24 hours into the future.](images/raw_window_24h.png)\n", + " ![One prediction 24 hours into the future.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/raw_window_24h.png?raw=1)\n", "\n", "2. A model that makes a prediction one hour into the future, given six hours of history, would need a window like this:\n", "\n", - " ![One prediction one hour into the future.](images/raw_window_1h.png)" + " ![One prediction one hour into the future.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/raw_window_1h.png?raw=1)" ] }, { @@ -744,7 +744,7 @@ "\n", "The example `w2` you define earlier will be split like this:\n", "\n", - "![The initial window is all consecutive samples, this splits it into an (inputs, labels) pairs](images/split_window.png)\n", + "![The initial window is all consecutive samples, this splits it into an (inputs, labels) pairs](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/split_window.png?raw=1)\n", "\n", "This diagram doesn't show the `features` axis of the data, but this `split_window` function also handles the `label_columns` so it can be used for both the single output and multi-output examples." ] @@ -1069,7 +1069,7 @@ "\n", "So, start by building models to predict the `T (degC)` value one hour into the future.\n", "\n", - "![Predict the next time step](images/narrow_window.png)\n", + "![Predict the next time step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/narrow_window.png?raw=1)\n", "\n", "Configure a `WindowGenerator` object to produce these single-step `(input, label)` pairs:" ] @@ -1120,11 +1120,11 @@ "\n", "Before building a trainable model it would be good to have a performance baseline as a point for comparison with the later more complicated models.\n", "\n", - "This first task is to predict temperature one hour into the future, given the current value of all features. The current values include the current temperature. \n", + "This first task is to predict temperature one hour into the future, given the current value of all features. The current values include the current temperature.\n", "\n", "So, start with a model that just returns the current temperature as the prediction, predicting \"No change\". This is a reasonable baseline since temperature changes slowly. Of course, this baseline will work less well if you make a prediction further in the future.\n", "\n", - "![Send the input to the output](images/baseline.png)" + "![Send the input to the output](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/baseline.png?raw=1)" ] }, { @@ -1171,8 +1171,8 @@ "\n", "val_performance = {}\n", "performance = {}\n", - "val_performance['Baseline'] = baseline.evaluate(single_step_window.val)\n", - "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0)" + "val_performance['Baseline'] = baseline.evaluate(single_step_window.val, return_dict=True)\n", + "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0, return_dict=True)" ] }, { @@ -1211,7 +1211,7 @@ "source": [ "This expanded window can be passed directly to the same `baseline` model without any code changes. This is possible because the inputs and labels have the same number of time steps, and the baseline just forwards the input to the output:\n", "\n", - "![One prediction 1h into the future, ever hour.](images/last_window.png)" + "![One prediction 1h into the future, ever hour.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/last_window.png?raw=1)" ] }, { @@ -1269,7 +1269,7 @@ "\n", "The simplest **trainable** model you can apply to this task is to insert linear transformation between the input and output. In this case the output from a time step only depends on that step:\n", "\n", - "![A single step prediction](images/narrow_window.png)\n", + "![A single step prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/narrow_window.png?raw=1)\n", "\n", "A `tf.keras.layers.Dense` layer with no `activation` set is a linear model. The layer only transforms the last axis of the data from `(batch, time, inputs)` to `(batch, time, units)`; it is applied independently to every item across the `batch` and `time` axes." ] @@ -1352,8 +1352,8 @@ "source": [ "history = compile_and_fit(linear, single_step_window)\n", "\n", - "val_performance['Linear'] = linear.evaluate(single_step_window.val)\n", - "performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0)" + "val_performance['Linear'] = linear.evaluate(single_step_window.val, return_dict=True)\n", + "performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0, return_dict=True)" ] }, { @@ -1364,7 +1364,7 @@ "source": [ "Like the `baseline` model, the linear model can be called on batches of wide windows. Used this way the model makes a set of independent predictions on consecutive time steps. The `time` axis acts like another `batch` axis. There are no interactions between the predictions at each time step.\n", "\n", - "![A single step prediction](images/wide_window.png)" + "![A single step prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/wide_window.png?raw=1)" ] }, { @@ -1430,7 +1430,7 @@ "id": "Ylng7215boIY" }, "source": [ - "Sometimes the model doesn't even place the most weight on the input `T (degC)`. This is one of the risks of random initialization. " + "Sometimes the model doesn't even place the most weight on the input `T (degC)`. This is one of the risks of random initialization." ] }, { @@ -1443,7 +1443,7 @@ "\n", "Before applying models that actually operate on multiple time-steps, it's worth checking the performance of deeper, more powerful, single input step models.\n", "\n", - "Here's a model similar to the `linear` model, except it stacks several a few `Dense` layers between the input and the output: " + "Here's a model similar to the `linear` model, except it stacks several a few `Dense` layers between the input and the output:" ] }, { @@ -1462,8 +1462,8 @@ "\n", "history = compile_and_fit(dense, single_step_window)\n", "\n", - "val_performance['Dense'] = dense.evaluate(single_step_window.val)\n", - "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)" + "val_performance['Dense'] = dense.evaluate(single_step_window.val, return_dict=True)\n", + "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0, return_dict=True)" ] }, { @@ -1476,7 +1476,7 @@ "\n", "A single-time-step model has no context for the current values of its inputs. It can't see how the input features are changing over time. To address this issue the model needs access to multiple time steps when making predictions:\n", "\n", - "![Three time steps are used for each prediction.](images/conv_window.png)\n" + "![Three time steps are used for each prediction.](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/conv_window.png?raw=1)\n" ] }, { @@ -1526,7 +1526,7 @@ "outputs": [], "source": [ "conv_window.plot()\n", - "plt.title(\"Given 3 hours of inputs, predict 1 hour into the future.\")" + "plt.suptitle(\"Given 3 hours of inputs, predict 1 hour into the future.\")" ] }, { @@ -1581,8 +1581,8 @@ "history = compile_and_fit(multi_step_dense, conv_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.val)\n", - "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0)" + "val_performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.val, return_dict=True)\n", + "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0, return_dict=True)" ] }, { @@ -1602,7 +1602,7 @@ "id": "gWfrsP8mq8lV" }, "source": [ - "The main down-side of this approach is that the resulting model can only be executed on input windows of exactly this shape. " + "The main down-side of this approach is that the resulting model can only be executed on input windows of exactly this shape." ] }, { @@ -1636,7 +1636,7 @@ }, "source": [ "### Convolution neural network\n", - " \n", + "\n", "A convolution layer (`tf.keras.layers.Conv1D`) also takes multiple time steps as input to each prediction." ] }, @@ -1646,7 +1646,7 @@ "id": "cdLBwoaHmsWb" }, "source": [ - "Below is the **same** model as `multi_step_dense`, re-written with a convolution. \n", + "Below is the **same** model as `multi_step_dense`, re-written with a convolution.\n", "\n", "Note the changes:\n", "* The `tf.keras.layers.Flatten` and the first `tf.keras.layers.Dense` are replaced by a `tf.keras.layers.Conv1D`.\n", @@ -1712,8 +1712,8 @@ "history = compile_and_fit(conv_model, conv_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['Conv'] = conv_model.evaluate(conv_window.val)\n", - "performance['Conv'] = conv_model.evaluate(conv_window.test, verbose=0)" + "val_performance['Conv'] = conv_model.evaluate(conv_window.val, return_dict=True)\n", + "performance['Conv'] = conv_model.evaluate(conv_window.test, verbose=0, return_dict=True)" ] }, { @@ -1724,7 +1724,7 @@ "source": [ "The difference between this `conv_model` and the `multi_step_dense` model is that the `conv_model` can be run on inputs of any length. The convolutional layer is applied to a sliding window of inputs:\n", "\n", - "![Executing a convolutional model on a sequence](images/wide_conv_window.png)\n", + "![Executing a convolutional model on a sequence](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/wide_conv_window.png?raw=1)\n", "\n", "If you run it on wider input, it produces wider output:" ] @@ -1749,7 +1749,7 @@ "id": "h_WGxtLIHhRF" }, "source": [ - "Note that the output is shorter than the input. To make training or plotting work, you need the labels, and prediction to have the same length. So build a `WindowGenerator` to produce wide windows with a few extra input time steps so the label and prediction lengths match: " + "Note that the output is shorter than the input. To make training or plotting work, you need the labels, and prediction to have the same length. So build a `WindowGenerator` to produce wide windows with a few extra input time steps so the label and prediction lengths match:" ] }, { @@ -1828,15 +1828,15 @@ "source": [ "An important constructor argument for all Keras RNN layers, such as `tf.keras.layers.LSTM`, is the `return_sequences` argument. This setting can configure the layer in one of two ways:\n", "\n", - "1. If `False`, the default, the layer only returns the output of the final time step, giving the model time to warm up its internal state before making a single prediction: \n", + "1. If `False`, the default, the layer only returns the output of the final time step, giving the model time to warm up its internal state before making a single prediction:\n", "\n", - "![An LSTM warming up and making a single prediction](images/lstm_1_window.png)\n", + "![An LSTM warming up and making a single prediction](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/lstm_1_window.png?raw=1)\n", "\n", "2. If `True`, the layer returns an output for each input. This is useful for:\n", - " * Stacking RNN layers. \n", + " * Stacking RNN layers.\n", " * Training a model on multiple time steps simultaneously.\n", "\n", - "![An LSTM making a prediction after every time step](images/lstm_many_window.png)" + "![An LSTM making a prediction after every time step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/lstm_many_window.png?raw=1)" ] }, { @@ -1889,8 +1889,8 @@ "history = compile_and_fit(lstm_model, wide_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)\n", - "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)" + "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val, return_dict=True)\n", + "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0, return_dict=True)" ] }, { @@ -1922,6 +1922,29 @@ "With this dataset typically each of the models does slightly better than the one before it:" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dMPev9Nzd4mD" + }, + "outputs": [], + "source": [ + "cm = lstm_model.metrics[1]\n", + "cm.metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6is3g113eIIa" + }, + "outputs": [], + "source": [ + "val_performance" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1933,9 +1956,8 @@ "x = np.arange(len(performance))\n", "width = 0.3\n", "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in val_performance.values()]\n", - "test_mae = [v[metric_index] for v in performance.values()]\n", + "val_mae = [v[metric_name] for v in val_performance.values()]\n", + "test_mae = [v[metric_name] for v in performance.values()]\n", "\n", "plt.ylabel('mean_absolute_error [T (degC), normalized]')\n", "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", @@ -1954,7 +1976,7 @@ "outputs": [], "source": [ "for name, value in performance.items():\n", - " print(f'{name:12s}: {value[1]:0.4f}')" + " print(f'{name:12s}: {value[metric_name]:0.4f}')" ] }, { @@ -1979,7 +2001,7 @@ "outputs": [], "source": [ "single_step_window = WindowGenerator(\n", - " # `WindowGenerator` returns all features as labels if you \n", + " # `WindowGenerator` returns all features as labels if you\n", " # don't set the `label_columns` argument.\n", " input_width=1, label_width=1, shift=1)\n", "\n", @@ -2034,8 +2056,8 @@ "source": [ "val_performance = {}\n", "performance = {}\n", - "val_performance['Baseline'] = baseline.evaluate(wide_window.val)\n", - "performance['Baseline'] = baseline.evaluate(wide_window.test, verbose=0)" + "val_performance['Baseline'] = baseline.evaluate(wide_window.val, return_dict=True)\n", + "performance['Baseline'] = baseline.evaluate(wide_window.test, verbose=0, return_dict=True)" ] }, { @@ -2073,8 +2095,8 @@ "history = compile_and_fit(dense, single_step_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['Dense'] = dense.evaluate(single_step_window.val)\n", - "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)" + "val_performance['Dense'] = dense.evaluate(single_step_window.val, return_dict=True)\n", + "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0, return_dict=True)" ] }, { @@ -2108,8 +2130,8 @@ "history = compile_and_fit(lstm_model, wide_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['LSTM'] = lstm_model.evaluate( wide_window.val)\n", - "performance['LSTM'] = lstm_model.evaluate( wide_window.test, verbose=0)\n", + "val_performance['LSTM'] = lstm_model.evaluate( wide_window.val, return_dict=True)\n", + "performance['LSTM'] = lstm_model.evaluate( wide_window.test, verbose=0, return_dict=True)\n", "\n", "print()" ] @@ -2132,7 +2154,7 @@ "\n", "That is how you take advantage of the knowledge that the change should be small.\n", "\n", - "![A model with a residual connection](images/residual.png)\n", + "![A model with a residual connection](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/residual.png?raw=1)\n", "\n", "Essentially, this initializes the model to match the `Baseline`. For this task it helps models converge faster, with slightly better performance." ] @@ -2143,7 +2165,7 @@ "id": "yP58A_ORx0kM" }, "source": [ - "This approach can be used in conjunction with any model discussed in this tutorial. \n", + "This approach can be used in conjunction with any model discussed in this tutorial.\n", "\n", "Here, it is being applied to the LSTM model, note the use of the `tf.initializers.zeros` to ensure that the initial predicted changes are small, and don't overpower the residual connection. There are no symmetry-breaking concerns for the gradients here, since the `zeros` are only used on the last layer." ] @@ -2192,8 +2214,8 @@ "history = compile_and_fit(residual_lstm, wide_window)\n", "\n", "IPython.display.clear_output()\n", - "val_performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.val)\n", - "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0)\n", + "val_performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.val, return_dict=True)\n", + "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0, return_dict=True)\n", "print()" ] }, @@ -2227,9 +2249,8 @@ "width = 0.3\n", "\n", "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in val_performance.values()]\n", - "test_mae = [v[metric_index] for v in performance.values()]\n", + "val_mae = [v[metric_name] for v in val_performance.values()]\n", + "test_mae = [v[metric_name] for v in performance.values()]\n", "\n", "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", "plt.bar(x + 0.17, test_mae, width, label='Test')\n", @@ -2248,7 +2269,7 @@ "outputs": [], "source": [ "for name, value in performance.items():\n", - " print(f'{name:15s}: {value[1]:0.4f}')" + " print(f'{name:15s}: {value[metric_name]:0.4f}')" ] }, { @@ -2327,7 +2348,7 @@ "source": [ "A simple baseline for this task is to repeat the last input time step for the required number of output time steps:\n", "\n", - "![Repeat the last input, for each output step](images/multistep_last.png)" + "![Repeat the last input, for each output step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_last.png?raw=1)" ] }, { @@ -2349,8 +2370,8 @@ "multi_val_performance = {}\n", "multi_performance = {}\n", "\n", - "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val)\n", - "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(last_baseline)" ] }, @@ -2362,7 +2383,7 @@ "source": [ "Since this task is to predict 24 hours into the future, given 24 hours of the past, another simple approach is to repeat the previous day, assuming tomorrow will be similar:\n", "\n", - "![Repeat the previous day](images/multistep_repeat.png)" + "![Repeat the previous day](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_repeat.png?raw=1)" ] }, { @@ -2381,8 +2402,8 @@ "repeat_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", "\n", - "multi_val_performance['Repeat'] = repeat_baseline.evaluate(multi_window.val)\n", - "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['Repeat'] = repeat_baseline.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(repeat_baseline)" ] }, @@ -2409,7 +2430,7 @@ "\n", "A simple linear model based on the last input time step does better than either baseline, but is underpowered. The model needs to predict `OUTPUT_STEPS` time steps, from a single input time step with a linear projection. It can only capture a low-dimensional slice of the behavior, likely based mainly on the time of day and time of year.\n", "\n", - "![Predict all timesteps from the last time-step](images/multistep_dense.png)" + "![Predict all timesteps from the last time-step](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_dense.png?raw=1)" ] }, { @@ -2434,8 +2455,8 @@ "history = compile_and_fit(multi_linear_model, multi_window)\n", "\n", "IPython.display.clear_output()\n", - "multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val)\n", - "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(multi_linear_model)" ] }, @@ -2474,8 +2495,8 @@ "history = compile_and_fit(multi_dense_model, multi_window)\n", "\n", "IPython.display.clear_output()\n", - "multi_val_performance['Dense'] = multi_dense_model.evaluate(multi_window.val)\n", - "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['Dense'] = multi_dense_model.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(multi_dense_model)" ] }, @@ -2496,7 +2517,7 @@ "source": [ "A convolutional model makes predictions based on a fixed-width history, which may lead to better performance than the dense model since it can see how things are changing over time:\n", "\n", - "![A convolutional model sees how things change over time](images/multistep_conv.png)" + "![A convolutional model sees how things change over time](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_conv.png?raw=1)" ] }, { @@ -2524,8 +2545,8 @@ "\n", "IPython.display.clear_output()\n", "\n", - "multi_val_performance['Conv'] = multi_conv_model.evaluate(multi_window.val)\n", - "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['Conv'] = multi_conv_model.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(multi_conv_model)" ] }, @@ -2548,7 +2569,7 @@ "\n", "In this single-shot format, the LSTM only needs to produce an output at the last time step, so set `return_sequences=False` in `tf.keras.layers.LSTM`.\n", "\n", - "![The LSTM accumulates state over the input window, and makes a single prediction for the next 24 hours](images/multistep_lstm.png)\n" + "![The LSTM accumulates state over the input window, and makes a single prediction for the next 24 hours](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_lstm.png?raw=1)\n" ] }, { @@ -2574,8 +2595,8 @@ "\n", "IPython.display.clear_output()\n", "\n", - "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val)\n", - "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(multi_lstm_model)" ] }, @@ -2595,7 +2616,7 @@ "\n", "You could take any of the single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here you'll focus on building a model that's been explicitly trained to do that.\n", "\n", - "![Feedback a model's output to its input](images/multistep_autoregressive.png)" + "![Feedback a model's output to its input](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/images/multistep_autoregressive.png?raw=1)" ] }, { @@ -2794,8 +2815,8 @@ "\n", "IPython.display.clear_output()\n", "\n", - "multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val)\n", - "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0)\n", + "multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val, return_dict=True)\n", + "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0, return_dict=True)\n", "multi_window.plot(feedback_model)" ] }, @@ -2829,9 +2850,8 @@ "width = 0.3\n", "\n", "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in multi_val_performance.values()]\n", - "test_mae = [v[metric_index] for v in multi_performance.values()]\n", + "val_mae = [v[metric_name] for v in multi_val_performance.values()]\n", + "test_mae = [v[metric_name] for v in multi_performance.values()]\n", "\n", "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", "plt.bar(x + 0.17, test_mae, width, label='Test')\n", @@ -2847,7 +2867,7 @@ "id": "Zq3hUsedCEmJ" }, "source": [ - "The metrics for the multi-output models in the first half of this tutorial show the performance averaged across all output features. These performances are similar but also averaged across output time steps. " + "The metrics for the multi-output models in the first half of this tutorial show the performance averaged across all output features. These performances are similar but also averaged across output time steps." ] }, { @@ -2859,7 +2879,7 @@ "outputs": [], "source": [ "for name, value in multi_performance.items():\n", - " print(f'{name:8s}: {value[1]:0.4f}')" + " print(f'{name:8s}: {value[metric_name]:0.4f}')" ] }, { @@ -2894,8 +2914,8 @@ "metadata": { "accelerator": "GPU", "colab": { - "collapsed_sections": [], "name": "time_series.ipynb", + "provenance": [], "toc_visible": true }, "kernelspec": { From 98ef4850dc548f25929ac748019923c0422b20e3 Mon Sep 17 00:00:00 2001 From: Raunak Date: Tue, 19 Mar 2024 12:55:22 -0700 Subject: [PATCH 40/85] add clang documentation --- site/en/install/source_windows.md | 117 +++++++++++++++++++----------- 1 file changed, 75 insertions(+), 42 deletions(-) diff --git a/site/en/install/source_windows.md b/site/en/install/source_windows.md index 758e5dbea45..6c8deed3571 100644 --- a/site/en/install/source_windows.md +++ b/site/en/install/source_windows.md @@ -1,6 +1,6 @@ # Build from source on Windows -Build a TensorFlow *pip* package from source and install it on Windows. +Build a TensorFlow *pip* package from the source and install it on Windows. Note: We already provide well-tested, pre-built [TensorFlow packages](./pip.md) for Windows systems. @@ -20,6 +20,7 @@ variable. Install the TensorFlow *pip* package dependencies: 
+pip3 install -U pip
 pip3 install -U six numpy wheel packaging
 pip3 install -U keras_preprocessing --no-deps
@@ -47,22 +48,35 @@ build TensorFlow. If MSYS2 is installed to `C:\msys64`, add run: 
+pacman -Syu (requires a console restart)
 pacman -S git patch unzip
+pacman -S git patch unzip rsync
-### Install Visual C++ Build Tools 2019 +Note: Clang will be the preferred compiler to build TensorFlow CPU wheels on the Windows Platform starting with TF 2.16.1 The currently supported version is LLVM/clang 17.0.6. -Install the *Visual C++ build tools 2019*. This comes with *Visual Studio 2019* +Note: To build with Clang on Windows, it is required to install both LLVM and Visual C++ Build tools as although Windows uses clang-cl.exe as the compiler, Visual C++ Build tools are needed to link to Visual C++ libraries + +### Install Visual C++ Build Tools 2022 + +Install the *Visual C++ build tools 2022*. This comes with *Visual Studio Community 2022* but can be installed separately: 1. Go to the [Visual Studio downloads](https://visualstudio.microsoft.com/downloads/){:.external}, -2. Select *Redistributables and Build Tools*, +2. Select *Tools for Visual Studio or Other Tools, Framework and Redistributables*, 3. Download and install: - - *Microsoft Visual C++ 2019 Redistributable* - - *Microsoft Build Tools 2019* + - *Build Tools for Visual Studio 2022* + - *Microsoft Visual C++ Redistributables for Visual Studio 2022* + +Note: TensorFlow is tested against the *Visual Studio Community 2022*. + +### Install LLVM + +1. Go to the + [LLVM downloads](https://github.com/llvm/llvm-project/releases/){:.external}, +2. Download and install Windows-compatible LLVM in C:/Program Files/LLVM e.g., LLVM-17.0.6-win64.exe -Note: TensorFlow is tested against the *Visual Studio 2019*. ### Install GPU support (optional) @@ -94,31 +108,32 @@ Key Point: If you're having build problems on the latest development branch, try a release branch that is known to work. ## Optional: Environmental Variable Set Up -Run following commands before running build command to avoid issue with package creation: -(If the below commands were set up while installing the packages, please ignore them). Run `set` check if all the paths were set correctly, run `echo %Environmental Variable%` e.g., `echo %BAZEL_VC%` to check path set up for a specific Environmental Variable +Run the following commands before running the build command to avoid issues with package creation: +(If the below commands were set up while installing the packages, please ignore them). Run `set` to check if all the paths were set correctly, run `echo %Environmental Variable%` e.g., `echo %BAZEL_VC%` to check the path set up for a specific Environmental Variable Python path set up issue [tensorflow:issue#59943](https://github.com/tensorflow/tensorflow/issues/59943),[tensorflow:issue#9436](https://github.com/tensorflow/tensorflow/issues/9436),[tensorflow:issue#60083](https://github.com/tensorflow/tensorflow/issues/60083) 
-set PATH=path/to/python # [e.g. (C:/Python310)]
-set PATH=path/to/python/Scripts # [e.g. (C:/Python310/Scripts)] 
+set PATH=path/to/python;%PATH% # [e.g. (C:/Python311)]
+set PATH=path/to/python/Scripts;%PATH% # [e.g. (C:/Python311/Scripts)] 
 set PYTHON_BIN_PATH=path/to/python_virtualenv/Scripts/python.exe 
 set PYTHON_LIB_PATH=path/to/python virtualenv/lib/site-packages 
 set PYTHON_DIRECTORY=path/to/python_virtualenv/Scripts
-Bazel/MSVC path set up issue [tensorflow:issue#54578](https://github.com/tensorflow/tensorflow/issues/54578) +Bazel/MSVC/CLANG path set up issue [tensorflow:issue#54578](https://github.com/tensorflow/tensorflow/issues/54578) 
 set BAZEL_SH=C:/msys64/usr/bin/bash.exe 
-set BAZEL_VS=C:/Program Files(x86)/Microsoft Visual Studio/2019/BuildTools 
-set BAZEL_VC=C:/Program Files(x86)/Microsoft Visual Studio/2019/BuildTools/VC 
+set BAZEL_VS=C:/Program Files/Microsoft Visual Studio/2022/BuildTools 
+set BAZEL_VC=C:/Program Files/Microsoft Visual Studio/2022/BuildTools/VC 
+set Bazel_LLVM=C:/Program Files/LLVM (explicitly tell Bazel where LLVM is installed by BAZEL_LLVM, needed while using CLANG)
+set PATH=C:/Program Files/LLVM/bin;%PATH% (Optional, needed while using CLANG as Compiler)
- ## Optional: Configure the build -TensorFlow builds are configured by the `.bazelrc` file in the respoitory's +TensorFlow builds are configured by the `.bazelrc` file in the repository's root directory. The `./configure` or `./configure.py` scripts can be used to adjust common settings. @@ -138,21 +153,27 @@ differ):

View sample configuration session

 python ./configure.py
-You have bazel 5.3.0 installed.
-Please specify the location of python. [Default is C:\Python310\python.exe]:
+You have bazel 6.5.0 installed.
+Please specify the location of python. [Default is C:\Python311\python.exe]:
+
 Found possible Python library paths:
-C:\Python310\lib\site-packages
-Please input the desired Python library path to use.  Default is [C:\Python310\lib\site-packages]
+C:\Python311\lib\site-packages
+Please input the desired Python library path to use.  Default is [C:\Python311\lib\site-packages]
 
 Do you wish to build TensorFlow with ROCm support? [y/N]:
 No ROCm support will be enabled for TensorFlow.
 
-
 WARNING: Cannot build with CUDA support on Windows.
-Starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2.
+Starting in TF 2.11, CUDA build is not supported for Windows. To use TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2.
 
-Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]:
+Do you want to use Clang to build TensorFlow? [Y/n]:
+Please use "--config=win_clang" to compile TensorFlow with CLANG.
 
+Please specify the path to clang executable. [Default is C:\Program Files\LLVM\bin\clang.EXE]:
+
+You have Clang 17.0.6 installed.
+
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]:
 
 Would you like to override eigen strong inline for some C++ compilation to reduce the compilation time? [Y/n]:
 Eigen strong inline overridden.
@@ -170,13 +191,12 @@ Preconfigured Bazel build configs. You can use any of the below by adding "--con
 Preconfigured Bazel build configs to DISABLE default on features:
         --config=nogcp          # Disable GCP support.
         --config=nonccl         # Disable NVIDIA NCCL support.
-
## Build and install the pip package -The pip package gets built in two steps. A `bazel build` commands creates a +The pip package is built in two steps. A `bazel build` command creates a "package-builder" program. You then run the package-builder to create the package. @@ -187,15 +207,23 @@ tensorflow:master repo has been updated to build 2.x by default. `bazel build ` to create the TensorFlow package-builder. 
-bazel build //tensorflow/tools/pip_package:build_pip_package
+bazel build //tensorflow/tools/pip_package:wheel
#### CPU-only Use `bazel` to make the TensorFlow package builder with CPU-only support: +##### Build with MSVC +
+bazel build --config=opt --repo_env=TF_PYTHON_VERSION=3.11 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
+
+ +##### Build with CLANG +Use --config=`win_clang` to build TenorFlow with the CLANG Compiler: + 
-bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+bazel build --config=win_clang --repo_env=TF_PYTHON_VERSION=3.11 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
#### GPU support @@ -217,7 +245,7 @@ bazel clean --expunge #### Bazel build options -Use this option when building to avoid issue with package creation: +Use this option when building to avoid issues with package creation: [tensorflow:issue#22390](https://github.com/tensorflow/tensorflow/issues/22390) 
@@ -236,33 +264,37 @@ to suppress nvcc warning messages.
 
 ### Build the package
 
-The `bazel build` command creates an executable named `build_pip_package`—this
-is the program that builds the `pip` package. For example, the following builds
-a `.whl` package in the `C:/tmp/tensorflow_pkg` directory:
+To build a pip package, you need to specify the --repo_env=WHEEL_NAME flag. 
+Depending on the provided name, the package will be created. For example:
 
-
-bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/tmp/tensorflow_pkg
+To build tensorflow CPU package:
+
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
 

 
-Although it is possible to build both CUDA and non-CUDA configs under the
-same source tree, we recommend running `bazel clean` when switching between
-these two configurations in the same source tree.
+To build nightly package, set `tf_nightly` instead of `tensorflow`, e.g.
+to build CPU nightly package:
+
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly_cpu
+

+
+As a result, generated wheel will be located in
+
+bazel-bin/tensorflow/tools/pip_package/wheel_house/
+

 
 
 ### Install the package
 
 The filename of the generated `.whl` file depends on the TensorFlow version and
-your platform. Use `pip3 install` to install the package, for example:
+your platform. Use `pip install` to install the package, for example:
 
-
-pip3 install C:/tmp/tensorflow_pkg/tensorflow-version-tags.whl
-
-e.g., pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
+
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
 

 
 Success: TensorFlow is now installed.
 
-
 ## Build using the MSYS shell
 
 TensorFlow can also be built using the MSYS shell. Make the changes listed
@@ -309,6 +341,7 @@ Note: Starting in TF 2.11, CUDA build is not supported for Windows. For using Te
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow-2.16.13.9-3.12Clang 17.0.1Bazel 6.5.08.912.3
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.08.912.3
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.912.2
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
+ From e944e76e1f177cb9a7322f238914914f229d57c5 Mon Sep 17 00:00:00 2001 From: mraunak <83710963+mraunak@users.noreply.github.com> Date: Mon, 25 Mar 2024 13:49:23 -0700 Subject: [PATCH 41/85] Update source_windows.md --- site/en/install/source_windows.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/install/source_windows.md b/site/en/install/source_windows.md index 6c8deed3571..1bb5a8b5f4a 100644 --- a/site/en/install/source_windows.md +++ b/site/en/install/source_windows.md @@ -167,7 +167,7 @@ WARNING: Cannot build with CUDA support on Windows. Starting in TF 2.11, CUDA build is not supported for Windows. To use TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2. Do you want to use Clang to build TensorFlow? [Y/n]: -Please use "--config=win_clang" to compile TensorFlow with CLANG. +Add "--config=win_clang" to compile TensorFlow with CLANG. Please specify the path to clang executable. [Default is C:\Program Files\LLVM\bin\clang.EXE]: From ff989f0d94cd81cce45a8db0f540e605ce05512b Mon Sep 17 00:00:00 2001 From: Jongbin Park Date: Tue, 26 Mar 2024 18:00:55 -0700 Subject: [PATCH 42/85] Fix signature generation when the method is dataclass instance. PiperOrigin-RevId: 619368090 --- tools/tensorflow_docs/api_generator/signature.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/tensorflow_docs/api_generator/signature.py b/tools/tensorflow_docs/api_generator/signature.py index 7ef8f1f856d..dacf5d2bada 100644 --- a/tools/tensorflow_docs/api_generator/signature.py +++ b/tools/tensorflow_docs/api_generator/signature.py @@ -580,7 +580,7 @@ def generate_signature( sig = sig.replace(parameters=params) - if dataclasses.is_dataclass(func): + if dataclasses.is_dataclass(func) and inspect.isclass(func): sig = sig.replace(return_annotation=EMPTY) extract_fn = _extract_class_defaults_and_annotations else: From 4a66c14119ecab8081fe4e2458d6ece2d1ba2782 Mon Sep 17 00:00:00 2001 From: kinarr Date: Mon, 8 Apr 2024 19:33:12 +0530 Subject: [PATCH 43/85] Update the notebook to be compatible with Keras 3 - Added kwargs (expand_nested=True, dpi=64) to `plot_model` - Convert the y_true, y_pred objects into numpy arrays --- site/en/tutorials/images/segmentation.ipynb | 35 ++++++++++++--------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/site/en/tutorials/images/segmentation.ipynb b/site/en/tutorials/images/segmentation.ipynb index 4bf59cbbd5a..d7633697143 100644 --- a/site/en/tutorials/images/segmentation.ipynb +++ b/site/en/tutorials/images/segmentation.ipynb @@ -97,7 +97,10 @@ }, "outputs": [], "source": [ - "!pip install git+https://github.com/tensorflow/examples.git" + "!pip install git+https://github.com/tensorflow/examples.git\n", + "!pip install -U keras\n", + "!pip install -q tensorflow_datasets\n", + "!pip install -q -U tensorflow-text tensorflow" ] }, { @@ -108,8 +111,9 @@ }, "outputs": [], "source": [ - "import tensorflow as tf\n", + "import numpy as np\n", "\n", + "import tensorflow as tf\n", "import tensorflow_datasets as tfds" ] }, @@ -252,7 +256,7 @@ " # both use the same seed, so they'll make the same random changes.\n", " self.augment_inputs = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n", " self.augment_labels = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n", - " \n", + "\n", " def call(self, inputs, labels):\n", " inputs = self.augment_inputs(inputs)\n", " labels = self.augment_labels(labels)\n", @@ -450,7 +454,7 @@ "source": [ "## Train the model\n", "\n", - "Now, all that is left to do is to compile and train the model. \n", + "Now, all that is left to do is to compile and train the model.\n", "\n", "Since this is a multiclass classification problem, use the `tf.keras.losses.SparseCategoricalCrossentropy` loss function with the `from_logits` argument set to `True`, since the labels are scalar integers instead of vectors of scores for each pixel of every class.\n", "\n", @@ -490,7 +494,7 @@ }, "outputs": [], "source": [ - "tf.keras.utils.plot_model(model, show_shapes=True)" + "tf.keras.utils.plot_model(model, show_shapes=True, expand_nested=True, dpi=64)" ] }, { @@ -695,12 +699,14 @@ }, "outputs": [], "source": [ - "label = [0,0]\n", - "prediction = [[-3., 0], [-3, 0]] \n", - "sample_weight = [1, 10] \n", + "label = np.array([0,0])\n", + "prediction = np.array([[-3., 0], [-3, 0]])\n", + "sample_weight = [1, 10]\n", "\n", - "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,\n", - " reduction=tf.keras.losses.Reduction.NONE)\n", + "loss = tf.keras.losses.SparseCategoricalCrossentropy(\n", + " from_logits=True,\n", + " reduction=tf.keras.losses.Reduction.NONE\n", + ")\n", "loss(label, prediction, sample_weight).numpy()" ] }, @@ -729,7 +735,7 @@ " class_weights = tf.constant([2.0, 2.0, 1.0])\n", " class_weights = class_weights/tf.reduce_sum(class_weights)\n", "\n", - " # Create an image of `sample_weights` by using the label at each pixel as an \n", + " # Create an image of `sample_weights` by using the label at each pixel as an\n", " # index into the `class weights` .\n", " sample_weights = tf.gather(class_weights, indices=tf.cast(label, tf.int32))\n", "\n", @@ -811,9 +817,8 @@ "metadata": { "accelerator": "GPU", "colab": { - "collapsed_sections": [], - "name": "segmentation.ipynb", - "toc_visible": true + "toc_visible": true, + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -822,4 +827,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 2f4e5fae2f054e2e5cb4cc00ccf4a5d75674932c Mon Sep 17 00:00:00 2001 From: Kinar Date: Tue, 9 Apr 2024 16:26:04 +0000 Subject: [PATCH 44/85] Formatted notebook --- site/en/tutorials/images/segmentation.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/site/en/tutorials/images/segmentation.ipynb b/site/en/tutorials/images/segmentation.ipynb index d7633697143..285ef538664 100644 --- a/site/en/tutorials/images/segmentation.ipynb +++ b/site/en/tutorials/images/segmentation.ipynb @@ -817,8 +817,8 @@ "metadata": { "accelerator": "GPU", "colab": { - "toc_visible": true, - "provenance": [] + "name": "segmentation.ipynb", + "toc_visible": true }, "kernelspec": { "display_name": "Python 3", @@ -827,4 +827,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From d6b06f555b0a5370ca12f5a419ed354e906b8cfd Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Thu, 11 Apr 2024 11:53:12 +0100 Subject: [PATCH 45/85] Update TF 2 and Keras in Video classification tutorial --- site/en/tutorials/video/video_classification.ipynb | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/site/en/tutorials/video/video_classification.ipynb b/site/en/tutorials/video/video_classification.ipynb index 9356c7cc9d3..4265b6387e3 100644 --- a/site/en/tutorials/video/video_classification.ipynb +++ b/site/en/tutorials/video/video_classification.ipynb @@ -84,9 +84,7 @@ "## Setup\n", "\n", "Begin by installing and importing some necessary libraries, including:\n", - "[remotezip](https://github.com/gtsystem/python-remotezip) to inspect the contents of a ZIP file, [tqdm](https://github.com/tqdm/tqdm) to use a progress bar, [OpenCV](https://opencv.org/) to process video files, [einops](https://github.com/arogozhnikov/einops/tree/master/docs) for performing more complex tensor operations, and [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs) for embedding data in a Jupyter notebook.\n", - "\n", - "**Note**: Use TensorFlow 2.10 to run this tutorial. Versions above TensorFlow 2.10 may not run successfully." + "[remotezip](https://github.com/gtsystem/python-remotezip) to inspect the contents of a ZIP file, [tqdm](https://github.com/tqdm/tqdm) to use a progress bar, [OpenCV](https://opencv.org/) to process video files, [einops](https://github.com/arogozhnikov/einops/tree/master/docs) for performing more complex tensor operations, and [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs) for embedding data in a Jupyter notebook." ] }, { @@ -98,8 +96,7 @@ "outputs": [], "source": [ "!pip install remotezip tqdm opencv-python einops \n", - "# Install TensorFlow 2.10\n", - "!pip install tensorflow==2.10.0" + "!pip install -U tensorflow keras" ] }, { From f2c7ee7137140351563525ee643c50c91947c98e Mon Sep 17 00:00:00 2001 From: BALAGANESH <85802233+balaganesh102004@users.noreply.github.com> Date: Thu, 11 Apr 2024 22:30:32 +0530 Subject: [PATCH 46/85] Documentation Fix - Ragged Tensor --- site/en/guide/ragged_tensor.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/site/en/guide/ragged_tensor.ipynb b/site/en/guide/ragged_tensor.ipynb index d36010699db..a4d9b0b8814 100644 --- a/site/en/guide/ragged_tensor.ipynb +++ b/site/en/guide/ragged_tensor.ipynb @@ -674,7 +674,7 @@ "source": [ "### Keras\n", "\n", - "[tf.keras](https://www.tensorflow.org/guide/keras) is TensorFlow's high-level API for building and training deep learning models. Ragged tensors may be passed as inputs to a Keras model by setting `ragged=True` on `tf.keras.Input` or `tf.keras.layers.InputLayer`. Ragged tensors may also be passed between Keras layers, and returned by Keras models. The following example shows a toy LSTM model that is trained using ragged tensors." + "[tf.keras](https://www.tensorflow.org/guide/keras) is TensorFlow's high-level API for building and training deep learning models. Ragged tensors can be passed as inputs to a Keras model by using ragged tensors between Keras layers, and returning ragged tensors by Keras models. The following example shows a toy LSTM model that is trained using ragged tensors:" ] }, { @@ -700,9 +700,9 @@ "\n", "# Build the Keras model.\n", "keras_model = tf.keras.Sequential([\n", - " tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True),\n", - " tf.keras.layers.Embedding(hash_buckets, 16),\n", - " tf.keras.layers.LSTM(32, use_bias=False),\n", + " tf.keras.layers.Embedding(hash_buckets, 16, input_length=hashed_words.shape[1]),\n", + " tf.keras.layers.LSTM(32, return_sequences=True, use_bias=False),\n", + " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dense(32),\n", " tf.keras.layers.Activation(tf.nn.relu),\n", " tf.keras.layers.Dense(1)\n", @@ -710,7 +710,7 @@ "\n", "keras_model.compile(loss='binary_crossentropy', optimizer='rmsprop')\n", "keras_model.fit(hashed_words, is_question, epochs=5)\n", - "print(keras_model.predict(hashed_words))" + "print(keras_model.predict(hashed_words))\n" ] }, { From 29fd0f4fe59582b65ec33102385189ea3a9ba9c5 Mon Sep 17 00:00:00 2001 From: Raviteja Gorijala Date: Fri, 12 Apr 2024 08:08:23 -0700 Subject: [PATCH 47/85] TF 2.16: Update documentation for wheel locations and toolchain changes PiperOrigin-RevId: 624182071 --- site/en/install/lang_c.ipynb | 13 ++++-- site/en/install/pip.md | 88 ++++++++++++++++++++++++++++-------- 2 files changed, 78 insertions(+), 23 deletions(-) diff --git a/site/en/install/lang_c.ipynb b/site/en/install/lang_c.ipynb index cfff20db10b..6d8d716fe92 100644 --- a/site/en/install/lang_c.ipynb +++ b/site/en/install/lang_c.ipynb @@ -130,16 +130,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -178,7 +181,7 @@ "outputs": [], "source": [ "%%bash\n", - "FILENAME=libtensorflow-cpu-linux-x86_64-2.15.0.tar.gz\n", + "FILENAME=libtensorflow-cpu-linux-x86_64-2.16.1.tar.gz\n", "wget -q --no-check-certificate https://storage.googleapis.com/tensorflow/libtensorflow/${FILENAME}\n", "sudo tar -C /usr/local -xzf ${FILENAME}" ] diff --git a/site/en/install/pip.md b/site/en/install/pip.md index 4add60b11d7..ed434bb9cdd 100644 --- a/site/en/install/pip.md +++ b/site/en/install/pip.md @@ -478,58 +478,110 @@ The value you specify depends on your Python version.
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12CLANG 17.0.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11MSVC 2019Bazel 6.1.0
tensorflow-2.14.03.9-3.11MSVC 2019Bazel 6.1.0
tensorflow-2.12.03.8-3.11MSVC 2019Bazel 5.3.0
Linux
Linux CPU onlyhttps://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.15.0.tar.gzhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-linux-x86_64.tar.gz
Linux GPU supporthttps://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.15.0.tar.gzhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-gpu-linux-x86_64.tar.gz
macOS
macOS CPU onlyhttps://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.15.0.tar.gzhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-x86_64.tar.gz
macOS ARM64 CPU onlyhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-arm64.tar.gz
Windows\n", "
Windows CPU onlyhttps://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.15.0.ziphttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-windows-x86_64.zip
Windows GPU only
- + - + - + - + - + - + - + + + + + + + + + - + - + + + + + + + + + + + + + + + + + + - + - + + + + + - + + - - + + - - + + - - + + + + + + + + + + + + + + + + + + + + + + + +
VersionURL
Linux
Linux x86
Python 3.9 GPU supporthttps://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.15.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.9 CPU-onlyhttps://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.15.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.10 GPU supporthttps://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.10 CPU-onlyhttps://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.11 GPU supporthttps://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.15.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.11 CPU-onlyhttps://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.15.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.12 GPU supporthttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.12 CPU-onlyhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
macOS (CPU-only)
Linux Arm64 (CPU-only)
Python 3.9https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.15.0-cp39-cp39-macosx_10_15_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Python 3.10https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Python 3.11https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Python 3.12https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
macOS x86 (CPU-only)
Python 3.9https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-macosx_10_15_x86_64.whl
Python 3.10https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.15.0-cp310-cp310-macosx_10_15_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-macosx_10_15_x86_64.whl
Python 3.11https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.15.0-cp311-cp311-macosx_10_15_x86_64.whlhttps://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-macosx_10_15_x86_64.whl
Python 3.12https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-macosx_10_15_x86_64.whl
Windows
macOS Arm64 (CPU-only)
Python 3.9 CPU-onlyhttps://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.15.0-cp39-cp39-win_amd64.whlPython 3.9https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-macosx_12_0_arm64.whl
Python 3.10 CPU-onlyhttps://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.15.0-cp310-cp310-win_amd64.whlPython 3.10https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-macosx_12_0_arm64.whl
Python 3.11 CPU-onlyhttps://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.15.0-cp311-cp311-win_amd64.whlPython 3.11https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-macosx_12_0_arm64.whl
Python 3.12https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-macosx_12_0_arm64.whl
Windows (CPU-only)
Python 3.9https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-win_amd64.whl
Python 3.10https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-win_amd64.whl
Python 3.11https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-win_amd64.whl
Python 3.12https://storage.googleapis.com/tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-win_amd64.whl
From f278011af8e25b060bf7d91640baf175d35ba7da Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 12 Apr 2024 15:52:27 -0700 Subject: [PATCH 48/85] Fix notebook failure with Keras 3. PiperOrigin-RevId: 624315589 --- site/en/tutorials/load_data/csv.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/site/en/tutorials/load_data/csv.ipynb b/site/en/tutorials/load_data/csv.ipynb index 0d4287a425e..7778af974b3 100644 --- a/site/en/tutorials/load_data/csv.ipynb +++ b/site/en/tutorials/load_data/csv.ipynb @@ -449,8 +449,8 @@ }, "outputs": [], "source": [ - "print(calc(1).numpy())\n", - "print(calc(2).numpy())" + "print(calc(np.array([1])).numpy())\n", + "print(calc(np.array([2])).numpy())" ] }, { @@ -1751,7 +1751,7 @@ "\n", "for row in font_rows.take(10):\n", " fonts_dict['font_name'].append(row[0].numpy().decode())\n", - " fonts_dict['character'].append(chr(row[2].numpy()))\n", + " fonts_dict['character'].append(chr(int(row[2].numpy())))\n", "\n", "pd.DataFrame(fonts_dict)" ] From fc4112c06c4e403cddc0e8d8bdc4293a4d6003f6 Mon Sep 17 00:00:00 2001 From: 8bitmp3 <19637339+8bitmp3@users.noreply.github.com> Date: Mon, 15 Apr 2024 09:40:40 +0000 Subject: [PATCH 49/85] Update TPU config in Use TPU notebook --- site/en/guide/tpu.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/site/en/guide/tpu.ipynb b/site/en/guide/tpu.ipynb index 0ac2f8216fc..78e5d7ce0e4 100644 --- a/site/en/guide/tpu.ipynb +++ b/site/en/guide/tpu.ipynb @@ -6,7 +6,7 @@ "id": "Tce3stUlHN0L" }, "source": [ - "##### Copyright 2018 The TensorFlow Authors.\n" + "##### Copyright 2024 The TensorFlow Authors.\n" ] }, { @@ -81,7 +81,7 @@ "id": "ebf7f8489bb7" }, "source": [ - "Before you run this Colab notebook, make sure that your hardware accelerator is a TPU by checking your notebook settings: **Runtime** > **Change runtime type** > **Hardware accelerator** > **TPU**.\n", + "Before you run this Colab notebook, make sure that your hardware accelerator is a TPU by checking your notebook settings: **Runtime** > **Change runtime type** > **Hardware accelerator** > **TPU v2**.\n", "\n", "Import some necessary libraries, including TensorFlow Datasets:" ] @@ -128,7 +128,7 @@ }, "outputs": [], "source": [ - "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')\n", + "resolver = tf.distribute.cluster_resolver.TPUClusterResolver()\n", "tf.config.experimental_connect_to_cluster(resolver)\n", "# This is the TPU initialization code that has to be at the beginning.\n", "tf.tpu.experimental.initialize_tpu_system(resolver)\n", @@ -588,7 +588,6 @@ "metadata": { "accelerator": "TPU", "colab": { - "collapsed_sections": [], "name": "tpu.ipynb", "toc_visible": true }, From 9efadf0a84eae76e347787e344626650ea4ed85b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 15 Apr 2024 13:53:35 -0700 Subject: [PATCH 50/85] Fix notebook failure with Keras 3. PiperOrigin-RevId: 625072490 --- .../en/tutorials/generative/autoencoder.ipynb | 47 +++++++++++++------ 1 file changed, 32 insertions(+), 15 deletions(-) diff --git a/site/en/tutorials/generative/autoencoder.ipynb b/site/en/tutorials/generative/autoencoder.ipynb index d81628fb401..1b2a6fcd2a8 100644 --- a/site/en/tutorials/generative/autoencoder.ipynb +++ b/site/en/tutorials/generative/autoencoder.ipynb @@ -6,9 +6,16 @@ "id": "Ndo4ERqnwQOU" }, "source": [ - "##### Copyright 2020 The TensorFlow Authors." + "##### Copyright 2024 The TensorFlow Authors." ] }, + { + "metadata": { + "id": "13rwRG5Jec7n" + }, + "cell_type": "markdown", + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -76,7 +83,7 @@ "source": [ "This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.\n", "\n", - "An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error. \n", + "An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.\n", "\n", "To learn more about autoencoders, please consider reading chapter 14 from [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville." ] @@ -117,7 +124,7 @@ }, "source": [ "## Load the dataset\n", - "To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels. " + "To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels." ] }, { @@ -169,7 +176,7 @@ " layers.Dense(latent_dim, activation='relu'),\n", " ])\n", " self.decoder = tf.keras.Sequential([\n", - " layers.Dense(tf.math.reduce_prod(shape), activation='sigmoid'),\n", + " layers.Dense(tf.math.reduce_prod(shape).numpy(), activation='sigmoid'),\n", " layers.Reshape(shape)\n", " ])\n", "\n", @@ -331,8 +338,8 @@ "outputs": [], "source": [ "noise_factor = 0.2\n", - "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape) \n", - "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape) \n", + "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape)\n", + "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape)\n", "\n", "x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)\n", "x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)" @@ -657,7 +664,7 @@ "id": "wVcTBDo-CqFS" }, "source": [ - "Plot a normal ECG. " + "Plot a normal ECG." ] }, { @@ -721,12 +728,12 @@ " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(8, activation=\"relu\")])\n", - " \n", + "\n", " self.decoder = tf.keras.Sequential([\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(140, activation=\"sigmoid\")])\n", - " \n", + "\n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", @@ -763,8 +770,8 @@ }, "outputs": [], "source": [ - "history = autoencoder.fit(normal_train_data, normal_train_data, \n", - " epochs=20, \n", + "history = autoencoder.fit(normal_train_data, normal_train_data,\n", + " epochs=20,\n", " batch_size=512,\n", " validation_data=(test_data, test_data),\n", " shuffle=True)" @@ -908,7 +915,7 @@ "id": "uEGlA1Be50Nj" }, "source": [ - "Note: There are other strategies you could use to select a threshold value above which test examples should be classified as anomalous, the correct approach will depend on your dataset. You can learn more with the links at the end of this tutorial. " + "Note: There are other strategies you could use to select a threshold value above which test examples should be classified as anomalous, the correct approach will depend on your dataset. You can learn more with the links at the end of this tutorial." ] }, { @@ -917,7 +924,7 @@ "id": "zpLSDAeb51D_" }, "source": [ - "If you examine the reconstruction error for the anomalous examples in the test set, you'll notice most have greater reconstruction error than the threshold. By varing the threshold, you can adjust the [precision](https://developers.google.com/machine-learning/glossary#precision) and [recall](https://developers.google.com/machine-learning/glossary#recall) of your classifier. " + "If you examine the reconstruction error for the anomalous examples in the test set, you'll notice most have greater reconstruction error than the threshold. By varing the threshold, you can adjust the [precision](https://developers.google.com/machine-learning/glossary#precision) and [recall](https://developers.google.com/machine-learning/glossary#recall) of your classifier." ] }, { @@ -992,8 +999,18 @@ "metadata": { "accelerator": "GPU", "colab": { - "collapsed_sections": [], - "name": "autoencoder.ipynb", + "gpuType": "T4", + "private_outputs": true, + "provenance": [ + { + "file_id": "17gKB2bKebV2DzoYIMFzyEXA5uDnwWOvT", + "timestamp": 1712793165979 + }, + { + "file_id": "https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/autoencoder.ipynb", + "timestamp": 1712792176273 + } + ], "toc_visible": true }, "kernelspec": { From 0aeef4bab17793e32c7cd8178861cf31d0431c6d Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 22 Apr 2024 13:50:30 -0700 Subject: [PATCH 51/85] Apply suggestion: "tpu='local'". --- site/en/guide/tpu.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/guide/tpu.ipynb b/site/en/guide/tpu.ipynb index 78e5d7ce0e4..8c12d71cb99 100644 --- a/site/en/guide/tpu.ipynb +++ b/site/en/guide/tpu.ipynb @@ -128,7 +128,7 @@ }, "outputs": [], "source": [ - "resolver = tf.distribute.cluster_resolver.TPUClusterResolver()\n", + "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='local')\n", "tf.config.experimental_connect_to_cluster(resolver)\n", "# This is the TPU initialization code that has to be at the beginning.\n", "tf.tpu.experimental.initialize_tpu_system(resolver)\n", From e520630d1505d122c4e1fb0e266eff1976f5b7dc Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Tue, 23 Apr 2024 16:33:02 -0700 Subject: [PATCH 52/85] Support button-cells without tfo class (#2304) --- tools/tensorflow_docs/tools/nblint/style/tensorflow.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py index f6ca2381a54..acd7cc8b698 100644 --- a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py +++ b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py @@ -81,7 +81,9 @@ def not_translation(args): # Button checks -is_button_cell_re = re.compile(r"class.*tfo-notebook-buttons") +# Look for class="tfo-notebook-buttons" (CSS used on website versions) or the +# run-in-colab logo (for notebooks that stick to GitHub/Colab). +is_button_cell_re = re.compile(r"class.*tfo-notebook-buttons|colab_logo_32px\.png") def get_arg_or_fail(user_args, arg_name, arg_fmt): From 880385b008b1a8e350915a2790e829ca381cc05a Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 25 Apr 2024 12:42:08 -0700 Subject: [PATCH 53/85] Update colab to TPUv2. PiperOrigin-RevId: 628160440 --- site/en/guide/tpu.ipynb | 4 +++- tools/tensorflow_docs/tools/nblint/style/tensorflow.py | 4 +--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/site/en/guide/tpu.ipynb b/site/en/guide/tpu.ipynb index 8c12d71cb99..d4376d14558 100644 --- a/site/en/guide/tpu.ipynb +++ b/site/en/guide/tpu.ipynb @@ -589,7 +589,9 @@ "accelerator": "TPU", "colab": { "name": "tpu.ipynb", - "toc_visible": true + "toc_visible": true, + "machine_shape": "hm", + "gpuType": "V28" }, "kernelspec": { "display_name": "Python 3", diff --git a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py index acd7cc8b698..f6ca2381a54 100644 --- a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py +++ b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py @@ -81,9 +81,7 @@ def not_translation(args): # Button checks -# Look for class="tfo-notebook-buttons" (CSS used on website versions) or the -# run-in-colab logo (for notebooks that stick to GitHub/Colab). -is_button_cell_re = re.compile(r"class.*tfo-notebook-buttons|colab_logo_32px\.png") +is_button_cell_re = re.compile(r"class.*tfo-notebook-buttons") def get_arg_or_fail(user_args, arg_name, arg_fmt): From e30c5f5f58586d7ca97755d0ff98821018c08d9f Mon Sep 17 00:00:00 2001 From: Prianka Liz Kariat Date: Thu, 2 May 2024 08:24:08 +0530 Subject: [PATCH 54/85] Fixed the imbalanced classification notebook to work for TensorFlow 2.16 --- .../structured_data/imbalanced_data.ipynb | 2196 +++++++++++++++-- 1 file changed, 2024 insertions(+), 172 deletions(-) diff --git a/site/en/tutorials/structured_data/imbalanced_data.ipynb b/site/en/tutorials/structured_data/imbalanced_data.ipynb index 16d08e53385..bc19774d648 100644 --- a/site/en/tutorials/structured_data/imbalanced_data.ipynb +++ b/site/en/tutorials/structured_data/imbalanced_data.ipynb @@ -153,9 +153,428 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "pR_SnbMArXr7" - }, - "outputs": [], + "id": "pR_SnbMArXr7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "outputId": "b02780f2-6d2b-44af-abec-82c62941dac1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Time V1 V2 V3 V4 V5 V6 V7 \\\n", + "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", + "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", + "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", + "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", + "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", + "\n", + " V8 V9 ... 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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']]\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"Time\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 88923.63361429356,\n \"min\": 0.0,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 94813.85957508067,\n 84692.0,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100697.08771991085,\n \"min\": -56.407509631329,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 1.1683749838001528e-15,\n 0.0181087991615309,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100696.94591374432,\n \"min\": -72.7157275629303,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 3.416908049651284e-16,\n 0.0654855563960555,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V3\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100696.3564401747,\n \"min\": -48.3255893623954,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n -1.379536707896593e-15,\n 0.179846343563544,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V4\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100693.85024469436,\n \"min\": -5.68317119816995,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.0740951198584196e-15,\n -0.0198465294811989,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V5\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100698.41415139876,\n \"min\": -113.743306711146,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 9.604066317127324e-16,\n -0.0543358267364858,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V26\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100694.41704783794,\n \"min\": -2.60455055280817,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 1.6834371984034178e-15,\n -0.0521391080182019,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V27\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100694.0031827918,\n \"min\": -22.5656793207827,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n -3.6600908126037946e-16,\n 0.0013421459786502,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"V28\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100693.53270660152,\n \"min\": -15.4300839055349,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n -1.2273899954199695e-16,\n 0.011243831564982,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Amount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 99778.01856206656,\n \"min\": 0.0,\n \"max\": 284807.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 88.34961925093133,\n 22.0,\n 284807.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Class\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100694.42782298056,\n \"min\": 0.0,\n \"max\": 284807.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.001727485630620034,\n 1.0,\n 0.04152718963546506\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ], "source": [ "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()" ] @@ -188,9 +1014,24 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "HCJFrtuY2iLF" - }, - "outputs": [], + "id": "HCJFrtuY2iLF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "92e418bb-4cd9-4eef-85fe-fe893081343a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Examples:\n", + " Total: 284807\n", + " Positive: 492 (0.17% of total)\n", + "\n" + ] + } + ], "source": [ "neg, pos = np.bincount(raw_df['Class'])\n", "total = neg + pos\n", @@ -258,10 +1099,10 @@ "train_df, val_df = train_test_split(train_df, test_size=0.2)\n", "\n", "# Form np arrays of labels and features.\n", - "train_labels = np.array(train_df.pop('Class'))\n", - "bool_train_labels = train_labels != 0\n", - "val_labels = np.array(val_df.pop('Class'))\n", - "test_labels = np.array(test_df.pop('Class'))\n", + "train_labels = np.array(train_df.pop('Class')).reshape(-1, 1)\n", + "bool_train_labels = train_labels[:, 0] != 0\n", + "val_labels = np.array(val_df.pop('Class')).reshape(-1, 1)\n", + "test_labels = np.array(test_df.pop('Class')).reshape(-1, 1)\n", "\n", "train_features = np.array(train_df)\n", "val_features = np.array(val_df)\n", @@ -281,9 +1122,23 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "96520cffee66" - }, - "outputs": [], + "id": "96520cffee66", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "737f82dc-e318-48c2-f22e-f7730d6c22fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Average class probability in training set: 0.0017\n", + "Average class probability in validation set: 0.0016\n", + "Average class probability in test set: 0.0020\n" + ] + } + ], "source": [ "print(f'Average class probability in training set: {train_labels.mean():.4f}')\n", "print(f'Average class probability in validation set: {val_labels.mean():.4f}')\n", @@ -291,10 +1146,9 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "8a_Z_kBmr7Oh" + "id": "ueKV4cmcoRnf" }, "source": [ "Given the small number of positive labels, this seems about right.\n", @@ -302,16 +1156,33 @@ "Normalize the input features using the sklearn StandardScaler.\n", "This will set the mean to 0 and standard deviation to 1.\n", "\n", - "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. " + "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "IO-qEUmJ5JQg" - }, - "outputs": [], + "id": "IO-qEUmJ5JQg", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fc994363-63a9-4b84-ce5f-ea84bc8d1fbf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training labels shape: (182276, 1)\n", + "Validation labels shape: (45569, 1)\n", + "Test labels shape: (56962, 1)\n", + "Training features shape: (182276, 29)\n", + "Validation features shape: (45569, 29)\n", + "Test features shape: (56962, 29)\n" + ] + } + ], "source": [ "scaler = StandardScaler()\n", "train_features = scaler.fit_transform(train_features)\n", @@ -352,7 +1223,7 @@ "\n", "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n", "\n", - "* Do these distributions make sense? \n", + "* Do these distributions make sense?\n", " * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n", "* Can you see the difference between the distributions?\n", " * Yes the positive examples contain a much higher rate of extreme values." @@ -362,9 +1233,35 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "raK7hyjd_vf6" - }, - "outputs": [], + "id": "raK7hyjd_vf6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "4e764b7b-2627-493f-8174-f9421797a0f5" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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+ }, + "metadata": {} + } + ], "source": [ "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n", "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n", @@ -386,7 +1283,7 @@ "source": [ "## Define the model and metrics\n", "\n", - "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: " + "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent:" ] }, { @@ -403,7 +1300,7 @@ " keras.metrics.TruePositives(name='tp'),\n", " keras.metrics.FalsePositives(name='fp'),\n", " keras.metrics.TrueNegatives(name='tn'),\n", - " keras.metrics.FalseNegatives(name='fn'), \n", + " keras.metrics.FalseNegatives(name='fn'),\n", " keras.metrics.BinaryAccuracy(name='accuracy'),\n", " keras.metrics.Precision(name='precision'),\n", " keras.metrics.Recall(name='recall'),\n", @@ -432,7 +1329,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "SU0GX6E6mieP" @@ -456,7 +1352,7 @@ "In the end, one often wants to predict a class label, 0 or 1, *no fraud* or *fraud*.\n", "This is called a deterministic classifier.\n", "To get a label prediction from our probabilistic classifier, one needs to choose a probability threshold $t$.\n", - "The default is to predict label 1 (fraud) if the predicted probability is larger than $t=50\\%$ and all the following metrics implicitly use this default. \n", + "The default is to predict label 1 (fraud) if the predicted probability is larger than $t=50\\%$ and all the following metrics implicitly use this default.\n", "\n", "* **False** negatives and **false** positives are samples that were **incorrectly** classified\n", "* **True** negatives and **true** positives are samples that were **correctly** classified\n", @@ -474,7 +1370,7 @@ "The following metrics take into account all possible choices of thresholds $t$.\n", "\n", "* **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.\n", - "* **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds. \n", + "* **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds.\n", "\n", "\n", "#### Read more:\n", @@ -520,8 +1416,9 @@ "EPOCHS = 100\n", "BATCH_SIZE = 2048\n", "\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_prc', \n", + "def early_stopping():\n", + " return tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_prc',\n", " verbose=1,\n", " patience=10,\n", " mode='max',\n", @@ -532,9 +1429,104 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "1xlR_dekzw7C" - }, - "outputs": [], + "id": "1xlR_dekzw7C", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "outputId": "84bec388-8283-40d4-969d-5e08ad51039f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+              "│ dense (Dense)                        │ (None, 16)                  │             480 │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dropout (Dropout)                    │ (None, 16)                  │               0 │\n",
+              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+              "│ dense_1 (Dense)                      │ (None, 1)                   │              17 │\n",
+              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+              "
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+              "
\n" + ] + }, + "metadata": {} + } + ], "source": [ "model = make_model()\n", "model.summary()" @@ -553,9 +1545,40 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "LopSd-yQqO3a" - }, - "outputs": [], + "id": "LopSd-yQqO3a", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4370b50f-4971-4be0-ab8d-75d8a0d880df" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.06593819],\n", + " [0.03485148],\n", + " [0.242398 ],\n", + " [0.15501657],\n", + " [0.1954891 ],\n", + " [0.10886171],\n", + " [0.08471535],\n", + " [0.20510358],\n", + " [0.06699 ],\n", + " [0.08843432]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], "source": [ "model.predict(train_features[:10])" ] @@ -584,16 +1607,28 @@ "id": "PdbfWDuVpo6k" }, "source": [ - "With the default bias initialization the loss should be about `math.log(2) = 0.69314` " + "With the default bias initialization the loss should be about `math.log(2) = 0.69314`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "H-oPqh3SoGXk" - }, - "outputs": [], + "id": "H-oPqh3SoGXk", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b41f7913-5178-4619-b4ff-c5394f3a1409" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss: 0.1409\n" + ] + } + ], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" @@ -616,9 +1651,24 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "F5KWPSjjstUS" - }, - "outputs": [], + "id": "F5KWPSjjstUS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e221f48b-e952-4c53-94d4-157e576a1557" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-6.35935934])" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], "source": [ "initial_bias = np.log([pos/neg])\n", "initial_bias" @@ -630,7 +1680,7 @@ "id": "d1juXI9yY1KD" }, "source": [ - "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n", + "Set that as the initial bias, and the model will give much more reasonable initial guesses.\n", "\n", "It should be near: `pos/total = 0.0018`" ] @@ -639,9 +1689,40 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "50oyu1uss0i-" - }, - "outputs": [], + "id": "50oyu1uss0i-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a7064997-d4ac-401d-98d2-950edd1e219c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.00354745],\n", + " [0.01596842],\n", + " [0.0011654 ],\n", + " [0.00274411],\n", + " [0.00660007],\n", + " [0.00329903],\n", + " [0.01171673],\n", + " [0.0127765 ],\n", + " [0.0021166 ],\n", + " [0.00054859]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], "source": [ "model = make_model(output_bias=initial_bias)\n", "model.predict(train_features[:10])" @@ -662,9 +1743,21 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xVDqCWXDqHSc" - }, - "outputs": [], + "id": "xVDqCWXDqHSc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "41e07c3a-9eb6-4fa0-b762-6d30b14078b6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss: 0.0167\n" + ] + } + ], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" @@ -700,7 +1793,7 @@ }, "outputs": [], "source": [ - "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')\n", + "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial.weights.h5')\n", "model.save_weights(initial_weights)" ] }, @@ -714,7 +1807,7 @@ "\n", "Before moving on, confirm quick that the careful bias initialization actually helped.\n", "\n", - "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: " + "Train the model for 20 epochs, with and without this careful initialization, and compare the losses:" ] }, { @@ -733,7 +1826,7 @@ " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=20,\n", - " validation_data=(val_features, val_labels), \n", + " validation_data=(val_features, val_labels),\n", " verbose=0)" ] }, @@ -752,7 +1845,7 @@ " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=20,\n", - " validation_data=(val_features, val_labels), \n", + " validation_data=(val_features, val_labels),\n", " verbose=0)" ] }, @@ -780,9 +1873,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "dxFaskm7beC7" - }, - "outputs": [], + "id": "dxFaskm7beC7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "595eaab3-4d79-442d-eb33-331287891143" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], "source": [ "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n", "plot_loss(careful_bias_history, \"Careful Bias\", 1)" @@ -794,7 +1903,7 @@ "id": "fKMioV0ddG3R" }, "source": [ - "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. " + "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage." ] }, { @@ -810,9 +1919,180 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yZKAc8NCDnoR" - }, - "outputs": [], + "id": "yZKAc8NCDnoR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5b8df429-2a35-429f-c669-3574f2ba5ff9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - Brier score: 0.0015 - accuracy: 0.9984 - auc: 0.7616 - cross entropy: 0.0117 - fn: 166.3517 - fp: 62.9890 - loss: 0.0178 - prc: 0.2995 - precision: 0.5583 - recall: 0.3453 - tn: 139407.7188 - tp: 72.5165 - val_Brier score: 0.0014 - val_accuracy: 0.9984 - val_auc: 0.8811 - val_cross entropy: 0.0075 - val_fn: 73.0000 - val_fp: 0.0000e+00 - val_loss: 0.0075 - val_prc: 0.5444 - val_precision: 1.0000 - val_recall: 0.0267 - val_tn: 45494.0000 - val_tp: 2.0000\n", + "Epoch 2/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - Brier score: 0.0014 - accuracy: 0.9985 - auc: 0.7690 - cross entropy: 0.0107 - fn: 114.0659 - fp: 18.7473 - loss: 0.0107 - prc: 0.2769 - precision: 0.6627 - recall: 0.2525 - tn: 93966.2188 - tp: 41.5385 - val_Brier score: 8.3124e-04 - val_accuracy: 0.9990 - val_auc: 0.9053 - val_cross entropy: 0.0049 - val_fn: 41.0000 - val_fp: 5.0000 - val_loss: 0.0049 - val_prc: 0.7088 - val_precision: 0.8718 - val_recall: 0.4533 - val_tn: 45489.0000 - val_tp: 34.0000\n", + "Epoch 3/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0010 - accuracy: 0.9989 - auc: 0.8831 - cross entropy: 0.0067 - fn: 89.7582 - fp: 10.6813 - loss: 0.0067 - prc: 0.5459 - precision: 0.8675 - recall: 0.4342 - tn: 93970.2344 - tp: 69.9011 - val_Brier score: 6.7792e-04 - val_accuracy: 0.9992 - val_auc: 0.8993 - val_cross entropy: 0.0043 - val_fn: 33.0000 - val_fp: 5.0000 - val_loss: 0.0043 - val_prc: 0.7099 - val_precision: 0.8936 - val_recall: 0.5600 - val_tn: 45489.0000 - val_tp: 42.0000\n", + "Epoch 4/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.6771e-04 - accuracy: 0.9990 - auc: 0.9013 - cross entropy: 0.0057 - fn: 75.0440 - fp: 13.6154 - loss: 0.0057 - prc: 0.5899 - precision: 0.8136 - recall: 0.4896 - tn: 93974.9453 - tp: 76.9670 - val_Brier score: 6.0917e-04 - val_accuracy: 0.9993 - val_auc: 0.9061 - val_cross entropy: 0.0040 - val_fn: 27.0000 - val_fp: 5.0000 - val_loss: 0.0040 - val_prc: 0.7290 - val_precision: 0.9057 - val_recall: 0.6400 - val_tn: 45489.0000 - val_tp: 48.0000\n", + "Epoch 5/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.6912e-04 - accuracy: 0.9991 - auc: 0.9175 - cross entropy: 0.0051 - fn: 60.9670 - fp: 18.1758 - loss: 0.0051 - prc: 0.6670 - precision: 0.8258 - recall: 0.5942 - tn: 93968.9219 - tp: 92.5055 - val_Brier score: 5.9678e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0039 - val_fn: 27.0000 - val_fp: 5.0000 - val_loss: 0.0039 - val_prc: 0.7305 - val_precision: 0.9057 - val_recall: 0.6400 - val_tn: 45489.0000 - val_tp: 48.0000\n", + "Epoch 6/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 9.2291e-04 - accuracy: 0.9990 - auc: 0.9047 - cross entropy: 0.0057 - fn: 71.8462 - fp: 14.6593 - loss: 0.0057 - prc: 0.5688 - precision: 0.8455 - recall: 0.5218 - tn: 93968.8672 - tp: 85.1978 - val_Brier score: 5.8535e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0038 - val_fn: 25.0000 - val_fp: 5.0000 - val_loss: 0.0038 - val_prc: 0.7251 - val_precision: 0.9091 - val_recall: 0.6667 - val_tn: 45489.0000 - val_tp: 50.0000\n", + "Epoch 7/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.5874e-04 - accuracy: 0.9990 - auc: 0.9201 - cross entropy: 0.0050 - fn: 76.1099 - fp: 15.4066 - loss: 0.0050 - prc: 0.6658 - precision: 0.8489 - recall: 0.4997 - tn: 93969.4609 - tp: 79.5934 - val_Brier score: 5.8049e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0037 - val_fn: 25.0000 - val_fp: 5.0000 - val_loss: 0.0037 - val_prc: 0.7429 - val_precision: 0.9091 - val_recall: 0.6667 - val_tn: 45489.0000 - val_tp: 50.0000\n", + "Epoch 8/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 9.0201e-04 - accuracy: 0.9990 - auc: 0.9116 - cross entropy: 0.0052 - fn: 78.9890 - fp: 12.8352 - loss: 0.0052 - prc: 0.6366 - precision: 0.8686 - recall: 0.5048 - tn: 93965.5625 - tp: 83.1868 - val_Brier score: 5.5696e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0036 - val_fn: 24.0000 - val_fp: 5.0000 - val_loss: 0.0036 - val_prc: 0.7449 - val_precision: 0.9107 - val_recall: 0.6800 - val_tn: 45489.0000 - val_tp: 51.0000\n", + "Epoch 9/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.1022e-04 - accuracy: 0.9991 - auc: 0.9392 - cross entropy: 0.0047 - fn: 67.0330 - fp: 16.9341 - loss: 0.0047 - prc: 0.6616 - precision: 0.8335 - recall: 0.5710 - tn: 93966.1562 - tp: 90.4505 - val_Brier score: 5.5489e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0035 - val_fn: 24.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7442 - val_precision: 0.9107 - val_recall: 0.6800 - val_tn: 45489.0000 - val_tp: 51.0000\n", + "Epoch 10/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.9357e-04 - accuracy: 0.9990 - auc: 0.9163 - cross entropy: 0.0043 - fn: 73.6484 - fp: 18.3297 - loss: 0.0043 - prc: 0.6833 - precision: 0.8038 - recall: 0.5348 - tn: 93966.5625 - tp: 82.0330 - val_Brier score: 5.4526e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7437 - val_precision: 0.9123 - val_recall: 0.6933 - val_tn: 45489.0000 - val_tp: 52.0000\n", + "Epoch 11/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.3662e-04 - accuracy: 0.9992 - auc: 0.9137 - cross entropy: 0.0043 - fn: 66.7253 - fp: 15.3077 - loss: 0.0043 - prc: 0.7015 - precision: 0.8679 - recall: 0.5775 - tn: 93968.1406 - tp: 90.3956 - val_Brier score: 5.3281e-04 - val_accuracy: 0.9994 - val_auc: 0.9061 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7435 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 12/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - Brier score: 8.2712e-04 - accuracy: 0.9991 - auc: 0.8945 - cross entropy: 0.0052 - fn: 72.5934 - fp: 13.6264 - loss: 0.0052 - prc: 0.6423 - precision: 0.8663 - recall: 0.5552 - tn: 93966.0234 - tp: 88.3297 - val_Brier score: 5.1658e-04 - val_accuracy: 0.9995 - val_auc: 0.9128 - val_cross entropy: 0.0034 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7498 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 13/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 8.0335e-04 - accuracy: 0.9991 - auc: 0.9298 - cross entropy: 0.0047 - fn: 68.5495 - fp: 15.3626 - loss: 0.0047 - prc: 0.6476 - precision: 0.8322 - recall: 0.5705 - tn: 93967.8828 - tp: 88.7802 - val_Brier score: 5.1140e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0034 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7536 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 14/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7173e-04 - accuracy: 0.9991 - auc: 0.9307 - cross entropy: 0.0044 - fn: 68.8022 - fp: 17.8352 - loss: 0.0044 - prc: 0.6730 - precision: 0.8344 - recall: 0.5772 - tn: 93967.0234 - tp: 86.9121 - val_Brier score: 5.1549e-04 - val_accuracy: 0.9995 - val_auc: 0.9129 - val_cross entropy: 0.0034 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7522 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 15/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.9408e-04 - accuracy: 0.9991 - auc: 0.9324 - cross entropy: 0.0042 - fn: 70.7582 - fp: 18.1538 - loss: 0.0042 - prc: 0.7199 - precision: 0.8482 - recall: 0.5600 - tn: 93963.6797 - tp: 87.9780 - val_Brier score: 5.1984e-04 - val_accuracy: 0.9995 - val_auc: 0.9129 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7546 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 16/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.3687e-04 - accuracy: 0.9991 - auc: 0.9155 - cross entropy: 0.0041 - fn: 64.7582 - fp: 14.6703 - loss: 0.0041 - prc: 0.7072 - precision: 0.8566 - recall: 0.5865 - tn: 93966.4688 - tp: 94.6703 - val_Brier score: 5.1591e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7601 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 17/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.4693e-04 - accuracy: 0.9992 - auc: 0.9257 - cross entropy: 0.0039 - fn: 61.0330 - fp: 15.3407 - loss: 0.0039 - prc: 0.7023 - precision: 0.8408 - recall: 0.5937 - tn: 93972.6797 - tp: 91.5165 - val_Brier score: 5.1012e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7647 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 18/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.4157e-04 - accuracy: 0.9992 - auc: 0.9369 - cross entropy: 0.0040 - fn: 61.2418 - fp: 15.1319 - loss: 0.0040 - prc: 0.7374 - precision: 0.8788 - recall: 0.6364 - tn: 93960.3984 - tp: 103.8022 - val_Brier score: 5.2259e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 20.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7690 - val_precision: 0.9167 - val_recall: 0.7333 - val_tn: 45489.0000 - val_tp: 55.0000\n", + "Epoch 19/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.1597e-04 - accuracy: 0.9992 - auc: 0.9065 - cross entropy: 0.0039 - fn: 60.9890 - fp: 15.5604 - loss: 0.0039 - prc: 0.6899 - precision: 0.8270 - recall: 0.5961 - tn: 93977.8047 - tp: 86.2198 - val_Brier score: 5.1175e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7670 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 20/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0362e-04 - accuracy: 0.9992 - auc: 0.9146 - cross entropy: 0.0038 - fn: 61.7363 - fp: 15.2747 - loss: 0.0038 - prc: 0.7365 - precision: 0.8786 - recall: 0.6190 - tn: 93970.2500 - tp: 93.3077 - val_Brier score: 5.0103e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7687 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 21/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.2299e-04 - accuracy: 0.9992 - auc: 0.9247 - cross entropy: 0.0040 - fn: 62.0989 - fp: 17.2198 - loss: 0.0040 - prc: 0.6831 - precision: 0.8256 - recall: 0.6047 - tn: 93971.5703 - tp: 89.6813 - val_Brier score: 5.0661e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7705 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 22/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5303e-04 - accuracy: 0.9991 - auc: 0.9384 - cross entropy: 0.0039 - fn: 69.7363 - fp: 14.5714 - loss: 0.0039 - prc: 0.7114 - precision: 0.8500 - recall: 0.5737 - tn: 93967.2969 - tp: 88.9670 - val_Brier score: 5.0563e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7739 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 23/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.2432e-04 - accuracy: 0.9992 - auc: 0.9361 - cross entropy: 0.0039 - fn: 56.0110 - fp: 15.3516 - loss: 0.0039 - prc: 0.7303 - precision: 0.8631 - recall: 0.6273 - tn: 93967.5703 - tp: 101.6374 - val_Brier score: 5.2901e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7761 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 24/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 8.0118e-04 - accuracy: 0.9991 - auc: 0.9335 - cross entropy: 0.0043 - fn: 70.5165 - fp: 15.8352 - loss: 0.0043 - prc: 0.6537 - precision: 0.8258 - recall: 0.5326 - tn: 93965.4141 - tp: 88.8022 - val_Brier score: 4.9249e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7781 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 25/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7466e-04 - accuracy: 0.9992 - auc: 0.9051 - cross entropy: 0.0044 - fn: 65.0549 - fp: 15.5604 - loss: 0.0044 - prc: 0.6598 - precision: 0.8677 - recall: 0.5912 - tn: 93967.4297 - tp: 92.5275 - val_Brier score: 4.9957e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7741 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 26/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.2408e-04 - accuracy: 0.9993 - auc: 0.9504 - cross entropy: 0.0034 - fn: 55.3956 - fp: 17.5275 - loss: 0.0034 - prc: 0.7545 - precision: 0.8694 - recall: 0.6804 - tn: 93964.5312 - tp: 103.1209 - val_Brier score: 5.0585e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7784 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 27/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.1493e-04 - accuracy: 0.9993 - auc: 0.9330 - cross entropy: 0.0033 - fn: 52.7143 - fp: 11.6593 - loss: 0.0033 - prc: 0.7650 - precision: 0.9083 - recall: 0.6557 - tn: 93973.6562 - tp: 102.5385 - val_Brier score: 4.9942e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7781 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 28/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 9.8291e-04 - accuracy: 0.9989 - auc: 0.9099 - cross entropy: 0.0050 - fn: 78.3407 - fp: 19.3626 - loss: 0.0050 - prc: 0.6648 - precision: 0.8219 - recall: 0.5242 - tn: 93951.8906 - tp: 90.9780 - val_Brier score: 5.1021e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7821 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", + "Epoch 29/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.2133e-04 - accuracy: 0.9993 - auc: 0.9352 - cross entropy: 0.0032 - fn: 58.9890 - fp: 14.0659 - loss: 0.0032 - prc: 0.7587 - precision: 0.8891 - recall: 0.6213 - tn: 93972.6016 - tp: 94.9121 - val_Brier score: 5.0344e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7813 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 30/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - Brier score: 6.7470e-04 - accuracy: 0.9992 - auc: 0.9465 - cross entropy: 0.0036 - fn: 60.6703 - fp: 13.8462 - loss: 0.0036 - prc: 0.7446 - precision: 0.8787 - recall: 0.6431 - tn: 93966.3281 - tp: 99.7253 - val_Brier score: 5.0940e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 20.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7802 - val_precision: 0.9167 - val_recall: 0.7333 - val_tn: 45489.0000 - val_tp: 55.0000\n", + "Epoch 31/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.5162e-04 - accuracy: 0.9992 - auc: 0.9420 - cross entropy: 0.0034 - fn: 60.9560 - fp: 13.5934 - loss: 0.0034 - prc: 0.7858 - precision: 0.8839 - recall: 0.6286 - tn: 93960.9453 - tp: 105.0769 - val_Brier score: 5.2065e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7825 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 32/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5032e-04 - accuracy: 0.9991 - auc: 0.9109 - cross entropy: 0.0039 - fn: 69.4945 - fp: 13.1758 - loss: 0.0039 - prc: 0.7179 - precision: 0.8657 - recall: 0.5539 - tn: 93965.1016 - tp: 92.8022 - val_Brier score: 5.1955e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7838 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 33/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9713e-04 - accuracy: 0.9992 - auc: 0.9564 - cross entropy: 0.0033 - fn: 62.0659 - fp: 13.9670 - loss: 0.0033 - prc: 0.7726 - precision: 0.8806 - recall: 0.6050 - tn: 93970.2500 - tp: 94.2857 - val_Brier score: 5.1435e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7818 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 34/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.8773e-04 - accuracy: 0.9992 - auc: 0.9417 - cross entropy: 0.0035 - fn: 61.5055 - fp: 13.9670 - loss: 0.0035 - prc: 0.7517 - precision: 0.8687 - recall: 0.6379 - tn: 93962.8828 - tp: 102.2198 - val_Brier score: 5.1135e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7846 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 35/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.6725e-04 - accuracy: 0.9991 - auc: 0.9264 - cross entropy: 0.0041 - fn: 62.0110 - fp: 18.2308 - loss: 0.0041 - prc: 0.6783 - precision: 0.8326 - recall: 0.5701 - tn: 93967.3438 - tp: 92.9890 - val_Brier score: 5.1539e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0033 - val_prc: 0.7841 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 36/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.3579e-04 - accuracy: 0.9991 - auc: 0.9536 - cross entropy: 0.0041 - fn: 65.5275 - fp: 17.3516 - loss: 0.0041 - prc: 0.7264 - precision: 0.8460 - recall: 0.5842 - tn: 93960.2109 - tp: 97.4835 - val_Brier score: 5.0522e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7808 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", + "Epoch 37/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.1126e-04 - accuracy: 0.9990 - auc: 0.9367 - cross entropy: 0.0039 - fn: 66.8901 - fp: 18.0220 - loss: 0.0039 - prc: 0.7194 - precision: 0.8227 - recall: 0.5816 - tn: 93960.9531 - tp: 94.7033 - val_Brier score: 5.0323e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7809 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", + "Epoch 38/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.3380e-04 - accuracy: 0.9991 - auc: 0.9402 - cross entropy: 0.0038 - fn: 63.3077 - fp: 19.4945 - loss: 0.0038 - prc: 0.7475 - precision: 0.8182 - recall: 0.6160 - tn: 93954.4297 - tp: 103.3407 - val_Brier score: 5.1817e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7832 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 39/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.0546e-04 - accuracy: 0.9993 - auc: 0.9490 - cross entropy: 0.0031 - fn: 58.7143 - fp: 14.3516 - loss: 0.0031 - prc: 0.7651 - precision: 0.8702 - recall: 0.5991 - tn: 93976.9531 - tp: 90.5495 - val_Brier score: 5.0504e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7814 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", + "Epoch 40/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 7.6890e-04 - accuracy: 0.9991 - auc: 0.9364 - cross entropy: 0.0040 - fn: 63.1319 - fp: 15.4835 - loss: 0.0040 - prc: 0.7046 - precision: 0.8488 - recall: 0.5873 - tn: 93969.2344 - tp: 92.7253 - val_Brier score: 5.1110e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7847 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", + "Epoch 41/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7761e-04 - accuracy: 0.9992 - auc: 0.9165 - cross entropy: 0.0036 - fn: 60.7692 - fp: 8.9890 - loss: 0.0036 - prc: 0.7558 - precision: 0.9148 - recall: 0.5847 - tn: 93973.3750 - tp: 97.4396 - val_Brier score: 4.9544e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7828 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", + "Epoch 42/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.3288e-04 - accuracy: 0.9991 - auc: 0.9468 - cross entropy: 0.0036 - fn: 67.1758 - fp: 16.2418 - loss: 0.0036 - prc: 0.7541 - precision: 0.8636 - recall: 0.5802 - tn: 93964.4609 - tp: 92.6923 - val_Brier score: 5.3282e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7825 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 43/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7773e-04 - accuracy: 0.9992 - auc: 0.9439 - cross entropy: 0.0034 - fn: 58.4396 - fp: 17.2198 - loss: 0.0034 - prc: 0.7504 - precision: 0.8419 - recall: 0.6661 - tn: 93964.6797 - tp: 100.2308 - val_Brier score: 5.1650e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7847 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 44/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2941e-04 - accuracy: 0.9992 - auc: 0.9365 - cross entropy: 0.0038 - fn: 64.6484 - fp: 14.7253 - loss: 0.0038 - prc: 0.7319 - precision: 0.8782 - recall: 0.5995 - tn: 93968.1797 - tp: 93.0220 - val_Brier score: 5.2610e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7863 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 45/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9266e-04 - accuracy: 0.9992 - auc: 0.9221 - cross entropy: 0.0037 - fn: 63.4615 - fp: 13.6484 - loss: 0.0037 - prc: 0.7195 - precision: 0.8849 - recall: 0.5841 - tn: 93971.1641 - tp: 92.2967 - val_Brier score: 5.4107e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7843 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 46/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.5421e-04 - accuracy: 0.9991 - auc: 0.9328 - cross entropy: 0.0039 - fn: 63.8462 - fp: 15.9890 - loss: 0.0039 - prc: 0.7441 - precision: 0.8518 - recall: 0.6037 - tn: 93959.2344 - tp: 101.5055 - val_Brier score: 5.2481e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7843 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 47/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 7.1543e-04 - accuracy: 0.9992 - auc: 0.9391 - cross entropy: 0.0037 - fn: 62.7363 - fp: 16.2418 - loss: 0.0037 - prc: 0.7472 - precision: 0.8673 - recall: 0.5994 - tn: 93968.3281 - tp: 93.2637 - val_Brier score: 5.4077e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 24.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7862 - val_precision: 0.9273 - val_recall: 0.6800 - val_tn: 45490.0000 - val_tp: 51.0000\n", + "Epoch 48/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 6.9187e-04 - accuracy: 0.9992 - auc: 0.9307 - cross entropy: 0.0038 - fn: 59.6044 - fp: 14.0330 - loss: 0.0038 - prc: 0.6986 - precision: 0.8686 - recall: 0.6019 - tn: 93975.8984 - tp: 91.0330 - val_Brier score: 5.2833e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7852 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 49/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.4433e-04 - accuracy: 0.9992 - auc: 0.9338 - cross entropy: 0.0038 - fn: 64.2198 - fp: 15.1319 - loss: 0.0038 - prc: 0.7242 - precision: 0.8649 - recall: 0.5966 - tn: 93966.2891 - tp: 94.9341 - val_Brier score: 5.3255e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7871 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 50/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.4424e-04 - accuracy: 0.9993 - auc: 0.9491 - cross entropy: 0.0030 - fn: 56.7253 - fp: 13.2088 - loss: 0.0030 - prc: 0.7941 - precision: 0.8804 - recall: 0.6463 - tn: 93971.6406 - tp: 99.0000 - val_Brier score: 5.0646e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7880 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 51/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 5.1395e-04 - accuracy: 0.9994 - auc: 0.9586 - cross entropy: 0.0027 - fn: 52.9121 - fp: 13.2198 - loss: 0.0027 - prc: 0.8149 - precision: 0.9037 - recall: 0.6944 - tn: 93973.1328 - tp: 101.3077 - val_Brier score: 5.4684e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 24.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7872 - val_precision: 0.9273 - val_recall: 0.6800 - val_tn: 45490.0000 - val_tp: 51.0000\n", + "Epoch 52/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0941e-04 - accuracy: 0.9992 - auc: 0.9302 - cross entropy: 0.0036 - fn: 64.3626 - fp: 10.4505 - loss: 0.0036 - prc: 0.7560 - precision: 0.9159 - recall: 0.6133 - tn: 93968.9531 - tp: 96.8022 - val_Brier score: 5.2056e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7885 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 53/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.5743e-04 - accuracy: 0.9991 - auc: 0.9293 - cross entropy: 0.0040 - fn: 64.5165 - fp: 13.2747 - loss: 0.0040 - prc: 0.7323 - precision: 0.8854 - recall: 0.5952 - tn: 93965.9922 - tp: 96.7912 - val_Brier score: 5.2784e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7890 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 54/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7563e-04 - accuracy: 0.9991 - auc: 0.9317 - cross entropy: 0.0038 - fn: 66.0000 - fp: 16.6923 - loss: 0.0038 - prc: 0.7352 - precision: 0.8552 - recall: 0.5805 - tn: 93963.6953 - tp: 94.1868 - val_Brier score: 5.1810e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7869 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 55/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.5146e-04 - accuracy: 0.9992 - auc: 0.9631 - cross entropy: 0.0032 - fn: 58.3187 - fp: 15.7473 - loss: 0.0032 - prc: 0.7844 - precision: 0.8693 - recall: 0.6499 - tn: 93962.5625 - tp: 103.9451 - val_Brier score: 5.2094e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7873 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 56/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.5884e-04 - accuracy: 0.9992 - auc: 0.9536 - cross entropy: 0.0032 - fn: 60.0989 - fp: 15.5934 - loss: 0.0032 - prc: 0.7572 - precision: 0.8564 - recall: 0.6243 - tn: 93968.0078 - tp: 96.8681 - val_Brier score: 4.9789e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7847 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", + "Epoch 57/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9108e-04 - accuracy: 0.9992 - auc: 0.9444 - cross entropy: 0.0036 - fn: 57.8791 - fp: 19.1538 - loss: 0.0036 - prc: 0.7387 - precision: 0.8582 - recall: 0.6350 - tn: 93962.5078 - tp: 101.0330 - val_Brier score: 5.3066e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7834 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 58/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2124e-04 - accuracy: 0.9991 - auc: 0.9520 - cross entropy: 0.0034 - fn: 65.9451 - fp: 16.7033 - loss: 0.0034 - prc: 0.7818 - precision: 0.8441 - recall: 0.5964 - tn: 93959.9375 - tp: 97.9890 - val_Brier score: 5.0541e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7845 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 59/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.3754e-04 - accuracy: 0.9993 - auc: 0.9333 - cross entropy: 0.0033 - fn: 55.2198 - fp: 12.8791 - loss: 0.0033 - prc: 0.7493 - precision: 0.8872 - recall: 0.6369 - tn: 93978.5938 - tp: 93.8791 - val_Brier score: 5.1594e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7872 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 60/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9694e-04 - accuracy: 0.9992 - auc: 0.9242 - cross entropy: 0.0035 - fn: 65.5275 - fp: 12.9560 - loss: 0.0035 - prc: 0.7379 - precision: 0.8468 - recall: 0.5466 - tn: 93972.7812 - tp: 89.3077 - val_Brier score: 5.1725e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7866 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 61/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.0213e-04 - accuracy: 0.9993 - auc: 0.9603 - cross entropy: 0.0028 - fn: 56.4725 - fp: 14.0000 - loss: 0.0028 - prc: 0.8052 - precision: 0.8714 - recall: 0.6505 - tn: 93972.7109 - tp: 97.3846 - val_Brier score: 5.1001e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 21.0000 - val_fp: 6.0000 - val_loss: 0.0035 - val_prc: 0.7883 - val_precision: 0.9000 - val_recall: 0.7200 - val_tn: 45488.0000 - val_tp: 54.0000\n", + "Epoch 62/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.0087e-04 - accuracy: 0.9993 - auc: 0.9195 - cross entropy: 0.0033 - fn: 54.5165 - fp: 13.3077 - loss: 0.0033 - prc: 0.7294 - precision: 0.8737 - recall: 0.6360 - tn: 93970.6406 - tp: 102.1099 - val_Brier score: 5.3151e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7870 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 63/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7005e-04 - accuracy: 0.9992 - auc: 0.9438 - cross entropy: 0.0034 - fn: 63.0220 - fp: 13.7802 - loss: 0.0034 - prc: 0.7454 - precision: 0.8733 - recall: 0.5965 - tn: 93970.3047 - tp: 93.4615 - val_Brier score: 5.0064e-04 - val_accuracy: 0.9995 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7900 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", + "Epoch 64/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 6.4124e-04 - accuracy: 0.9993 - auc: 0.9291 - cross entropy: 0.0037 - fn: 59.1319 - fp: 10.4396 - loss: 0.0037 - prc: 0.7319 - precision: 0.9070 - recall: 0.6550 - tn: 93967.7812 - tp: 103.2198 - val_Brier score: 5.0829e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7891 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 65/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - Brier score: 7.2464e-04 - accuracy: 0.9992 - auc: 0.9486 - cross entropy: 0.0036 - fn: 59.9011 - fp: 17.9560 - loss: 0.0036 - prc: 0.7716 - precision: 0.8604 - recall: 0.6405 - tn: 93959.6016 - tp: 103.1099 - val_Brier score: 5.2800e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7906 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 66/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.7741e-04 - accuracy: 0.9992 - auc: 0.9405 - cross entropy: 0.0035 - fn: 61.6044 - fp: 13.7582 - loss: 0.0035 - prc: 0.7451 - precision: 0.8714 - recall: 0.6217 - tn: 93974.7500 - tp: 90.4615 - val_Brier score: 4.9498e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 18.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7909 - val_precision: 0.9344 - val_recall: 0.7600 - val_tn: 45490.0000 - val_tp: 57.0000\n", + "Epoch 67/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.0034e-04 - accuracy: 0.9991 - auc: 0.9481 - cross entropy: 0.0034 - fn: 62.9011 - fp: 18.5604 - loss: 0.0034 - prc: 0.7612 - precision: 0.8439 - recall: 0.6191 - tn: 93960.0312 - tp: 99.0769 - val_Brier score: 5.4392e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7875 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 68/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.5760e-04 - accuracy: 0.9992 - auc: 0.9452 - cross entropy: 0.0033 - fn: 63.1099 - fp: 11.2527 - loss: 0.0033 - prc: 0.7626 - precision: 0.8964 - recall: 0.6040 - tn: 93973.9375 - tp: 92.2747 - val_Brier score: 5.3727e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7887 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 69/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.1643e-04 - accuracy: 0.9991 - auc: 0.9494 - cross entropy: 0.0034 - fn: 65.6484 - fp: 16.4725 - loss: 0.0034 - prc: 0.7572 - precision: 0.8369 - recall: 0.5890 - tn: 93965.8438 - tp: 92.6044 - val_Brier score: 5.1218e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7917 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 70/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.9824e-04 - accuracy: 0.9992 - auc: 0.9349 - cross entropy: 0.0036 - fn: 66.1868 - fp: 16.5385 - loss: 0.0036 - prc: 0.7260 - precision: 0.8530 - recall: 0.6125 - tn: 93962.1953 - tp: 95.6484 - val_Brier score: 5.0980e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7907 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 71/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5123e-04 - accuracy: 0.9991 - auc: 0.9493 - cross entropy: 0.0036 - fn: 67.1429 - fp: 18.9451 - loss: 0.0036 - prc: 0.7621 - precision: 0.8247 - recall: 0.5947 - tn: 93960.0469 - tp: 94.4396 - val_Brier score: 5.2164e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7908 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 72/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.8855e-04 - accuracy: 0.9992 - auc: 0.9132 - cross entropy: 0.0035 - fn: 66.1978 - fp: 14.0769 - loss: 0.0035 - prc: 0.6707 - precision: 0.8301 - recall: 0.5121 - tn: 93980.5703 - tp: 79.7253 - val_Brier score: 5.2068e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7893 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 73/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.1969e-04 - accuracy: 0.9993 - auc: 0.9236 - cross entropy: 0.0033 - fn: 56.7363 - fp: 11.8462 - loss: 0.0033 - prc: 0.7282 - precision: 0.8801 - recall: 0.6023 - tn: 93976.6797 - tp: 95.3077 - val_Brier score: 5.0412e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7890 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", + "Epoch 74/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.0848e-04 - accuracy: 0.9993 - auc: 0.9391 - cross entropy: 0.0031 - fn: 57.9231 - fp: 17.1429 - loss: 0.0031 - prc: 0.7422 - precision: 0.8436 - recall: 0.6043 - tn: 93973.5625 - tp: 91.9451 - val_Brier score: 5.1496e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7913 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 75/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.2375e-04 - accuracy: 0.9992 - auc: 0.9530 - cross entropy: 0.0030 - fn: 60.0659 - fp: 13.5604 - loss: 0.0030 - prc: 0.8093 - precision: 0.8831 - recall: 0.6218 - tn: 93973.0469 - tp: 93.9011 - val_Brier score: 5.1856e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7885 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 76/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2307e-04 - accuracy: 0.9992 - auc: 0.9528 - cross entropy: 0.0035 - fn: 63.2637 - fp: 11.8791 - loss: 0.0035 - prc: 0.7700 - precision: 0.9061 - recall: 0.5787 - tn: 93972.4609 - tp: 92.9670 - val_Brier score: 5.0187e-04 - val_accuracy: 0.9995 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 19.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7880 - val_precision: 0.9333 - val_recall: 0.7467 - val_tn: 45490.0000 - val_tp: 56.0000\n", + "Epoch 77/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.3179e-04 - accuracy: 0.9993 - auc: 0.9447 - cross entropy: 0.0032 - fn: 54.8571 - fp: 13.6703 - loss: 0.0032 - prc: 0.7511 - precision: 0.8843 - recall: 0.6671 - tn: 93971.8984 - tp: 100.1429 - val_Brier score: 5.1987e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7870 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 78/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0267e-04 - accuracy: 0.9992 - auc: 0.9536 - cross entropy: 0.0033 - fn: 60.8462 - fp: 13.7143 - loss: 0.0033 - prc: 0.7800 - precision: 0.8991 - recall: 0.6105 - tn: 93967.9141 - tp: 98.0989 - val_Brier score: 5.1156e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7879 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 79/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.2558e-04 - accuracy: 0.9993 - auc: 0.9506 - cross entropy: 0.0033 - fn: 55.2857 - fp: 12.1978 - loss: 0.0033 - prc: 0.7819 - precision: 0.8887 - recall: 0.6283 - tn: 93977.7656 - tp: 95.3187 - val_Brier score: 5.1267e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7888 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", + "Epoch 79: early stopping\n", + "Restoring model weights from the end of the best epoch: 69.\n" + ] + } + ], "source": [ "model = make_model()\n", "model.load_weights(initial_weights)\n", @@ -821,7 +2101,7 @@ " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", - " callbacks=[early_stopping],\n", + " callbacks=[early_stopping()],\n", " validation_data=(val_features, val_labels))" ] }, @@ -870,9 +2150,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "u6LReDsqlZlk" - }, - "outputs": [], + "id": "u6LReDsqlZlk", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "bf04aa07-636a-459a-d5ae-20dd581614e8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_metrics(baseline_history)" ] @@ -901,9 +2197,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "aNS796IJKrev" - }, - "outputs": [], + "id": "aNS796IJKrev", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8f71218b-57d8-4eb6-f922-ea8087e3f414" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", + "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n" + ] + } + ], "source": [ "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)" @@ -945,9 +2254,39 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "poh_hZngt2_9" - }, - "outputs": [], + "id": "poh_hZngt2_9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 623 + }, + "outputId": "53e02b44-2af1-418f-b705-ef104c87cac5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "loss : 0.003293460002169013\n", + "compile_metrics : 0.003293460002169013\n", + "\n", + "Legitimate Transactions Detected (True Negatives): 56843\n", + "Legitimate Transactions Incorrectly Detected (False Positives): 7\n", + "Fraudulent Transactions Missed (False Negatives): 27\n", + "Fraudulent Transactions Detected (True Positives): 85\n", + "Total Fraudulent Transactions: 112\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "baseline_results = model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -987,19 +2326,60 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "52bd793e04bb" - }, - "outputs": [], + "id": "52bd793e04bb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "0b8dc494-b271-435a-c6d8-0f25c8aa01b9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Legitimate Transactions Detected (True Negatives): 56832\n", + "Legitimate Transactions Incorrectly Detected (False Positives): 18\n", + "Fraudulent Transactions Missed (False Negatives): 20\n", + "Fraudulent Transactions Detected (True Positives): 92\n", + "Total Fraudulent Transactions: 112\n", + "Legitimate Transactions Detected (True Negatives): 56731\n", + "Legitimate Transactions Incorrectly Detected (False Positives): 119\n", + "Fraudulent Transactions Missed (False Negatives): 14\n", + "Fraudulent Transactions Detected (True Positives): 98\n", + "Total Fraudulent Transactions: 112\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_cm(test_labels, test_predictions_baseline, threshold=0.1)\n", "plot_cm(test_labels, test_predictions_baseline, threshold=0.01)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { - "id": "P-QpQsip_F2Q" + "id": "kF8k-g9goRni" }, "source": [ "### Plot the ROC\n", @@ -1032,9 +2412,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "DfHHspttKJE0" - }, - "outputs": [], + "id": "DfHHspttKJE0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "066fe74c-0416-4c38-c0e3-04329dad8dc3" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -1075,9 +2471,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FdQs_PcqEsiL" - }, - "outputs": [], + "id": "FdQs_PcqEsiL", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "bf226778-45d6-42bf-e8cc-79c0856e3632" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -1117,9 +2529,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "qjGWErngGny7" - }, - "outputs": [], + "id": "qjGWErngGny7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "112a112e-7d3f-47b8-c3cd-58fef7f0cef0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Weight for class 0: 0.50\n", + "Weight for class 1: 289.44\n" + ] + } + ], "source": [ "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", "# The sum of the weights of all examples stays the same.\n", @@ -1149,9 +2574,54 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "UJ589fn8ST3x" - }, - "outputs": [], + "id": "UJ589fn8ST3x", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8ab120c7-9c19-4f27-f369-95f681a8a395" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 18ms/step - Brier score: 0.0020 - accuracy: 0.9978 - auc: 0.8179 - cross entropy: 0.0129 - fn: 151.4066 - fp: 224.7363 - loss: 2.6473 - prc: 0.3597 - precision: 0.4048 - recall: 0.4473 - tn: 150619.6094 - tp: 106.8242 - val_Brier score: 0.0013 - val_accuracy: 0.9986 - val_auc: 0.9300 - val_cross entropy: 0.0104 - val_fn: 41.0000 - val_fp: 21.0000 - val_loss: 0.0104 - val_prc: 0.4581 - val_precision: 0.6182 - val_recall: 0.4533 - val_tn: 45473.0000 - val_tp: 34.0000\n", + "Epoch 2/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0060 - accuracy: 0.9931 - auc: 0.8712 - cross entropy: 0.0292 - fn: 75.2308 - fp: 612.5604 - loss: 0.9810 - prc: 0.2220 - precision: 0.1202 - recall: 0.4986 - tn: 93372.8906 - tp: 79.8901 - val_Brier score: 0.0022 - val_accuracy: 0.9978 - val_auc: 0.9428 - val_cross entropy: 0.0155 - val_fn: 16.0000 - val_fp: 85.0000 - val_loss: 0.0155 - val_prc: 0.6574 - val_precision: 0.4097 - val_recall: 0.7867 - val_tn: 45409.0000 - val_tp: 59.0000\n", + "Epoch 3/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0095 - accuracy: 0.9888 - auc: 0.9128 - cross entropy: 0.0447 - fn: 50.1978 - fp: 1033.1758 - loss: 0.6856 - prc: 0.2480 - precision: 0.0889 - recall: 0.6417 - tn: 92952.0547 - tp: 105.1429 - val_Brier score: 0.0043 - val_accuracy: 0.9943 - val_auc: 0.9476 - val_cross entropy: 0.0237 - val_fn: 16.0000 - val_fp: 244.0000 - val_loss: 0.0237 - val_prc: 0.6520 - val_precision: 0.1947 - val_recall: 0.7867 - val_tn: 45250.0000 - val_tp: 59.0000\n", + "Epoch 4/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 0.0132 - accuracy: 0.9843 - auc: 0.9118 - cross entropy: 0.0619 - fn: 43.1209 - fp: 1458.9890 - loss: 0.5465 - prc: 0.2216 - precision: 0.0719 - recall: 0.7105 - tn: 92524.0469 - tp: 114.4176 - val_Brier score: 0.0062 - val_accuracy: 0.9917 - val_auc: 0.9483 - val_cross entropy: 0.0315 - val_fn: 16.0000 - val_fp: 364.0000 - val_loss: 0.0315 - val_prc: 0.6518 - val_precision: 0.1395 - val_recall: 0.7867 - val_tn: 45130.0000 - val_tp: 59.0000\n", + "Epoch 5/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0166 - accuracy: 0.9802 - auc: 0.9420 - cross entropy: 0.0767 - fn: 32.6044 - fp: 1842.9670 - loss: 0.4640 - prc: 0.2562 - precision: 0.0749 - recall: 0.8069 - tn: 92124.6719 - tp: 140.3297 - val_Brier score: 0.0084 - val_accuracy: 0.9901 - val_auc: 0.9534 - val_cross entropy: 0.0410 - val_fn: 13.0000 - val_fp: 440.0000 - val_loss: 0.0410 - val_prc: 0.6102 - val_precision: 0.1235 - val_recall: 0.8267 - val_tn: 45054.0000 - val_tp: 62.0000\n", + "Epoch 6/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0196 - accuracy: 0.9761 - auc: 0.9495 - cross entropy: 0.0900 - fn: 30.9890 - fp: 2289.6375 - loss: 0.3925 - prc: 0.2116 - precision: 0.0563 - recall: 0.8074 - tn: 91690.8438 - tp: 129.0989 - val_Brier score: 0.0104 - val_accuracy: 0.9879 - val_auc: 0.9562 - val_cross entropy: 0.0501 - val_fn: 12.0000 - val_fp: 541.0000 - val_loss: 0.0501 - val_prc: 0.5908 - val_precision: 0.1043 - val_recall: 0.8400 - val_tn: 44953.0000 - val_tp: 63.0000\n", + "Epoch 7/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0233 - accuracy: 0.9720 - auc: 0.9509 - cross entropy: 0.1045 - fn: 24.0659 - fp: 2611.6045 - loss: 0.3303 - prc: 0.2385 - precision: 0.0542 - recall: 0.8642 - tn: 91360.3438 - tp: 144.5604 - val_Brier score: 0.0112 - val_accuracy: 0.9873 - val_auc: 0.9590 - val_cross entropy: 0.0543 - val_fn: 12.0000 - val_fp: 569.0000 - val_loss: 0.0543 - val_prc: 0.5775 - val_precision: 0.0997 - val_recall: 0.8400 - val_tn: 44925.0000 - val_tp: 63.0000\n", + "Epoch 8/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0241 - accuracy: 0.9707 - auc: 0.9557 - cross entropy: 0.1079 - fn: 25.4835 - fp: 2768.0989 - loss: 0.2904 - prc: 0.2209 - precision: 0.0436 - recall: 0.8428 - tn: 91219.0312 - tp: 127.9560 - val_Brier score: 0.0127 - val_accuracy: 0.9863 - val_auc: 0.9603 - val_cross entropy: 0.0614 - val_fn: 12.0000 - val_fp: 613.0000 - val_loss: 0.0614 - val_prc: 0.5583 - val_precision: 0.0932 - val_recall: 0.8400 - val_tn: 44881.0000 - val_tp: 63.0000\n", + "Epoch 9/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0268 - accuracy: 0.9672 - auc: 0.9618 - cross entropy: 0.1191 - fn: 19.7143 - fp: 3080.3845 - loss: 0.2417 - prc: 0.1898 - precision: 0.0384 - recall: 0.8885 - tn: 90914.2891 - tp: 126.1868 - val_Brier score: 0.0135 - val_accuracy: 0.9853 - val_auc: 0.9625 - val_cross entropy: 0.0658 - val_fn: 12.0000 - val_fp: 658.0000 - val_loss: 0.0658 - val_prc: 0.5371 - val_precision: 0.0874 - val_recall: 0.8400 - val_tn: 44836.0000 - val_tp: 63.0000\n", + "Epoch 10/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - Brier score: 0.0298 - accuracy: 0.9634 - auc: 0.9549 - cross entropy: 0.1327 - fn: 21.3516 - fp: 3426.9670 - loss: 0.2952 - prc: 0.2044 - precision: 0.0405 - recall: 0.8664 - tn: 90547.7500 - tp: 144.5055 - val_Brier score: 0.0141 - val_accuracy: 0.9847 - val_auc: 0.9628 - val_cross entropy: 0.0685 - val_fn: 12.0000 - val_fp: 684.0000 - val_loss: 0.0685 - val_prc: 0.5282 - val_precision: 0.0843 - val_recall: 0.8400 - val_tn: 44810.0000 - val_tp: 63.0000\n", + "Epoch 11/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - Brier score: 0.0307 - accuracy: 0.9620 - auc: 0.9655 - cross entropy: 0.1353 - fn: 21.6703 - fp: 3583.5056 - loss: 0.2558 - prc: 0.1959 - precision: 0.0387 - recall: 0.8808 - tn: 90397.5859 - tp: 137.8132 - val_Brier score: 0.0154 - val_accuracy: 0.9835 - val_auc: 0.9641 - val_cross entropy: 0.0745 - val_fn: 12.0000 - val_fp: 742.0000 - val_loss: 0.0745 - val_prc: 0.5155 - val_precision: 0.0783 - val_recall: 0.8400 - val_tn: 44752.0000 - val_tp: 63.0000\n", + "Epoch 12/100\n", + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - Brier score: 0.0326 - accuracy: 0.9591 - auc: 0.9397 - cross entropy: 0.1407 - fn: 26.8242 - fp: 3843.8682 - loss: 0.3581 - prc: 0.1931 - precision: 0.0348 - recall: 0.8298 - tn: 90134.2344 - tp: 135.6483 - val_Brier score: 0.0153 - val_accuracy: 0.9835 - val_auc: 0.9654 - val_cross entropy: 0.0743 - val_fn: 12.0000 - val_fp: 741.0000 - val_loss: 0.0743 - val_prc: 0.5055 - val_precision: 0.0784 - val_recall: 0.8400 - val_tn: 44753.0000 - val_tp: 63.0000\n", + "Epoch 12: early stopping\n", + "Restoring model weights from the end of the best epoch: 2.\n" + ] + } + ], "source": [ "weighted_model = make_model()\n", "weighted_model.load_weights(initial_weights)\n", @@ -1161,10 +2631,10 @@ " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", - " callbacks=[early_stopping],\n", + " callbacks=[early_stopping()],\n", " validation_data=(val_features, val_labels),\n", " # The class weights go here\n", - " class_weight=class_weight) " + " class_weight=class_weight)" ] }, { @@ -1180,9 +2650,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "BBe9FMO5ucTC" - }, - "outputs": [], + "id": "BBe9FMO5ucTC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "be1f7941-652c-4e8e-b044-f8a544d9b8b6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_metrics(weighted_history)" ] @@ -1200,9 +2686,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "nifqscPGw-5w" - }, - "outputs": [], + "id": "nifqscPGw-5w", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8ac89a0c-110e-4b9e-ed2b-fca440be5853" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", + "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n" + ] + } + ], "source": [ "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)" @@ -1212,9 +2711,39 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "owKL2vdMBJr6" - }, - "outputs": [], + "id": "owKL2vdMBJr6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 623 + }, + "outputId": "fe3f19be-a3ac-4dd8-9f00-3e43e8440e3b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "loss : 0.016584360972046852\n", + "compile_metrics : 0.016584360972046852\n", + "\n", + "Legitimate Transactions Detected (True Negatives): 56747\n", + "Legitimate Transactions Incorrectly Detected (False Positives): 103\n", + "Fraudulent Transactions Missed (False Negatives): 22\n", + "Fraudulent Transactions Detected (True Positives): 90\n", + "Total Fraudulent Transactions: 112\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "weighted_results = weighted_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -1249,9 +2778,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "3hzScIVZS1Xm" - }, - "outputs": [], + "id": "3hzScIVZS1Xm", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "54f23c34-db79-49ff-b16f-ce4d77ab6a12" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -1276,9 +2821,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7jHnmVebOWOC" - }, - "outputs": [], + "id": "7jHnmVebOWOC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "ebab4ec2-bd58-4c4b-9191-2a446f2ebeda" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -1333,7 +2894,7 @@ "source": [ "#### Using NumPy\n", "\n", - "You can balance the dataset manually by choosing the right number of random \n", + "You can balance the dataset manually by choosing the right number of random\n", "indices from the positive examples:" ] }, @@ -1341,9 +2902,24 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "BUzGjSkwqT88" - }, - "outputs": [], + "id": "BUzGjSkwqT88", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "10efae2b-a1f2-4e47-be0b-2c5a1475a2f8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(181971, 29)" + ] + }, + "metadata": {}, + "execution_count": 47 + } + ], "source": [ "ids = np.arange(len(pos_features))\n", "choices = np.random.choice(ids, len(neg_features))\n", @@ -1358,9 +2934,24 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7ie_FFet6cep" - }, - "outputs": [], + "id": "7ie_FFet6cep", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b2652b4-2772-449c-d11e-e51bff6b25f2" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(363942, 29)" + ] + }, + "metadata": {}, + "execution_count": 48 + } + ], "source": [ "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n", "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n", @@ -1423,9 +3014,28 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "llXc9rNH7Fbz" - }, - "outputs": [], + "id": "llXc9rNH7Fbz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "16a5bf80-80b9-4edb-99e8-109b0d3a6570" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Features:\n", + " [-1.71909383 2.38497281 -4.59481652 1.17323447 -0.78699302 -2.69341776\n", + " -2.93305552 1.60763628 -2.78266045 -5. 2.92735061 -5.\n", + " -0.16521428 -5. 0.04789472 -4.72027875 -5. -2.35087136\n", + " 0.53308929 -0.00701804 1.40398131 0.35945914 -0.75014734 -0.53770716\n", + " -0.29864578 0.23727469 0.86211054 0.8456621 -1.93388031]\n", + "\n", + "Label: [1]\n" + ] + } + ], "source": [ "for features, label in pos_ds.take(1):\n", " print(\"Features:\\n\", features.numpy())\n", @@ -1458,9 +3068,21 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "EWXARdTdAuQK" - }, - "outputs": [], + "id": "EWXARdTdAuQK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fe92c1d9-fba5-49c9-fbb0-c85018f6d929" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.5009765625\n" + ] + } + ], "source": [ "for features, label in resampled_ds.take(1):\n", " print(label.numpy().mean())" @@ -1481,11 +3103,26 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xH-7K46AAxpq" - }, - "outputs": [], - "source": [ - "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n", + "id": "xH-7K46AAxpq", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ca89ac53-dd21-4ea2-bd20-3feae503c065" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "278" + ] + }, + "metadata": {}, + "execution_count": 53 + } + ], + "source": [ + "resampled_steps_per_epoch = int(np.ceil(2.0*neg/BATCH_SIZE))\n", "resampled_steps_per_epoch" ] }, @@ -1499,32 +3136,85 @@ "\n", "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n", "\n", - "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. " + "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "soRQ89JYqd6b" - }, - "outputs": [], + "id": "soRQ89JYqd6b", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7105bd8e-365d-41e1-9e6e-7159499f5228" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 87ms/step - Brier score: 0.1800 - accuracy: 0.7457 - auc: 0.8137 - cross entropy: 0.6766 - fn: 31326.3906 - fp: 54831.5156 - loss: 0.9703 - prc: 0.7956 - precision: 0.6252 - recall: 0.7266 - tn: 145406.4531 - tp: 112110.3047 - val_Brier score: 0.0533 - val_accuracy: 0.9647 - val_auc: 0.9642 - val_cross entropy: 0.2261 - val_fn: 10.0000 - val_fp: 1598.0000 - val_loss: 0.2261 - val_prc: 0.7297 - val_precision: 0.0391 - val_recall: 0.8667 - val_tn: 43896.0000 - val_tp: 65.0000\n", + "Epoch 2/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 78ms/step - Brier score: 0.0652 - accuracy: 0.9160 - auc: 0.9698 - cross entropy: 0.2180 - fn: 12836.7168 - fp: 10300.5518 - loss: 0.2180 - prc: 0.9767 - precision: 0.9217 - recall: 0.9095 - tn: 132932.8281 - tp: 130642.5703 - val_Brier score: 0.0228 - val_accuracy: 0.9833 - val_auc: 0.9727 - val_cross entropy: 0.1132 - val_fn: 11.0000 - val_fp: 751.0000 - val_loss: 0.1132 - val_prc: 0.7277 - val_precision: 0.0785 - val_recall: 0.8533 - val_tn: 44743.0000 - val_tp: 64.0000\n", + "Epoch 3/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 75ms/step - Brier score: 0.0464 - accuracy: 0.9404 - auc: 0.9837 - cross entropy: 0.1586 - fn: 10936.4404 - fp: 5890.3799 - loss: 0.1586 - prc: 0.9864 - precision: 0.9565 - recall: 0.9230 - tn: 137368.5000 - tp: 132517.3438 - val_Brier score: 0.0164 - val_accuracy: 0.9854 - val_auc: 0.9744 - val_cross entropy: 0.0801 - val_fn: 11.0000 - val_fp: 654.0000 - val_loss: 0.0801 - val_prc: 0.7347 - val_precision: 0.0891 - val_recall: 0.8533 - val_tn: 44840.0000 - val_tp: 64.0000\n", + "Epoch 4/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 70ms/step - Brier score: 0.0385 - accuracy: 0.9500 - auc: 0.9891 - cross entropy: 0.1321 - fn: 9469.4658 - fp: 4698.1758 - loss: 0.1321 - prc: 0.9904 - precision: 0.9659 - recall: 0.9330 - tn: 138342.9375 - tp: 134202.0781 - val_Brier score: 0.0139 - val_accuracy: 0.9863 - val_auc: 0.9741 - val_cross entropy: 0.0655 - val_fn: 12.0000 - val_fp: 613.0000 - val_loss: 0.0655 - val_prc: 0.7255 - val_precision: 0.0932 - val_recall: 0.8400 - val_tn: 44881.0000 - val_tp: 63.0000\n", + "Epoch 5/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 80ms/step - Brier score: 0.0332 - accuracy: 0.9569 - auc: 0.9922 - cross entropy: 0.1141 - fn: 8225.2656 - fp: 4017.6809 - loss: 0.1141 - prc: 0.9929 - precision: 0.9710 - recall: 0.9422 - tn: 138940.4531 - tp: 135529.2500 - val_Brier score: 0.0123 - val_accuracy: 0.9875 - val_auc: 0.9733 - val_cross entropy: 0.0566 - val_fn: 12.0000 - val_fp: 559.0000 - val_loss: 0.0566 - val_prc: 0.7203 - val_precision: 0.1013 - val_recall: 0.8400 - val_tn: 44935.0000 - val_tp: 63.0000\n", + "Epoch 6/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 70ms/step - Brier score: 0.0304 - accuracy: 0.9608 - auc: 0.9937 - cross entropy: 0.1047 - fn: 7469.0322 - fp: 3682.0574 - loss: 0.1047 - prc: 0.9939 - precision: 0.9731 - recall: 0.9476 - tn: 140014.3906 - tp: 135547.1875 - val_Brier score: 0.0112 - val_accuracy: 0.9880 - val_auc: 0.9705 - val_cross entropy: 0.0502 - val_fn: 12.0000 - val_fp: 536.0000 - val_loss: 0.0502 - val_prc: 0.7182 - val_precision: 0.1052 - val_recall: 0.8400 - val_tn: 44958.0000 - val_tp: 63.0000\n", + "Epoch 7/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 88ms/step - Brier score: 0.0282 - accuracy: 0.9629 - auc: 0.9948 - cross entropy: 0.0960 - fn: 7075.3188 - fp: 3527.6309 - loss: 0.0960 - prc: 0.9949 - precision: 0.9747 - recall: 0.9507 - tn: 139543.1719 - tp: 136566.5469 - val_Brier score: 0.0102 - val_accuracy: 0.9888 - val_auc: 0.9706 - val_cross entropy: 0.0447 - val_fn: 12.0000 - val_fp: 500.0000 - val_loss: 0.0447 - val_prc: 0.7181 - val_precision: 0.1119 - val_recall: 0.8400 - val_tn: 44994.0000 - val_tp: 63.0000\n", + "Epoch 8/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 90ms/step - Brier score: 0.0266 - accuracy: 0.9640 - auc: 0.9956 - cross entropy: 0.0899 - fn: 6878.3228 - fp: 3399.3513 - loss: 0.0899 - prc: 0.9954 - precision: 0.9757 - recall: 0.9517 - tn: 140075.2500 - tp: 136359.7344 - val_Brier score: 0.0092 - val_accuracy: 0.9895 - val_auc: 0.9707 - val_cross entropy: 0.0398 - val_fn: 12.0000 - val_fp: 467.0000 - val_loss: 0.0398 - val_prc: 0.7185 - val_precision: 0.1189 - val_recall: 0.8400 - val_tn: 45027.0000 - val_tp: 63.0000\n", + "Epoch 9/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 83ms/step - Brier score: 0.0248 - accuracy: 0.9657 - auc: 0.9963 - cross entropy: 0.0839 - fn: 6540.0322 - fp: 3233.0896 - loss: 0.0839 - prc: 0.9961 - precision: 0.9767 - recall: 0.9540 - tn: 140536.8125 - tp: 136402.7188 - val_Brier score: 0.0084 - val_accuracy: 0.9903 - val_auc: 0.9708 - val_cross entropy: 0.0356 - val_fn: 12.0000 - val_fp: 432.0000 - val_loss: 0.0356 - val_prc: 0.7087 - val_precision: 0.1273 - val_recall: 0.8400 - val_tn: 45062.0000 - val_tp: 63.0000\n", + "Epoch 10/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 82ms/step - Brier score: 0.0235 - accuracy: 0.9674 - auc: 0.9967 - cross entropy: 0.0795 - fn: 6132.1685 - fp: 3185.6416 - loss: 0.0795 - prc: 0.9965 - precision: 0.9773 - recall: 0.9572 - tn: 139754.8438 - tp: 137640.0000 - val_Brier score: 0.0074 - val_accuracy: 0.9912 - val_auc: 0.9661 - val_cross entropy: 0.0314 - val_fn: 12.0000 - val_fp: 389.0000 - val_loss: 0.0314 - val_prc: 0.7116 - val_precision: 0.1394 - val_recall: 0.8400 - val_tn: 45105.0000 - val_tp: 63.0000\n", + "Epoch 11/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 77ms/step - Brier score: 0.0225 - accuracy: 0.9683 - auc: 0.9970 - cross entropy: 0.0761 - fn: 5892.4697 - fp: 3121.7490 - loss: 0.0761 - prc: 0.9967 - precision: 0.9778 - recall: 0.9584 - tn: 140245.8906 - tp: 137452.5625 - val_Brier score: 0.0070 - val_accuracy: 0.9914 - val_auc: 0.9671 - val_cross entropy: 0.0292 - val_fn: 12.0000 - val_fp: 382.0000 - val_loss: 0.0292 - val_prc: 0.7027 - val_precision: 0.1416 - val_recall: 0.8400 - val_tn: 45112.0000 - val_tp: 63.0000\n", + "Epoch 12/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 79ms/step - Brier score: 0.0215 - accuracy: 0.9703 - auc: 0.9972 - cross entropy: 0.0730 - fn: 5490.4980 - fp: 3039.1074 - loss: 0.0730 - prc: 0.9968 - precision: 0.9784 - recall: 0.9617 - tn: 140386.8594 - tp: 137796.2031 - val_Brier score: 0.0068 - val_accuracy: 0.9915 - val_auc: 0.9620 - val_cross entropy: 0.0279 - val_fn: 12.0000 - val_fp: 375.0000 - val_loss: 0.0279 - val_prc: 0.6917 - val_precision: 0.1438 - val_recall: 0.8400 - val_tn: 45119.0000 - val_tp: 63.0000\n", + "Epoch 13/100\n", + "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 83ms/step - Brier score: 0.0210 - accuracy: 0.9701 - auc: 0.9973 - cross entropy: 0.0710 - fn: 5511.9858 - fp: 3034.5950 - loss: 0.0710 - prc: 0.9970 - precision: 0.9786 - recall: 0.9615 - tn: 139996.2500 - tp: 138169.8281 - val_Brier score: 0.0065 - val_accuracy: 0.9919 - val_auc: 0.9510 - val_cross entropy: 0.0263 - val_fn: 12.0000 - val_fp: 357.0000 - val_loss: 0.0263 - val_prc: 0.6914 - val_precision: 0.1500 - val_recall: 0.8400 - val_tn: 45137.0000 - val_tp: 63.0000\n", + "Epoch 13: early stopping\n", + "Restoring model weights from the end of the best epoch: 3.\n" + ] + } + ], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", "\n", "# Reset the bias to zero, since this dataset is balanced.\n", - "output_layer = resampled_model.layers[-1] \n", + "output_layer = resampled_model.layers[-1]\n", "output_layer.bias.assign([0])\n", "\n", "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n", - "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n", + "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2)\n", "\n", "resampled_history = resampled_model.fit(\n", " resampled_ds,\n", " epochs=EPOCHS,\n", " steps_per_epoch=resampled_steps_per_epoch,\n", - " callbacks=[early_stopping],\n", + " callbacks=[early_stopping()],\n", " validation_data=val_ds)" ] }, @@ -1536,7 +3226,7 @@ "source": [ "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n", "\n", - "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n", + "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight.\n", "\n", "This smoother gradient signal makes it easier to train the model." ] @@ -1549,16 +3239,32 @@ "source": [ "### Check training history\n", "\n", - "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. " + "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "YoUGfr1vuivl" - }, - "outputs": [], + "id": "YoUGfr1vuivl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "900013c8-0a75-4071-f972-96c7a74721ea" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_metrics(resampled_history)" ] @@ -1578,7 +3284,7 @@ "id": "KFLxRL8eoDE5" }, "source": [ - "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n", + "Because training is easier on the balanced data, the above training procedure may overfit quickly.\n", "\n", "So break up the epochs to give the `tf.keras.callbacks.EarlyStopping` finer control over when to stop training." ] @@ -1587,15 +3293,72 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "e_yn9I26qAHU" - }, - "outputs": [], + "id": "e_yn9I26qAHU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a0c08ef9-9288-482c-d791-30ba9ee88f14" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 133ms/step - Brier score: 0.1260 - accuracy: 0.8303 - auc: 0.7891 - cross entropy: 0.5845 - fn: 5591.7144 - fp: 6751.8096 - loss: 1.8949 - prc: 0.5380 - precision: 0.4449 - recall: 0.4974 - tn: 49988.6172 - tp: 5667.3335 - val_Brier score: 0.3379 - val_accuracy: 0.3906 - val_auc: 0.5873 - val_cross entropy: 0.9111 - val_fn: 24.0000 - val_fp: 27746.0000 - val_loss: 0.9111 - val_prc: 0.0134 - val_precision: 0.0018 - val_recall: 0.6800 - val_tn: 17748.0000 - val_tp: 51.0000\n", + "Epoch 2/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 172ms/step - Brier score: 0.3219 - accuracy: 0.5424 - auc: 0.6023 - cross entropy: 1.2477 - fn: 3986.5239 - fp: 6108.9048 - loss: 1.2477 - prc: 0.7228 - precision: 0.5391 - recall: 0.6340 - tn: 5025.7617 - tp: 7309.2856 - val_Brier score: 0.3100 - val_accuracy: 0.4563 - val_auc: 0.9047 - val_cross entropy: 0.8457 - val_fn: 5.0000 - val_fp: 24773.0000 - val_loss: 0.8457 - val_prc: 0.1116 - val_precision: 0.0028 - val_recall: 0.9333 - val_tn: 20721.0000 - val_tp: 70.0000\n", + "Epoch 3/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 117ms/step - Brier score: 0.2662 - accuracy: 0.6107 - auc: 0.7148 - cross entropy: 0.9069 - fn: 2916.3809 - fp: 5682.6191 - loss: 0.9069 - prc: 0.8052 - precision: 0.5887 - recall: 0.7345 - tn: 5535.5713 - tp: 8295.9043 - val_Brier score: 0.2728 - val_accuracy: 0.5428 - val_auc: 0.9333 - val_cross entropy: 0.7601 - val_fn: 6.0000 - val_fp: 20826.0000 - val_loss: 0.7601 - val_prc: 0.4609 - val_precision: 0.0033 - val_recall: 0.9200 - val_tn: 24668.0000 - val_tp: 69.0000\n", + "Epoch 4/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - Brier score: 0.2149 - accuracy: 0.6830 - auc: 0.7974 - cross entropy: 0.6877 - fn: 2166.2856 - fp: 4835.0000 - loss: 0.6877 - prc: 0.8606 - precision: 0.6462 - recall: 0.8009 - tn: 6444.3809 - tp: 8984.8096 - val_Brier score: 0.2349 - val_accuracy: 0.6326 - val_auc: 0.9392 - val_cross entropy: 0.6739 - val_fn: 6.0000 - val_fp: 16738.0000 - val_loss: 0.6739 - val_prc: 0.5582 - val_precision: 0.0041 - val_recall: 0.9200 - val_tn: 28756.0000 - val_tp: 69.0000\n", + "Epoch 5/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - Brier score: 0.1844 - accuracy: 0.7266 - auc: 0.8463 - cross entropy: 0.5815 - fn: 1737.0952 - fp: 4309.8096 - loss: 0.5815 - prc: 0.8937 - precision: 0.6842 - recall: 0.8418 - tn: 6921.1431 - tp: 9462.4287 - val_Brier score: 0.2002 - val_accuracy: 0.7173 - val_auc: 0.9430 - val_cross entropy: 0.5943 - val_fn: 6.0000 - val_fp: 12878.0000 - val_loss: 0.5943 - val_prc: 0.6364 - val_precision: 0.0053 - val_recall: 0.9200 - val_tn: 32616.0000 - val_tp: 69.0000\n", + "Epoch 6/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 85ms/step - Brier score: 0.1574 - accuracy: 0.7700 - auc: 0.8854 - cross entropy: 0.4912 - fn: 1416.6190 - fp: 3682.4761 - loss: 0.4912 - prc: 0.9206 - precision: 0.7248 - recall: 0.8722 - tn: 7523.4761 - tp: 9807.9043 - val_Brier score: 0.1690 - val_accuracy: 0.7844 - val_auc: 0.9462 - val_cross entropy: 0.5219 - val_fn: 7.0000 - val_fp: 9818.0000 - val_loss: 0.5219 - val_prc: 0.6896 - val_precision: 0.0069 - val_recall: 0.9067 - val_tn: 35676.0000 - val_tp: 68.0000\n", + "Epoch 7/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 80ms/step - Brier score: 0.1382 - accuracy: 0.8019 - auc: 0.9053 - cross entropy: 0.4331 - fn: 1326.8572 - fp: 3077.5239 - loss: 0.4331 - prc: 0.9332 - precision: 0.7591 - recall: 0.8801 - tn: 8216.4287 - tp: 9809.6670 - val_Brier score: 0.1427 - val_accuracy: 0.8369 - val_auc: 0.9494 - val_cross entropy: 0.4599 - val_fn: 7.0000 - val_fp: 7427.0000 - val_loss: 0.4599 - val_prc: 0.7112 - val_precision: 0.0091 - val_recall: 0.9067 - val_tn: 38067.0000 - val_tp: 68.0000\n", + "Epoch 8/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - Brier score: 0.1266 - accuracy: 0.8178 - auc: 0.9158 - cross entropy: 0.3961 - fn: 1301.3810 - fp: 2720.4761 - loss: 0.3961 - prc: 0.9405 - precision: 0.7809 - recall: 0.8815 - tn: 8511.5713 - tp: 9897.0479 - val_Brier score: 0.1216 - val_accuracy: 0.8722 - val_auc: 0.9523 - val_cross entropy: 0.4087 - val_fn: 7.0000 - val_fp: 5817.0000 - val_loss: 0.4087 - val_prc: 0.7195 - val_precision: 0.0116 - val_recall: 0.9067 - val_tn: 39677.0000 - val_tp: 68.0000\n", + "Epoch 9/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 90ms/step - Brier score: 0.1146 - accuracy: 0.8401 - auc: 0.9286 - cross entropy: 0.3630 - fn: 1216.6190 - fp: 2340.0476 - loss: 0.3630 - prc: 0.9486 - precision: 0.8092 - recall: 0.8907 - tn: 8866.9521 - tp: 10006.8574 - val_Brier score: 0.1034 - val_accuracy: 0.9024 - val_auc: 0.9556 - val_cross entropy: 0.3636 - val_fn: 8.0000 - val_fp: 4441.0000 - val_loss: 0.3636 - val_prc: 0.7246 - val_precision: 0.0149 - val_recall: 0.8933 - val_tn: 41053.0000 - val_tp: 67.0000\n", + "Epoch 10/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 141ms/step - Brier score: 0.1019 - accuracy: 0.8630 - auc: 0.9407 - cross entropy: 0.3283 - fn: 1137.5238 - fp: 1933.7142 - loss: 0.3283 - prc: 0.9574 - precision: 0.8400 - recall: 0.8985 - tn: 9235.6670 - tp: 10123.5713 - val_Brier score: 0.0886 - val_accuracy: 0.9234 - val_auc: 0.9583 - val_cross entropy: 0.3257 - val_fn: 8.0000 - val_fp: 3483.0000 - val_loss: 0.3257 - val_prc: 0.7331 - val_precision: 0.0189 - val_recall: 0.8933 - val_tn: 42011.0000 - val_tp: 67.0000\n", + "Epoch 11/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 87ms/step - Brier score: 0.0954 - accuracy: 0.8722 - auc: 0.9462 - cross entropy: 0.3064 - fn: 1127.0952 - fp: 1727.5714 - loss: 0.3064 - prc: 0.9611 - precision: 0.8538 - recall: 0.9002 - tn: 9409.7617 - tp: 10166.0479 - val_Brier score: 0.0767 - val_accuracy: 0.9397 - val_auc: 0.9607 - val_cross entropy: 0.2939 - val_fn: 9.0000 - val_fp: 2737.0000 - val_loss: 0.2939 - val_prc: 0.7362 - val_precision: 0.0235 - val_recall: 0.8800 - val_tn: 42757.0000 - val_tp: 66.0000\n", + "Epoch 12/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - Brier score: 0.0889 - accuracy: 0.8828 - auc: 0.9506 - cross entropy: 0.2921 - fn: 1129.1904 - fp: 1491.3334 - loss: 0.2921 - prc: 0.9634 - precision: 0.8706 - recall: 0.8988 - tn: 9768.9521 - tp: 10041.0000 - val_Brier score: 0.0671 - val_accuracy: 0.9512 - val_auc: 0.9625 - val_cross entropy: 0.2669 - val_fn: 9.0000 - val_fp: 2215.0000 - val_loss: 0.2669 - val_prc: 0.7375 - val_precision: 0.0289 - val_recall: 0.8800 - val_tn: 43279.0000 - val_tp: 66.0000\n", + "Epoch 13/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0832 - accuracy: 0.8917 - auc: 0.9556 - cross entropy: 0.2738 - fn: 1092.8572 - fp: 1334.7620 - loss: 0.2738 - prc: 0.9666 - precision: 0.8821 - recall: 0.9028 - tn: 9941.6670 - tp: 10061.1904 - val_Brier score: 0.0592 - val_accuracy: 0.9591 - val_auc: 0.9644 - val_cross entropy: 0.2439 - val_fn: 9.0000 - val_fp: 1854.0000 - val_loss: 0.2439 - val_prc: 0.7288 - val_precision: 0.0344 - val_recall: 0.8800 - val_tn: 43640.0000 - val_tp: 66.0000\n", + "Epoch 14/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 79ms/step - Brier score: 0.0784 - accuracy: 0.8973 - auc: 0.9592 - cross entropy: 0.2589 - fn: 1079.2858 - fp: 1219.2380 - loss: 0.2589 - prc: 0.9692 - precision: 0.8923 - recall: 0.9037 - tn: 9997.1904 - tp: 10134.7617 - val_Brier score: 0.0531 - val_accuracy: 0.9647 - val_auc: 0.9660 - val_cross entropy: 0.2253 - val_fn: 10.0000 - val_fp: 1599.0000 - val_loss: 0.2253 - val_prc: 0.7295 - val_precision: 0.0391 - val_recall: 0.8667 - val_tn: 43895.0000 - val_tp: 65.0000\n", + "Epoch 15/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 77ms/step - Brier score: 0.0740 - accuracy: 0.9025 - auc: 0.9634 - cross entropy: 0.2437 - fn: 1043.6666 - fp: 1119.0952 - loss: 0.2437 - prc: 0.9724 - precision: 0.9003 - recall: 0.9070 - tn: 10041.3330 - tp: 10226.3809 - val_Brier score: 0.0479 - val_accuracy: 0.9687 - val_auc: 0.9674 - val_cross entropy: 0.2090 - val_fn: 10.0000 - val_fp: 1418.0000 - val_loss: 0.2090 - val_prc: 0.7313 - val_precision: 0.0438 - val_recall: 0.8667 - val_tn: 44076.0000 - val_tp: 65.0000\n", + "Epoch 16/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - Brier score: 0.0700 - accuracy: 0.9100 - auc: 0.9666 - cross entropy: 0.2328 - fn: 995.7619 - fp: 1030.0952 - loss: 0.2328 - prc: 0.9742 - precision: 0.9069 - recall: 0.9115 - tn: 10276.5713 - tp: 10128.0479 - val_Brier score: 0.0435 - val_accuracy: 0.9717 - val_auc: 0.9683 - val_cross entropy: 0.1946 - val_fn: 11.0000 - val_fp: 1278.0000 - val_loss: 0.1946 - val_prc: 0.7319 - val_precision: 0.0477 - val_recall: 0.8533 - val_tn: 44216.0000 - val_tp: 64.0000\n", + "Epoch 17/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 140ms/step - Brier score: 0.0674 - accuracy: 0.9139 - auc: 0.9682 - cross entropy: 0.2265 - fn: 1035.0476 - fp: 884.1429 - loss: 0.2265 - prc: 0.9756 - precision: 0.9201 - recall: 0.9080 - tn: 10257.5234 - tp: 10253.7617 - val_Brier score: 0.0400 - val_accuracy: 0.9740 - val_auc: 0.9694 - val_cross entropy: 0.1825 - val_fn: 11.0000 - val_fp: 1176.0000 - val_loss: 0.1825 - val_prc: 0.7334 - val_precision: 0.0516 - val_recall: 0.8533 - val_tn: 44318.0000 - val_tp: 64.0000\n", + "Epoch 18/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - Brier score: 0.0632 - accuracy: 0.9185 - auc: 0.9712 - cross entropy: 0.2126 - fn: 998.7143 - fp: 832.3333 - loss: 0.2126 - prc: 0.9776 - precision: 0.9244 - recall: 0.9117 - tn: 10379.8096 - tp: 10219.6191 - val_Brier score: 0.0369 - val_accuracy: 0.9761 - val_auc: 0.9702 - val_cross entropy: 0.1717 - val_fn: 11.0000 - val_fp: 1078.0000 - val_loss: 0.1717 - val_prc: 0.7340 - val_precision: 0.0560 - val_recall: 0.8533 - val_tn: 44416.0000 - val_tp: 64.0000\n", + "Epoch 19/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 76ms/step - Brier score: 0.0625 - accuracy: 0.9207 - auc: 0.9717 - cross entropy: 0.2095 - fn: 1008.9524 - fp: 757.5714 - loss: 0.2095 - prc: 0.9781 - precision: 0.9303 - recall: 0.9115 - tn: 10366.4287 - tp: 10297.5234 - val_Brier score: 0.0345 - val_accuracy: 0.9777 - val_auc: 0.9703 - val_cross entropy: 0.1624 - val_fn: 11.0000 - val_fp: 1005.0000 - val_loss: 0.1624 - val_prc: 0.7342 - val_precision: 0.0599 - val_recall: 0.8533 - val_tn: 44489.0000 - val_tp: 64.0000\n", + "Epoch 20/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0601 - accuracy: 0.9228 - auc: 0.9737 - cross entropy: 0.2039 - fn: 996.7143 - fp: 726.3333 - loss: 0.2039 - prc: 0.9792 - precision: 0.9335 - recall: 0.9111 - tn: 10425.5713 - tp: 10281.8574 - val_Brier score: 0.0324 - val_accuracy: 0.9788 - val_auc: 0.9705 - val_cross entropy: 0.1543 - val_fn: 11.0000 - val_fp: 956.0000 - val_loss: 0.1543 - val_prc: 0.7349 - val_precision: 0.0627 - val_recall: 0.8533 - val_tn: 44538.0000 - val_tp: 64.0000\n", + "Epoch 21/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0578 - accuracy: 0.9262 - auc: 0.9753 - cross entropy: 0.1954 - fn: 978.0952 - fp: 676.4762 - loss: 0.1954 - prc: 0.9805 - precision: 0.9380 - recall: 0.9123 - tn: 10584.3809 - tp: 10191.5234 - val_Brier score: 0.0306 - val_accuracy: 0.9794 - val_auc: 0.9708 - val_cross entropy: 0.1471 - val_fn: 11.0000 - val_fp: 926.0000 - val_loss: 0.1471 - val_prc: 0.7357 - val_precision: 0.0646 - val_recall: 0.8533 - val_tn: 44568.0000 - val_tp: 64.0000\n", + "Epoch 22/1000\n", + "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 86ms/step - Brier score: 0.0563 - accuracy: 0.9297 - auc: 0.9764 - cross entropy: 0.1900 - fn: 960.0476 - fp: 637.4762 - loss: 0.1900 - prc: 0.9812 - precision: 0.9422 - recall: 0.9154 - tn: 10543.0000 - tp: 10289.9521 - val_Brier score: 0.0290 - val_accuracy: 0.9804 - val_auc: 0.9709 - val_cross entropy: 0.1406 - val_fn: 11.0000 - val_fp: 880.0000 - val_loss: 0.1406 - val_prc: 0.7359 - val_precision: 0.0678 - val_recall: 0.8533 - val_tn: 44614.0000 - val_tp: 64.0000\n", + "Epoch 22: early stopping\n", + "Restoring model weights from the end of the best epoch: 12.\n" + ] + } + ], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", "\n", "# Reset the bias to zero, since this dataset is balanced.\n", - "output_layer = resampled_model.layers[-1] \n", + "output_layer = resampled_model.layers[-1]\n", "output_layer.bias.assign([0])\n", "\n", "resampled_history = resampled_model.fit(\n", @@ -1603,7 +3366,7 @@ " # These are not real epochs\n", " steps_per_epoch=20,\n", " epochs=10*EPOCHS,\n", - " callbacks=[early_stopping],\n", + " callbacks=[early_stopping()],\n", " validation_data=(val_ds))" ] }, @@ -1620,9 +3383,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FMycrpJwn39w" - }, - "outputs": [], + "id": "FMycrpJwn39w", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "a09cbb77-4d2f-4861-ec09-5c4cf599fe7b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_metrics(resampled_history)" ] @@ -1640,9 +3419,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "C0fmHSgXxFdW" - }, - "outputs": [], + "id": "C0fmHSgXxFdW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "18daf060-f084-4bec-d167-b3adca61ed9b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", + "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n" + ] + } + ], "source": [ "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)" @@ -1652,9 +3444,39 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FO0mMOYUDWFk" - }, - "outputs": [], + "id": "FO0mMOYUDWFk", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 623 + }, + "outputId": "c48eb61f-fb8b-4154-a6d4-b6f6fcf4d25e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "loss : 0.26978760957717896\n", + "compile_metrics : 0.26978760957717896\n", + "\n", + "Legitimate Transactions Detected (True Negatives): 53944\n", + "Legitimate Transactions Incorrectly Detected (False Positives): 2906\n", + "Fraudulent Transactions Missed (False Negatives): 9\n", + "Fraudulent Transactions Detected (True Positives): 103\n", + "Total Fraudulent Transactions: 112\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "resampled_results = resampled_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -1677,9 +3499,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "fye_CiuYrZ1U" - }, - "outputs": [], + "id": "fye_CiuYrZ1U", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "a4d4cbc5-5cad-41ee-989d-1f633bc9e2f9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -1696,25 +3534,41 @@ "id": "vayGnv0VOe_v" }, "source": [ - "### Plot the AUPRC\r\n" + "### Plot the AUPRC\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "wgWXQ8aeOhCZ" - }, - "outputs": [], + "id": "wgWXQ8aeOhCZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 850 + }, + "outputId": "e27d71e0-0901-442d-d6fe-3e969f3cf51c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ - "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\r\n", - "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\r\n", - "\r\n", - "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\r\n", - "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\r\n", - "\r\n", - "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\r\n", - "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\r\n", + "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", + "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", + "\n", + "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n", + "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n", + "\n", + "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n", + "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n", "plt.legend(loc='lower right');" ] }, @@ -1732,9 +3586,7 @@ ], "metadata": { "colab": { - "collapsed_sections": [], - "name": "imbalanced_data.ipynb", - "toc_visible": true + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -1743,4 +3595,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 0675307b1e40355f789c3cbd3e93f8f3109b2f4e Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Fri, 3 May 2024 09:55:08 +0800 Subject: [PATCH 55/85] Support spaces in license headers When running `pyink` (black) on a notebook with a license, it adds a space. We should permit this. --- tools/tensorflow_docs/tools/nbfmt/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/tensorflow_docs/tools/nbfmt/__main__.py b/tools/tensorflow_docs/tools/nbfmt/__main__.py index f09b0c27192..b806d093a25 100644 --- a/tools/tensorflow_docs/tools/nbfmt/__main__.py +++ b/tools/tensorflow_docs/tools/nbfmt/__main__.py @@ -237,7 +237,7 @@ def update_license_cells(data: Dict[str, Any]) -> None: data: object representing a parsed JSON notebook. """ # This pattern in Apache and MIT license boilerplate. - license_re = re.compile(r"#@title.*License") + license_re = re.compile(r"#\s?@title.*License") for idx, cell in enumerate(data["cells"]): src_text = "".join(cell["source"]) From 397701037d4332f421066f3fe19754c6d1de6a5f Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Fri, 3 May 2024 16:49:03 +0800 Subject: [PATCH 56/85] [nblint] Support spaces in license headers --- tools/tensorflow_docs/tools/nblint/style/tensorflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py index 3b77a169919..49fc9dc1025 100644 --- a/tools/tensorflow_docs/tools/nblint/style/tensorflow.py +++ b/tools/tensorflow_docs/tools/nblint/style/tensorflow.py @@ -56,7 +56,7 @@ def copyright_check(args): return any(re.search(pattern, cell_source) for pattern in copyrights_re) -license_re = re.compile("#@title Licensed under the Apache License") +license_re = re.compile("#\s?@title Licensed under the Apache License") @lint( From 623441822aec3c36d872577993f1e92eaf0a25f1 Mon Sep 17 00:00:00 2001 From: Yang Chen Date: Mon, 6 May 2024 14:18:06 -0700 Subject: [PATCH 57/85] Internal change ValueError: PiperOrigin-RevId: 631183129 --- site/en/tutorials/generative/style_transfer.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/site/en/tutorials/generative/style_transfer.ipynb b/site/en/tutorials/generative/style_transfer.ipynb index 06469c33c91..c8f1376624e 100644 --- a/site/en/tutorials/generative/style_transfer.ipynb +++ b/site/en/tutorials/generative/style_transfer.ipynb @@ -1110,10 +1110,9 @@ "\n", "try:\n", " from google.colab import files\n", - "except ImportError:\n", - " pass\n", - "else:\n", - " files.download(file_name)" + " files.download(file_name)\n", + "except (ImportError, AttributeError):\n", + " pass" ] }, { From 0cc42b470815d1d15cdaefd556568fed5508bfe4 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 7 May 2024 10:05:19 -0700 Subject: [PATCH 58/85] Redirect "install from source" to docker build readme. PiperOrigin-RevId: 631461253 --- site/en/install/_toc.yaml | 3 +- site/en/install/source.md | 547 -------------------------------------- 2 files changed, 2 insertions(+), 548 deletions(-) delete mode 100644 site/en/install/source.md diff --git a/site/en/install/_toc.yaml b/site/en/install/_toc.yaml index 26cdb270bb8..cbb1b28b08e 100644 --- a/site/en/install/_toc.yaml +++ b/site/en/install/_toc.yaml @@ -13,7 +13,8 @@ toc: path: /install/errors - heading: Build from source - title: Linux / macOS - path: /install/source + status: external + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles#readme - title: Windows path: /install/source_windows - title: SIG Build diff --git a/site/en/install/source.md b/site/en/install/source.md deleted file mode 100644 index 6a0aa08ed4b..00000000000 --- a/site/en/install/source.md +++ /dev/null @@ -1,547 +0,0 @@ -# Build from source - -Build a TensorFlow *pip* package from source and install it on Ubuntu Linux and -macOS. While the instructions might work for other systems, it is only tested -and supported for Ubuntu and macOS. - -Note: Well-tested, pre-built [TensorFlow packages](./pip.md) for Linux and macOS -systems are already provided. - -## Setup for Linux and macOS - -Install the following build tools to configure your development environment. - -### Install Python and the TensorFlow package dependencies - -
-
-

Ubuntu

-
-sudo apt install python3-dev python3-pip
-
-
-
-

macOS

-

Requires Xcode 9.2 or later.

-

Install using the Homebrew package manager:

-
-brew install python
-
-
-
- -Install the TensorFlow *pip* package dependencies (if using a virtual -environment, omit the `--user` argument): - -
-pip install -U --user pip
-
- -Note: A `pip` version >19.0 is required to install the TensorFlow 2 `.whl` -package. Additional required dependencies are listed in the -setup.py -file under `REQUIRED_PACKAGES`. - -### Install Bazel - -To build TensorFlow, you will need to install Bazel. -[Bazelisk](https://github.com/bazelbuild/bazelisk) is an easy way to install -Bazel and automatically downloads the correct Bazel version for TensorFlow. For -ease of use, add Bazelisk as the `bazel` executable in your `PATH`. - -If Bazelisk is not available, you can manually -[install Bazel](https://bazel.build/install). Make -sure to install the correct Bazel version from TensorFlow's -[.bazelversion](https://github.com/tensorflow/tensorflow/blob/master/.bazelversion) -file. - -### Install Clang (recommended, Linux only) - -Clang is a C/C++/Objective-C compiler that is compiled in C++ based on LLVM. It -is the default compiler to build TensorFlow starting with TensorFlow 2.13. The -current supported version is LLVM/Clang 17. - -[LLVM Debian/Ubuntu nightly packages](https://apt.llvm.org) provide an automatic -installation script and packages for manual installation on Linux. Make sure you -run the following command if you manually add llvm apt repository to your -package sources: - -
-sudo apt-get update && sudo apt-get install -y llvm-17 clang-17
-
- -Now that `/usr/lib/llvm-17/bin/clang` is the actual path to clang in this case. - -Alternatively, you can download and unpack the pre-built -[Clang + LLVM 17](https://github.com/llvm/llvm-project/releases/tag/llvmorg-17.0.2). - -Below is an example of steps you can take to set up the downloaded Clang + LLVM -17 binaries on Debian/Ubuntu operating systems: - -1. Change to the desired destination directory: `cd ` - -1. Load and extract an archive file...(suitable to your architecture): - 
-    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-17.0.2/clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
-    
-    tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
-    
-
- -1. Copy the extracted contents (directories and files) to `/usr` (you may need - sudo permissions, and the correct directory may vary by distribution). This - effectively installs Clang and LLVM, and adds it to the path. You should not - have to replace anything, unless you have a previous installation, in which - case you should replace the files: - 
-    cp -r clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04/* /usr
-
- -1. Check the obtained Clang + LLVM 17 binaries version: - 
-    clang --version
-
- -1. Now that `/usr/bin/clang` is the actual path to your new clang. You can run - the `./configure` script or manually set environment variables `CC` and - `BAZEL_COMPILER` to this path. - -### Install GPU support (optional, Linux only) - -There is *no* GPU support for macOS. - -Read the [GPU support](./pip.md) guide to install the drivers and additional -software required to run TensorFlow on a GPU. - -Note: It is easier to set up one of TensorFlow's GPU-enabled [Docker images](#docker_linux_builds). - -### Download the TensorFlow source code - -Use [Git](https://git-scm.com/){:.external} to clone the -[TensorFlow repository](https://github.com/tensorflow/tensorflow){:.external}: - -
-git clone https://github.com/tensorflow/tensorflow.git
-cd tensorflow
-
- -The repo defaults to the `master` development branch. You can also check out a -[release branch](https://github.com/tensorflow/tensorflow/releases){:.external} -to build: - -
-git checkout branch_name  # r2.2, r2.3, etc.
-
- - -## Configure the build - -TensorFlow builds are configured by the `.bazelrc` file in the repository's -root directory. The `./configure` or `./configure.py` scripts can be used to -adjust common settings. - -Please run the `./configure` script from the repository's root directory. This -script will prompt you for the location of TensorFlow dependencies and asks for -additional build configuration options (compiler flags, for example). Refer to -the _Sample session_ section for details. - -
-./configure
-
- -There is also a python version of this script, `./configure.py`. If using a -virtual environment, `python configure.py` prioritizes paths -within the environment, whereas `./configure` prioritizes paths outside -the environment. In both cases you can change the default. - -### Sample session - -The following shows a sample run of `./configure` script (your -session may differ): - - - -### Configuration options - -#### GPU support - -For [GPU support](./pip.md), set `cuda=Y` during configuration and specify the -versions of CUDA and cuDNN. If your system has multiple versions of CUDA or -cuDNN installed, explicitly set the version instead of relying on the default. -`./configure` creates symbolic links to your system's CUDA libraries—so if you -update your CUDA library paths, this configuration step must be run again before -building. - -#### Optimizations - -For compilation optimization flags, the default (`-march=native`) optimizes the -generated code for your machine's CPU type. However, if building TensorFlow for -a different CPU type, consider a more specific optimization flag. Check the -[GCC manual](https://gcc.gnu.org/onlinedocs/gcc-4.5.3/gcc/i386-and-x86_002d64-Options.html){:.external} -for examples. - -#### Preconfigured configurations - -There are some preconfigured build configs available that can be added to the -`bazel build` command, for example: - -* `--config=dbg` —Build with debug info. See - [CONTRIBUTING.md](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) - for details. -* `--config=mkl` —Support for the - [Intel® MKL-DNN](https://github.com/intel/mkl-dnn){:.external}. -* `--config=monolithic` —Configuration for a mostly static, monolithic build. - - -## Build and install the pip package - -#### Bazel build options - -Refer to the Bazel -[command-line reference](https://bazel.build/reference/command-line-reference) -for -[build options](https://bazel.build/reference/command-line-reference#build-options). - -Building TensorFlow from source can use a lot of RAM. If your system is -memory-constrained, limit Bazel's RAM usage with: `--local_ram_resources=2048`. - -The [official TensorFlow packages](./pip.md) are built with a Clang toolchain -that complies with the manylinux2014 package standard. - -### Build the package - -To build pip package, you need to specify `--repo_env=WHEEL_NAME` flag. -depending on the provided name, package will be created, e.g: - -To build tensorflow CPU package: -
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
-
- -To build tensorflow GPU package: -
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda
-
- -To build tensorflow TPU package: -
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_tpu --config=tpu
-
- -To build nightly package, set `tf_nightly` instead of `tensorflow`, e.g. -to build CPU nightly package: -
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly_cpu
-
- -As a result, generated wheel will be located in -
-bazel-bin/tensorflow/tools/pip_package/wheel_house/
-
- -### Install the package - -The filename of the generated `.whl` file depends on the TensorFlow version and -your platform. Use `pip install` to install the package, for example: - -
-pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
-
- -Success: TensorFlow is now installed. - - -## Docker Linux builds - -TensorFlow's Docker development images are an easy way to set up an environment -to build Linux packages from source. These images already contain the source -code and dependencies required to build TensorFlow. Go to the TensorFlow -[Docker guide](./docker.md) for installation instructions and the -[list of available image tags](https://hub.docker.com/r/tensorflow/tensorflow/tags/){:.external}. - -### CPU-only - -The following example uses the `:devel` image to build a CPU-only package from -the latest TensorFlow source code. Check the [Docker guide](./docker.md) for -available TensorFlow `-devel` tags. - -Download the latest development image and start a Docker container that you'll -use to build the *pip* package: - -
-docker pull tensorflow/tensorflow:devel
-docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
-    tensorflow/tensorflow:devel bash
-
-git pull  # within the container, download the latest source code
-
- -The above `docker run` command starts a shell in the `/tensorflow_src` -directory—the root of the source tree. It mounts the host's current directory in -the container's `/mnt` directory, and passes the host user's information to the -container through an environmental variable (used to set permissions—Docker can -make this tricky). - -Alternatively, to build a host copy of TensorFlow within a container, mount the -host source tree at the container's `/tensorflow` directory: - -
-docker run -it -w /tensorflow -v /path/to/tensorflow:/tensorflow -v $PWD:/mnt \
-    -e HOST_PERMS="$(id -u):$(id -g)" tensorflow/tensorflow:devel bash
-
- -With the source tree set up, build the TensorFlow package within the container's -virtual environment: - -1. Optional: Configure the build—this prompts the user to answer build - configuration questions. -2. Build the *pip* package. -3. Adjust the ownership permissions of the file for outside the container. - -
-./configure  # if necessary
-
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu --config=opt
-`
-chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
-
- -Install and verify the package within the container: - -
-pip uninstall tensorflow  # remove current version
-
-pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
-cd /tmp  # don't import from source directory
-python -c "import tensorflow as tf; print(tf.__version__)"
-
- -Success: TensorFlow is now installed. - -On your host machine, the TensorFlow *pip* package is in the current directory -(with host user permissions): -./tensorflow-version-tags.whl - -### GPU support - -Docker is the easiest way to build GPU support for TensorFlow since the *host* -machine only requires the -[NVIDIA® driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver){:.external} -(the *NVIDIA® CUDA® Toolkit* doesn't have to be installed). Refer to the -[GPU support guide](./pip.md) and the TensorFlow [Docker guide](./docker.md) to -set up [nvidia-docker](https://github.com/NVIDIA/nvidia-docker){:.external} -(Linux only). - -The following example downloads the TensorFlow `:devel-gpu` image and uses -`nvidia-docker` to run the GPU-enabled container. This development image is -configured to build a *pip* package with GPU support: - -
-docker pull tensorflow/tensorflow:devel-gpu
-docker run --gpus all -it -w /tensorflow -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
-    tensorflow/tensorflow:devel-gpu bash
-git pull  # within the container, download the latest source code
-
- -Then, within the container's virtual environment, build the TensorFlow package -with GPU support: - -
-./configure  # if necessary
-
-bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=opt
-
-chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
-
- -Install and verify the package within the container and check for a GPU: - -
-pip uninstall tensorflow  # remove current version
-
-pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
-cd /tmp  # don't import from source directory
-python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
-
- -Success: TensorFlow is now installed. - - - -## Tested build configurations - -### Linux - -#### CPU - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.0
tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.0
tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.0
tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.1
tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.0
tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.1
tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.0
tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.0
tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.0
tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
tensorflow-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
tensorflow-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.1
tensorflow-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.2
tensorflow-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.0
tensorflow-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
tensorflow-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
tensorflow-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.0
tensorflow-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.0
tensorflow-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.4
tensorflow-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
tensorflow-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
tensorflow-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2
tensorflow-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2
- -#### GPU - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.08.912.3
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.912.2
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.08.611.8
tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.08.111.2
tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.18.111.2
tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.08.111.2
tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.18.111.2
tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.08.011.0
tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.07.610.1
tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.07.610.1
tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.17.610.1
tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
tensorflow_gpu-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
tensorflow_gpu-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.17.410.0
tensorflow_gpu-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.27.410.0
tensorflow_gpu-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow_gpu-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.079
tensorflow_gpu-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow_gpu-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow_gpu-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow_gpu-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.468
tensorflow_gpu-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.568
tensorflow_gpu-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.55.18
tensorflow_gpu-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18
tensorflow_gpu-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18
- -### macOS - -#### CPU - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang from xcode 13.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
tensorflow-2.12.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
tensorflow-2.11.03.7-3.10Clang from xcode 10.14Bazel 5.3.0
tensorflow-2.10.03.7-3.10Clang from xcode 10.14Bazel 5.1.1
tensorflow-2.9.03.7-3.10Clang from xcode 10.14Bazel 5.0.0
tensorflow-2.8.03.7-3.10Clang from xcode 10.14Bazel 4.2.1
tensorflow-2.7.03.7-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.6.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.5.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.4.03.6-3.8Clang from xcode 10.3Bazel 3.1.0
tensorflow-2.3.03.5-3.8Clang from xcode 10.1Bazel 3.1.0
tensorflow-2.2.03.5-3.8Clang from xcode 10.1Bazel 2.0.0
tensorflow-2.1.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
tensorflow-1.15.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
tensorflow-1.14.02.7, 3.3-3.7Clang from xcodeBazel 0.24.1
tensorflow-1.13.12.7, 3.3-3.7Clang from xcodeBazel 0.19.2
tensorflow-1.12.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.11.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.10.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.9.02.7, 3.3-3.6Clang from xcodeBazel 0.11.0
tensorflow-1.8.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
tensorflow-1.7.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
tensorflow-1.6.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
tensorflow-1.5.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
tensorflow-1.4.02.7, 3.3-3.6Clang from xcodeBazel 0.5.4
tensorflow-1.3.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
tensorflow-1.2.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
tensorflow-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2
tensorflow-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2
- -#### GPU - - - - - -
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow_gpu-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18
tensorflow_gpu-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18
From 7175b2b30abcbdbf62937fd84c875c607e371858 Mon Sep 17 00:00:00 2001 From: Vadym Matsishevskyi Date: Thu, 9 May 2024 11:27:25 -0700 Subject: [PATCH 59/85] Automated rollback of commit 0cc42b470815d1d15cdaefd556568fed5508bfe4 PiperOrigin-RevId: 632215280 --- site/en/install/_toc.yaml | 3 +- site/en/install/source.md | 547 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 548 insertions(+), 2 deletions(-) create mode 100644 site/en/install/source.md diff --git a/site/en/install/_toc.yaml b/site/en/install/_toc.yaml index cbb1b28b08e..26cdb270bb8 100644 --- a/site/en/install/_toc.yaml +++ b/site/en/install/_toc.yaml @@ -13,8 +13,7 @@ toc: path: /install/errors - heading: Build from source - title: Linux / macOS - status: external - path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles#readme + path: /install/source - title: Windows path: /install/source_windows - title: SIG Build diff --git a/site/en/install/source.md b/site/en/install/source.md new file mode 100644 index 00000000000..6a0aa08ed4b --- /dev/null +++ b/site/en/install/source.md @@ -0,0 +1,547 @@ +# Build from source + +Build a TensorFlow *pip* package from source and install it on Ubuntu Linux and +macOS. While the instructions might work for other systems, it is only tested +and supported for Ubuntu and macOS. + +Note: Well-tested, pre-built [TensorFlow packages](./pip.md) for Linux and macOS +systems are already provided. + +## Setup for Linux and macOS + +Install the following build tools to configure your development environment. + +### Install Python and the TensorFlow package dependencies + +
+
+

Ubuntu

+
+sudo apt install python3-dev python3-pip
+
+
+
+

macOS

+

Requires Xcode 9.2 or later.

+

Install using the Homebrew package manager:

+
+brew install python
+
+
+
+ +Install the TensorFlow *pip* package dependencies (if using a virtual +environment, omit the `--user` argument): + +
+pip install -U --user pip
+
+ +Note: A `pip` version >19.0 is required to install the TensorFlow 2 `.whl` +package. Additional required dependencies are listed in the +setup.py +file under `REQUIRED_PACKAGES`. + +### Install Bazel + +To build TensorFlow, you will need to install Bazel. +[Bazelisk](https://github.com/bazelbuild/bazelisk) is an easy way to install +Bazel and automatically downloads the correct Bazel version for TensorFlow. For +ease of use, add Bazelisk as the `bazel` executable in your `PATH`. + +If Bazelisk is not available, you can manually +[install Bazel](https://bazel.build/install). Make +sure to install the correct Bazel version from TensorFlow's +[.bazelversion](https://github.com/tensorflow/tensorflow/blob/master/.bazelversion) +file. + +### Install Clang (recommended, Linux only) + +Clang is a C/C++/Objective-C compiler that is compiled in C++ based on LLVM. It +is the default compiler to build TensorFlow starting with TensorFlow 2.13. The +current supported version is LLVM/Clang 17. + +[LLVM Debian/Ubuntu nightly packages](https://apt.llvm.org) provide an automatic +installation script and packages for manual installation on Linux. Make sure you +run the following command if you manually add llvm apt repository to your +package sources: + +
+sudo apt-get update && sudo apt-get install -y llvm-17 clang-17
+
+ +Now that `/usr/lib/llvm-17/bin/clang` is the actual path to clang in this case. + +Alternatively, you can download and unpack the pre-built +[Clang + LLVM 17](https://github.com/llvm/llvm-project/releases/tag/llvmorg-17.0.2). + +Below is an example of steps you can take to set up the downloaded Clang + LLVM +17 binaries on Debian/Ubuntu operating systems: + +1. Change to the desired destination directory: `cd ` + +1. Load and extract an archive file...(suitable to your architecture): + 
+    wget https://github.com/llvm/llvm-project/releases/download/llvmorg-17.0.2/clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
+    
+    tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
+    
+
+ +1. Copy the extracted contents (directories and files) to `/usr` (you may need + sudo permissions, and the correct directory may vary by distribution). This + effectively installs Clang and LLVM, and adds it to the path. You should not + have to replace anything, unless you have a previous installation, in which + case you should replace the files: + 
+    cp -r clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04/* /usr
+
+ +1. Check the obtained Clang + LLVM 17 binaries version: + 
+    clang --version
+
+ +1. Now that `/usr/bin/clang` is the actual path to your new clang. You can run + the `./configure` script or manually set environment variables `CC` and + `BAZEL_COMPILER` to this path. + +### Install GPU support (optional, Linux only) + +There is *no* GPU support for macOS. + +Read the [GPU support](./pip.md) guide to install the drivers and additional +software required to run TensorFlow on a GPU. + +Note: It is easier to set up one of TensorFlow's GPU-enabled [Docker images](#docker_linux_builds). + +### Download the TensorFlow source code + +Use [Git](https://git-scm.com/){:.external} to clone the +[TensorFlow repository](https://github.com/tensorflow/tensorflow){:.external}: + +
+git clone https://github.com/tensorflow/tensorflow.git
+cd tensorflow
+
+ +The repo defaults to the `master` development branch. You can also check out a +[release branch](https://github.com/tensorflow/tensorflow/releases){:.external} +to build: + +
+git checkout branch_name  # r2.2, r2.3, etc.
+
+ + +## Configure the build + +TensorFlow builds are configured by the `.bazelrc` file in the repository's +root directory. The `./configure` or `./configure.py` scripts can be used to +adjust common settings. + +Please run the `./configure` script from the repository's root directory. This +script will prompt you for the location of TensorFlow dependencies and asks for +additional build configuration options (compiler flags, for example). Refer to +the _Sample session_ section for details. + +
+./configure
+
+ +There is also a python version of this script, `./configure.py`. If using a +virtual environment, `python configure.py` prioritizes paths +within the environment, whereas `./configure` prioritizes paths outside +the environment. In both cases you can change the default. + +### Sample session + +The following shows a sample run of `./configure` script (your +session may differ): + + + +### Configuration options + +#### GPU support + +For [GPU support](./pip.md), set `cuda=Y` during configuration and specify the +versions of CUDA and cuDNN. If your system has multiple versions of CUDA or +cuDNN installed, explicitly set the version instead of relying on the default. +`./configure` creates symbolic links to your system's CUDA libraries—so if you +update your CUDA library paths, this configuration step must be run again before +building. + +#### Optimizations + +For compilation optimization flags, the default (`-march=native`) optimizes the +generated code for your machine's CPU type. However, if building TensorFlow for +a different CPU type, consider a more specific optimization flag. Check the +[GCC manual](https://gcc.gnu.org/onlinedocs/gcc-4.5.3/gcc/i386-and-x86_002d64-Options.html){:.external} +for examples. + +#### Preconfigured configurations + +There are some preconfigured build configs available that can be added to the +`bazel build` command, for example: + +* `--config=dbg` —Build with debug info. See + [CONTRIBUTING.md](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) + for details. +* `--config=mkl` —Support for the + [Intel® MKL-DNN](https://github.com/intel/mkl-dnn){:.external}. +* `--config=monolithic` —Configuration for a mostly static, monolithic build. + + +## Build and install the pip package + +#### Bazel build options + +Refer to the Bazel +[command-line reference](https://bazel.build/reference/command-line-reference) +for +[build options](https://bazel.build/reference/command-line-reference#build-options). + +Building TensorFlow from source can use a lot of RAM. If your system is +memory-constrained, limit Bazel's RAM usage with: `--local_ram_resources=2048`. + +The [official TensorFlow packages](./pip.md) are built with a Clang toolchain +that complies with the manylinux2014 package standard. + +### Build the package + +To build pip package, you need to specify `--repo_env=WHEEL_NAME` flag. +depending on the provided name, package will be created, e.g: + +To build tensorflow CPU package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
+
+ +To build tensorflow GPU package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda
+
+ +To build tensorflow TPU package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_tpu --config=tpu
+
+ +To build nightly package, set `tf_nightly` instead of `tensorflow`, e.g. +to build CPU nightly package: +
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly_cpu
+
+ +As a result, generated wheel will be located in +
+bazel-bin/tensorflow/tools/pip_package/wheel_house/
+
+ +### Install the package + +The filename of the generated `.whl` file depends on the TensorFlow version and +your platform. Use `pip install` to install the package, for example: + +
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
+
+ +Success: TensorFlow is now installed. + + +## Docker Linux builds + +TensorFlow's Docker development images are an easy way to set up an environment +to build Linux packages from source. These images already contain the source +code and dependencies required to build TensorFlow. Go to the TensorFlow +[Docker guide](./docker.md) for installation instructions and the +[list of available image tags](https://hub.docker.com/r/tensorflow/tensorflow/tags/){:.external}. + +### CPU-only + +The following example uses the `:devel` image to build a CPU-only package from +the latest TensorFlow source code. Check the [Docker guide](./docker.md) for +available TensorFlow `-devel` tags. + +Download the latest development image and start a Docker container that you'll +use to build the *pip* package: + +
+docker pull tensorflow/tensorflow:devel
+docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
+    tensorflow/tensorflow:devel bash
+
+git pull  # within the container, download the latest source code
+
+ +The above `docker run` command starts a shell in the `/tensorflow_src` +directory—the root of the source tree. It mounts the host's current directory in +the container's `/mnt` directory, and passes the host user's information to the +container through an environmental variable (used to set permissions—Docker can +make this tricky). + +Alternatively, to build a host copy of TensorFlow within a container, mount the +host source tree at the container's `/tensorflow` directory: + +
+docker run -it -w /tensorflow -v /path/to/tensorflow:/tensorflow -v $PWD:/mnt \
+    -e HOST_PERMS="$(id -u):$(id -g)" tensorflow/tensorflow:devel bash
+
+ +With the source tree set up, build the TensorFlow package within the container's +virtual environment: + +1. Optional: Configure the build—this prompts the user to answer build + configuration questions. +2. Build the *pip* package. +3. Adjust the ownership permissions of the file for outside the container. + +
+./configure  # if necessary
+
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu --config=opt
+`
+chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
+
+ +Install and verify the package within the container: + +
+pip uninstall tensorflow  # remove current version
+
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
+cd /tmp  # don't import from source directory
+python -c "import tensorflow as tf; print(tf.__version__)"
+
+ +Success: TensorFlow is now installed. + +On your host machine, the TensorFlow *pip* package is in the current directory +(with host user permissions): +./tensorflow-version-tags.whl + +### GPU support + +Docker is the easiest way to build GPU support for TensorFlow since the *host* +machine only requires the +[NVIDIA® driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver){:.external} +(the *NVIDIA® CUDA® Toolkit* doesn't have to be installed). Refer to the +[GPU support guide](./pip.md) and the TensorFlow [Docker guide](./docker.md) to +set up [nvidia-docker](https://github.com/NVIDIA/nvidia-docker){:.external} +(Linux only). + +The following example downloads the TensorFlow `:devel-gpu` image and uses +`nvidia-docker` to run the GPU-enabled container. This development image is +configured to build a *pip* package with GPU support: + +
+docker pull tensorflow/tensorflow:devel-gpu
+docker run --gpus all -it -w /tensorflow -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
+    tensorflow/tensorflow:devel-gpu bash
+git pull  # within the container, download the latest source code
+
+ +Then, within the container's virtual environment, build the TensorFlow package +with GPU support: + +
+./configure  # if necessary
+
+bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=opt
+
+chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
+
+ +Install and verify the package within the container and check for a GPU: + +
+pip uninstall tensorflow  # remove current version
+
+pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
+cd /tmp  # don't import from source directory
+python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
+
+ +Success: TensorFlow is now installed. + + + +## Tested build configurations + +### Linux + +#### CPU + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.0
tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.0
tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.0
tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.1
tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.0
tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.1
tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.2
tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.0
tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.0
tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.0
tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
tensorflow-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
tensorflow-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.1
tensorflow-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.2
tensorflow-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
tensorflow-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.0
tensorflow-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
tensorflow-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
tensorflow-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.0
tensorflow-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.0
tensorflow-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.4
tensorflow-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
tensorflow-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
tensorflow-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2
tensorflow-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2
+ +#### GPU + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow-2.16.13.9-3.12Clang 17.0.6Bazel 6.5.08.912.3
tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.912.2
tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.08.611.8
tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.08.111.2
tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.18.111.2
tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.08.111.2
tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.18.111.2
tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.08.011.0
tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.07.610.1
tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.07.610.1
tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.17.610.1
tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
tensorflow_gpu-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
tensorflow_gpu-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.17.410.0
tensorflow_gpu-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.27.410.0
tensorflow_gpu-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
tensorflow_gpu-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow_gpu-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.079
tensorflow_gpu-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow_gpu-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow_gpu-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow_gpu-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.468
tensorflow_gpu-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.568
tensorflow_gpu-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.55.18
tensorflow_gpu-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18
tensorflow_gpu-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18
+ +### macOS + +#### CPU + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VersionPython versionCompilerBuild tools
tensorflow-2.16.13.9-3.12Clang from xcode 13.6Bazel 6.5.0
tensorflow-2.15.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.14.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
tensorflow-2.13.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
tensorflow-2.12.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
tensorflow-2.11.03.7-3.10Clang from xcode 10.14Bazel 5.3.0
tensorflow-2.10.03.7-3.10Clang from xcode 10.14Bazel 5.1.1
tensorflow-2.9.03.7-3.10Clang from xcode 10.14Bazel 5.0.0
tensorflow-2.8.03.7-3.10Clang from xcode 10.14Bazel 4.2.1
tensorflow-2.7.03.7-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.6.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.5.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
tensorflow-2.4.03.6-3.8Clang from xcode 10.3Bazel 3.1.0
tensorflow-2.3.03.5-3.8Clang from xcode 10.1Bazel 3.1.0
tensorflow-2.2.03.5-3.8Clang from xcode 10.1Bazel 2.0.0
tensorflow-2.1.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
tensorflow-2.0.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
tensorflow-1.15.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
tensorflow-1.14.02.7, 3.3-3.7Clang from xcodeBazel 0.24.1
tensorflow-1.13.12.7, 3.3-3.7Clang from xcodeBazel 0.19.2
tensorflow-1.12.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.11.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.10.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
tensorflow-1.9.02.7, 3.3-3.6Clang from xcodeBazel 0.11.0
tensorflow-1.8.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
tensorflow-1.7.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
tensorflow-1.6.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
tensorflow-1.5.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
tensorflow-1.4.02.7, 3.3-3.6Clang from xcodeBazel 0.5.4
tensorflow-1.3.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
tensorflow-1.2.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
tensorflow-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2
tensorflow-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2
+ +#### GPU + + + + + +
VersionPython versionCompilerBuild toolscuDNNCUDA
tensorflow_gpu-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18
tensorflow_gpu-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18
From 75b2672b5bed8ca0995663536db84bd9a39b8896 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 15 May 2024 01:54:35 -0700 Subject: [PATCH 60/85] Update email address for support requests. PiperOrigin-RevId: 633857848 --- site/en/hub/portability_and_deletion.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/site/en/hub/portability_and_deletion.md b/site/en/hub/portability_and_deletion.md index 30341306bea..67fa401d161 100644 --- a/site/en/hub/portability_and_deletion.md +++ b/site/en/hub/portability_and_deletion.md @@ -1,14 +1,14 @@ ## I want to see what I’ve uploaded to TensorFlow Hub. Can I get a copy of my data? -Yes. If you’d like the TensorFlow Hub team to **send you a copy** of all of the -data you have uploaded, please send us an email at [hi-tf-hub@google.com](mailto:hi-tf-hub@google.com) +Yes. If you’d like the Kaggle Team to **send you a copy** of all of the +data you have uploaded, please send us an email at [support@kaggle.com](mailto:support@kaggle.com) and we’ll respond as soon as possible. ## How do I delete what I’ve uploaded to TensorFlow Hub? Similarly, if you’d like us to **delete or remove content**, please send us an -email at [hi-tf-hub@google.com](mailto:hi-tf-hub@google.com) and we’ll delete +email at [support@kaggle.com](mailto:support@kaggle.com) and we’ll delete all copies that we have and stop serving it on tfhub.dev. Please note: * Because TensorFlow Hub is an open-source platform, copies of your assets may From c5941e0f6da89e375ae7aa22c1d4206666c2036e Mon Sep 17 00:00:00 2001 From: Prianka Liz Kariat Date: Thu, 30 May 2024 08:03:59 +0530 Subject: [PATCH 61/85] Cleared outputs in imbalanced_data.ipy --- .../structured_data/imbalanced_data.ipynb | 2096 +---------------- 1 file changed, 120 insertions(+), 1976 deletions(-) diff --git a/site/en/tutorials/structured_data/imbalanced_data.ipynb b/site/en/tutorials/structured_data/imbalanced_data.ipynb index bc19774d648..b261d12f429 100644 --- a/site/en/tutorials/structured_data/imbalanced_data.ipynb +++ b/site/en/tutorials/structured_data/imbalanced_data.ipynb @@ -153,428 +153,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "pR_SnbMArXr7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 233 - }, - "outputId": "b02780f2-6d2b-44af-abec-82c62941dac1" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Time V1 V2 V3 V4 V5 V6 V7 \\\n", - "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", - "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", - "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", - "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", - "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", - "\n", - " V8 V9 ... V21 V22 V23 V24 V25 \\\n", - "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n", - "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n", - "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n", - "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n", - "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n", - "\n", - " V26 V27 V28 Amount Class \n", - "0 -0.189115 0.133558 -0.021053 149.62 0 \n", - "1 0.125895 -0.008983 0.014724 2.69 0 \n", - "2 -0.139097 -0.055353 -0.059752 378.66 0 \n", - "3 -0.221929 0.062723 0.061458 123.50 0 \n", - "4 0.502292 0.219422 0.215153 69.99 0 \n", - "\n", - "[5 rows x 31 columns]" - ], - "text/html": [ - "\n", - "
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} - }, - "metadata": {}, - "execution_count": 8 - } - ], + "id": "-fgdQgmwUFuj" + }, + "outputs": [], "source": [ "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()" ] @@ -1014,24 +188,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "HCJFrtuY2iLF", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "92e418bb-4cd9-4eef-85fe-fe893081343a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Examples:\n", - " Total: 284807\n", - " Positive: 492 (0.17% of total)\n", - "\n" - ] - } - ], + "id": "HCJFrtuY2iLF" + }, + "outputs": [], "source": [ "neg, pos = np.bincount(raw_df['Class'])\n", "total = neg + pos\n", @@ -1122,23 +281,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "96520cffee66", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "737f82dc-e318-48c2-f22e-f7730d6c22fd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Average class probability in training set: 0.0017\n", - "Average class probability in validation set: 0.0016\n", - "Average class probability in test set: 0.0020\n" - ] - } - ], + "id": "96520cffee66" + }, + "outputs": [], "source": [ "print(f'Average class probability in training set: {train_labels.mean():.4f}')\n", "print(f'Average class probability in validation set: {val_labels.mean():.4f}')\n", @@ -1163,26 +308,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "IO-qEUmJ5JQg", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fc994363-63a9-4b84-ce5f-ea84bc8d1fbf" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Training labels shape: (182276, 1)\n", - "Validation labels shape: (45569, 1)\n", - "Test labels shape: (56962, 1)\n", - "Training features shape: (182276, 29)\n", - "Validation features shape: (45569, 29)\n", - "Test features shape: (56962, 29)\n" - ] - } - ], + "id": "IO-qEUmJ5JQg" + }, + "outputs": [], "source": [ "scaler = StandardScaler()\n", "train_features = scaler.fit_transform(train_features)\n", @@ -1233,35 +361,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "raK7hyjd_vf6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "4e764b7b-2627-493f-8174-f9421797a0f5" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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EvfrVr1a/+93v6sq4r+O+ffvqbo9Kk0BE8yOU4kxBol4ipcTq1avxute9Dtdff31L6x4fH8dZH/8aBkbXwK4UUZyZwNDKg0J/BpDsvqlxXH/RCzE2NoaxsbGWzjkiIlpqnONE1MVKpVLDPKibb74Z4+PjdVuutIJSqj1L1TWBiz9/D/7y37+DHTt2cFUXEfU0znEi6mI///nP8Z73vAdvfOMbsXLlStx33334j//4Dxx77LF44xvf2NK2JiYm8NZPfhMi3drl+QCQHhyGXSnibdd9D7ddNtZ0tR0RUbdi4ETUxQ4//HBs2LABn/jEJ7yJxmeffTY+8pGPxO6ptlDGwDBsu3ESc6ukBuefo4qIqJswcCLqYocffji+/vWvt7UN9xLdUmSUdttyL9etWLGCc56IqKcwcCJaxpRS9ekH2jjaBABmqYCLP38PpFmEVTHxmbe/wJswzonjRNQLGDgRLWPuvKbh9ZuRGhCwZto/6uTMdzJgWRM4/9++gcGV62CkdNz8jhdy7hMRdT0GTkTLnDEw3Lm2B3NIDw5DT/GjiIh6Az+tiJahpZzXRETUTxg4ES1DExMTOPvT312SeU1JuIEc5zkRUbdjAkyiZcYNUlKDI0h18DKdnzk3i7dd9z2OgBFR12PgRLTMuBPCbbPzI01+zPFERL2Al+qIlgF//qSJiYmOTggnIuplDJyIlgH/nKbS9BSMwVynu0RE1JMYOBEtE6nBESglYFnddYnOxaziRNQLGDgRUVfwZxW3TQu3XfZ6JsQkoq7DyeFEfc4dyekF6cFhpAaGYQzk6kafiIi6BQMnoj6llML4+Dh27NjRlavo4pilAtMTEFFX4qU6oj4SXD337lvvR2VuFiI90OmuzRvTExBRN2LgRNRHgqvnRjccuWSb9xIRLQcMnIj6TLevnkvKPzeLW7EQUbfgHCeiPtFLk8CTcFfZnf3p7/bV8yKi3sbAiahPdOtWKouRHhxGanCk090gIvIwcCLqI/24lUowMSYRUScxcCLqYW7KgfHx8b4NLMy5WVx47XexY8eOvn6eRNQbODmcqIe5q+iUUvi7l2/udHfaRxO4+PP3wEjpuPkdL2RGcSLqGI44EfU4Zw6Qhvfc+GPYdv/MbwpKDw7DGBjmZTsi6igGTkR9whjMdboLbWfOzTKjOBF1FAMnoh7Vb+kHkvJnFHfneHEEioiWCgMnoh7Ty3vQtYIbMLqT4t/08a8uywCSiDqDk8OJeox/W5Ve3INusdzEmLqh4e9evhmpwf5LwUBE3YsjTkQ9KDU4glQf5mxKKj04DG9CfI9vLUNEvYWBE1EPWa7zmqIshwnxRNRdGDgR9Qil1LKd1xTHP+eJk8SJqN0YOBH1CHcvuuU4rykONwMmoqXEyeFEPcQYGO7rJJcLlR4chp7ixxkRtR9HnIi6nJt+gKMp8bgZMBEtBX5FI+pi7rymv77lPlTmZjnaFMPdDPj6i4DR0VEAwIoVKyCE6GzHiKivMHAi6kLu6Ik7r2l4/WakBgSsGY46xapuBizNImzTwm2XvZ4bAhNRSzFwIupCyz3J5WKkB4dhVwxoKbPTXSGiPsQ5TkRdxh1tWu5JLheLaQqIqB0YOBF1GffyHHM1LY6bpuAv//072LFjB4MnImoJBk5EXcjgSFNLuFuzXHjtdxk8EVFLMHAi6gJKKRw4cAAHDhxg2oF20IQXPPHSHREtBieHE3WBiYkJvOEfvohMfo2zIoxpB1qvuuLOSOm4+R0v5Go7IloQBk5EHeJP2DgxMQFjYNhbEca0A+2RHhyGZujeqJMQAmNjY8z1RESJMXAi6hB/yoHS9BSMwVynu7QsmHOzOP/fvoHBleugGxo+8eYTsGnTJgZPRJQI5zgRdUAw5QCDpqVlDOa8ieNvu+57nFdGRIlxxIloCYVlBKfOMgZyvHRHRIkxcCJaQswI3n3MUiH00h3gvF4MpIjIj5fqiNpMKYXx8XEcOHAA4+PjzAjehfyX7ty0BTt27MCbPv5VXsYjojoccSJqI6UUduzYgb++5T5vEvjohiM73S2K49soWOOoIBEFMHAiaiP/XKbUgIBlMT9TL/DSQpgVL2WEOwdqxYoVvHRHtIwxcCJqE39+JupN7n530iw6KSOyg7j+ohdibGzMm/vkvs6jo6OYnJwEAM6LIupjDJyIWiy4co6TwHubN/pUHS28+PP31E0in5iYwFkfuxMfecMWXPltZz885oYi6l8MnIhaIJgF/N233s+Vc33KCaSKuPDa7+L6i6o3Cg3vufHHGN1wZN19/pEpoLZQAEBD6gP3vcPRKqLuxsCJaJGiJoCnBgS3Tulnvknktm3VJzGt3qcbGv7tTc+qu6zn7knovw9w5sO983M/wi3vfQ330SPqYssucBofH+c3Olq04AjThZ/6b04AX4bi9hZ0R6b8OaL+7uWbfXsS1u5z51ANrFjjpT/g5xS5ny8AGEx3kWUXOL3p41/lNzpakKjLcdxnjuK4OaLsShHvufHHde+V2n3OHCp3MnpUIk4A3qU+ru7rf27CXAD4xgff2OHekGvZBU6pQa5womT881EANARL3uU4jjBRQkkC7LA5VBd99ge49q1nAICz4MDI4vqLXojR0VEvTQKAup8BBlf9IDU40ukuUMCyC5yI/MPfo6OjdZmh/Sce9xJcJr/Gu5TCYImWjH8OlZTez+6CA3+aBP/lPvdnq2LiM29/gTdSBdQCqbhJ6kQUj4ETdUSzFUTB4MbNj+P+7CYkBBD6bTvuPnfkSCmFv3v5Zrz3Cz+tC478J6HgpRSipeSfQxWcT+V/X4b/PFE3h8qqmN4olf9LQXCSetzvEVCba8MVgLRcLavASSmF4sRe/PGPf8T09HSnu7OsTU5O4l2fuQtb3/YSjI6Oht5/6ed/CgC48s+OxYe//mDdz2axgNLcDABgML8a0iyhNDdT93Pcffn1GyHNEi7a+hukMjno2TlIswSrUoJV9v1cKUHTdOdxM9ORPwNY0H2tLsc6eru/7XjO7vu5NDON8z/xTe93wH3fV2ZLePM/3JTo98iWEv9+0csAIPb3l1pjcnISc+O7AQDT09MYHh5moNoFhHK/UiwD09PTyOfzne4GERHRvE1NTWFkhHOeOm1ZBU5KKczMzHS6G4lMT09jw4YNePzxx/mL0kI8ru3B49oePK7t04vHliNO3WFZXaoTQvTML4hrZGSk5/rcC3hc24PHtT14XNuHx5bmS+t0B4iIiIh6BQMnIiIiooQYOHWpTCaDK6+8EplMptNd6Ss8ru3B49oePK7tw2NLC7WsJocTERERLQZHnIiIiIgSYuBERERElBADJyIiIqKEGDgRERERJcTAiYiIiCghBk5ERERECTFwIiIiIkpoWQVOSilMT0+DqauIiGg54Hmv9ZZV4DQzM4N8Po8n909huixhSb6RiIiof7nnvZdd+QVMTEx0ujt9YVkFTkRERESLsWwDJwFAE+DwJRER9b3PXHAaRkdHO92NvmB0ugOdkNaBobTodDeIiIiWxNjYGITgea8VlmXglNGdNw/fRERERDQfy/JSnRCCQRMRERHN27IMnIiIiIgWgoETERERUUIMnIiIiIgSYuBERERElBADJyIiIqKElmXgxJyXREREtBDLMnByKcUgioiIiJJbloGTAsD9fYmIiGi+lmXg5Mc8mERERJTUsg+ciIiIiJJi4ERERESUEAMnIiIiooSWfeDEVXVERESU1LIMnIT7hxPDiYiIaB6WZeAE1IImBk9ERESU1LIMnBgsERER0UIsy8CJiIiIaCEYOBERERElxMCJiIiIKCEGTkREREQJMXAiIiIiSmhZBk5KMfElERERzd/yDJw63QEiIiLqScsycAKc4ImjTkRERDQfyzZwIiIiIpovBk5ERERECTFwIiIiIkqIgRMRERFRQss2cHL3+eUEcSIiIkrK6HQHOkGrRk1CxJcjIiIi8lu+I04MmoiIiGielm3gRERERDRfDJyIiIiIEmLgRERERJRQzwZOH/nIRyCEwLvf/e5Od4WIiIiWiZ4MnO655x5cd911OO644zrdFSLqMkwxQkTt1HOB0+zsLN7ylrfg+uuvx9jYWKe7Q0Qt4AY7wX8XUof7c1gd7u2LaYeIlreeC5wuvvhivPKVr8SLXvSipmXL5TKmp6fr/hBRdwgGL1IBqvpv2P3zqUMFytQFVZh/O0S9gue99uupwOmWW27Bfffdh6uvvjpR+auvvhr5fN77s2HDhjb3kIjmQ4UEOkDttvkEM2F1SN+NblAV9hgGTdQveN5rP6FUb3xkPP744zjxxBNx9913e3ObzjjjDGzZsgXXXHNN6GPK5TLK5bL3/+npaWzYsAFTU1MYGRlZim4TUQSZ4JNHID5ZrQoJhhZCY0Jc6hM877VfzwROX/3qV/Ha174Wuq57t9m2DSEENE1DuVyuuy/M9PQ08vk830BEXYCBE1H78bzXej2zV90LX/hC/OY3v6m77bzzzsPTn/50/M3f/E3ToImIiIhosXomcBoeHsaxxx5bd1sul8PKlSsbbiciIiJqh54JnIiov7hXx+IutbkTxOMu2SWppxn3smGzS4NhvFV5vv5wE3Gi/tXTgdMPfvCDTneBiBZJE83nKsUFUELU7lvsfCfl/ZUs+Anrd5Jgj4h6V0+lIyCi/iHEAkZ3UJ86wH28+28rJnknTYWQNNgjov7CwImIOk6I2iW32HJN6miVZlW1ajUfEfUeBk5ERCF4mY2IwjBwIiIiIkqIgRMRERFRQgyciKjPtGL2UatmMMXXw8njRL2HgRMRdYWWhDtKARCI20nKvS+2TNP7m9eBBO34ihFRj2DgREQd5+Y9alrOVz4YcHgBiu8O1eTnYFCjlIJSClI5STHd/wfLiCR1+7rSWEfjyjwGUES9oacTYBJR7/IHCvOJGfwZup16GgOVutsjApzgbaK6jM7NIq6qP+sa6gKl+bTjJcMUtTrcdrwg0C3jq5cr+oi6FwMnIuoI56Lawi/RKQBSKi9z+IL7Ue2Ikiq0L7YEpFDQsIi+KsBWCpoQXmDWUAbJRt2IqLN4qY6IOqY185paU0dsFnCFyIBnXu0svgqiBWk2146SY+BERETU5yYnJzvdhb7BwImIiKjPjY6OdroLfYNznIiorzWbcO1u6gtEzzFSvh/C6gnbeDi8L/UT0dvVDhG1D0eciGhJtWq+kCsyCHFTCqC2Qi44zUMqQKJWxh9EwX2c718bYWkQ6h8b1k5j3xrTHNTd36J2iFwTExOd7kLf4IgTES0JL6dRC+v0B01CiGpAEt2Oqv7lBkKh98f8H3CCJ9Ek75Ty/mo+AhU3+tQ0AEvYDhG1DgMnIloS7Rwc8QcfzdqRCcokvb9Z8LQUsYwCoDFoIloyvFRHRH0javSmLW0tWUvx/Ikziaj9GDgRERERJcTAiYiIiCghBk5ERERECTFwIqKWWKp5NkuxdQS3pyCiKAyciGhR/DHGQuMNN69RXH4j/31xZbyfF9aVujaa5VqKriP85+gyjYVa0Q8i19TUFL8QtAgDJyJaMPdz2E00GbzdTxPBlWjRQVBUkCQDyTODwYUEvD+1FuoJhK+Ic+uSCiiZCiVLeW2F9hH1yTNr9Tj/2gowla8vKvAvAEsBlmx8nmHPvT6Aqt0uwBxO1Nx7Pv9T7lfXIszjRETzFpVk0k3aKHzl/Cd1IWoPms93XylVXXlZbcjNX6TgBCrBvgjfv37Cd5+qdlIBqJiyVo8CypaCrimk9ejIxGvHDSIDz82uBnu6L3C0fYGmG0BpADREJ8QMcksxaKIkrjn7T7lfXYswcCKieUuSIDJqJMS9TUlANanJbrI3i10NoKJKxQVP8N1XMmXkNjC2BEwopPT4PebCMpH773eDoyiyWlAo1SQYUhAQDJhoXkZGRpY0z1k/46U6Iuppi521IdDavfPajZfmiDqLgRMREVGf4+Tw1mHgRERE1Ofed/uvOTm8RTjHiYjmzZv8vcDHJ/nim/TLsW++eUgdClZ18lFKC5+jVLEVypaCpgGGCC8jFVCxFAwd0EN21LWlQrnaUMbQGupQylmhZ0oFXRfORPGQMlb1iRgaoIX0Q6nqXCnhTIwPq8OdoO92k/NaCOD7oJUYOBHRgoUFLe4cnOCKOiCwGi8i4lFq/gGZhvq0AG6g4p+7VJGALpQXtNhSYbYiUakGVlICNoCUpuqCI2/FnABMW8GWCildQAgBpRTKtoLpW9I3Z0qkdQFDc8pIpWDbtQnslq2cdnRA00RdsOMyA30NHhP3uenCmUju9sX/fN2f57NSj/rX375kI/L5fKe70Rd4qY6I5s05WVd/Dvzxl/FzT/7+DERCiLqT+nyDpoY2URtlCpvwbSugbDsB03ipFjR57cMJsCqWgoA7qlP/XGQ1TUHZlChUZF3Q5KrYCkVTwrQkLLtx1Z+qlqlYzmq+sNE1Wzl9sWX0qkFbOav+bKkiJ7hLVU3nwPkty9oH77ifl+pahIETES1YMKgI/t8v7rQdNqoy775U/1hxeQGq9xet+JYMPb4OpRQqMQGN2x/ZJFjRRPNLkkJEDM3V9adZHfH3U/8zUqlOd6FvMHAiokVrxYm5m07uzuXGpelQXDMi5KeFtSF4uW6Z04wMV9a1CAMnImoJnpeJulelVMDbP/sDXq5rAQZOREREfc4qzeHaC07ntistwMCJiIioz2WHRjA6OspLti3AwImIukLS3E7JpmhEF2q2P5776ERzQRY5XSRxO4vsglJcVbfcuXOcJiYm+F5YJAZORNRWyQIip1DUd2EV8nOwXn9w4ARYjQ0rpWAIQI/80u08pmTVEmcG63H/L6WCQnRAIuv6Gl7GtmvBU2h/UU1HEBP41OevCn9WbqoCni+Xr0qpgItu/gXe+M9fwcTERKe709OYAJOI2sJ/kg7LMeQFS9VLB94VBF9aAuX95SSn9AcIWqAOWc3R5CV+FE7yRz9bOekIdA3Qqj9XQy2vv5aUUMpJdpnWBQZStZwBQghY0gms3JpTGqBr9c/F355UygvU/MfE/VHaTiCna87zCdYh4Sa7rOWpcg+W/zjVjknjRH0hBKo5POv6wKs2y4tVKkBVSp3uRs9j4ERELVeXIbxp2fpSQtRO8G5up2BqJgUnkIJyypq2bMjf5GbXdpOAW4G8S0I4W5tIpWBKpy07EOFVbIWKrTCYEtA1gYrV2I4pnQAsE/FpqlDdJkUpaNUAJshWzuhTSneeeNg8lIYgLOKYaAgmIhV1ZYDFJjegXpTNjQBQuPZtr8DY2Finu9PTGDgRUVss9qqQEE6wEMeujgzFMaNSalfb0OBsgxKnaCoIEb11iTviEzeCI9E8YHFHlqK4QWRcPQrcl4waaZlBQNrI5/N8fywSAyciIqI+Nze+G1CSE8NbgJPDiYiIiBLiiBMRda3gBPKw+90v0Iu5+iAS7BnXjCUVdAC6Ft2R5s/HvdQW3Y6qzpiPOyZSxW8bk6QfLl7V6Q/uHCdepls8Bk5E1BbuBrYNk5gTRChKqbpJ2GEnelsqmLav/og5Rlrt7sh5V7m0BqkUypaCHbJJsLN5cfgJRyqFOVOiZCkIAPmshqG0FlrerVoLTAD3VvQpwAKgS4WUjoYy0nc8DU1BE8EyTtBk205fUjqgBe5352MBgJJhdQSOU5O5W9Qb3DlOU1NTGBsbYwC1CLxUR0Qt5/9M1kRtMnOzoEkpBVsqVGTjSjr3fqkUyqZE2apfJadQDSwCTbgb3AqEf+B56RAADKQ0ZA3h9VcTzghS2ElGKYWSJTFRtFGy3KQGwGRJYveshXJw+Z2PBGAr5Y2YBYM6WwElyxnFkrIaDAUCGksCpu0Ebu5x88+DV3BWBZq29PJABY+PrKZkkNV+yGDQhOjjSr1lbnw3KsVZvPW672Lnzp2c67QIDJyIqG388UaSoMmUzqhLFFsqlEzVdLVdeF+Et4ou6n7AyaeUNgR0LSYhp1KYLtuYrcjQUSxLAhNFG7LJc3aSZEaXMe1aGoLQfgCw7PA8WS4nd5WKLyPdNqIL+UeqqIcJgbde910mwVwEXqojorYTAk3zE8Sfth1hl9Hm2ZOmW644yTSr6SIjIiepnPxNcQZSWsJ8SdGl4uYpeWVE81QIWrM6krQDXrLrZdncCFLZQQCAWSp0uDe9jYETERFRn9Myg848JwApEX75mZJh4ERERNTn5sZ3w8gMwCoV8G/nnIZ8Pt/pLvUsznEiIiJaJoxsDu+/7X5MTU11uis9iyNOREREfc6d42SWCrj2/NMwOjra6S71LI44EfUxb6PcRayGakUdSSSZcRGTW3JenOcTs4ddk8cLqKZlKtWlf1HtuLe7qQLCuKkG4upwJ4ZHl6nVE0U26UewPuo97hwnLZXlfnWLxMCJqA/5Ax0VuH0+dXg/h9Q7X06ixej7hRBIa/HBUcoQyBrxgY2IWAznBgZFU2GyZHur4vzBgv/5xZ5XhMBwRkM6ZkfesqWwr9DYjtuPsq2wc6KC3bNWNU+Svx+qmp7B6WtYEOYms5wu2xgv2k6ep5AXTQEo206ah2Bw5F/JaCn/69z4Irv5nNzjRL1lbnw3CgeewtzEHkxOTmJiYoK5nBaIl+qI+lQwOaRS/kSU0YGB/75gQkUg2chQGDcJpUJ4TiE3UWUazgiJaau6Nt3s3ZouoGtO1vCKL6GTm2jTe16BZI6mVJgpSy8H1ExZIq0DuZTmLem3q0kgIQS0al9VIKBwE1EKITCQEkjrCkVT1uWW0qr3WxLYX7AxmBIYyTrpCaQC9hUsTJSciKpo2Zgu21g7lMJoVgeqZUqW9I5ToeIk/MylNejCTb7pZCx3m90/ZyOXEl7WcgVASl9gJAEbQEoD/PFeMPGmVMp3fzUtA+rLCzRPgUDdycjm8I7P/xJ2qYAvv++1WLFiRae71HMYOBH1kbgkh0DtpNfshBdXj6r+tdCTphACunCSWfpv89OEQFoHKlKF5hgSQiBtAEI4AVTYc3JHnqbLNixZH2S5KjZQsSXyWT0mmHMSSNoyPAOUrgnk0hoKFVkNJhoPzJypUKhYEBowW5YNCTxtBeyaMWHaErmUFprg05IKUyUbKV04/QkpUzAVbCUxnNZD+6oAVCSQgorcRkbBGX1KCSAqTFa+Hxg89QZ/HicAgMHT/0LxyBFRRzRP7CiazmnShIAm4qNFOyJo8msWcALxyTmdvorQbWL8bcyW4rNmmraCpcdvxFpO8FyaPZ3GcSTqd/48TgCg2dy3bqE4x4mIiKjPuXOc3D/uvnXcemX+GDgREREtQ1Z5jhPEF4CX6ojazL+6LWp+kTsB2b1rISPnST//pIpuRyW4zAPU5jmF1eGvJ+75JqGJ+D7VluJH15FLa8gqhelS+Ia8mkB1wjUiL7UZGpDSBEpW9E53aV1AqujLgpoARjIaiqazWi6MaSvMViRyaS10fznhtaNgRXRWKaBoSqR1AT3mWmezuWruCTV0HpS7QhC1ifDU3RrmOAEwDYOv3QIwcCJqo+BJPxhwBFMGLHTSbdKAp64fgXbaUYd/Nd9C2tEACM0JFPzxiFIKtqwPzty2vMcKwNAFMobT8HBGx/ichTmzVmgwpWGger/QBOxAQCJQXYWmaVBKIaMLFC1VN89IoBbQoPpvcJVdShcwqkFMLi0wZ0rMlG1vbpWAE7xVpDMhfs60MZxx+uae2AzNqQcAhNBgS6cf/hjMXQ1XsRUqtkJaF8j66hAAdO86gwgNgLVqudrqufoAKjhC4QTi4ZP4qXsE5zgBzjwnmj8GTkRtEDtK4isT+Xg0BhzzbSeJYLDRrjoW2o7/hG8IeIGNP1gQvojMDUaN6miLUrWJ1joU1gylMFdxgpasoVVzS9UOsAYgrTmr3IRw2gy2M2AAGV2gYIavotOgMJTRUbElTFv5gp1auQFDIGsYmK7maAqOLikA02WJORNYMaBjsJoyoa6vwqmnYjsrFMMGlyq2k9ZhICWc5xs4Jm5bACBULfAKHyWMfgHd96GG+Int1DnuXnV+VqnAS3ULwMCJqA2W6qNouXzkuSdjaYfngKqVqQUq/sf5f07rQC6tNwQQ/jKGiB5BEUJAKAlAQISs6PMCNSEgjPAgQviGG8MuyXnl4FxqDD4X//81oWKzhioAWUPzgqHQ5wRnRKsVuZnCjit1J13T+VotAAMnoi7Fj7NGzQPFxtGdxjqcACuujIBomjm86aJ+t0hkFaJpGgRdEy05sWlN8josZm5dXT08CXet4Bwns1TAtW97BcbGxjrYq97EwImIiKjP+ec4GboOCIHR0VEGuwvAwImIiKjPuXOcrFIB/3bOaTj00JMwOjra6W71JOZxIiIiWiaMbA7vv+1+b29Imj+OOBEtc3H5epayDmrUqsn/nLBN7hwns1TAteefxtGmReCIE1EbtOIUFZe2oFUriP1LkRe6LDn4uIXUo5Ty/kTVoZSCoTU/tsqfnCiEodWW3S+UJgQyepMyaN7OSEaL3Y+vbClUojJdVhla7KI6AEDJkog7JhK1FA+LeW9J73VceB3UHlpmEOnBYaQGhji3aZE44kTUBu6iq6jzh5e0sUkeJhX4wZ80M8nGtFHtRAU3SUeOosr5Ax/RZOVZkjqCnIBFwVIIzZyd9oIIgajUfoYQGM4ImLbCnNmYCTytC6SqQZFph2fo1jVgKKMjKxUKFdlQJmMIDKd1CAHMmQpTJbuuHQFgKK0ha+hYMwTsmTUxFdgAOGsIrB9OYSClQSonH1OwrykNSFczWpYsVZfcE3COxVBGQ0Z30nTGvWUs5eRyMsRi0hKIuldc+d631FnTu3bg6re9hHObWoAjTkRtIoRz4vKfM9ws07WcOrXb4gRHn5rFTFHtAGrBI0uh/WpBXfOtQwiBlCaQ1WsjLYYAsnpt+b6bvNL/AacB0FE7JildYCRTGznSBTCYFkgbwpv/kTY0ZFPCe3004Y7wuNm8BUYyGobSwsv+PTqgI5/VoVX7kktrWDdsYLCa0ymrC6wc1DGQ0iCEk1H84JE0Dh9LI6ML6AI4aNjAxrE0BlJatV2BtC/7uJv8MmNoXl8HUhpGsxrS1eczmHLayXplGt+PDa8FAFM5I1DzfWmDSTWTBPa0dIzsIOc2tQhHnIjaTDQ5Wfm3IomT5DwUl4+n2ShYXVstmxPTvg9okeRymYj/diiEsyVJKuaTUBMCaSM6EBDC2dZlIBU9UqcJgdEBHUMxV90GUxo2r8zE9tUQTtbyqJdG1wSGM3rsHnXN9vYDnMzpesKv1c3eJzxHdwfDMHDdW8/gaFMLcMSJiIioz2lGBvl8nqNNLcDAiYiIqM/NTe7jvnQtwsCJaAko1ZqVcM0++Pwr09rdl2b4Id05PPQUZmpqir+XLcDAiajN3BVtCuFBS5JAJrhUP2q5vtuODCmjlIIt6/uStM242+Zbh3t7t9CEsxrPiPk0TOsCA4aILKNX60hVNweOKpPVnZVwYQSAlO5sQhw1RckQzuObpTlwJ2eHHWYBICWcP3F9bV3Ki9bUQ4sztHId3vWf92FycrLTXel5PRM4XX311TjppJMwPDyMNWvW4DWveQ22bdvW6W4RRVKqevLy34b6oMUfVIXXER2o+HMfNQY47onTuU8qZ1l9sC9RJ9dgO/5/FyKur+3kn88RNrdDE9XJ+0JAgxP8+IMSrbo6T8C3ms8QXmAjAGR0J7BybwkGNgLV1XxwVjO5AZS/jKHBWc1X/X+qmhLBv5ovozvlhHDKGaJ5/qZgsB7MLZXSnHq85wsnoNKQbFJ31HwZgeZ9o6WlZQY5v6lFeiZw+uEPf4iLL74YP//5z3H33XfDNE285CUvQaFQ6HTXiBokyc8UDKoa61h8gGFLJ9+RHbOaK+noU68KW36tCXgrz9z73HK6qAUUWuDxbtCS0UX1j38lY221mwbn8TqqgYrwp4ZwfkhpbtBVCzLq2oIzApXRqyvpQvrqBkL1fQjkxYI/eHPL+foDJ1gyRG3Urdn5NWpJezC1hr8d6qzpXTvwiTc9k6vqWqBn0hF8+9vfrvv/jTfeiDVr1uDee+/FaaedFvqYcrmMcrns/X96erqtfSRy9VKY0Y/ntciREC/wiC4nhHDSMfgLhtzvJtoMbwd1uT+Dpdx2m7UD1L7dRvVVKuUEZjGvpB4TwIhAH5METdH3xT+W2i/uvMdVda3RMyNOQVNTUwCAFStWRJa5+uqrkc/nvT8bNmxYqu4R9RR+lgYt0VBJS4ZkguM84c20Ak+63S/qvJcdGuHr1yJC9eAYvJQSf/Znf4bJyUn85Cc/iSwXFnlv2LABU1NTGBkZWYqu0jLViqzJrcnKnTDLeJ99njY7QTSbf9Ps9Uvy2iQ59vG70Dmafbu1E7STbtFX5MUeV2q/qPPeiy7/HG5975/HDjZQMj1zqc7v4osvxoMPPhgbNAFAJpNBJhOdiZeIiKifRJ33mMepdXoucLrkkkvwjW98Az/60Y9wyCGHdLo7tEwtfBNUouWNvzudwUt1rdMzc5yUUrjkkktw55134nvf+x42btzY6S5RD2pF8kf3gz+qnqX6UudPaRBZJvBvVJnYOpK006RM0jqWIlFoK9IrLKVW9LLZcU3y+iQpkKSdJHvlUetpBq++tErPjDhdfPHF+NKXvoSvfe1rGB4exu7duwE4qwQGBgY63Dvqdt6JwbvB+Wc+X8D835Sls5Cp4dvzUgdNbj+C/avrK6p9Rf0UYn9QpfluCHs+tqpNQQ5rRwJQspY4Ma6OYDv+8jacZfPB2xfKv1mx/2dbOqvigpsZJ339RPXsn2T+WFwZA86xi5vrpDWpw70/rg5b1fI3Rb5PIt7TriSvhf89ttB2qD14qa51embE6dOf/jSmpqZwxhlnYP369d6fW2+9tdNdoy4V/PYbTP4YVS6uHn/uJRVyf7DuxQjNyeNry98PdzKz/35b1U6mKvAn2FcJJ2gJPif/7bJap/9+BcCUtbbM6r/BftbVEXg+bl/d2+1AOwsVlmndf+Jwk38Gk3ImCYYEAE0TXgLNMJomamVC6jA0QNeFk+xSayyjV3MrxbUjAOjVrOdhmcDd/E2aiH6fRL2n656LADQhoIm4pAe1OmTI6xf3u8Pz+dLgliut0TMjTnyxaaGiVkgpOB/YSbIvN7tPxLSzWP4Rk7j++IOiuDIC0aMTbtATVcYNgtwGwsq4QZQuagFQQxkAIkE7esh9rVR7D4hEr19wBaKbEFNB1T0+mLFciNrrJ6pBiJ8mBFKaclbIKTejeaAOXzthOZeEcIInWzrHNCr7d7On6d7vPj4YvLs5qpLWE3n51m2Ho05Lwt1y5QvvHMPY2Finu9PTeiZwIlqIpQq3u6WdJP1IUkezMs2W0Te7dJS0naWSpB9xaRtEgpEYIURskOBmAo+ryCkT39uwEa6F0GI6mzR4ou7BLVdap2cu1REREdHCcMuV1mHgREREtAxwy5XW4KU66kqqbr7IwsvMp63Q+SBK+e5v3weOfw5fVDv+TNRhJYKZqsOfT/z9ACCrzzk4z8bfV3fTYD3ico5SznydqDrcMu6cq8h24Hy7C84J8j+fWh3h9/tFv5fc+UfRr7Gq/tXp806zlXqAc9zj5mx5K99a1Cfqfszj1DoMnKirNKQN8P3Hv7S54QM/4oSmiYg6G6sPaad+wm+Sk+tCBBc+BNtRSkUuWfcv/baDd6r6Y1a9yddOfcDhBkT+Cd2GpuqCHykVynYtmLSlQkoXdX11J1wDzslbE/V1NBxXABqCaQFqZWzl3G9o/joa51FpDc8n+fvE3y4QE+zF1KHFtYvwZfoLqUNoouE4e/eLWrCqqdpkcz9dqwWi0hcE++vQfe+JuPe7+/sV2dcWHBNqDeZxah0GTtQ1oj5c/atzor5tK9/93m7vvn/jHtvQjmyyeq1FAVSzlaJu8BC16swpEz3JOuzEGlpGOu2ErYCzpLt0XjppBwKdkQooW05Q4y55D5KqFkBFJT+U1c5GvUYSQEUCRrUOGTLm5nZNi3nOYe+T0HIxr3GSOvzPoyFFQMK3jT8ocduqG/1UteOpUAu6/H02NOEFR5poXMHmvLa118gNqpK8x/13xfXV39ZijwktHPM4tQ4DJ+oKUUFTQ7mEdQU/iJNmK16qD5Yk7UQFTXVlWtAXS8YfV6kUSlZ8HbZUEHr82S/R8W9yv3P5r0k7SLaqrNkoR5LAOOq95nbEC9rnGRjUpRmIur0a4Lir34KjQ+7PbnDkvy1YRoOqC6rm86UgWV+rPyzimBB1CwZO1BVaFa7EnwiTnby7RQ911ctT1PY5FC1qpxXdbFZHKwKE5m2EB01hZZLUsxhJqmDQ1Dmc49Q6XFVHRNQmS3We6qUTYg91ta9oRoaZw1uEgRMREVGfq5QKePtnf4DJyclOd6XnMXAiIiLqc1ZpDv/8xmcin893uis9j4ETUTss2XB4Lw27L1FfeSmCqMHQynX44De3Y2pqqtNd6XkMnKgrtGraQ9xO660+n0bNFajl2FGhZYKb9YZV4y53R+zzUfUPiGonoh/ufe7E7vAyKlB1eDtuzqW4dqRKcEy8hFNR9TRvRyG+jFtP01QNMcctpotEXUnLDELaNuc4tQBX1VFXaNWmoSrww3xX8bhLvBO358t3E3+SVXVlbF/OGw21Pnr/Bsqgrkwt8HJ+rAUb/lVWAGDaEko5eXZ035J0tz9SAWY1gZMWaKjWVyeRooCbGNFtR3lpJExboWwDaV0gpTUek4qtUDSdJe+5tIaU3nhMLKmqTSunnYiVYrJ6HETIcXXzEYlqwsyoOsLeJ3EalvrHFyfqOnPjuwElMTk5ibGxsZ5aUNBtOOJEXcPLetyCuoIhjJxHUCaEaOmHin80RQKwfH1RcDJ+14IgJ69SsIz0B0jV/4ePVDmjJLZUMH0Zvt0AyRsZAlCxpBc0uWVsWRuxkQoo28pLjKmqfZdeO87/TV8eqIqtULRqj7EVMFOWmDOVFwxOlyVmy7J2TFQ1l5RvsMn///Bj6vTV+3/1uPmfn1nNgh474hbdhK8tVZ+fCNXAjecd6jFGNocLP/M9TExMdLorPY0jTtR1WpVvyR+MLKwf9aM3sW01KeOOHkWRiN9bDKhl147jjtpE3m8rWLaK7YspVd0lr7B+WDEVSAUUTemN/oQp2wqoAGk9eim9rQDRJGeTLYG4Z2NXR6fiviH6R/XiqabJN4m6VTY3glR2EGap0Omu9DwGTtSVmm2PQguXaJSlBe00CwR7DUMm6mVaZtD5YzfsaknzxMCJiIioz82N74aRGYBVKnCC+CJxjhMREdEywgzii8PAibpO0g1/F9+OavgTVmbx7ThzbaImdAOArE7mtuzoftiyNgE6rB6pqnXIqDpqq87iJpebtkLFciaYh9VjS4WSJVGyJGRMX902op6zaSsUTKe/YaRyVuKVzOh2rOpxkxF9dZ+3jYjni9qkdzumr+4E/ej3SbPjWiuTJBUCUatlcyMYGMpjeNVBeNd/3scM4ovAS3XUVZYiaJpP2oDFteNM+vYtXPMmrLsrs5xApX4FnSUBTThL952UAU4g4nLjDN1fh1SwfGUsBWiqVocbvNX1z/vL+cGS1Unbbh3S6aehO5OilVIoW6puYnjJUkhpgKHV+moFno/7Q3BetazePmcqGJpC1hBeO/6VelIBlqmQ1oGU5rvN93ztagRkaLXJ5F7KAgACAjYAoZxvi27KB/8hUXACLM33+gS5x16D8o5r8J3iP67uQodgO1HHhKhd3DlOAGDOzWBychKjo6NMS7AAHHGiruB9G297O4tfIZeEVIAZEqzU7ndGlyp2+HN2AwPTrg+a/GxVW/5vhZRxUx9YUkX2Q8HJ01Q0VV3Q5L/ftIGSKZ3RoZB6TKlQspxRKjPi+dRGbML7YUlgtuL0oSLD63BzQVVshD5ftx4plZfWIphawg2O4t5rEtEjVF4Z5bQTtaLPDcri2ml2TIhaaW58NwoHnkLhwFOoFGe5b90iMHCirtBv546oQGW+ZZqJusxVL/4bZcV28ktFUSo8YAoKu5wW7EXcl9tE33tF88DW0N0Rp4U3luiDsQXf1PldnzrBKhVw7QWnY3R0tNNd6Um8VEfUBs3ijH4LFAEsSRSQtIlWXH5IVsVStUO0OG4eJwAwDYOX6RaBI05ERER9zsvjlBmElspyZd0iMHAiIiLqc8E5Tm+97rvcemWBGDgRUWvwyytRz7DKc5icnOSo0wIwcKK+4d9Md+F11P8bVWaxnzUiwePjNqdNaqk+E5P1M+nziUsXsfj+JD2mzd8Di399mrVD1CpuHif3D/M5LRwDJ+oKi52iGAyaFnJCcx8iI4InL6iCs2Q9LIByb4vdVLaayCnyObvPAdFJF93bREQ/vDJQqNgysg7A6UfUirja8YwPGN3UCHF9NWX8/nVO6gPlBSX1dTj/mtKpx9+3YDtlS0YGNu7/3eSeUe8TCV8uqoj3Qe19sohAPaReonaom+NU/SNtmyNOC8BVddQVvGSFEXlvagkjnf+7oUWz33knoaVbQ221lf/DQvkSEXonSwR/cFgS3tJ9G4Dh67ebVNEOeQ517fnbVP4TdK2Mbau6E7emue2oaqJJJxVBXTuqvsOmDS/IMG2FtC6Q0mvtSAXMmbaXE0mDguGL+IQQsFWtDgDQvN2Xa+2UfXmkKrbCgKHB0Gt99QIiABYAXTp9cdvwkwqo2E5CTS1wFMuWG6w42cKzhoAuau0AtaSaRUvC0ATSumh4rZWvLT1wXOv64h571N537mssfXVowkk26t4/368B3vsh0A5RK7l71flx37qFYeBEXUV4J2ZUMz77bvf9iwSXy8JOhrV2RHVExhE1EqJq3QnNu2Qp57KbLuBsMxLfpYZ2/AGje6IOllFw65ZOMCNlaH/dESpUgw8VuK9sK5i2RErXYMrGhJcSQEU6gYImACskv5PXbjUAKQcSPEkFFEyJlC2QMQSklA3HzVZA0VLI6MLLwB3kZi3XtcYs4W4dBVMhoztBmB3yZrCks21MxtDge1vV1yMBCeXlfgpScAJkUf0T9h6Q1dfOEPVB+Hz53+9E1L0YOFHX8YKkJmUWPc8oEDxFaZaoUgFNE0RGjaTV+gLYdnwdzl5o8Q1Zdnx/bQWUzfjwzlbNn0/Zij9ullTQmkSR7mhNFAUnAIxjScDQ4+uwZW3rmagyzfgD6LgyGoeKqEv58zi5TMNgLqcFYOBERETU5/x71Xm32TampqYwNjbGAGoeODmciIioz/nzOAXzOe3cuZNzneaBgRP1tWZLxlvxWeG2EdeOVKrpZTbZtK/KmRC+iHaUUt6Ksrg6mrXT/LgmbydOq46JHdOOUsrZsLdJO837srgVdkQdIQQ3/J0nXqqjntNsvlD4Y9xVT7VVdUnmrDjzVoKr+err9Z+Tg5OdlVKo2M4fANCEM5nZPxdGSoWiJb0J0IbmTDb399WUwGxFef0ZTAGGVt9O0VLeZG1NKKQ0UdcXSyoUKrXJ2llDwPCVUcqZMD5XbcfQgMGUBj3QTtmScKceiWpKBH87UilnNZ7l3JcxGvtqKaBYfb5pzcZgWoMeqKNi145t2DEBABOAaTpzmLJh7chaOxldIpuqP/bu6+e+PikddfOh3Pvd10YAMHTVMJfJ/Z9U1WMSM6eqGV4xoXYIm+MEAGapgGvPP40b/s4DAyfqGQ3BixDeUvLkdThlm+UTqmsHtcno7uqsYMDkktW15ZpwJlmXTFlXj1RAyVJIaYAuFCqycWWaJRVsoJo6wFml5p/PLZUTRKU0hazhrAybM1VDO2Vbecv6i1bjKrqSpaAJhazhLPyfLdevgLMkMF2WyBgCWb2Wqyl4rBSc5fjeysDA/SVLQRcKGUNAKSe9gb9MRQKVksSgIZDRAVsJmDL6mAg0rl50Uis4xyStuykQ6suUqwHsQEogVQ2wgi+haTvBXqr6yWiFrE40bee10zUnOArGOdW3QOSKwTDB1aNErRY2xwlw5jkBwOTkJDf+TYiBE/WMqFjHXR2XqI4mo1Vxq6dqeZfCgya/kiljV6ZVbNmwxD7YjzlTohyzqsyUQKUc35GKLVGK6YhUwEy5MWWAX9lSsGX8Criw3FXB+4tW40iN35zlHNeoIk7QUgtYwjhJNqPbUQCKpoKejl9lV7Higx5bOR+eIiblhUwQPPnzk/F8Re0UlsfJddHNv4BdKuDL73stVqxYscQ96z2c40R9Iem3pEXPQEmYBiFJCoPF1pFEswAvaZm4oCmpZlVoaP46JulHsyJxgdd8aEI0DYqSYtBEnWSVCjCZDDMxjjgRUU/hpQSi+Yua4+RiTqfkGDgRERH1uag5Tt79zOmUGAMnIiKiPhc3x8n11uu+i8++/YUYHR3lRPEYnONEXYOX1ykJzsMgahMhcNHNv8Ab//krTIoZgyNO1HH+FUVRq4ta9fsbtdmr106Tx6tqjp64gsqXjqBZQ1EbESuloAvAcnfTjSjTjKg2FNeOJoSzKi6mjFRoyNdUu7++P1F1KIjqsQt/ke2m7ShIIaDHPB+gemibrHaL62tSMiSHVbAfbrqMZu1wVR21W7M5Tn6poTwu+dK9+MI7RzE2NtbmnvUeBk7UUe5J113ZJXy3J12mPZ9vRbWUAoE6qm3HxUTNckC5J0g3j09YXV7iRunkFUppgI76k7ibnLNkOikLdF3UDQ277ZgSKJoShgYMGCK0jv1zEpNFG2MDOoYyou4k7iaInChaUACGMzp01N8vhEDJUtg/JzGUFhjJaKHtTJYkypZCPqsha4iGYMGSQNG0oWsCQ2kNItAO4KQJGJ+zkc9qGEqHtzNTsr2+Onmu6ttxX8PY1W7CPfYCuggPbNzVe1HvBU248azwyoS16N/IOKyduvcKgydqo2ZznIJkpdjG3vQ2Bk7UMWHZuP0nEleS5fLz4R8xqksaGfMYVU3wGDaK5D8hVmzp9FdUn4OqJcwU1ZGdklXLA2VKwIKT2NEdSSlZqi73kmU7iSr1apZvpYBCxfaSYlZsJ79R1hDIVH+jp8sSu2ctL1fUvjkbMxWJVYNOwCGVwlRJYrZSe9YH5iwMppygRQgBWwLTlVodMxWFOdPGaFbDQMoJjgqmU4/b2/GiRNZQyGc06MJpp2jWMqfbtsJE0ckUnjVQDQAVCr5cUpMliUJFYmxArybNbDwmE0UbWUNgKKN57xVdIDZPlCacDOTua2Uppz+GVp+Asi7wCrxPnMzhtXbigiaXPylqrbbGAIrBE7VTkjlOLqtUwL+dcxry+Xybe9WbGDhRRzQLhtwAKrbMIq7f+S8NxgVMgBO4NLvqZroBU0g7Ak6wZEoVHnjBCX4s24ZU4cdGKqBiOm2YIR1WcBJM7itYqNhOpvCgkqXwxLSFobSo287Eb86UKFkShiZC27EVcKAoka5ILwt4WDsVy8ZQWsCSjaM2CkChIlEynQAkmCUccOrdW7AxmlGQCD8mTiDlBFhxo0xOQAVoIUmgVLWtgeonYbAO979a9Wf/dizBeoDmAZQuoksxeKJuYWRz+L+3/Aqfzedx+OGHc5J4AAMnoiaShGdJAsFmCS3jMom7wgIVv6igyS8ukzgAb++2uA/LSkxGc6AWkMSxlTOvKY4p4/uhED/KBLgBT7MySepgDinqXfOZ4+TiPKdwDJyIiIj63HznOAGAOTfDPexCMHAiIiLqc/OZ4+TH3E6NmMeJOmKxv3qtyC/iVhHXF3e+SdxnhWzSF6WcuU3x81+cMs5S+fA6ypZC0bRhRVwXlEqhZElYUkUen5Il8cSUiYmiHVpGKYWpko09sybKEdcOLamwb9bCgTkr8rkXKhI7JyuYKoVf01NKYbYiMVWyYcc8n6mSjUJFxr7e02UbZsx1UKkQe0wAZ3K93aSMP41Buygkm99HtGSquZ3e9C9fw+TkZKd70xU44kQd46UemMdjFnLiclai1T9OBVISBPviX/GnoZZKyX+OV8qZ8B03v8mSzqoy9/HBVAhOHfBWnbn3ab5l7JZUmC7b3rwiSzopCDKGBk24q9skxou21xdLOWkO3EnTtlTYP2dhsuQEQ1NliYmiwPphAwMp5/tTyZLYV7BRrvZlpmJiNKtj5aDutTNVkjgwZ3v9nC5JrM4ZyKWdFX+m7UxQn6mu1pssSQynbRwyYiBjOO2ULYlp3yq6omVjKK0hlxLea1WxlTdHqmRLzJlAPqsjrQvv9apWh4qtULFtZHSBXFqrm8/kf10tCehCVQPhWj0C8CblO6vmVGgdqJYTqr6OYJkw7jyrJDmd3P66B5lf8KkVFjLHyWWWCrj2gtMxOjra2k71KAZO1BH+VW2aSJZyYL5BU1SyxmAt/pOrexKtKyNENbWAkxJASumkEYjptKwuoQ8O2vhTIZjSGUUKq0UCkFKhaEoUzMYSlgSsioQmnNQD5ZARF7s6dFEwbewr2A3HuGgp7JgwMZbVoGnAbKWxjsmSjZmyjXxWR6EiGyaF2wrYPWshaziB3ETRbng+MxWJh/dXsHbIQFoPn1g+W5EomsBQWms8/tV2xos2srrAikG9GjTVv75lW6FcdIKwAUOEvv52dUgnpdWCI385Z4WkE2B5aQcCnXFHhDT46mh8St7t/m7M5zKH8v3A4IkWayFznLzH2s4vLec7ORg4UUe1+vdPRHyrd/8fd1ktNGiqrwSAc/KNC5oAJ5lj1NUjIWqBVZyCKVEMCZr89s81Bip1/bAU9szGL4GbqUjoIUv1XW7QImLGVeZMJ0CL4qRccEPTcFI1X3mYTYm6XEyhZfT4N5UmAF3zxhHDy/hyO0V1WQNiM5S7QdNiTzJJE8ESxVnoHCfXRTf/AnapgOvf9oJln6KAc5yo7yzmF7pVHwXNxsaSDJ4lKtPk/qj5Q35JnnNc0JRUk3gmUWDQLK2AW0+y90B0mbi8ULV2wke12mEZn6OoS1ilAhScyeITExOd7k5HccSJiIiozy1mjpOfWSq0oDe9jYETERFRn1vMHKe6emwbU1NTEEIs2/lODJyIiIj63GLnOPkt9/lOnONENE+JFvd1SR6epEvfW5KfKMG2M4tvJ9njW5Pnq0teRKIu45/vtHPnTkxMTCyr3xeOOFFX8K3SXxR3RV1wZZ37Sy1Q29Q3GFK4jxGqlpspGHgopWBo0fuwuXWkdKAcschMKQVRzbHkrvILayetCxQtFTqF2WtHABUZXUdGd1YAGlr4akOlnDxTad3pc9j9ADBVtjGSCd9MV1WP15wpkUtrkbmKxos2VmR16Fp4P2zlJM4cTDWmCXDNlCWyhoaUHnJQfGWGM9H9kMqZNK9rAgoqdOK7KYG0jthVc7ZU1VxbInTytn/NXtL8TWH856Nl9sWeWqhVc5yCLr1zG8xSAZ9/xwuWzZ52DJyoK/jzG4UFUALO7vZKxSecBGon++A3IKlqQZOvcLV954xkyca8Sv56LOnk+RFwTrrBcgpA0ZSNG/YqNwmmkyRyppoAUgBI6aK65LxW30xFYqbsVKIJwKieMN2g0JLA/jnTC6yyhqgLJoQQ2D1j4aH9ZZQthQFDeHmU3DoUnHQGblLMVYM61g7p3sleCIHZisS2/ZVqwCJw5MoMVueMur7uLVh4YtqErYB8RsOho2mkNFXXzpypYNoK43M2VuV0rBzQ6467aTu5nGwFpDSnL9lUfZSQMQQyukDBVEhLhawhGgI5QxOQEJguO/dnjMagRRPO+0BJBV3ACZ4aAjmgbDlJNg0tIjAVAjacYXuB8JQBspr0SRP+fsSnZQhyj3QtCKv+n0EUzUOr5jgF2VLCtiyOOBF1gnciUI0nC+ELHHSBhAFUNQcOwvMD+duwpWoMqlALhpRy8hDVffuvBk9OUk0F00ZoIkrAyeQtAMyWbZR9+Zvc/EaaVNA0ActWmCzZdf2VCqgoQMA50U+VbUyVZC3LOZx8TRVbednNf7e3jPFibciraCnsnKxlAp8znSzh/nacIMrGmpyOobSGnRMV7JqxvPtLlsJv9pSwYkDH01ZlYEmFx6YqmPPlmpoqSzy4t4SDhg2syRmoWArFwPPdV3D6f9CwE8jNBhJrmhJ4atZGLi2xNmdA14ABQ4PmyzVVsZ1tUgZTTsCoCwHNl4bAf0yG0s5t/vvdMpZyAh8tJHgCnEDZlkDaqA91/GUl3ISY0SGRrL4X49NlxnPfh4yXaCFaOccpyCoVMDk5ibGxsWUx34mBE3Wd2gkmroxzSa3Zd5wmOSa9k16cshndjoCAhArNuu1XMmVdgNHQDwXsn7WaJrM8MGdF3m8r4Pf7ytg/F52IcrJkY6occZ0RTqDwh3EThUp0mfGijV/vLtYFMn5KAXtnLWT06CmUFVthb8GOzPANAIWKwuCYBiOqHThBVi4d3Y6tAFsJpGOSZko4gVcUBSd40kIuZfrLAM33PWxV1LMMzk3UQ4xsDhd+5nveZsAA+nrFHQMnIiKiPteuOU5+l965DUIIVIqzfT3niYETERFRn2vXHCc/W0aPVPcTBk5ESyDZFiu1CeRR90ulYrcdkQnKWLazN11cO7aU1f3cwtm2DUBA06KXt1UsWZ34HtcOYMTsxWJXJ3Av5pgoparz3eLLAM3KxF8iS1bHwlfXES1GO+c4+VmlAv7tnNOqv3f9+X5n4EQ9y50LpSI25hXCeYMrhG/em3QRSKb6W2LajZPMpXJWi6U0AanCN/Y1bWfieVp35soEyyilMFuRXooDXWsMBIqmxI4JE7MVZ4XcaFar25hXKWfy92/2lGFLYGxAw1Baq/vQMm2Jx/ZOYfdkAUPZFI5YvwLDA+m6dqbnytj25AEUKxZWDOewemy4LoBSUmLvrj/iqUcfgW7oOOLpx2DV2nV17VQsGzsPzOCeuQrWDGewZcMYhrL1HzWFisRjkxYsCRw8ksJBw0bdc1ZKwZQK9+4qIWsIbF6RRj5bH6RJpVAwFWaqKQzGskbDMVEApsvOysOhjLP6MJimwrQVSsqZPJ41tIYNjwWc+xQAKVXDXnhuwGQDgAL0aqqJho2mA+UXckLxr6zrw/MR9QEjm8PffO1h2KV7cf3bXtCXc56EWkZrCKenp5HP5zE1NYWRkZFOd4cWKSzdQJC7JB5wJgFLFQy0nBm78b8GtRELWzqr52Q1JYDpazTYjlSqITWBW8ZZraVQthWmy7Kh7wKArjn1PDlt4alZq+H+kYyGobTAVEninieLDZPCUxqwclBHWhfYMzWHR/dMepv+uovi143mcPjaUSil8OieSeyZLNTl1NI1gbUr8sjnBjA7NYHHH/ktSnP1e1WNrliJI55xDLKDOeybmsPuyUJtyXy1zNPWDeNp60YgFbBvzmqYKJ/RBTaOpZHPaLCVE2wGX5GVgzoOH00hrQtYsjFIFQBGs86KQCD8/WBowHBGh6E5AawVEummNIFMNcAyNOcYBL85N1vAIIC6kbLg6tC6sglPJv46GDhRUu557yVX3NT2OU5BqYGhvpzzxBEn6lnBb/2acHMr1Z/o3H81N2ipr8VXRkWMQtUSHGrCyeszU2lMh+Bvp2JLFEJW0bllDE1hvGijFLFITgGYKknsmKiEJttUcJb+/3ZvBY9OmKEncVMCu6ZNHJiYQKliNTweAHZPFrB3ygl03GSc/l7bUuHJfRN44pHfojJ9IPRkPTUxjnt/+QvkDz8GMrAZgVvXw7tnMFG0sXZsKPT5lm2FbfvLOGJFGgOp8EuEB+ZslC2JI1amQy/NKQATJRtSKeTSeugxsSQwWbSRS4vIy3umVBA2MJzxB0qNeZ6c20Or8FJgpITzfmx2iS8ueBJoDJYYNNF8LcUcpyB3zpNtWZicnKy7r5dHoBg4UV9wRpaA+kCosYwdm/zJP9YS3Y4p43NICSHqcheFqdgqMmhyHSjakRnKXTsnTADRvS6VKw1BU1D8MQEgLVSmDzjthOXDUgrpoTHYKj5AGBuKn18xkBKRQZNrxUB4QOSXS2mxZXQNsXOiAHjJN2MDmiYd0RIGOc1OHgyWqBWWao5TlItu/kUt0XBxFl9+32uxYsWKjvVnMRg4EXWx5qFc92h6Xu+xE3+vfhsm6kZWqVD38+TkJIQQPTnyxMCJiIiozy1FHqfEhvL4v1/9fc/uccfAiYiIqM91Yo5THHePO//cp14ZfWLgRD2DK4m6W9MdRXrlmmMP4u8GNdPpOU5R3LlPVnHWS1/Q7QHUggMny7Lw/e9/H3/84x9x2GGH4fnPfz50PToZHtFieBv2xpwgmp2XlVItmTMUsXVaXU90Eb4cPnkdQEaP76tSCllDoGhJRIUstRxMUWGN8upyjmtYGQEIDZB25MGXZsn5oIt8gRTKloWUoUV+IJq2aprjqGwrL8VEVBnbzaUUmTTT/TemDgkoLX61W5KVde6ihcWsqmvaToLfDaJu5Z/7dNHNvwDMEm75v3/e1ZfvEgdO73rXu/DSl74Ur3rVq/DEE0/gxS9+MR555BGsWrUK+/fvx9FHH41vfetbOPjgg9vZX1qG3BOHVLWEl0DtJOGeMOKCIveE7IYRUYvV3JN/WD1uHboQyOjOyrjGpJrObWk9PCGm21epBAYMgZIVXofb14wOlEP27FVKYa4iUTErKFtA2jDqA59qQ2apADU3BWRyELrRUAekhfKT2wBNQ3r9Uc7zF/XBlj03icr+x6Bnc9BzKxAMwpRSMKf2o2CWMLD+CAgj05ggUtp4+KGHsG7VChx8yCHOJs3BJJ+Wwq93l7BpRRojGR0KCiIQyO2fs6GUwrrhVENw5B63J2dMjGV1DFfzOQXbsRScpJmGgBYRYJWqqyLdvJ1RgU18QARYUkFPGDxFteM/2v56vADQvR0MnihaV81ximCWCrj2gtO9pJndKnECzHXr1uE73/kOjj32WJx11lkYHx/Hf/7nf2LVqlUYHx/HOeecg2w2i9tuu62tHf7Upz6Fj33sY9i9ezeOP/54bN26FSeffHKixzIBZu/wvytDs4Kj9i07qoz/ZCRVfY4mpZzgyf+4YJJCZzuP2oiAm/xSoXa/mwTTbcuUTkDlkkrBqmYOd+ubKUsvGHJvc+sQQmC2bGNvwfJSESilUJHOSdjJtwRs21vAjgNF7znpmsBgJg1d151+mWXsf+pJlIpzteeXHoCWzcHJKyRQOfA4Krt+D2VVnPuNDDIHPQ3GioOc52ObmNv+S1R2P+LVoWWHkV51GGCkq0GmrD6+Fs1mVm5AZtUG70CaM+OoTO/3XqzswAA2bT4Co2NjXqBZtuqDzJUDOjaNpWFozusnAKR0X64sAazJGVgxUEtfEMzKntEFVg3qyBha7TVDvbQGDPiyiQsBGL7taAScbOMp3Q3LRcP7JIwm6oMgDfFJMOHdFz+aFkyCGXw+wUcxiCL3vPeyv7+t+wOnuRn8+1+e3PWX6xIHTgMDA/jd736HjRs3YsOGDbjjjjvqApYHH3wQz3/+87Fv3762dfbWW2/F2WefjWuvvRannHIKrrnmGtx2223Ytm0b1qxZ0/TxDJx6R9Q2KvOrQzWtx78VS9jvqFIKZcupJ2w7FcDZymS2ImGr8MtzSjnZwSu2asiY7S/z5LSJkqVQiEjetGemgiemKnhisoRiRBlZKkCaZcxMTYS3A0AWJmDNjkMWJsOfUDXAquza5gVV9QQyBz0NQk8BKmQ4DIDIDCKz6lBYczMRdQCbnvYMjK5YWZdZ3U8XwLMPGYRezd4dZjSrYW3OiHxtAGD9sIGMk8Y7vK+o5ocS0fmdsoYziihiklm6A35OUB9eyEh4HljsCSPJpWBaHtzz3mnv2dqVc5yCjGwOdqlQt10L0F0TxxNfqjvqqKPwy1/+Ehs3bsTw8DCmp6fr7p+ZmYFs887I/+///T9ceOGFOO+88wAA1157Lb75zW/ic5/7HC677LK2tk29qVnwlSQ5oVTNE17GJap06kBk0OSWmSzZsQGAVAqP7JuLLgBgrjALszgbXUBJVPbsiK3DntkPc29cGQVYZSBmE2BllmFWk2ZG9nWuiNyIhIiox65edooKmgBndC/umAFOtvC0Hj1xXSG+DaB2mTh+tIi5n6h79cKlOldqKI9L79zm/T5125YtiQOn97znPXjve9+LtWvX4vLLL8df/dVfYevWrXjGM56Bbdu24a//+q/xute9rm0drVQquPfee3H55Zd7t2mahhe96EX42c9+FvqYcrmMcrns/T8Y7BEREfWTqPNet6UjaMb2DcT40xZ0w8hT4sDp3HPPxfj4OF75yldCKQXbtvGSl7zEu//P/uzP8K//+q9t6SQA7N+/H7ZtY+3atXW3r127Fg8//HDoY66++mp8+MMfblufiIiIuknUea9b0xEk1U0r7hIHTg8++CAuvfRSnH/++bjrrrvw6KOPQkqJ9evX43nPex6OPPLIdvZzQS6//HJceuml3v+np6exYcOGDvaIltpi0w+4KQz8E9EbyzgTkeP2sLOVghaTosDdYDeuv0VTIZ0yYFo2wqYmKqVgm2UoaQFCD1+hJW2IdBbKsgAZvoedNXsA9sRuGGPrnHlMDXVIlJ58GNrAENJrjwhvxzZhTjwFPTcGLZ0NbadcKuLAvr1YsXoNtJDLdVIpPLK/iBWDBtYNp8P7KhX2zFpYMaBXJ3AH+qEUxosWZsoCa4eMyG+qRVMipQkYIXW4TKmQ0qIvxzkvSXw6haSapSggitKv573S5F782zmnVeeudvb3I3HgdNxxx+Gkk07CW9/6VrzpTW/C8PBwO/vVYNWqVdB1HXv27Km7fc+ePVi3bl3oYzKZDDKZzFJ0j9rAW4K9mDqqE36Dq+qCbYS146yIc6byaKimF/DNZVLVdAMSQMYQyECgbDuTyV2WVJgu27AkYGjubbUASimFmYrE7lkTmiagVdv1t2NJhUcOVPDohIVMKoVMKoVyxUTFqgU+VrmIwoGnYJWL1SdmA1rKmz+klIIszcIujENLD0KlnXlIqjznPXNZKaH42P0w9z0GAKgc+CPSazfDyK/1PqTs2XGUd22DqjhzrYyx9cgd/XwYwytr7cxNw56bcvo1sQvG6DoYo+sgtFqeN6HrmJwYx+TEOPbveQoHHXo4RkbHvDrKlsRMsYJ91avrh+TTeObBOeTSulemYivMVgBAYveshfXDBlYP1gLGiq0wWaytTnxqxsTmlRmMZHz9gLPirWhKFOFMAB9Ma3WTxN1YyqqubEzrzm3BD243LQCAhjQHbjvzSRewkJODP20BwFV1y1HUea+X5jiFGsrjw9/bDbP0h47Pd0q8qu7HP/4xbrjhBtx+++2QUuINb3gDLrjgApx66qnt7qPnlFNOwcknn4ytW7cCAKSUOPTQQ3HJJZckmhzOVXW9KS7lQPI6qsFBk0oUqgGTbCzrr8OSKnRCuLu8fq5iY6qsMFsJnzVuS4VCNWAqhEwaV0rBlgpPTlv43b5KXYoDrx2lUJgrYnZ8N0rT4+FPSGhQUkIWxhtXtykFQMEuzaL8+G9RfOx/nVVygY8EfWAExurDYE08CXtqL+rGxYQAFJA9fAuyhx0POTcNJRtX2gk9hdSqDdCHVjrBXMgZfTg/irUHH4qSraFi1x83d9TvT9YNYtPKbORk/IwucMiIAUtGr2Bck9OxcSyDjB6+Qk4AGExrGDCEN2k8WE4DkDZE5Co8tx5NOHm/wuqYjyQBVJJ0B7T89FI6giRkpdjxwCnxiNOpp56KU089FVu3bsWXv/xl3HjjjTj99NNxxBFH4IILLsA555wTOfLTKpdeeinOOeccnHjiiTj55JNxzTXXoFAoeKvsqD/FXSZLXodIlDncGcmIrgNwAvaoE7ebQHOyJEMDIpeUwI6JSmRfhBDYOVnBb/eGL+V3EkgCU0/tgFkuRbajzDLs2YjVbUIAECjufADlP/4msg57bhr2zvv9tfp+dH62JvfAXhmeAgFwLt0JI+0M30Wc2ecKBUyVZXhaiGpTM5XoYw84mcXHizaMmFVyRcvJuB7ZVzgjSnp0V+Hmao/NuVTNCdWKpJTNRp80weSXFK/X5zgBTpZx93LdxETt82apJ4zPe8uVXC6H8847D+eddx62b9+OG264AZ/61Kfwt3/7t3jZy16Gr3/96+3oJwDgrLPOwr59+/ChD30Iu3fvxpYtW/Dtb3+7YcI49Z9WBU+ySSVJfvWahV9CiMjgy1WR4dnJ/Qpm05ZgVsqR9wJw5js1EZnPqVZL0zq0TA5KSQgRnaJASw/EfrgJIxX7eADIpbWmQUTMNCUATsLLZvSYuUxAfK4mf5lWzMVI1g6DJup/RjaHv/nawxBim3ebu8fd4YcfvmTB06I2+T3iiCPwgQ98AIcddhguv/xyfPOb32xVvyJdcskluOSSS9reDhHND8/bRN2r5+c4RTCG8rjkS/fiC+8cXbLLdwsOnH70ox/hc5/7HO644w5omoYzzzwTF1xwQSv7RkRERC3Qa3mc5sOcm/HyPLnaefluXoHTrl27cOONN+LGG2/E9u3b8dznPhef+MQncOaZZyKXy7Wlg0RERLQ4/TDHKc5FN//CC5Ss4iy+/L7XYsWKFW1pK3Hg9PKXvxzf+c53sGrVKpx99tk4//zz8bSnPa0tnSLqfYvdaa+VtSwN5dsAN/T+6uT8XrEUuWI4N4moNaxSwftZVaIXzLRC4sAplUrh9ttvx6te9Srout78AV1ssZOMqTMWm8wSQGwSyiRllFLQRfykbaUUxrI69s5FzxAfSAnk0hoKEekKlFI4ZMTAE1Nm5Ca4SimMrFyL6f27o3oCYWSaZu/MbvgTmJNPAbYd86zij7458SRSqw4FMrnwYEMpmONPIL1mU3UxX3ASuIJVKqI8M4HM8Fh4RKEUHp8oY91QCmlDC0+8qRQKpkKumrczrMxE0cacKTGYip5oPmcqpHUReb+Ck5LCWTUXXkYqZ6J6VHDkpdloknOpWQCnwAni1Fy/znEKMksFXPu2V7R1vlPiPE79wM1nMTlZn8eJHza9Q6nFB08qZNNeFfjXTX6pag8CRC0Jppv80p9eyf1VKlkKRVPClqph+Xwtz5NExXZyDY0XLa8e9yQ5XbKxe9ZC2XKW10+WZEM7xVIZpUoFdqWE4sQe2L5vWUopwDZhz+yHMsuApjtZwAXgTuNWSkFZFahKEbIyh+LOB2DufTQm0FJOHoXgKyBtSNNZ3Zc5+BnIbPgT50QvNABO4lFVmoEszkBkBjGw8ZkwRtd5x9TpqwVrYhdkaRYDqzdg7KgToKWy1YDBCQ0s00SpXIahAccePIqnrRuGgpPgFNXjaknn9UlpwIoBHWmjPkBL6cDqQQPZlIZcSsNoVmtYIacJZ9NfTQgMpgQyRmNwJKp/NE3A0MJX2WnVJJlu8kug8dC6PwrffcHPo6SjXv5S/EwjV7/lcWrGnJvBFy95cdsu0wHLNHCaqAZOTBjXm1qRENOpxwmggkGT/35bAVY1qrFVYzyhlII7aGTaEoWKbAjKypbCdEVCKYWS5fzxk0phqmRjsmTDlMBTM1ZD4syyJbF7xsmCXa6YmCuV6rZdUUrBLEyjOP4UFBTk7ARkMWRTaz0FoRtQtgVZmQMCySqtqb0obPspVKXo3eY/cSulACW9JJfKLDds3SIyOQxsPgmpsYOhzBLswmRDGWNsPbKbToQw0rCn98Ge2V93cIVuYOSwYzB86DOc41Yqwbbr+zo6kMJzjliF0cE0bKlg2o3viFxKYMWgAQFgxaCOkYzWECTlMzqG0k4ApeuNSS0NDRhKCWia8AKmYDCjC3jbtQgREUihlm8pblzP/7CFXCqMC8Jo+XHPe6e9Z2tfz3ECAEPXYRYLuP2Db25r4LSodAT9gB8svcd9zVqR18kdqYi63xBAxYreg04IAV0o7C9YkfVkDAFUVN2okZ8mBMYGDNz3VAllK7w/GUNDPg08sncmdJ86IQTSQ3mU9u6EObk78uAoqwJZmkXUqdvIr0Fmw7Eo/eGe0JO2M5KkQ5ULoRnCAUCVCyhu/wXEEc8GVPhztiaeQnH7L2AMrQrti7ItTO34NZAP3y8PACaLJu57bALPPWJ16P2Akwvr0LTAykEdeuh+eMBkycbYgF57fsG+SqBkAzkNkZnCbeV8mLqjTGEk0Dzarz50MXOr3Et3/Gyj5cQqFXD9u16FsbExjI6OtrWtZR840fLmXQlapITnw1gyZiQCcEcREiTwbBpRNqlDiNZMio4Immr3O6NXsWf4FgyICxG/NYpbpmk9Ce7nxrzUrfp9jpNpGBgdXZpcTgyciIiI+lw/53ECAM22MTU1FfvlpVW5nRg4ERER9bl+z+ME1OdyCmplbqdlHzhxAmXvcl+2xVzMEQCMavqBqAtLWUNAwZnk3bgaz0lNsDqno2QpzJZlQ3+kUkjrGtbmNEyV7YbJ4UopmFLh+LUZzFYkdkyYKIdMdF4xmMIZR63CYwfmsPPAXEM7KUPHoUdvgZB/gicfvh+FiX2NT0ZPQUsPQNkmVGWu4VKYNCuwC5PQ8+sgS9NQ5bmGKoSRgbFms7MSbnIXlFmfM0UpBVmYxPS9X0dq1eHIHnI0hF7/USPLcyj99geQZgm5o56L1JqNjZOpB/MoTe6BZmSQGl4BTW/8uCpUJL7z8D5sXDmIjSsHoQU2980Yzr6Bewo2xrK6M98sIKMLTBZtpHRgIKWFXtaTUmGm7GwOnNLDL2VWbAVhK6SM8EuD8/mYWUwOKX6c0XLlz+UUdt/k5GTd79VCR6CW5aq6yckp5PMjTfOnUHfzv34LTVPgvv3dDYDD5hn5f0VM2zlBAk7QFCwnFTBTlihZqhoQwUtH4J4MS5bEZMmGLQFbVlMX+NIRKACPT5l4YtqZcJ7WBAZSwptDo5RC0bTx26dmMF6oQAiBkcEMctm01xchBCZ2PYZd2x6AVSkBQoNIZWr5k6rPSZYLUFYZSilY47tQ2bezbm6SsiqwCxOAbQKaDn1wFCLtfmt1Ug3YswdgT+0BlIQ0y5ClmWragmpfUllkD9+C1IpDACVR2bcT5r4/+lJDKaRWbkDu6NOhD+adwGzlwdAy9bsRGLlRGIMjzpwlTYOu69XsCs4vcC6t49iDRrBqKA1NACsHDQxnNO94AM4Gv6NZDXo1jUAupTdM6B5MCaSrwZGG2qbBbhFdOAGWXg3SwiZi6xpgaLUAy1vBi3BxqQTm88HOlcIU5J73XnLFTX09xymJ1MCQ9/tUKc7i8+94wYLmRC3rwIn6R6vyOwHVtAMxZdwki1H3u3mY9s81jj7563hyxsJMOToB5mxF4rFJMzLRoxACj42XMFVRzhL4QDmlJGzLwvZf/QiVcnQmXXv2AIo77oUMGV3y+mtVIIx09YQsggUgKyWU/vhA6AiVSx9e7aRCsCqNdzpZMbHixRchc+if1AVEflo6g9zKg0JHn9w47OTD8jjp0JHIFW4agI1jKeTSeuTITloD8lkNAiIyCHEDLMSUyVZHn9yVbmGSBDvNgiemIKAoyy2Pk58Rk6x7MYHTsrxUxw+W/uOujlvUZbvqaE78yjbhjThF3Q8Ak6XwoMktU7ZUZNDklpmtyMgTpnv7rIWGy1O1MhoKkwdigyYAKO16JDJoctsSqUx0BULAntkbGzQBgLLN6JV2SiG1agOyhx0XW0d6cARCC/8wdI/3iRuGY9MCDGc05NLR6QcAYDClxS7pF3BSRMRxE2O65aPqQUw7cX0M1tGsHlrelsMcJz+rVMAN1fQEURaatmBZBk5Ei9GKc1OSAM/bSmMx7SQZUG6WNiBxO/G9bXrJKSIgqqujyX54gJNrKa6tRMFFTODVagx2iNqnVSvp/Bg4ERER9bl+z+PUYCiPd/3nffjCO8dantuJgRMREVGf6/c8TkDjnKZKcbY97bSlViIiIuoa/T7HKWpOUzu2X2HgRH0j0bwhX/qBdoqYr+3REzSvV1djNasnbhWgboTv8+YnjDQWO5vK2U8u/vFKSghdjywnfRsLR5ER++P5lW2JrNAiX2M7wZQuZ+Vj/PukVe+lJCvhFpPXiajfhK2WU5rOLVeIkko0/zlQKOqkJ4SADoXQhXPV+c+G5gQ0cavr1g4Z2D9nYy4kbYFSCild4JARA7tnLVgRJ/J1QzoMDdg9Gx0sHL0mi52TFUxHrNBbuWo18s98Fn774G9gWVboZPHhP3kByo//FnM77oUQGlRwsrhmwBhZDbsw0ZDs0pU5+BlIja1H4aEfO5PN/XUIDRACA0eeAlUqoLzr4dra+VohwLZgTjyF1NhBCAZXblg3lFIYyqUxOWc2hHoCgK4J7DxQxNPX1ueA8itZElMlCyOZ6JV1RVPB0JxNnqMCm4rtJDaNut+WTmDrrr1baNzjvmZRwVNcqgMiV7/McTJLBVx7/mmhI0nt3tzXxcCJepb/vBuXd6nZN/VgGU0ICChIwMsUrvz/Cid7tK4pVCwZGmSldIH1wwYKFYn9c3Y1OFJ1fR7O6MilNRyYs7F/rhYcOUvdBQzNWTa/Jifx6ISJmUotGMnoAkMZJwnjykEde2YtPHKg7CXb1ASwJmdgbECHWLUJGw89BP/7m9/gDzse9erQszkMrNsEPTuEoUOPwdBRp2Di51+BObnbK2Pk1yA1dpCTf2nFelhTe2GO7/ICI6GnkF5/JIyR1RBCYGDTCZi5/79RfuJ3XnCUPeRo5E/9Cxj5tQCA0hO/w8wvvgJ7bsopIzSMnPTnGH7myyGMtDPaY5uAbdWebzaDzZs2YjSfBwCsGrGwa2IOc+VamSNXD+K5m0aRS+uQChCqPr+VADCa1ZBLaZBKYKokMZASSOu1MppwkmTqmoBZTVCaqg6SuWVSukCmmiDTVk69GuoDIyf5JaCEgO3eH3iPxKU7iKKUQi2VllhQHbQ89cscJ822kc/nl2RkKcqyTIA5NTWFkREmwOx1XjATeX+yt3azyzFe5u+IMqYtY0efpFJ4asZCMSJpJgBULInHpkxoQiClhSWzVHhq1sLeWds52YfkEDJthW37yzBtidVDKRgh1wv37R/HD+/5NVK5PFL5NY3tSInJe7+B4hMPITW6LvSDVloVWFN7IYw00qsObdhOBQDKe3ag9Mf/xeBRz0X28Gc2tmOZmPv9/8Cem8TISa+BMbK6oQ4lJVaODCKXy+Gg9eugafXPWSkF27JgWhaOO2gIB+WzDXUAQC6lQdeAfEb3sn37GQIYG9RhaOHHHqgGU8LJ3RRWB+DkbdKEM+oVVoeAMwK12GBHCHhJGRg0UTPuee+092ztiTlOcUkrAcAsFnD7B9/ckj3nFoojTtSzliLiF9XMmnFt6Vp1olEETQhkDREbOKUNDYMprWEvPH8/RrM6yjFTfFK6wCH5VMNeeH4rVowhd/BRkfcLTcPAhmMgzXJkGc1II7v+yNi8S5n1RyH3jNOi2zFSGH7WK6qBWUQCT03D5k0bYRjhH1NCCKwZyeJP1oYHTK7BlIbBVHRuJ1s5ZZoZSEXPmwKcUaaoZKSuZnPfkuAoE/Urq1TA9U2SVgJLd0kuCgMnIiKiPtcLc5xMw1iyCd6LwcCJiIioz3XzHCfv8lyTrP/dgoETURPur3H85br4Ze66cC4HzZnRhfJZDUVTRV5q0wWwJqdj/5wdeUnP3Xg2apUdAKzLZzFRqKAcsZxPN1IYWrMBs3ufwIIviAoNIjMIVSlGLnvUjDSMdBZWJfqyYNbQoGKuhCZL69D80patVHWCd3RBqQAN8YsNmi1GkCr+Upv/UPXA+YN6SLfmcbJKBfzbOachn88jn893/DJcEgycqGe5JyAVk8fI3bi3uehF3SndqUMqICymcYIiHVIplC0FKxDV6EJgdS4FIQQKFRtPzVh1k8lT1ZVx2VQaUik8MWXi8SmzLjjKZ3WsHcrA0ATmTIkH95Swz7cSTxdOCoSxAeebm7PKroKyrx1NAINpDaceuRqWLfGbJ6fwh72zdccuN5DF8NOOg6ZtQXFqP5584EcoTu6rFRACwsjEzm/Sh1Yivf4oiFQGStqwp/dBlmZ9VWgYXHUQBleuhxACZrmIwuQBSN8qupFcFicceShWDmeglMJMRdatKgSAdUMGNq9IR/dDAKtzBvJZp6+2VA2pH1K6M2ncvd0QqmGD4IwhkDGEt8pSh4Lmu19U63Fvisvv5DavBQKo0PdwSJoDt2/M60T9wsjm8DdfexgwS7jl//55T7yvuaqOet5iVtfN55fUrcdSgZGDaszlnswsqVC2JJSCt7LNbcetw0lBYCGf1TGS0RrKVGyF7QcqKJgS64dTGExpXv3uv7tnTPxuXxlZQ2DtkFF3wncDvR0TFTw+ZSGtOykU/AdNAZgumbj3sQnMlCVGh3LQdc1XhwQgMPHYw3jqd7+AlBKakY4cChGpLNLrj4Q+tLJ2UKr/ykoR9vQ+pAdyGF53GISeqh37al9Ks1MwizM45rB1OOLgNQ3HRCpgomQjpQsctTKN4YweGUCMZjWsGmw8JoAzMqgADGc0DPiOq/c84KyQS+sCgymtIZByy+jCCardBY5R7yVn02GExuZumoK4D2EBQNNqD+2FEwt1D/e895IrburaOU5ubqbDDz+8J97fHHGinuf/1h5+f+3EGfZz8nacoEUPtifq29EFkNY0yJB23J9XDGgYTKdD+yGEk1voiJVpzPguuXn5iKr/rhkyoIC6USV/WV0Ah4wYmChKyODBEc6C9pFsCketH8NjkyaCZ3YhnIhgdMOROPD4I6jMzUQeG20wj8xhx3vL5BH4V0tlkdvwdGSy2YZ23L4MjYzi2ccdjrTeOM/BfT6Hj6axdkivuz3okJEUcmkt5Pk4P6d05Y1ChdWh4ARNcYGZgjMSFRZU1R0X/10hxZJ8a9WbBGZESXTzHKeUEBgdHe2Z9zgDJ+obTb+5hwQw825DCC8IiapCCAGJ+O04VLW3UfcLIWDK+OBOEyI0aPKbM53RnLh2pkruJb+I5fqWGRs0AYA2kHceH31QkEq527+El8llDGRC8lP5DaTqg8cwg6lg8FYvpcenFQCcwCmuHQFE5nPyl3HqiC3WtI5eOZlQd+uWOU7BPE1msYD/uOQVPTG3ycXAiagNmgVx/Ug02/yD53+iZcvQdZjFAq4NCZJ6abQJYOBERETU9zqZx8m/v1yvBUlhGDgRERH1uaWa4xS6ZUp1DlO3J7ZMioETEbVIk2+Ry+3a5RJSIakLiPzaNcfJHyjFzVfqpTlMzTBwIpoHb3f6Jtz8UlE00XweVFoTKDeJNgZTAnMxe+Dl0hoMrbYEv6GfAFYMGJgsVaChlmfIL5XKYDC/AnNT46FtCABybtKZ4yRE6PMWAjDNCjL6QOTznqvYqFgydANjr4wpvfQNUQoViaFMdJ4pS6qmqyrLtkIm5tNRwckLFbeqzkvIsIigRiE+L5S/bgZPtJSi5iz1w6W4Zhg4Ud9wc+W0a2DDO4EB0OEEGVFtpXUBKRXMiIBF1wSG0kBFAuWQrJpOqgANwxkN40U7NJu4oQEbR1OYqSg8OW02ZNfWBHBoPo2jVmbwv7tL2Hag0hC0DGc0/PkzhjFdlvjmthnsnq0loXSP50kbcnjXs/8PfnLfg7jtrp/CtC1IWTsWudwgXvr85+Dggw/Bj36/D7/fM+3L4OT8e/BYDqdsXoM5U+HXu2brMqhr1cV4p28axkmHDOKpWQtPTDn98Pd1wBDYNJZGLq1humSjEhLlrchqGM3qTqAWcr8AMJzWkNUFLBWekTylAbmUFhvYunsCKwivUNi5QqpaSoKFBDYCTtCrafAicREITt1EqW7VDKAoTCvnOPXbnKX5YgJM6ktx2cQXVl94bUoBtu//wYXwSilYCl5mamcZe/3ogZQKRUt6ZdK6QMpXRimFOVNhvGh7J+KsIbzkmoAz+rGnYGF/NZv4SMZJAOlfMj8+Z+EXTxRxoGjD0IAjV6axfsjw2pFK4f5dJdz9h1lUbIVDRgy85IhhrM7Vvl9NzRZwy7d/jJ8+8DCEEHjOiVvwvJNPQNpLNQD88UAB333oKUzOmchlDDx78xpsWDlU19ft+4vYtm8OUgFPX53FS582iny21k7RlPjDeAWTJQlNAIeNprB+2PAydiulULYVpssSUjnHY4OXv6l27E1ZC44GDYGRrFaX9VsqhYpdC/LyWQ0DhvAd+2pd1fLusdcCJwonbQDqb4spI9B8kWFYnf7M4VFjn+7jGEARUDvvvezvb2tZ4CQrRXz+HS/omzlL88XAifpW1H5u89XsV0T6grSo85QlFSTQcML1q1gSQkSXKVsS02VZ3XstvMxsxYYlEZkPSUqJh/dXMJTW6jOJ+8yUbeydtXHwiBHZzs9/vwvQUxjLh/8eVSyJ7fsKWD2chaGH92UoJZA1gIPymdD7lVKYrUhkdA1pI7wfhgZkdIGhdHRupqwhoGtAOqIfSink0jpSevSx10X1jxa9Cakbo8Z9+9Z9gVOcJimimn7Db/Z4Wj7c895p79m6qDlO7lwmdx5Tr2T5bgdeqqO+tVS5lJLNeRKIn5nTPKGiJupHmcKkdQ0R8YHXD3c/uyiDKQ2H5ON7u271qtjkm7omsH40/tvtUEbH6lx0X4RwsnfH0RKU0TXhXVqLaicTEZjVysQHTf66Yu+PvZeprqi7+IMl/1ym5Xh5zo+BExERUZ+b7xwn/zwmgMGSHwMnIiKiPjefPE6Grvdd7qVWYuBEfS3J5bpWXNLTRPyEdHduix1RRgBIa85KPSssJwCc+TxjWQ1zpgrf2BfAUFqDAjBbkaFzvAwNOCyfwmxF4kDRbiwAYNWgjoGUhj2zVmiqA0MDjl2TQclW+P3+cujqtXVDBp51UAo7JyvYOWGG92M0hXxWx4GiDTPk+aQ0YCSrw7QVZsoy9LiNZjWMZTUUTIVKSB2acCaF6xq8SeCN7TgbCCsVno4B8L1Hmk64jt92RvruXcx396gUBe7ttkLTDYjj669/Jhxo6H1J8zipSsnLxdRPuZdaiYET9S1vZVH1/8GTpv9c0CzwiaNpzion919/wBJcGaUJ56TmP8frAt78Jw2AoTtpCvzLzA0N0IQGpRQyhoaSJTFTll49aV0g65unM2DomK1IFExf2oCUQMbQoZTCUFrD2ICOXTOmFxwNpgTWDxtIVedR5bMZHJizsGvGgi2dOsYGNKz0zZHaMJLCb/eV8fiU6dVxzJosVuUMKKVw0EgKR6608Msn5jBVcsKSQ0ZSOH59FplqNDmU1jBRsjE+Z9etbhuqrpAbMARyaQ0TvrQMA4bAhnwKg6n6Y+KusnPrzWc173U2NKBiKy/Q04RTJmto1bxOzvH3B7eaAAxNVOsQ1bxKgRVywv9eil/pBgUogdDSCwlS/PmogosYZLWz8wmggr8DyvcDg6f+5uZlWu4Tv5PgqjpaFtyl2e67PWy5dnDpeWMdylsK7uf/gHHvcwMAt3xYGVv6V2I1tmNLVZcHKKyO2YqEEAK6Ft6OJYGSJb0gJXi/EAJTJRsKCsMZPbQOqYB9BQsDhpNMM6yOyaKNiZKNg/NOWoLgkn8BYMd4BVlD84KqYD22AiZLNgYM0XCyd8uXTImsIbCiGryFHZOiJTGQ0r0J4cEyqvri5KoF6u6HgoCAVApaxHF1aajmV0J0YFIXGEWch3S0P32AG9xFJuts8t736gEDqF7jnvdecsVNkXOclntepvniiBMtC2GfA415d+JTGDTk5Qmp1LvNF1yFPU4pBV0LP1m65d0RkLh2DF2LvGwjhIAmlG9EJbz/Q5n4OoSSGAkJqvzlh7MacpnwlABuELUhn4Zs0s5QOr6vYwN6XZ6lsDIjmcagyl9GFwrZVPhKPHdsSfdGmaKDjWZBU63N6Pv8vWjnuSpJ1UmCJupdUXOcOJ9p/hg40bLS7OSUaE6UaF7Kaaf5qSa+PyLRyTTuxN3s5J+ojurzjW+neV9VC9pJmp+oeTtNHp+gXKu+lS/Fl/te6iu1R9gcJ6tUwA3vehXGxsY4n2keGDgRLUizMwjPMETU/fL5PEea5omBExERUZ8Ly+NkGtG7A1A0Bk5ERER9LmyOU0o0z4ZPjRg4EfWzJJO2lkgrurK0TyU+J5NTZPFL4ZZiM96oyf/zr4fznHpVcI6TO7+Jc5vmr9n2WUTLSn1Ons5K0o+0Fl9OALH7tAGAIeInXQtRnyMqjJagnaxRSwQa2g7QtB3bVon29IsrId1Mlk3FtyOlavoiNXsNw1OQtp67WnQxyWe6JP6mFtE1nakHFogjTkQBXqLCqPtRy7uz0JNJVNLCYD/cj7SwNAmaAHRDQ0qpusSOLkMA6erSfVsqzJkKweTaGaO2cbBpKxStQI4qACldQDM0ZFMKcxXZkKE7aziBlRACFVthtiLr2tGqOZPSupOGYbaiMFOp72xKAwZSGjQhYEmFmbKNsi+qEABWDurIZ5zozJTAXKU+m7iuAUNpHalqOyWrMZt4RhfIpgQ04eRqskKyiad14W2ULFVjJncBJ0mpJkTke0CD732C6DJJVwkulhDCGz/zv+WCSTyjno8IlKfe45/jZJYKuPZtr+Ck8AVi4EQUIuxEEkz+1yzAStZOeAAV/BaoQdW2wQgkMxRCIGMIpKrBAgCkdUD31aFrAkNpZ9uRkq1gaE6A4G8nbQgYukLRlLClkzHbn4hSEwJDGWcLlLmKDSGAwZRWNwKU1gXGshqKlhOEZXRRl3dJCIHhjMBgSmCiZMOWTuCV8g1FGZrA2ICBkiUxZ0qkdYFVg4YX4LnPL5XVvOBoIKV5wZvbzkBKIK0rlEwn7flASqurQxMCKd1J8GlLJ/BKBY6JLgBNOIlElXLK+I9J2PvETWjpvZa+11GhPqhaCvUJPhv7VF+2VijqfU+9yT/HKVXN28TRpoVh4EQUo9mlu1YET049zXMGNfuM04TAQMxvtBDCCahirpdpQiBraJH75QFOcDGSDU8g6bYzYAhkY/qiawIjmfh2soazvUtkVu5qcDSUiX4+uuYEe1HHzkmI6QSNUYQQiMiX6StTC4hC73f704Fgaf6Prf7bor5Qd3DnOPn3oqOF4RwnIiKiZcAqFfDZi1/OvegWiSNOREREfS6bGwGgeImuBRg4EVGDVqQOSFJHszL8eO9e/g2zqftpmUFALtU6zv7GwImoBbQmq+yCE4Tne/98+gHErI4SzsRQpZwVdsEyAs4Eb80QMG1n4nWwTEoDUtWGyrYKXXWWTTmr9SzpTFgPrgo0NGAwpUMAKFoKhcAKOQEgl9YwYDirwUy7cUWgJqp9FQK2auwH4MwpctMk2AoNdbjHpBmBWpDX7PWLq2NpJchDtdCafe8dpTiBvBfMje+uWzBCC8fAiWiRwlZQBVckuYkD3ZNMMEiAr1yzICy0D3X9cVoQCE9j4DI0Z0m+G0w4K8iEt8LP0JwybqoDXTir2dwVfUopZA0NtlQo205wlNZFXT4nXQC5lEDFdoIsTTgpAXTNebyoTmjP6jpmKhJlWyFrCAyltdrKQaWQqbbjBnJp3QnM3L5qcHJaucGRBiCl+4+tgA6nfUvWQookmz6HvV6hr19MHa3kHnvhe62C90W1vNi0AlHvS39qLJ6bu5NVmsMN730DUxC0AAMnohZxTxhucBR2XzA48gdV/nKJ20SwjtpyfKWUEwhVUxn45zV4y+nh5Huq72t9HW4w5LSj6u4HnOcyYDTW7f85ratqfqXwdgCFfFaHdPsc0U4wQWawjA4n1UKwDq+MUkhpzgiVCvQ17NgGf/b/qwL/xtWx2GBCRByTqLJh/Qi+1+arWTDfvvEtaoXs0AjnN7UIV9URtVhdUsGQz6hgkBRXZr5t1t9WO83HLel3/w094fryFdXXWV+m2Qk9aTtaTD1J2/GPMoXdr6pPqFnQ5P6JK4MmZapNLUrcc05yTPx9WExfEuVa5zm5a2lGptNd6BsMnIjaoJdOIK36Btq8nt5qp5dwFIGamZvcF7tTASXHwImIiKjP6ZrGALtFGDgRERH1uU++7SWcGN4iDJyIiIj63MjICEecWoSBE1GXSvoRx2kL87OUp45WnKeUUouem6JU/PvEvT+qTNLmm7VD1A8YOBF1KSGSneTjTs5J64ivX7Qk2HDyRMW0Uy0T15bWpA63TOxqOAE02bO3JQktBVoXRCx2pMCfrNLfJ////V0NlpkPDmpQv2MeJ6IuJtxEQUBk0syF1uHW488/FXY/AIhqtKJUYxZwp43a/WHtC18ZTSnYsr4tXaulItAAJzGnLxO4qJbx1yFVfYJPXbgpIJr3VQhAVHM5+bOJi2r7ccfEDdzi2vEf18Vo5aWVqLSYKvCzPw9ZWFAVplXPl6gXMHAi6nLeCUmF3NbCOtwAR8WWEdBQCxSCJ3Z/MBEMmPxlDN0JjqDqgx2XJgSE5iTuhKgFVf46dFELoLSQOoJ9DfbXG0lzk2EmOCZhfXXbafXebe2aj+IGR4st42LQRMsNAyeiHtGaEYzFl0ly6U4IkeCSWvw1NXdkqFk7erM64qtIdDkzLgFo7fbWTe7plUm8DJpoOeIcJyIiIqKEGDgRERERJcTAiYiWvZZcBkV/bfbST8+Feufyby9g4ERE8yYQHSi4t3dLTp+4vgK19AW6Fj2nqtlJx53ro2nN53bF1tMlJze3F1GpHQSap4Wg7jI6OtrpLvQNTg4nonnzL9fXRG3Z/nzSJCyV5n31rbSrlg9NzRDypPzl/fdronFlmv/+uISWnQyeol4/d+p7sGfd9DpTvG4JyvsBR5yIaMH8n8W1/Ead6UszSfrqrORzhlnc1AORK+ki6vY/Jvivv0zw306f2KKPSf3KQ/f/RMtVTwROO3fuxAUXXICNGzdiYGAAmzdvxpVXXolKpdLprhER/KMune5JcyJxgNfk8lyTAEJUx2mapTDodMDk1zz9w9L0g6ib9cSluocffhhSSlx33XU44ogj8OCDD+LCCy9EoVDAxz/+8U53j4jQbyfVVj2ZphmiWtRO+/XX60u0cEItdvfIDvnYxz6GT3/609ixY0fix0xPTyOfz2NqagojIyNt7B0R9bKwrVqCmk2ODpsn1c042bs/8bzXej0x4hRmamoKK1asiC1TLpdRLpe9/09PT7e7W0RERB3D81779cQcp6Dt27dj69atePvb3x5b7uqrr0Y+n/f+bNiwYYl6SEREtPR43mu/jl6qu+yyy/DRj340tsxDDz2Epz/96d7/n3zySZx++uk444wz8NnPfjb2sWGR94YNGzhkSUSxklxmi0u90GuX6YBgaoVO94YWiue99uto4LRv3z4cOHAgtsymTZuQTqcBALt27cIZZ5yBZz/72bjxxhuhafMbMOO1XiJKKi74Cdvc1v0kjcoDFVaH99iFdbGunlZ9kPsDQgZRvY/nvdbr6Byn1atXY/Xq1YnKPvnkk3j+85+PE044ATfccMO8gyYiovlwcxcFg6CwoMkvycTyYB1h7SxFHWHcRJe9uWyIqP16YnL4k08+iTPOOAOHHXYYPv7xj2Pfvn3efevWretgz4io3wk3bTbiA6YkcUZc0OWO8CTqU0Q986kjjgJX2RFF6YnA6e6778b27duxfft2HHLIIXX39Wg2BSLqIa26XNWyzYRjE2/23vwqol7SE9e7zj33XCilQv8QERERLZWeCJyIiIiIugEDJyKiFhCI30DFW/0WM1DerA5Xs8H2JH1pdr9SnCBOFIaBExFRC2miMSiZ79SmqDqS1uNtZBxSh3/Sd1xfmYaAKFxPTA4nIup2wUAjKh9S7MTuhHUk6Yf7GC1kpV0r+kq0XDFwIiJqsWBagIUEIA3BzSLqmFewxmCJKBYDJyKiNmAAQtSfOMeJiIiIKCEGTkREREQJMXAiIiIiSoiBExEREVFCDJyIiIiIEmLgRERERJQQAyciIiKihBg4ERERESXEwImIiIgoIQZORERERAkxcCIiIiJKiIETERERUUIMnIiIiIgSYuBERERElBADJyIiIqKEGDgRERERJcTAiYiIiCghBk5ERERECTFwIiIiIkqIgRMRERFRQgyciIiIiBJi4ERERESUEAMnIiIiooQYOBERERElxMCJiIiIKCEGTkREREQJMXAiIiIiSoiBExEREVFCDJyIiIiIEmLgRERERJQQAyciIiKihBg4ERERESXEwImIiIgoIQZORERERAkxcCIiIiJKiIETERERUUIMnIiIiIgSYuBERERElBADJyIiIqKEGDgRERERJcTAiYiIiCghBk5ERERECTFwIiIiIkqIgRMRERFRQgyciIiIiBJi4ERERESUEAMnIiIiooQYOBERERElxMCJiIiIKCEGTkRERF1MqU73gPwYOBEREXUpBk3dh4ETERFRl/EHTIydugsDJyIioi7hD5ikYtDUjYxOd4CIiIhqJKOlrsYRJyIiIqKEGDgRERERJcTAiYiIiCghBk5ERERdgtObuh8nhxMREXWY4gq6nsHAiYiIqEPc9AMMmnoHL9URERF1EIOm3sLAiYiIqEMYNPUeBk5ERERECTFwIiIiIkqIgRMRERFRQgyciIiIiBJi4ERERNQhotMdoHlj4ERERNQhQjjBEwOo3sEEmERERB0k3KiJ2cN7AkeciIiIuoA7+kTdjYETERERUUI9FziVy2Vs2bIFQgg88MADne4OERERLSM9Fzi9//3vx0EHHdTpbhAREdEy1FOTw7/1rW/hrrvuwh133IFvfetbTcuXy2WUy2Xv/9PT0+3sHhER0aJpAlALnCjO81779cyI0549e3DhhRfi85//PAYHBxM95uqrr0Y+n/f+bNiwoc29JCIiWjjhmx2+kIniPO+1n1BKdf3qR6UUXvGKV+B5z3serrjiCuzcuRMbN27E/fffjy1btkQ+Lizy3rBhA6ampjAyMrIEPSciIloY/9lZJIyieN5rv45eqrvsssvw0Y9+NLbMQw89hLvuugszMzO4/PLL51V/JpNBJpNZTBeJiIg6wg2W5jO8wfNe+3V0xGnfvn04cOBAbJlNmzbhzDPPxH/9139B+EJu27ah6zre8pa34KabbkrU3vT0NPL5PCNvIiJaFnjea72euFT3xz/+sW6C265du/DSl74Ut99+O0455RQccsghierhG4iIiJYTnvdarydW1R166KF1/x8aGgIAbN68OXHQRERERLRYPbOqjoiIaDnq/utCy0tPjDgFHX744eiBK4xERESLolTyFXW0NDjiRERE1IXc8QHJcYKu0pMjTkRERP0oeDGFMVP3YeBERETUZRgwdS9eqiMiIuoiDJq6GwMnIiIiooQYOBERERElxMCJiIiIKCEGTkRERF1EEwBTN3UvBk5ERERdwk12KarBEwOo7sPAiYiIqAu5QZTG6KmrMHAiIiLqUkJwr7puw8CJiIioi3Gvuu7CwImIiIgoIQZORERERAkxcCIiIiJKiIETERERUUIMnIiIiIgSYuBERERElBADJyIiIqKEGDgRERERJcTAiYiIiCghBk5ERERECTFwIiIiIkqIgRMRERFRQgyciIiIiBJi4ERERESUEAMnIiIiooQYOBERERElxMCJiIiIKCEGTkRERF1MKdXpLpAPAyciIiKihBg4ERERdSGlFJRSKNscceomDJyIiIi6iHtpzpLAbEWibDFw6iYMnIiIiLqELRUsCRQqNuZMCdmimGliYoJzpVqEgRMREVGXUADmTAlLtrbet/3HjzA5OdnaSpcpo9MdWEputD09Pd3hnhARETWypEKh0hg1iYqO4eFhCCE60CvyE2oZjd098cQT2LBhQ6e7QURENG9TU1MYGRmZ12Omp6eRz+cX9FgKt6wCJykldu3a1RNR+/T0NDZs2IDHH3+cb/YW4nFtDx7X9uBxbZ9ePLYLOXcppTAzM9MT571esawu1WmahkMOOaTT3ZiXkZGRnvml7iU8ru3B49oePK7t0+/HVgjR18+vEzg5nIiIiCghBk5ERERECTFw6lKZTAZXXnklMplMp7vSV3hc24PHtT14XNuHx5YWallNDiciIiJaDI44ERERESXEwImIiIgoIQZORERERAkxcCIiIiJKiIFTDymXy9iyZQuEEHjggQc63Z2etnPnTlxwwQXYuHEjBgYGsHnzZlx55ZWoVCqd7lpP+tSnPoXDDz8c2WwWp5xyCn75y192uks97eqrr8ZJJ52E4eFhrFmzBq95zWuwbdu2Tner73zkIx+BEALvfve7O90V6iEMnHrI+9//fhx00EGd7kZfePjhhyGlxHXXXYff/va3+Nd//Vdce+21+MAHPtDprvWcW2+9FZdeeimuvPJK3HfffTj++OPx0pe+FHv37u1013rWD3/4Q1x88cX4+c9/jrvvvhumaeIlL3kJCoVCp7vWN+655x5cd911OO644zrdFeoxTEfQI771rW/h0ksvxR133IFjjjkG999/P7Zs2dLpbvWVj33sY/j0pz+NHTt2dLorPeWUU07BSSedhE9+8pMAnD0hN2zYgHe961247LLLOty7/rBv3z6sWbMGP/zhD3Haaad1ujs9b3Z2Fs961rPw7//+7/iHf/gHbNmyBddcc02nu0U9giNOPWDPnj248MIL8fnPfx6Dg4Od7k7fmpqawooVKzrdjZ5SqVRw77334kUvepF3m6ZpeNGLXoSf/exnHexZf5mamgIAvj9b5OKLL8YrX/nKuvctUVLLapPfXqSUwrnnnouLLroIJ554Inbu3NnpLvWl7du3Y+vWrfj4xz/e6a70lP3798O2baxdu7bu9rVr1+Lhhx/uUK/6i5QS7373u/G85z0Pxx57bKe70/NuueUW3Hfffbjnnns63RXqURxx6pDLLrsMQojYPw8//DC2bt2KmZkZXH755Z3uck9Ielz9nnzySbzsZS/DG9/4Rlx44YUd6jlRuIsvvhgPPvggbrnllk53pec9/vjj+Ou//mt88YtfRDab7XR3qEdxjlOH7Nu3DwcOHIgts2nTJpx55pn4r//6LwghvNtt24au63jLW96Cm266qd1d7SlJj2s6nQYA7Nq1C2eccQae/exn48Ybb4Sm8bvEfFQqFQwODuL222/Ha17zGu/2c845B5OTk/ja177Wuc71gUsuuQRf+9rX8KMf/QgbN27sdHd63le/+lW89rWvha7r3m22bUMIAU3TUC6X6+4jCsPAqcv98Y9/xPT0tPf/Xbt24aUvfSluv/12nHLKKTjkkEM62Lve9uSTT+L5z38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- }, - "metadata": {} - } - ], + "id": "raK7hyjd_vf6" + }, + "outputs": [], "source": [ "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n", "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n", @@ -1429,104 +531,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "1xlR_dekzw7C", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "outputId": "84bec388-8283-40d4-969d-5e08ad51039f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential\"\u001b[0m\n" - ], - "text/html": [ - "
Model: \"sequential\"\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n", - "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m480\u001b[0m │\n", - "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", - "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", - "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m17\u001b[0m │\n", - "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n" - ], - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-              "┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-              "│ dense (Dense)                        │ (None, 16)                  │             480 │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ dropout (Dropout)                    │ (None, 16)                  │               0 │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ dense_1 (Dense)                      │ (None, 1)                   │              17 │\n",
-              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m497\u001b[0m (1.94 KB)\n" - ], - "text/html": [ - "
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\n" - ] - }, - "metadata": {} - } - ], + "id": "1xlR_dekzw7C" + }, + "outputs": [], "source": [ "model = make_model()\n", "model.summary()" @@ -1545,40 +552,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "LopSd-yQqO3a", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4370b50f-4971-4be0-ab8d-75d8a0d880df" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0.06593819],\n", - " [0.03485148],\n", - " [0.242398 ],\n", - " [0.15501657],\n", - " [0.1954891 ],\n", - " [0.10886171],\n", - " [0.08471535],\n", - " [0.20510358],\n", - " [0.06699 ],\n", - " [0.08843432]], dtype=float32)" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ], + "id": "LopSd-yQqO3a" + }, + "outputs": [], "source": [ "model.predict(train_features[:10])" ] @@ -1614,21 +590,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "H-oPqh3SoGXk", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "b41f7913-5178-4619-b4ff-c5394f3a1409" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loss: 0.1409\n" - ] - } - ], + "id": "H-oPqh3SoGXk" + }, + "outputs": [], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" @@ -1651,24 +615,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "F5KWPSjjstUS", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e221f48b-e952-4c53-94d4-157e576a1557" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([-6.35935934])" - ] - }, - "metadata": {}, - "execution_count": 20 - } - ], + "id": "F5KWPSjjstUS" + }, + "outputs": [], "source": [ "initial_bias = np.log([pos/neg])\n", "initial_bias" @@ -1689,40 +638,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "50oyu1uss0i-", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a7064997-d4ac-401d-98d2-950edd1e219c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[0.00354745],\n", - " [0.01596842],\n", - " [0.0011654 ],\n", - " [0.00274411],\n", - " [0.00660007],\n", - " [0.00329903],\n", - " [0.01171673],\n", - " [0.0127765 ],\n", - " [0.0021166 ],\n", - " [0.00054859]], dtype=float32)" - ] - }, - "metadata": {}, - "execution_count": 21 - } - ], + "id": "50oyu1uss0i-" + }, + "outputs": [], "source": [ "model = make_model(output_bias=initial_bias)\n", "model.predict(train_features[:10])" @@ -1743,21 +661,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xVDqCWXDqHSc", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "41e07c3a-9eb6-4fa0-b762-6d30b14078b6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loss: 0.0167\n" - ] - } - ], + "id": "xVDqCWXDqHSc" + }, + "outputs": [], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" @@ -1873,25 +779,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "dxFaskm7beC7", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "595eaab3-4d79-442d-eb33-331287891143" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], + "id": "dxFaskm7beC7" + }, + "outputs": [], "source": [ "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n", "plot_loss(careful_bias_history, \"Careful Bias\", 1)" @@ -1919,180 +809,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "yZKAc8NCDnoR", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5b8df429-2a35-429f-c669-3574f2ba5ff9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - Brier score: 0.0015 - accuracy: 0.9984 - auc: 0.7616 - cross entropy: 0.0117 - fn: 166.3517 - fp: 62.9890 - loss: 0.0178 - prc: 0.2995 - precision: 0.5583 - recall: 0.3453 - tn: 139407.7188 - tp: 72.5165 - val_Brier score: 0.0014 - val_accuracy: 0.9984 - val_auc: 0.8811 - val_cross entropy: 0.0075 - val_fn: 73.0000 - val_fp: 0.0000e+00 - val_loss: 0.0075 - val_prc: 0.5444 - val_precision: 1.0000 - val_recall: 0.0267 - val_tn: 45494.0000 - val_tp: 2.0000\n", - "Epoch 2/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - Brier score: 0.0014 - accuracy: 0.9985 - auc: 0.7690 - cross entropy: 0.0107 - fn: 114.0659 - fp: 18.7473 - loss: 0.0107 - prc: 0.2769 - precision: 0.6627 - recall: 0.2525 - tn: 93966.2188 - tp: 41.5385 - val_Brier score: 8.3124e-04 - val_accuracy: 0.9990 - val_auc: 0.9053 - val_cross entropy: 0.0049 - val_fn: 41.0000 - val_fp: 5.0000 - val_loss: 0.0049 - val_prc: 0.7088 - val_precision: 0.8718 - val_recall: 0.4533 - val_tn: 45489.0000 - val_tp: 34.0000\n", - "Epoch 3/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0010 - accuracy: 0.9989 - auc: 0.8831 - cross entropy: 0.0067 - fn: 89.7582 - fp: 10.6813 - loss: 0.0067 - prc: 0.5459 - precision: 0.8675 - recall: 0.4342 - tn: 93970.2344 - tp: 69.9011 - val_Brier score: 6.7792e-04 - val_accuracy: 0.9992 - val_auc: 0.8993 - val_cross entropy: 0.0043 - val_fn: 33.0000 - val_fp: 5.0000 - val_loss: 0.0043 - val_prc: 0.7099 - val_precision: 0.8936 - val_recall: 0.5600 - val_tn: 45489.0000 - val_tp: 42.0000\n", - "Epoch 4/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.6771e-04 - accuracy: 0.9990 - auc: 0.9013 - cross entropy: 0.0057 - fn: 75.0440 - fp: 13.6154 - loss: 0.0057 - prc: 0.5899 - precision: 0.8136 - recall: 0.4896 - tn: 93974.9453 - tp: 76.9670 - val_Brier score: 6.0917e-04 - val_accuracy: 0.9993 - val_auc: 0.9061 - val_cross entropy: 0.0040 - val_fn: 27.0000 - val_fp: 5.0000 - val_loss: 0.0040 - val_prc: 0.7290 - val_precision: 0.9057 - val_recall: 0.6400 - val_tn: 45489.0000 - val_tp: 48.0000\n", - "Epoch 5/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.6912e-04 - accuracy: 0.9991 - auc: 0.9175 - cross entropy: 0.0051 - fn: 60.9670 - fp: 18.1758 - loss: 0.0051 - prc: 0.6670 - precision: 0.8258 - recall: 0.5942 - tn: 93968.9219 - tp: 92.5055 - val_Brier score: 5.9678e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0039 - val_fn: 27.0000 - val_fp: 5.0000 - val_loss: 0.0039 - val_prc: 0.7305 - val_precision: 0.9057 - val_recall: 0.6400 - val_tn: 45489.0000 - val_tp: 48.0000\n", - "Epoch 6/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 9.2291e-04 - accuracy: 0.9990 - auc: 0.9047 - cross entropy: 0.0057 - fn: 71.8462 - fp: 14.6593 - loss: 0.0057 - prc: 0.5688 - precision: 0.8455 - recall: 0.5218 - tn: 93968.8672 - tp: 85.1978 - val_Brier score: 5.8535e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0038 - val_fn: 25.0000 - val_fp: 5.0000 - val_loss: 0.0038 - val_prc: 0.7251 - val_precision: 0.9091 - val_recall: 0.6667 - val_tn: 45489.0000 - val_tp: 50.0000\n", - "Epoch 7/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.5874e-04 - accuracy: 0.9990 - auc: 0.9201 - cross entropy: 0.0050 - fn: 76.1099 - fp: 15.4066 - loss: 0.0050 - prc: 0.6658 - precision: 0.8489 - recall: 0.4997 - tn: 93969.4609 - tp: 79.5934 - val_Brier score: 5.8049e-04 - val_accuracy: 0.9993 - val_auc: 0.9062 - val_cross entropy: 0.0037 - val_fn: 25.0000 - val_fp: 5.0000 - val_loss: 0.0037 - val_prc: 0.7429 - val_precision: 0.9091 - val_recall: 0.6667 - val_tn: 45489.0000 - val_tp: 50.0000\n", - "Epoch 8/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 9.0201e-04 - accuracy: 0.9990 - auc: 0.9116 - cross entropy: 0.0052 - fn: 78.9890 - fp: 12.8352 - loss: 0.0052 - prc: 0.6366 - precision: 0.8686 - recall: 0.5048 - tn: 93965.5625 - tp: 83.1868 - val_Brier score: 5.5696e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0036 - val_fn: 24.0000 - val_fp: 5.0000 - val_loss: 0.0036 - val_prc: 0.7449 - val_precision: 0.9107 - val_recall: 0.6800 - val_tn: 45489.0000 - val_tp: 51.0000\n", - "Epoch 9/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.1022e-04 - accuracy: 0.9991 - auc: 0.9392 - cross entropy: 0.0047 - fn: 67.0330 - fp: 16.9341 - loss: 0.0047 - prc: 0.6616 - precision: 0.8335 - recall: 0.5710 - tn: 93966.1562 - tp: 90.4505 - val_Brier score: 5.5489e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0035 - val_fn: 24.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7442 - val_precision: 0.9107 - val_recall: 0.6800 - val_tn: 45489.0000 - val_tp: 51.0000\n", - "Epoch 10/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.9357e-04 - accuracy: 0.9990 - auc: 0.9163 - cross entropy: 0.0043 - fn: 73.6484 - fp: 18.3297 - loss: 0.0043 - prc: 0.6833 - precision: 0.8038 - recall: 0.5348 - tn: 93966.5625 - tp: 82.0330 - val_Brier score: 5.4526e-04 - val_accuracy: 0.9994 - val_auc: 0.9062 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7437 - val_precision: 0.9123 - val_recall: 0.6933 - val_tn: 45489.0000 - val_tp: 52.0000\n", - "Epoch 11/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.3662e-04 - accuracy: 0.9992 - auc: 0.9137 - cross entropy: 0.0043 - fn: 66.7253 - fp: 15.3077 - loss: 0.0043 - prc: 0.7015 - precision: 0.8679 - recall: 0.5775 - tn: 93968.1406 - tp: 90.3956 - val_Brier score: 5.3281e-04 - val_accuracy: 0.9994 - val_auc: 0.9061 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7435 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 12/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - Brier score: 8.2712e-04 - accuracy: 0.9991 - auc: 0.8945 - cross entropy: 0.0052 - fn: 72.5934 - fp: 13.6264 - loss: 0.0052 - prc: 0.6423 - precision: 0.8663 - recall: 0.5552 - tn: 93966.0234 - tp: 88.3297 - val_Brier score: 5.1658e-04 - val_accuracy: 0.9995 - val_auc: 0.9128 - val_cross entropy: 0.0034 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7498 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 13/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 8.0335e-04 - accuracy: 0.9991 - auc: 0.9298 - cross entropy: 0.0047 - fn: 68.5495 - fp: 15.3626 - loss: 0.0047 - prc: 0.6476 - precision: 0.8322 - recall: 0.5705 - tn: 93967.8828 - tp: 88.7802 - val_Brier score: 5.1140e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0034 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7536 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 14/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7173e-04 - accuracy: 0.9991 - auc: 0.9307 - cross entropy: 0.0044 - fn: 68.8022 - fp: 17.8352 - loss: 0.0044 - prc: 0.6730 - precision: 0.8344 - recall: 0.5772 - tn: 93967.0234 - tp: 86.9121 - val_Brier score: 5.1549e-04 - val_accuracy: 0.9995 - val_auc: 0.9129 - val_cross entropy: 0.0034 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7522 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 15/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.9408e-04 - accuracy: 0.9991 - auc: 0.9324 - cross entropy: 0.0042 - fn: 70.7582 - fp: 18.1538 - loss: 0.0042 - prc: 0.7199 - precision: 0.8482 - recall: 0.5600 - tn: 93963.6797 - tp: 87.9780 - val_Brier score: 5.1984e-04 - val_accuracy: 0.9995 - val_auc: 0.9129 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7546 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 16/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.3687e-04 - accuracy: 0.9991 - auc: 0.9155 - cross entropy: 0.0041 - fn: 64.7582 - fp: 14.6703 - loss: 0.0041 - prc: 0.7072 - precision: 0.8566 - recall: 0.5865 - tn: 93966.4688 - tp: 94.6703 - val_Brier score: 5.1591e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7601 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 17/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.4693e-04 - accuracy: 0.9992 - auc: 0.9257 - cross entropy: 0.0039 - fn: 61.0330 - fp: 15.3407 - loss: 0.0039 - prc: 0.7023 - precision: 0.8408 - recall: 0.5937 - tn: 93972.6797 - tp: 91.5165 - val_Brier score: 5.1012e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7647 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 18/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.4157e-04 - accuracy: 0.9992 - auc: 0.9369 - cross entropy: 0.0040 - fn: 61.2418 - fp: 15.1319 - loss: 0.0040 - prc: 0.7374 - precision: 0.8788 - recall: 0.6364 - tn: 93960.3984 - tp: 103.8022 - val_Brier score: 5.2259e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 20.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7690 - val_precision: 0.9167 - val_recall: 0.7333 - val_tn: 45489.0000 - val_tp: 55.0000\n", - "Epoch 19/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.1597e-04 - accuracy: 0.9992 - auc: 0.9065 - cross entropy: 0.0039 - fn: 60.9890 - fp: 15.5604 - loss: 0.0039 - prc: 0.6899 - precision: 0.8270 - recall: 0.5961 - tn: 93977.8047 - tp: 86.2198 - val_Brier score: 5.1175e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7670 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 20/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0362e-04 - accuracy: 0.9992 - auc: 0.9146 - cross entropy: 0.0038 - fn: 61.7363 - fp: 15.2747 - loss: 0.0038 - prc: 0.7365 - precision: 0.8786 - recall: 0.6190 - tn: 93970.2500 - tp: 93.3077 - val_Brier score: 5.0103e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7687 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 21/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.2299e-04 - accuracy: 0.9992 - auc: 0.9247 - cross entropy: 0.0040 - fn: 62.0989 - fp: 17.2198 - loss: 0.0040 - prc: 0.6831 - precision: 0.8256 - recall: 0.6047 - tn: 93971.5703 - tp: 89.6813 - val_Brier score: 5.0661e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7705 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 22/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5303e-04 - accuracy: 0.9991 - auc: 0.9384 - cross entropy: 0.0039 - fn: 69.7363 - fp: 14.5714 - loss: 0.0039 - prc: 0.7114 - precision: 0.8500 - recall: 0.5737 - tn: 93967.2969 - tp: 88.9670 - val_Brier score: 5.0563e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7739 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 23/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.2432e-04 - accuracy: 0.9992 - auc: 0.9361 - cross entropy: 0.0039 - fn: 56.0110 - fp: 15.3516 - loss: 0.0039 - prc: 0.7303 - precision: 0.8631 - recall: 0.6273 - tn: 93967.5703 - tp: 101.6374 - val_Brier score: 5.2901e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7761 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 24/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 8.0118e-04 - accuracy: 0.9991 - auc: 0.9335 - cross entropy: 0.0043 - fn: 70.5165 - fp: 15.8352 - loss: 0.0043 - prc: 0.6537 - precision: 0.8258 - recall: 0.5326 - tn: 93965.4141 - tp: 88.8022 - val_Brier score: 4.9249e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7781 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 25/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7466e-04 - accuracy: 0.9992 - auc: 0.9051 - cross entropy: 0.0044 - fn: 65.0549 - fp: 15.5604 - loss: 0.0044 - prc: 0.6598 - precision: 0.8677 - recall: 0.5912 - tn: 93967.4297 - tp: 92.5275 - val_Brier score: 4.9957e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7741 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 26/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.2408e-04 - accuracy: 0.9993 - auc: 0.9504 - cross entropy: 0.0034 - fn: 55.3956 - fp: 17.5275 - loss: 0.0034 - prc: 0.7545 - precision: 0.8694 - recall: 0.6804 - tn: 93964.5312 - tp: 103.1209 - val_Brier score: 5.0585e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7784 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 27/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.1493e-04 - accuracy: 0.9993 - auc: 0.9330 - cross entropy: 0.0033 - fn: 52.7143 - fp: 11.6593 - loss: 0.0033 - prc: 0.7650 - precision: 0.9083 - recall: 0.6557 - tn: 93973.6562 - tp: 102.5385 - val_Brier score: 4.9942e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7781 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 28/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 9.8291e-04 - accuracy: 0.9989 - auc: 0.9099 - cross entropy: 0.0050 - fn: 78.3407 - fp: 19.3626 - loss: 0.0050 - prc: 0.6648 - precision: 0.8219 - recall: 0.5242 - tn: 93951.8906 - tp: 90.9780 - val_Brier score: 5.1021e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7821 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", - "Epoch 29/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.2133e-04 - accuracy: 0.9993 - auc: 0.9352 - cross entropy: 0.0032 - fn: 58.9890 - fp: 14.0659 - loss: 0.0032 - prc: 0.7587 - precision: 0.8891 - recall: 0.6213 - tn: 93972.6016 - tp: 94.9121 - val_Brier score: 5.0344e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7813 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 30/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - Brier score: 6.7470e-04 - accuracy: 0.9992 - auc: 0.9465 - cross entropy: 0.0036 - fn: 60.6703 - fp: 13.8462 - loss: 0.0036 - prc: 0.7446 - precision: 0.8787 - recall: 0.6431 - tn: 93966.3281 - tp: 99.7253 - val_Brier score: 5.0940e-04 - val_accuracy: 0.9995 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 20.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7802 - val_precision: 0.9167 - val_recall: 0.7333 - val_tn: 45489.0000 - val_tp: 55.0000\n", - "Epoch 31/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 6.5162e-04 - accuracy: 0.9992 - auc: 0.9420 - cross entropy: 0.0034 - fn: 60.9560 - fp: 13.5934 - loss: 0.0034 - prc: 0.7858 - precision: 0.8839 - recall: 0.6286 - tn: 93960.9453 - tp: 105.0769 - val_Brier score: 5.2065e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7825 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 32/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5032e-04 - accuracy: 0.9991 - auc: 0.9109 - cross entropy: 0.0039 - fn: 69.4945 - fp: 13.1758 - loss: 0.0039 - prc: 0.7179 - precision: 0.8657 - recall: 0.5539 - tn: 93965.1016 - tp: 92.8022 - val_Brier score: 5.1955e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7838 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 33/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9713e-04 - accuracy: 0.9992 - auc: 0.9564 - cross entropy: 0.0033 - fn: 62.0659 - fp: 13.9670 - loss: 0.0033 - prc: 0.7726 - precision: 0.8806 - recall: 0.6050 - tn: 93970.2500 - tp: 94.2857 - val_Brier score: 5.1435e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7818 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 34/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.8773e-04 - accuracy: 0.9992 - auc: 0.9417 - cross entropy: 0.0035 - fn: 61.5055 - fp: 13.9670 - loss: 0.0035 - prc: 0.7517 - precision: 0.8687 - recall: 0.6379 - tn: 93962.8828 - tp: 102.2198 - val_Brier score: 5.1135e-04 - val_accuracy: 0.9994 - val_auc: 0.9195 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7846 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 35/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.6725e-04 - accuracy: 0.9991 - auc: 0.9264 - cross entropy: 0.0041 - fn: 62.0110 - fp: 18.2308 - loss: 0.0041 - prc: 0.6783 - precision: 0.8326 - recall: 0.5701 - tn: 93967.3438 - tp: 92.9890 - val_Brier score: 5.1539e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0033 - val_prc: 0.7841 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 36/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.3579e-04 - accuracy: 0.9991 - auc: 0.9536 - cross entropy: 0.0041 - fn: 65.5275 - fp: 17.3516 - loss: 0.0041 - prc: 0.7264 - precision: 0.8460 - recall: 0.5842 - tn: 93960.2109 - tp: 97.4835 - val_Brier score: 5.0522e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7808 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", - "Epoch 37/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 8.1126e-04 - accuracy: 0.9990 - auc: 0.9367 - cross entropy: 0.0039 - fn: 66.8901 - fp: 18.0220 - loss: 0.0039 - prc: 0.7194 - precision: 0.8227 - recall: 0.5816 - tn: 93960.9531 - tp: 94.7033 - val_Brier score: 5.0323e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7809 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", - "Epoch 38/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.3380e-04 - accuracy: 0.9991 - auc: 0.9402 - cross entropy: 0.0038 - fn: 63.3077 - fp: 19.4945 - loss: 0.0038 - prc: 0.7475 - precision: 0.8182 - recall: 0.6160 - tn: 93954.4297 - tp: 103.3407 - val_Brier score: 5.1817e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0034 - val_prc: 0.7832 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 39/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.0546e-04 - accuracy: 0.9993 - auc: 0.9490 - cross entropy: 0.0031 - fn: 58.7143 - fp: 14.3516 - loss: 0.0031 - prc: 0.7651 - precision: 0.8702 - recall: 0.5991 - tn: 93976.9531 - tp: 90.5495 - val_Brier score: 5.0504e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7814 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", - "Epoch 40/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 7.6890e-04 - accuracy: 0.9991 - auc: 0.9364 - cross entropy: 0.0040 - fn: 63.1319 - fp: 15.4835 - loss: 0.0040 - prc: 0.7046 - precision: 0.8488 - recall: 0.5873 - tn: 93969.2344 - tp: 92.7253 - val_Brier score: 5.1110e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 22.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7847 - val_precision: 0.9138 - val_recall: 0.7067 - val_tn: 45489.0000 - val_tp: 53.0000\n", - "Epoch 41/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7761e-04 - accuracy: 0.9992 - auc: 0.9165 - cross entropy: 0.0036 - fn: 60.7692 - fp: 8.9890 - loss: 0.0036 - prc: 0.7558 - precision: 0.9148 - recall: 0.5847 - tn: 93973.3750 - tp: 97.4396 - val_Brier score: 4.9544e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0033 - val_fn: 17.0000 - val_fp: 5.0000 - val_loss: 0.0033 - val_prc: 0.7828 - val_precision: 0.9206 - val_recall: 0.7733 - val_tn: 45489.0000 - val_tp: 58.0000\n", - "Epoch 42/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.3288e-04 - accuracy: 0.9991 - auc: 0.9468 - cross entropy: 0.0036 - fn: 67.1758 - fp: 16.2418 - loss: 0.0036 - prc: 0.7541 - precision: 0.8636 - recall: 0.5802 - tn: 93964.4609 - tp: 92.6923 - val_Brier score: 5.3282e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7825 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 43/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7773e-04 - accuracy: 0.9992 - auc: 0.9439 - cross entropy: 0.0034 - fn: 58.4396 - fp: 17.2198 - loss: 0.0034 - prc: 0.7504 - precision: 0.8419 - recall: 0.6661 - tn: 93964.6797 - tp: 100.2308 - val_Brier score: 5.1650e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7847 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 44/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2941e-04 - accuracy: 0.9992 - auc: 0.9365 - cross entropy: 0.0038 - fn: 64.6484 - fp: 14.7253 - loss: 0.0038 - prc: 0.7319 - precision: 0.8782 - recall: 0.5995 - tn: 93968.1797 - tp: 93.0220 - val_Brier score: 5.2610e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7863 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 45/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9266e-04 - accuracy: 0.9992 - auc: 0.9221 - cross entropy: 0.0037 - fn: 63.4615 - fp: 13.6484 - loss: 0.0037 - prc: 0.7195 - precision: 0.8849 - recall: 0.5841 - tn: 93971.1641 - tp: 92.2967 - val_Brier score: 5.4107e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7843 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 46/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 7.5421e-04 - accuracy: 0.9991 - auc: 0.9328 - cross entropy: 0.0039 - fn: 63.8462 - fp: 15.9890 - loss: 0.0039 - prc: 0.7441 - precision: 0.8518 - recall: 0.6037 - tn: 93959.2344 - tp: 101.5055 - val_Brier score: 5.2481e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7843 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 47/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 7.1543e-04 - accuracy: 0.9992 - auc: 0.9391 - cross entropy: 0.0037 - fn: 62.7363 - fp: 16.2418 - loss: 0.0037 - prc: 0.7472 - precision: 0.8673 - recall: 0.5994 - tn: 93968.3281 - tp: 93.2637 - val_Brier score: 5.4077e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 24.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7862 - val_precision: 0.9273 - val_recall: 0.6800 - val_tn: 45490.0000 - val_tp: 51.0000\n", - "Epoch 48/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 6.9187e-04 - accuracy: 0.9992 - auc: 0.9307 - cross entropy: 0.0038 - fn: 59.6044 - fp: 14.0330 - loss: 0.0038 - prc: 0.6986 - precision: 0.8686 - recall: 0.6019 - tn: 93975.8984 - tp: 91.0330 - val_Brier score: 5.2833e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7852 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 49/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.4433e-04 - accuracy: 0.9992 - auc: 0.9338 - cross entropy: 0.0038 - fn: 64.2198 - fp: 15.1319 - loss: 0.0038 - prc: 0.7242 - precision: 0.8649 - recall: 0.5966 - tn: 93966.2891 - tp: 94.9341 - val_Brier score: 5.3255e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7871 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 50/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.4424e-04 - accuracy: 0.9993 - auc: 0.9491 - cross entropy: 0.0030 - fn: 56.7253 - fp: 13.2088 - loss: 0.0030 - prc: 0.7941 - precision: 0.8804 - recall: 0.6463 - tn: 93971.6406 - tp: 99.0000 - val_Brier score: 5.0646e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7880 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 51/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Brier score: 5.1395e-04 - accuracy: 0.9994 - auc: 0.9586 - cross entropy: 0.0027 - fn: 52.9121 - fp: 13.2198 - loss: 0.0027 - prc: 0.8149 - precision: 0.9037 - recall: 0.6944 - tn: 93973.1328 - tp: 101.3077 - val_Brier score: 5.4684e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 24.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7872 - val_precision: 0.9273 - val_recall: 0.6800 - val_tn: 45490.0000 - val_tp: 51.0000\n", - "Epoch 52/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0941e-04 - accuracy: 0.9992 - auc: 0.9302 - cross entropy: 0.0036 - fn: 64.3626 - fp: 10.4505 - loss: 0.0036 - prc: 0.7560 - precision: 0.9159 - recall: 0.6133 - tn: 93968.9531 - tp: 96.8022 - val_Brier score: 5.2056e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0034 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0034 - val_prc: 0.7885 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 53/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.5743e-04 - accuracy: 0.9991 - auc: 0.9293 - cross entropy: 0.0040 - fn: 64.5165 - fp: 13.2747 - loss: 0.0040 - prc: 0.7323 - precision: 0.8854 - recall: 0.5952 - tn: 93965.9922 - tp: 96.7912 - val_Brier score: 5.2784e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7890 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 54/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.7563e-04 - accuracy: 0.9991 - auc: 0.9317 - cross entropy: 0.0038 - fn: 66.0000 - fp: 16.6923 - loss: 0.0038 - prc: 0.7352 - precision: 0.8552 - recall: 0.5805 - tn: 93963.6953 - tp: 94.1868 - val_Brier score: 5.1810e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7869 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 55/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.5146e-04 - accuracy: 0.9992 - auc: 0.9631 - cross entropy: 0.0032 - fn: 58.3187 - fp: 15.7473 - loss: 0.0032 - prc: 0.7844 - precision: 0.8693 - recall: 0.6499 - tn: 93962.5625 - tp: 103.9451 - val_Brier score: 5.2094e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7873 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 56/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.5884e-04 - accuracy: 0.9992 - auc: 0.9536 - cross entropy: 0.0032 - fn: 60.0989 - fp: 15.5934 - loss: 0.0032 - prc: 0.7572 - precision: 0.8564 - recall: 0.6243 - tn: 93968.0078 - tp: 96.8681 - val_Brier score: 4.9789e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 18.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7847 - val_precision: 0.9194 - val_recall: 0.7600 - val_tn: 45489.0000 - val_tp: 57.0000\n", - "Epoch 57/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9108e-04 - accuracy: 0.9992 - auc: 0.9444 - cross entropy: 0.0036 - fn: 57.8791 - fp: 19.1538 - loss: 0.0036 - prc: 0.7387 - precision: 0.8582 - recall: 0.6350 - tn: 93962.5078 - tp: 101.0330 - val_Brier score: 5.3066e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7834 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 58/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2124e-04 - accuracy: 0.9991 - auc: 0.9520 - cross entropy: 0.0034 - fn: 65.9451 - fp: 16.7033 - loss: 0.0034 - prc: 0.7818 - precision: 0.8441 - recall: 0.5964 - tn: 93959.9375 - tp: 97.9890 - val_Brier score: 5.0541e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7845 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 59/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.3754e-04 - accuracy: 0.9993 - auc: 0.9333 - cross entropy: 0.0033 - fn: 55.2198 - fp: 12.8791 - loss: 0.0033 - prc: 0.7493 - precision: 0.8872 - recall: 0.6369 - tn: 93978.5938 - tp: 93.8791 - val_Brier score: 5.1594e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7872 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 60/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.9694e-04 - accuracy: 0.9992 - auc: 0.9242 - cross entropy: 0.0035 - fn: 65.5275 - fp: 12.9560 - loss: 0.0035 - prc: 0.7379 - precision: 0.8468 - recall: 0.5466 - tn: 93972.7812 - tp: 89.3077 - val_Brier score: 5.1725e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7866 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 61/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.0213e-04 - accuracy: 0.9993 - auc: 0.9603 - cross entropy: 0.0028 - fn: 56.4725 - fp: 14.0000 - loss: 0.0028 - prc: 0.8052 - precision: 0.8714 - recall: 0.6505 - tn: 93972.7109 - tp: 97.3846 - val_Brier score: 5.1001e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 21.0000 - val_fp: 6.0000 - val_loss: 0.0035 - val_prc: 0.7883 - val_precision: 0.9000 - val_recall: 0.7200 - val_tn: 45488.0000 - val_tp: 54.0000\n", - "Epoch 62/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.0087e-04 - accuracy: 0.9993 - auc: 0.9195 - cross entropy: 0.0033 - fn: 54.5165 - fp: 13.3077 - loss: 0.0033 - prc: 0.7294 - precision: 0.8737 - recall: 0.6360 - tn: 93970.6406 - tp: 102.1099 - val_Brier score: 5.3151e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7870 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 63/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.7005e-04 - accuracy: 0.9992 - auc: 0.9438 - cross entropy: 0.0034 - fn: 63.0220 - fp: 13.7802 - loss: 0.0034 - prc: 0.7454 - precision: 0.8733 - recall: 0.5965 - tn: 93970.3047 - tp: 93.4615 - val_Brier score: 5.0064e-04 - val_accuracy: 0.9995 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 19.0000 - val_fp: 5.0000 - val_loss: 0.0035 - val_prc: 0.7900 - val_precision: 0.9180 - val_recall: 0.7467 - val_tn: 45489.0000 - val_tp: 56.0000\n", - "Epoch 64/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Brier score: 6.4124e-04 - accuracy: 0.9993 - auc: 0.9291 - cross entropy: 0.0037 - fn: 59.1319 - fp: 10.4396 - loss: 0.0037 - prc: 0.7319 - precision: 0.9070 - recall: 0.6550 - tn: 93967.7812 - tp: 103.2198 - val_Brier score: 5.0829e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0035 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7891 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 65/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - Brier score: 7.2464e-04 - accuracy: 0.9992 - auc: 0.9486 - cross entropy: 0.0036 - fn: 59.9011 - fp: 17.9560 - loss: 0.0036 - prc: 0.7716 - precision: 0.8604 - recall: 0.6405 - tn: 93959.6016 - tp: 103.1099 - val_Brier score: 5.2800e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7906 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 66/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.7741e-04 - accuracy: 0.9992 - auc: 0.9405 - cross entropy: 0.0035 - fn: 61.6044 - fp: 13.7582 - loss: 0.0035 - prc: 0.7451 - precision: 0.8714 - recall: 0.6217 - tn: 93974.7500 - tp: 90.4615 - val_Brier score: 4.9498e-04 - val_accuracy: 0.9995 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 18.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7909 - val_precision: 0.9344 - val_recall: 0.7600 - val_tn: 45490.0000 - val_tp: 57.0000\n", - "Epoch 67/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.0034e-04 - accuracy: 0.9991 - auc: 0.9481 - cross entropy: 0.0034 - fn: 62.9011 - fp: 18.5604 - loss: 0.0034 - prc: 0.7612 - precision: 0.8439 - recall: 0.6191 - tn: 93960.0312 - tp: 99.0769 - val_Brier score: 5.4392e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7875 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 68/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.5760e-04 - accuracy: 0.9992 - auc: 0.9452 - cross entropy: 0.0033 - fn: 63.1099 - fp: 11.2527 - loss: 0.0033 - prc: 0.7626 - precision: 0.8964 - recall: 0.6040 - tn: 93973.9375 - tp: 92.2747 - val_Brier score: 5.3727e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7887 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 69/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.1643e-04 - accuracy: 0.9991 - auc: 0.9494 - cross entropy: 0.0034 - fn: 65.6484 - fp: 16.4725 - loss: 0.0034 - prc: 0.7572 - precision: 0.8369 - recall: 0.5890 - tn: 93965.8438 - tp: 92.6044 - val_Brier score: 5.1218e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7917 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 70/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.9824e-04 - accuracy: 0.9992 - auc: 0.9349 - cross entropy: 0.0036 - fn: 66.1868 - fp: 16.5385 - loss: 0.0036 - prc: 0.7260 - precision: 0.8530 - recall: 0.6125 - tn: 93962.1953 - tp: 95.6484 - val_Brier score: 5.0980e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0035 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0035 - val_prc: 0.7907 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 71/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.5123e-04 - accuracy: 0.9991 - auc: 0.9493 - cross entropy: 0.0036 - fn: 67.1429 - fp: 18.9451 - loss: 0.0036 - prc: 0.7621 - precision: 0.8247 - recall: 0.5947 - tn: 93960.0469 - tp: 94.4396 - val_Brier score: 5.2164e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7908 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 72/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.8855e-04 - accuracy: 0.9992 - auc: 0.9132 - cross entropy: 0.0035 - fn: 66.1978 - fp: 14.0769 - loss: 0.0035 - prc: 0.6707 - precision: 0.8301 - recall: 0.5121 - tn: 93980.5703 - tp: 79.7253 - val_Brier score: 5.2068e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7893 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 73/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.1969e-04 - accuracy: 0.9993 - auc: 0.9236 - cross entropy: 0.0033 - fn: 56.7363 - fp: 11.8462 - loss: 0.0033 - prc: 0.7282 - precision: 0.8801 - recall: 0.6023 - tn: 93976.6797 - tp: 95.3077 - val_Brier score: 5.0412e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 22.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7890 - val_precision: 0.9298 - val_recall: 0.7067 - val_tn: 45490.0000 - val_tp: 53.0000\n", - "Epoch 74/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.0848e-04 - accuracy: 0.9993 - auc: 0.9391 - cross entropy: 0.0031 - fn: 57.9231 - fp: 17.1429 - loss: 0.0031 - prc: 0.7422 - precision: 0.8436 - recall: 0.6043 - tn: 93973.5625 - tp: 91.9451 - val_Brier score: 5.1496e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7913 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 75/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.2375e-04 - accuracy: 0.9992 - auc: 0.9530 - cross entropy: 0.0030 - fn: 60.0659 - fp: 13.5604 - loss: 0.0030 - prc: 0.8093 - precision: 0.8831 - recall: 0.6218 - tn: 93973.0469 - tp: 93.9011 - val_Brier score: 5.1856e-04 - val_accuracy: 0.9994 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7885 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 76/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 7.2307e-04 - accuracy: 0.9992 - auc: 0.9528 - cross entropy: 0.0035 - fn: 63.2637 - fp: 11.8791 - loss: 0.0035 - prc: 0.7700 - precision: 0.9061 - recall: 0.5787 - tn: 93972.4609 - tp: 92.9670 - val_Brier score: 5.0187e-04 - val_accuracy: 0.9995 - val_auc: 0.9197 - val_cross entropy: 0.0036 - val_fn: 19.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7880 - val_precision: 0.9333 - val_recall: 0.7467 - val_tn: 45490.0000 - val_tp: 56.0000\n", - "Epoch 77/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 6.3179e-04 - accuracy: 0.9993 - auc: 0.9447 - cross entropy: 0.0032 - fn: 54.8571 - fp: 13.6703 - loss: 0.0032 - prc: 0.7511 - precision: 0.8843 - recall: 0.6671 - tn: 93971.8984 - tp: 100.1429 - val_Brier score: 5.1987e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7870 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 78/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 7.0267e-04 - accuracy: 0.9992 - auc: 0.9536 - cross entropy: 0.0033 - fn: 60.8462 - fp: 13.7143 - loss: 0.0033 - prc: 0.7800 - precision: 0.8991 - recall: 0.6105 - tn: 93967.9141 - tp: 98.0989 - val_Brier score: 5.1156e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7879 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 79/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 6.2558e-04 - accuracy: 0.9993 - auc: 0.9506 - cross entropy: 0.0033 - fn: 55.2857 - fp: 12.1978 - loss: 0.0033 - prc: 0.7819 - precision: 0.8887 - recall: 0.6283 - tn: 93977.7656 - tp: 95.3187 - val_Brier score: 5.1267e-04 - val_accuracy: 0.9994 - val_auc: 0.9196 - val_cross entropy: 0.0036 - val_fn: 23.0000 - val_fp: 4.0000 - val_loss: 0.0036 - val_prc: 0.7888 - val_precision: 0.9286 - val_recall: 0.6933 - val_tn: 45490.0000 - val_tp: 52.0000\n", - "Epoch 79: early stopping\n", - "Restoring model weights from the end of the best epoch: 69.\n" - ] - } - ], + "id": "yZKAc8NCDnoR" + }, + "outputs": [], "source": [ "model = make_model()\n", "model.load_weights(initial_weights)\n", @@ -2150,25 +869,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "u6LReDsqlZlk", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 855 - }, - "outputId": "bf04aa07-636a-459a-d5ae-20dd581614e8" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "u6LReDsqlZlk" + }, + "outputs": [], "source": [ "plot_metrics(baseline_history)" ] @@ -2197,22 +900,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "aNS796IJKrev", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8f71218b-57d8-4eb6-f922-ea8087e3f414" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", - "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n" - ] - } - ], + "id": "aNS796IJKrev" + }, + "outputs": [], "source": [ "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)" @@ -2254,39 +944,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "poh_hZngt2_9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 623 - }, - "outputId": "53e02b44-2af1-418f-b705-ef104c87cac5" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "loss : 0.003293460002169013\n", - "compile_metrics : 0.003293460002169013\n", - "\n", - "Legitimate Transactions Detected (True Negatives): 56843\n", - "Legitimate Transactions Incorrectly Detected (False Positives): 7\n", - "Fraudulent Transactions Missed (False Negatives): 27\n", - "Fraudulent Transactions Detected (True Positives): 85\n", - "Total Fraudulent Transactions: 112\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "poh_hZngt2_9" + }, + "outputs": [], "source": [ "baseline_results = model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -2326,51 +986,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "52bd793e04bb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "0b8dc494-b271-435a-c6d8-0f25c8aa01b9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Legitimate Transactions Detected (True Negatives): 56832\n", - "Legitimate Transactions Incorrectly Detected (False Positives): 18\n", - "Fraudulent Transactions Missed (False Negatives): 20\n", - "Fraudulent Transactions Detected (True Positives): 92\n", - "Total Fraudulent Transactions: 112\n", - "Legitimate Transactions Detected (True Negatives): 56731\n", - "Legitimate Transactions Incorrectly Detected (False Positives): 119\n", - "Fraudulent Transactions Missed (False Negatives): 14\n", - "Fraudulent Transactions Detected (True Positives): 98\n", - "Total Fraudulent Transactions: 112\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" - }, - "metadata": {} - } - ], + "id": "52bd793e04bb" + }, + "outputs": [], "source": [ "plot_cm(test_labels, test_predictions_baseline, threshold=0.1)\n", "plot_cm(test_labels, test_predictions_baseline, threshold=0.01)" @@ -2412,25 +1030,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "DfHHspttKJE0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "066fe74c-0416-4c38-c0e3-04329dad8dc3" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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jNjZWbdu21Y4dO8rcJjQ0VKGhoSXWOxyOgP3DCGRf8E9ZY+5w/PH/L4JsQbwuAcZ73RyMuzkYd3Mw7uZg3M3BuNesmhprywe1EyIiIhQREaHff/9dy5cv15w5c3yef/HFF9W1a1d17lzxGxPn5OQoNTVVN998c6DKxWnoP3sztT09R5KUW1hkcjUAAACoyywf1JYvXy7DMNSuXTvt2LFDEyZMUPv27TVixAjvNtnZ2Xr77bf12GOPldpHr169NHDgQI0ePVqSNH78eF1zzTVq3ry59u/fr6lTp8put2vYsGE1ckywnlXbDurWl78tf0MAAACgBlg+qGVlZWnixInau3ev6tevr0GDBmnmzJk+pxyXLFkiwzDKDFqpqak6fPiwd3nv3r0aNmyYMjIy1KhRI/Xs2VPr169Xo0aNqv14YE1bfsss87mOTWJqrhAAAABAp0FQGzx4sAYPHnzKbUaNGqVRo0aV+fyuXbt8lpcsWRKI0lBL3Xxhc7VtHClJahTlVK8OcSZXBAAAgLrG8kENqGm9OsTpsnaEMwAAAJgnyOwCAAAAAAC+CGoAAAAAYDEENQAAAACwGIIaAAAAAFgMQQ0AAAAALIagBgAAAAAWQ1ADAAAAAIvhPmqoM3ILimT877HLVaR8t5RTUCSHx6bCIo+ptQEAAADFEdRQ6xmGoVtf/larfzl00jPBeuCbz02pCQAAADgVLn1ErbfjYE4pIa1sDSNDq7EaAAAAoHycUUOtV+j+47LG+Gin2jSOlMdj6PDhQ2rYsJGCgmze5y9q3VAdE6PNKBMAAADwIqihTrmiQ5xmDewkl8ulpUuXqn//rnI4HGaXBQAAAPjg0kcAAAAAsBiCGgAAAABYDEENAAAAACyGoAYAAAAAFkNQAwAAAACLIagBAAAAgMUQ1AAAAADAYghqAAAAAGAx3PAap5Wt+7L0r1U7lHXM5XebnAJ3NVYEAAAABB5BDaeVR5f9rK+2H650+xA7J5EBAABgfXxqxWklI6ew0m0bRYVq4DlNAlgNAAAAUD04o4bTUog9SFum9q5Qm9Bgu+xBtmqqCAAAAAgcghpOTzYpPIS3LwAAAGonLn0EAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGIIaAAAAAFgMQQ0AAAAALIYbUcFH6qEcrd1xWIZhdiWlO5JbaHYJAAAAQLUjqMErM69Q/f/5lQqKPGaXAgAAANRpXPoIr9RDuadNSDu3WazZJQAAAADVhjNqKNWlbRtp4DlNzC6jVE5HkC5p28jsMgAAAIBqQ1BDqVrHReo6iwY1AAAAoLbj0kcAAAAAsBiCGgAAAABYDEENAAAAACyGoAYAAAAAFkNQAwAAAACLIagBAAAAgMUQ1AAAAADAYghq8Mp3uc0uAQAAAIAIavifr7Yf0k0vbDC7DAAAAAAiqOF/Pvp+v89yfLTTpEoAAAAAENQgSSryGN7H/TrGa8gFTU2sBgAAAKjbCGoo4f5+7RTtdJhdBgAAAFBnEdQAAAAAwGIIagAAAABgMQQ1AAAAALAYghoAAAAAWAxBDQAAAAAshqAGAAAAABZDUAMAAAAAiyGoAQAAAIDFBJtdAGre+l8z9NTnO3Q03+Vdt+dInokVAQAAACiOoFYH/f3Tn7Xlt8wynw912GuuGAAAAAAlENTqoKxjf5xJC7L9sd4eZNN1XZqoSWyYCVUBAAAAOIGgVofFhju0ZUofs8sAAAAAcBImEwEAAAAAiyGoAQAAAIDFENQAAAAAwGIIagAAAABgMUwmAgAAAMDrk/8c0LyUbcotcHvXrRh3qSJD/4gOL3z1q174ame5fZ3VJFovDD/fZ93Ihd9q677sctuOvDhJIy9u6V3OKShS8mOr/TkEPX/Leep0Rox3eeVP6Xrova3ltgsPtevzcZf5rJu19Cd9uGW/d7koP8evGqqKoAYAAADAa17KNqUeyvVZZxiGz/LR/CKlZeeX21dCrLPEuozcQr/aHs0vKlGDP+0kqdDt8VnOd3n8als8jJ6QlefyaespKPSrhqoiqAEAAADwOnEmLcgmxUUdD1o2m81nmyhnsOKjS4awkzWICCl1nT9to5y+UcVms/nVTpJC7L7f8HI6gvxqGx5qL7EuJtzh07Yov0i/+VVF1RDUAAAAAJQQF+XU+km9Sn1u5MUtfS5LrIiTL4X0V2RocJn1lKdXh8bq1aFxpdpO6t9Bk/p38C5nZGSo4YxKdVUhTCZSx6QeytHOw7nlbwgAAADANAS1OqSgyK0/PbPO7DIAAAAAlIOgVoccyS3Ukdw/vvzYvWUDE6sBAAAAUBaCWh0VG+7Qk8POMbsMAAAAAKVgMpE6qkerBgq2k9MBAADg6/lbzlOh21Ni5kTULIIaAAAAAK/iN4qGeYjJAAAAAGAxBDUAAAAAsBgufQQAAADgtfKndOW7PHI6gip9k2hUHUENAAAAgNdD721VWna+4qOdBDUTcekjAAAAAFgMQQ0AAAAALIagBgAAAAAWY/mgdvToUY0ZM0bNmzdXWFiYevTooW+//db7/K233iqbzebz069fv3L7XbBggVq0aCGn06lu3brpm2++qc7DsIRnV/9qdgkAAAAA/GD5oDZy5EilpKTotdde0w8//KA+ffooOTlZ+/bt827Tr18/HThwwPuzePHiU/b55ptvauzYsZo6dao2b96szp07q2/fvjp48GB1H46p1qVmeB83iQ0zsRIAAAAAp2LpoHbs2DG98847mjNnji655BK1bt1a06ZNU+vWrfX00097twsNDVV8fLz3p169eqfsd968ebrjjjs0YsQInXnmmXrmmWcUHh6ul156qboPyVRBQTbv479c1trESgAAAACciqWn5y8qKpLb7ZbT6fRZHxYWpjVr1niXV61apbi4ONWrV09XXHGFHnnkETVo0KDUPgsLC7Vp0yZNnDjRuy4oKEjJyclat25dmbUUFBSooKDAu5ydnS1JcrlccrlclTq+E060r2o/5TEMQ5IUGhykqBBbte/PympqzOGLcTcH424Oxt0cjLs5GHdzVNe4GzK8/+U1LammxsRmnPj0blE9evRQSEiIFi1apMaNG2vx4sUaPny4WrdurW3btmnJkiUKDw9XUlKSUlNTNWnSJEVGRmrdunWy2+0l+tu/f7+aNGmir7/+Wt27d/euv//++7V69Wpt2LCh1DqmTZum6dOnl1i/aNEihYeHB+6Aq9Gc7+3al2eTw2boHxe6zS4HAAAAFjRlk11ZhTbFhBh6uCufGU+Wl5enG2+8UVlZWYqOjq62/Vj6jJokvfbaa7rtttvUpEkT2e12nXvuuRo2bJg2bdokSRo6dKh3206dOunss89Wq1attGrVKvXq1StgdUycOFFjx471LmdnZ6tp06bq06dPlV8gl8ullJQU9e7dWw6Ho6qllunpneukvKMKstvVv3/fatvP6aCmxhy+GHdzMO7mYNzNwbibg3E3R3WN+z+3r1HR0QI1iApV//49A9ZvbZGRkVH+RgFg+aDWqlUrrV69Wrm5ucrOzlZCQoKGDBmili1blrp9y5Yt1bBhQ+3YsaPUoNawYUPZ7Xalp6f7rE9PT1d8fHyZdYSGhio0NLTEeofDEbB/GIHsqzQ22x/fUeOX6HHVPeYoHeNuDsbdHIy7ORh3czDu5gj0uH8+/vKA9VUb1dR73NKTiRQXERGhhIQE/f7771q+fLkGDBhQ6nZ79+5VRkaGEhISSn0+JCREXbt21cqVK73rPB6PVq5c6XMpJAAAAACYxfJBbfny5Vq2bJl27typlJQUXX755Wrfvr1GjBihnJwcTZgwQevXr9euXbu0cuVKDRgwQK1bt1bfvn9c2terVy899dRT3uWxY8fq+eef18KFC/XTTz/pL3/5i3JzczVixAgzDhEAAAAAfFj+0sesrCxNnDhRe/fuVf369TVo0CDNnDlTDodDRUVF+s9//qOFCxcqMzNTiYmJ6tOnj2bMmOFzmWJqaqoOHz7sXR4yZIgOHTqkKVOmKC0tTV26dNGyZcvUuHFjMw4RAAAAAHxYPqgNHjxYgwcPLvW5sLAwLV++vNw+du3aVWLd6NGjNXr06KqWBwAAANQqs5b+pKw8l2LCHZrUv4PZ5dRZlr/0EQAAAEDN+XDLfr258Td9uGW/2aXUaZY/o4aq27ovSzM+/lE/Hcg2uxQAAAAAfuCMWh3w7Je/asPOI97l8JCSNwIHAAAAYB0EtTrgaL7L+zg0OEj39W5rYjUAAAAAysOlj3XMN5OSFRPOjSgBAAAAK+OMGgAAAABYDGfUAACAKT75zwHNS9mm3AK3d92KcZcqMvSPjycvfPWrXvhqZ7l9ndUkWi8MP99n3ciF32rrvvIn0hp5cZJGXtzSu5xTUKTkx1b7cwh6/pbz1OmMGO/yyp/S9dB7W8ttFx5q1+fjLvNZN2vpT37Nsnd5+zjNvr6Tz7prnlyjQ0cLym07sX97DejSxLuceihHNz2/odx2kvTh6IsUF+30Li/asEdPrNwuSTJkKD/frln/XS2bbD7tkhpGaPGoC33W3bvkO2349YjKM/SCphqT7PuVjQtnrfSr3seHdFH3Vg28y+tSM3Tfm1v8art+Ui+f5fkrftGSb34rt123lvX1z6Hn+Kwb9tx67TycW27be3q10Y3dmnmXD2bn69qn1p6yzYlxb39+rtolxnrXf7Bln2Yv/bncfTaKCtVHf+3ps27iuz/o4NH8ctui+hHUAACAKealbFPqId8PsIZh+CwfzS9SWnb5HxoTYp0l1mXkFvrV9mh+UYka/GknSYVuj89yvsvjV9viYfSErDyXX22zjhWWWHfoaIFfbY8Vun2W3R7/j9V90muTV3jya2NTVmHJsBjlLHmsRyr52kiq9GtT6PbvtSmrDn/aHskt+doczvHvtckr9D1Wt9/vQ5vcHt/X5lihu9LHmnWsUCe6iwhlAjozEdQAAIApTpxJC7JJcVHHg5bN5ns2JsoZrPjokiHsZA0iQkpd50/bk4OEzWbzq50khdh9v0XidAT51Ta8lA/AMeEOv9rGhJU81kZRoeW2k6Swk2Z+tgf5f6z2k16b8JA/XpvjZ3by5XQ6S5xRaxhZsrb6lXxtJFX6tQmx+/falFWHP23rl/I+bBgZWmrgPFl4iO+x2v14H54Yd3uQ75iHhdj9qre0901M2PHXJiLUrnF92pXbB6qPzTj5f13BL9nZ2YqJiVFWVpaio6Or1JfL5dLSpUvVv39/ORyBn+jj1pe/0apthyRJ30/pw2Qiqv4xR+kYd3Mw7uZg3Mt34ayVSsvOV3y0s8SlZpXFuJuDcTcH426OjIwMNWzYMCA54FSYTAQAAAAALIagBgAAAAAWQ1ADAAAAAIshqNUB+zOPmV0CAAAAgApg1sc64Jf0nD8WbGVvBwBATXr+lvNU6PaUmJ0PAEBQqxMaR4cqPfv4fU2iS5nmFgAAMxS/UTQAwBf/C6sOiYsKLXF/GgAAAADWQ1CrQ4KDCGkAAADA6YDr4AAAgClW/pSufJdHTkeQenVobHY5AGApBDUAAGCKh97bqrTsfMVHOwlqAHASLn0EAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGIJaHZCTX2R2CQAAAAAqgKBWyx3IOqbcQrfZZQAAAACoAIJaLff1jgzv4/1Z+SZWAgAAAMBfBLVazij2+M5LW5pWBwAAJwsPtSsyNFjhoXazSwEAywk2uwDUnKb1ws0uAQAAr8/HXWZ2CQBgWZxRAwAAAACLIagBAAAAgMUQ1AAAAADAYviOGgAAMMWspT8pK8+lmHCHJvXvYHY5AGApnFEDAACm+HDLfr258Td9uGW/2aUAgOUQ1AAAAADAYghqAAAAAGAxBDUAAAAAsBiCWi23LjXD7BIAAAAAVBCzPtZyP+zL9D6OCLWbVwgAoEZ98p8DmpeyTbkFbu+6e3q10Y3dmnmXD2bn69qn1vrV3xt3dFOrRpHe5Q+27NPspT+X265RVKg++mtPn3UT3/1BX/x8UAeP5vu1bwCoiwhqtVxMmMP7+Ir2jU2sBABQk+albFPqoVyfdXmFRT7LbsNQWrZ/YcntMXyWjxW6/W57sqxjhT5t+R+JAFASQa0OiQjhDyEA1BUnzqQF2aS4KKckKTzE98++3WZTfLTTr/7sQTaf5bAQu19tG0WFllgXExbibRsRate4Pu38qgEA6hKCGgAAtVhclFPrJ/Uq/bnosp8rz4AuTTSgS5NKtZ19fadKtQOAuoTJRAAAAADAYghqAAAAAGAxBDUAAAAAsBi+owYAQC2U1DBCUc5gNYwsOZkHAMD6CGoAANRCi0ddaHYJAIAq4NJHAAAAALAYghoAAAAAWAyXPtZCWXkuvb5htw5kHdOujDyzywEAAABQQQS1Wui5r1K14ItUs8sAAJjo3iXf6UhuoepHhOifQ88xuxwAQAUR1GqhPUeOlVjX58zGCrZzpSsA1BUbfj2itOx8xUc7zS4FAFAJBLVa7qVbz1PTeuFqHRdpdikAAAAA/ERQq+XaxEWpaf1ws8sAAAAAUAFcCwcAAAAAFkNQAwAAAACLIagBAAAAgMUQ1AAAAADAYghqAAAAAGAxBDUAAAAAsBim5wcAoBYaekFTHc0vUpSTP/UAcDrit3ctlJFTYHYJAACTjUlua3YJAIAq4NLHWmZPRp6+Ts0wuwwAAAAAVUBQq2U27PQNaY2iQk2qBAAAAEBlEdRqsT9f2ExOh93sMgAAAABUEN9Rq8U6JESbXQIAwCQXzlqptOx8xUc7tX5SL7PLAQBUEGfUAAAAAMBiCGoAAAAAYDEENQAAAACwGIIaAAAAAFgMQQ0AAAAALIagBgAAAAAWw/T8AIDTyjVPrtGhowXlbjexf3sN6NLEu5x6KEc3Pb/Br318OPoixUU7vcuLNuzREyu3l9suqWGEFo+60Gfd2Lf/o293ZZbbdugFTTUmua3PugtnrfSr3seHdFH3Vg28y+tSM5SWne9XWwCANRHUAACnlUNHC/wKIccK3T7Lbo/hd3hxG4bPcl5hkV9to5wl/6z+nufyq+3R/KIS6/ytt9DtKXM5ItTuVx8AAGshqJ3mso65dMerG7V1X5YkqchtlNMCAE5vjaJC/douLMQ3oNiDbIovdpbsVOw2m89yeEiwX20bRpasrV64w6+2pYU8f+sNsQeVWI6Pdioi1K5xfdr51QcAwFoIaqe5lT+l65udR0p9LtrpqOFqACDwJr77g7KOFSomLESzr++kj/7as1L9tGoUqfWTelWq7Y3dmunGbs0q1Xben86Ww1G538eVrbd7qwaVbgsAsAaC2mnumMv30p52jaMkSWc1iVHvMxubURIABNQXPx9UWna+32eXAACoDQhqp7niX6OYe8PZ+tN5Tc0rBgAAAEBAMD1/LWI76TsVAAAAAE5PBDUAAAAAsBiC2mmu+ByPnE8DAAAAageCGgAAAABYjOWD2tGjRzVmzBg1b95cYWFh6tGjh7799ltJksvl0gMPPKBOnTopIiJCiYmJuuWWW7R///5T9jlt2jTZbDafn/bt29fE4QSewX3TAAAAgNrG8rM+jhw5Ulu3btVrr72mxMREvf7660pOTtaPP/6oyMhIbd68WZMnT1bnzp31+++/695779W1116rjRs3nrLfjh07asWKFd7l4GDLD0W5mEsEAAAAqB0snU6OHTumd955Rx988IEuueQSScfPhn300Ud6+umn9cgjjyglJcWnzVNPPaULLrhAe/bsUbNmZd+cNDg4WPHx8dVaf03gfBoAAABQ+1g6qBUVFcntdsvp9L3JaVhYmNasWVNqm6ysLNlsNsXGxp6y7+3btysxMVFOp1Pdu3fX7NmzTxnsCgoKVFBQ4F3Ozs6WdPzyS5fL5ecRle5E+8r043b/ccNrj9td5VrqiqqMOSqPcTfH6T7uV3VqrOz8IkU7g0+rYzjdx/10xbibg3E3B+Nujpoab5thWPtLTj169FBISIgWLVqkxo0ba/HixRo+fLhat26tbdu2+Wybn5+viy66SO3bt9cbb7xRZp+ffvqpcnJy1K5dOx04cEDTp0/Xvn37tHXrVkVFRZXaZtq0aZo+fXqJ9YsWLVJ4eHjVDrIKvkqz6d877ZKkP7d26/xGln45AQAAgNNaXl6ebrzxRmVlZSk6Orra9mP5oJaamqrbbrtNX375pex2u84991y1bdtWmzZt0k8//eTdzuVyadCgQdq7d69WrVpVoUHLzMxU8+bNNW/ePN1+++2lblPaGbWmTZvq8OHDVX6BXC6XUlJS1Lt3bzkcjgq1fW39Hj38yc+SpH/c0EkDOidUqZa6oipjjspj3M3BuJuDcTcH424Oxt0cjLs5MjIylJCQUO1BzdKXPkpSq1attHr1auXm5io7O1sJCQkaMmSIWrZs6d3G5XJp8ODB2r17tz7//PMKD1hsbKzatm2rHTt2lLlNaGioQkNDS6x3OBwB+4dRmb7sdrv3cbDdzj/SCgrk6wf/Me7mYNzNwbibg3E3B+NuDsa9ZtXUWFt+ev4TIiIilJCQoN9//13Lly/XgAEDJP0R0rZv364VK1aoQYMGFe47JydHqampSkg4/c5GFT8hyqyPAAAAQO1g+TNqy5cvl2EYateunXbs2KEJEyaoffv2GjFihFwul2644QZt3rxZH3/8sdxut9LS0iRJ9evXV0hIiCSpV69eGjhwoEaPHi1JGj9+vK655ho1b95c+/fv19SpU2W32zVs2DDTjhMAULorHlulg9kFiosO1efjLjO7HAAAaoTlg1pWVpYmTpyovXv3qn79+ho0aJBmzpwph8OhXbt26cMPP5QkdenSxafdF198ocsuu0zS8e+5HT582Pvc3r17NWzYMGVkZKhRo0bq2bOn1q9fr0aNGtXUYQWMpb9gCAABkFfgVk5BkSILLP8nCwCAgLH8X73Bgwdr8ODBpT7XokUL+TMXyq5du3yWlyxZEojSAAAAAKBanDbfUQMAAACAuoKgdporfkLRxmwiAAAAQK1AUAMAAAAAiyGoneaKf0OP82kAAABA7UBQAwAAAACLsfysjyjpcE6BNu46IsOQfjqQ7V3PV9QAAACA2oGgdpo5mu/SZXNXKaegyOxSAAAAAFQTgtpp5qcDR8sMae3jo2q4GgCofjMHnqV8l0dOB1frAwDqDoLaaaxbUn316hAnSeravL5axxHUANQ+vTo0NrsEAABqHEHtNNalaaxGXdLK7DIAAAAABBjXkQAAAACAxXBGDQBgaT/szVKh26MQe5A6nRFjdjkAANQIghoAS/jkPwc0L2Wbcgvc3nXdWtbXP4ee47PdsOfWa+fh3HL7u6dXG93YrZl3+WB2vq59aq1ftbxxRze1ahTpXf5gyz7NXvpzue0aRYXqo7/29Fk38d0f9MXPB8tte22XRE3q38Fn3RWPrVJesfEoy8yBZ/l8j+uHvVm649WN3mVDhvLz7Zr139Wyyfc+HivGXarI0D/+FLzw1a964aud5e7zrCbRemH4+T7rRi78Vlv3ZZfRoth2Fydp5MUtvcs5BUVKfmx1mdunZedLkuKjnVo/qVe5/QMAUBsQ1ABYwryUbUo95BvAjuQWltjucE6B94P7qeQV+s6O6jYMv9pJkttj+CwfK3T73fZkWccK/Wqblecqse5gdoFft+LId3l8lgvdnlL2aVNWYUGJtobhe6xH84v8qjch1lliXUauf8d6NN/3mAw/X5uIUHu52wAAUFsQ1ABYwokzaUE2KS7qeAioHxFSYruGkaElPuiXJjzE99eb3WZTfHTJcFEae5DvWaewELtfbRtFhZZYFxMW4lfbmHBHiXVx0aGKLCj/1/TJ09aH2IN89nn8jFq+nE5niTNqNpvvcpQz2K96G5Ty2jSI8O9Yo5y+x2Tz47WJCLVrXJ925fYNAEBtQVADYClxUae+vG3xqAsr128VLpsb0KWJBnRpUqm2s6/vVKl2kvT5uMsq1a7TGTE+x+pyubR06VL173+pHI6SgbC4kRe39LkssSJOvhTSX5GhwVzSCADASZj1EQAAAAAshqAGAAAAABbj16WP5557boU6tdls+vDDD9WkSeUuFULZfkk/anYJAAAAAKqZX0Fty5YtGjdunCIjI8vd1jAM/f3vf1dBQcnZxVB1P6f9MfX1MVf503YDp4uhFzTV0fyiEhNNAAAA1EV+fyKaMGGC4uLi/Nr2scceq3RBODVn8B/TU1/UuqGJlQCBNSa5rdklAAAAWIZfQW3nzp1q1KiR353++OOPSkxMrHRR8E9p02MDAAAAOP35FdSaN29eoU6bNm1aqWIAAAAAAFW4j1pRUZGeffZZrVq1Sm63WxdddJHuvvtuOZ3+3VAWAAAAAFC6Sge1e+65R7/88ouuv/56uVwuvfrqq9q4caMWL14cyPoA1BEXzlqptOx8xVfhxtQAAAC1hd9B7b333tPAgQO9y5999pm2bdsmu/345BZ9+/bVhRdeGPgKAQAAAKCO8fuG1y+99JKuu+467d+/X9Lxe6v93//9n5YtW6aPPvpI999/v84///xqKxQAAAAA6gq/g9pHH32kYcOG6bLLLtOTTz6p5557TtHR0XrooYc0efJkNW3aVIsWLarOWgEAAACgTqjQd9SGDBmivn376v7771ffvn31zDPPcM80AAAAAAgwv8+onRAbG6vnnntOc+fO1S233KIJEyYoPz+/OmoDAAAAgDrJ76C2Z88eDR48WJ06ddJNN92kNm3aaNOmTQoPD1fnzp316aefVmeddd7WfVkas+Q7ffZjutmlAAAAAKhmfge1W265RUFBQZo7d67i4uJ05513KiQkRNOnT9f777+v2bNna/DgwdVZa502/aP/6v0t+7XnSJ53XbC9widEAQAAAJwG/P6O2saNG/X999+rVatW6tu3r5KSkrzPdejQQV9++aWee+65aikS0sGjBT7L5zSL1VmJ0SZVAwAAAKA6+R3UunbtqilTpmj48OFasWKFOnXqVGKbUaNGBbQ4lBQb7tDn4y5TvXCHbDab2eUAAAAAqAZ+B7VXX31V48aN03333acuXbro2Wefrc66UIYgm031I0LMLgMIuMeHdFGh26MQLukFAADwP6g1b95c//73v6uzFgB1WPdWDcwuAQAAwDL8+l/X2dnZFer06NGjlSoGAAAAAOBnUKtXr54OHjzod6dNmjTRr7/+WumiAAAAAKAu8+vSR8Mw9MILLygyMtKvTl0uV5WKAlD3rEvN8H5HjcsgAQBAXedXUGvWrJmef/55vzuNj4+Xw+GodFEA6p773tyitOx8xUc7tX5SL7PLAQAAMJVfQW3Xrl3VXAYAAAAA4ATmwQYAAAAAiyGonQaO5BZqd0ae2WUAAAAAqCEENYvzeAxdt2Ct2WUAAAAAqEEENYvLPObSniN/nE3r1CTGxGoAAAAA1ASC2mlmwU3nml0CAAAAgGrm16yPxS1btkyRkZHq2bOnJGnBggV6/vnndeaZZ2rBggWqV69ewIvEcb3axykytMIvGf4np6BIyY+tliQZMpSfb9es/66WTbYS2z5/y3nqdMYfZy9X/pSuh97bWu4+wkPt+nzcZT7rZi39SR9u2V9u28vbx2n29Z181l3z5BodOlpQbtuJ/dtrQJcm3uXUQzm66fkN5baTpA9HX6S4aKd3edGGPXpi5fZy2yU1jNDiURf6rLt3yXfa8OuRMtucGPdUZ6rG9m3v81xadr5f9QIAANQFFf7UP2HCBD366KOSpB9++EHjxo3T2LFj9cUXX2js2LF6+eWXA14kEAiGYZwUBmzKKiw9BBW6PT7L+S6PX0GitCCdlefyq23WscIS6w4dLfCr7bFCt8+y23PysZbNbRg+y3mFRX61jXKWPNYjuYV+tLXpaEFRmc9GhNrL3TcAAEBtV+GgtnPnTp155pmSpHfeeUdXX321Zs2apc2bN6t///4BLxAIFJvNpvj/nTk6fmYnX06ns9QzaiF236uCnY4gb9tTCS8lZMSEO/xqGxMWUmJdo6jQcttJUliI737tQTa/9ilJdpvv8YeHBPvVtmFkydrqR4Scsu2JcY8qJdDGRzsVEWrXuD7t/KgaAACgdqtwUAsJCVFe3vHJLVasWKFbbrlFklS/fn1lZ2cHtjoggCJDg7V+Ui9Jksvl0tKlS9W//6VyOBzltu3VobF6dWhcqf1O6t9Bk/p3qFTbj/7as1LtWjWK9B5rRd3YrZlu7NasUm3/OfScUz7vHfcrWpV4rrL1AgAA1EYVDmo9e/bU2LFjddFFF+mbb77Rm2++KUn65ZdfdMYZZwS8QKCqXvjqVx3NL1KUM1gjL25pdjkAAABAuSo86+NTTz2l4OBg/fvf/9bTTz+tJk2OT2Dw6aefql+/fgEvEKiqF77aqX+u3K4XvtppdikAAACAXyp8Rq1Zs2b6+OOPS6x//PHHA1IQAAAAANR1lbqPWmpqqv72t79p2LBhOnjwoKTjZ9T++9//BrQ4AAAAAKiLKhzUVq9erU6dOmnDhg169913lZOTI0n6/vvvNXXq1IAXWNflnmIacwAAAAC1U4WD2oMPPqhHHnlEKSkpCgn5YzrxK664QuvXrw9ocXXd2Le26OI5X5hdBgAAAIAaVuGg9sMPP2jgwIEl1sfFxenw4cMBKQrHb5L87uZ9Puv8vacWAAAAgNNbhYNabGysDhw4UGL9d999550BElXn8ni8jxtEhOhPXc/Q3Ze3NrEiAAAAADWlwkFt6NCheuCBB5SWliabzSaPx6O1a9dq/Pjx3ptfI7DOaVZPc//UWU3rh5tdCgAAAIAaUOGgNmvWLLVv315NmzZVTk6OzjzzTF1yySXq0aOH/va3v1VHjQAAAABQp1T4PmohISF6/vnnNXnyZG3dulU5OTk655xz1KZNm+qoD6iys5pEKyHWqQYRIeVvDAAAAFhAhYPamjVr1LNnTzVr1kzNmjWrjpogyTDMrqD2eGH4+WaXAAAAAFRIhS99vOKKK5SUlKRJkybpxx9/rI6aAAAAAKBOq3BQ279/v8aNG6fVq1frrLPOUpcuXTR37lzt3bu3OuqDJJvN7AoAAAAA1KQKB7WGDRtq9OjRWrt2rVJTU/WnP/1JCxcuVIsWLXTFFVdUR40AAAAAUKdU+DtqxSUlJenBBx9U586dNXnyZK1evTpQdQEBM3Lht8rILVSDiBC+rwYAAIDTQoXPqJ2wdu1a3XXXXUpISNCNN96os846S5988kkga6vTDDGbSKBs3Zet7/Zkauu+bLNLAQAAAPxS4TNqEydO1JIlS7R//3717t1b//znPzVgwACFh3MzZgAAAAAIhAoHtS+//FITJkzQ4MGD1bBhw+qoCSdhLhEAAACgbqlwUFu7dm111AEAAAAA+B+/gtqHH36oK6+8Ug6HQx9++OEpt7322msDUhgAAAAA1FV+BbXrrrtOaWlpiouL03XXXVfmdjabTW63O1C11W3MJQIAAADUWX4FNY/HU+pj1AxueA0AAADULRWenv/VV19VQUFBifWFhYV69dVXA1IUAAAAANRlFQ5qI0aMUFZWVon1R48e1YgRIwJSFAAAAADUZRWe9dEwDNlKuRZv7969iomJCUhRQCCNvDhJR/OLFOWs8NsdAAAAMIXfn1zPOecc2Ww22Ww29erVS8HBfzR1u93auXOn+vXrVy1F1kXMJRI4Iy9uaXYJAAAAQIX4HdROzPa4ZcsW9e3bV5GRkd7nQkJC1KJFCw0aNCjgBUKycctrAAAAoE7xO6hNnTpVktSiRQsNGTJETqez2ooCAAAAgLqswl/aGT58eHXUgZMYJl37+MGWfZq99Odyt2sUFaqP/trTZ93Ed3/QFz8fLLfttV0SNal/B591Vzy2SnkF5d+Db+bAs9SrQ2Pv8g97s3THqxtP2SYkOEjP3txVTeuHKzKU76kBAADA+vz61Fq/fn398ssvatiwoerVq1fqZCInHDlyJGDF1WVbfsv0PjZq8BtrxwrdSsvOr1TbrGOFfrXNynOVWHcwu0A5BUXlts13+d7Hr9Dt8WufV/7zK7VqFKGV4y4rd1sAAADAbH4Ftccff1xRUVHex6cKagiMQ0f/CB+/55YMNoGSeihHbo8he5BNrRpFKizErvjo8i9rbRQVWmJdTFiIX21jwh0l1sVFhyqyoPy3o9Phe0eJEHuQX/uMCLVrXJ925W4HAAAAWIFfQa345Y633nprddWCYoqfQxtyftNq289Nz29QWna+4qOdWj+plwZ0aaIBXZpUqq/Z13eqdB2fV/JMV6czYrR+Uq9K7xcAAACwogrf8Hrz5s364YcfvMsffPCBrrvuOk2aNEmFhYUBLU46fiPtMWPGqHnz5goLC1OPHj307bffep83DENTpkxRQkKCwsLClJycrO3bt5fb74IFC9SiRQs5nU5169ZN33zzTcBrr4ri31ELqvCrBAAAAOB0VuEIcOedd+qXX36RJP36668aMmSIwsPD9fbbb+v+++8PeIEjR45USkqKXnvtNf3www/q06ePkpOTtW/fPknSnDlz9MQTT+iZZ57Rhg0bFBERob59+yo/v+zvLb355psaO3aspk6dqs2bN6tz587q27evDh4sfyKMmmIUS2pMzw8AAADULRUOar/88ou6dOkiSXr77bd16aWXatGiRXrllVf0zjvvBLS4Y8eO6Z133tGcOXN0ySWXqHXr1po2bZpat26tp59+WoZhaP78+frb3/6mAQMG6Oyzz9arr76q/fv36/333y+z33nz5umOO+7QiBEjdOaZZ+qZZ55ReHi4XnrppYDWXxXFL33kK4EAAABA3VLhucoNw5DHc3zmvRUrVujqq6+WJDVt2lSHDx8OaHFFRUVyu90l7tkWFhamNWvWaOfOnUpLS1NycrL3uZiYGHXr1k3r1q3T0KFDS/RZWFioTZs2aeLEid51QUFBSk5O1rp168qspaCgQAUFBd7l7OxsSZLL5ZLLVbXJPk60L95PkfuPqerdbneV91GWEzNKGjKqbR9WVNqYo/ox7uZg3M3BuJuDcTcH424Oxt0cNTXeFQ5q5513nh555BElJydr9erVevrppyVJO3fuVOPGjctpXTFRUVHq3r27ZsyYoQ4dOqhx48ZavHix1q1bp9atWystLU2SSuy3cePG3udOdvjwYbnd7lLb/Pxz2fcPmz17tqZPn15i/Weffabw8PCKHlqpUlJSvI9/PGCTZJckfb9li+x7vwvIPk6Wn2+XZFN+fr6WLl1aLfuwsuJjjprDuJuDcTcH424Oxt0cjLs5GPealZeXVyP7qXBQmz9/vm666Sa9//77euihh9S6dWtJ0r///W/16NEj4AW+9tpruu2229SkSRPZ7Xade+65GjZsmDZt2hTwfZ3KxIkTNXbsWO9ydna2mjZtqj59+ig6OrpKfbtcLqWkpKh3795yOI5PXZ/+9W69t2ubJOmcc85R/07xVdpHWWb9d7WyCgvkdDrVv/+l1bIPKyptzFH9GHdzMO7mYNzNwbibg3E3B+NujoyMjBrZT4WD2tlnn+0z6+MJc+fOld1uD0hRxbVq1UqrV69Wbm6usrOzlZCQoCFDhqhly5aKjz8eXtLT05WQkOBtk56e7v0e3ckaNmwou92u9PR0n/Xp6ene/koTGhqq0NCS9w5zOBwB+4dRvK+gYlM92u32avvHd2KiEptsdfIfeCBfP/iPcTcH424Oxt0cjLs5GHdzMO41q6bGutITv2/atEmvv/66Xn/9dW3evFlOp7Nai46IiFBCQoJ+//13LV++XAMGDFBSUpLi4+O1cuVK73bZ2dnasGGDunfvXmo/ISEh6tq1q08bj8ejlStXltnGbEwmAgAAANQtFT6jdvDgQQ0ZMkSrV69WbGysJCkzM1OXX365lixZokaNGgW0wOXLl8swDLVr1047duzQhAkT1L59e40YMUI2m01jxozRI488ojZt2igpKUmTJ09WYmKirrvuOm8fvXr10sCBAzV69GhJ0tixYzV8+HCdd955uuCCCzR//nzl5uZqxIgRAa29KorfR43p+QEAAIC6pcJB7a9//atycnL03//+Vx06dJAk/fjjjxo+fLjuueceLV68OKAFZmVlaeLEidq7d6/q16+vQYMGaebMmd6zd/fff79yc3M1atQoZWZmqmfPnlq2bJnPTJGpqak+M1IOGTJEhw4d0pQpU5SWlqYuXbpo2bJlAZ8MpSqMYhP0V+cZtQ9HXyS3YcjOaTsAAADAMioc1JYtW6YVK1Z4Q5oknXnmmVqwYIH69OkT0OIkafDgwRo8eHCZz9tsNj388MN6+OGHy9xm165dJdaNHj3ae4bNinzPqFWfuGhn+RsBAAAAqFEV/o6ax+Mp9btoDofDe381BBYnuwAAAIC6pcJB7YorrtC9996r/fv3e9ft27dP9913n3r16hXQ4uoyo/xNAAAAANRSFb708amnntK1116rFi1aqGnTppKk3377TWeddZZef/31gBdYVxk+Sa36Tqkt2rBHeYVFCg8J1o3dmlXbfgAAAAD4r8JBrWnTptq8ebNWrFihn3/+WZLUoUMHJScnB7y4uqymJhN5YuV2pWXnKz7aSVADAAAALKLCQU06PoFH79691bt370DXg/+pqclEAAAAAFhPpW54vXLlSl199dVq1aqVWrVqpauvvlorVqwIdG34HxuziQAAAAB1SoWD2r/+9S/169dPUVFRuvfee3XvvfcqOjpa/fv314IFC6qjxjrJKHZKjZgGAAAA1C0VvvRx1qxZevzxx33uQXbPPffooosu0qxZs3T33XcHtEAwPT8AAABQ11T4jFpmZqb69etXYn2fPn2UlZUVkKJw8qyPAAAAAOqSCge1a6+9Vu+9916J9R988IGuvvrqgBQF3/uocUYNAAAAqFsqfOnjmWeeqZkzZ2rVqlXq3r27JGn9+vVau3atxo0bpyeeeMK77T333BO4SuuQ347kaV7KL95lG99SAwAAAOqUCge1F198UfXq1dOPP/6oH3/80bs+NjZWL774onfZZrMR1CrptfW7fZadDrtJlQAAAAAwQ4WD2s6dO6ujDhSTlefyPm4SG6bzW9Srtn0lNYxQlDNYDSNDq20fAAAAACqmUje8Rs15ecT5CrZX6nZ3flk86sJq6xsAAABA5VRfAgAAAAAAVApBDQAAAAAshqAGAAAAABbDd9TquHuXfKcjuYWqHxGifw49x+xyAAAAAKiSZ9S++uor/fnPf1b37t21b98+SdJrr72mNWvWBLQ4VL8Nvx7RV9sPa8OvR8wuBQAAAMD/VDiovfPOO+rbt6/CwsL03XffqaCgQJKUlZWlWbNmBbxABNYn/zmgXo+t0oWzVurCWSt18Gi+2SUBAAAAOEmFg9ojjzyiZ555Rs8//7wcDod3/UUXXaTNmzcHtDgE3ryUbUo9lKu07HylZefLYxxfHxHKTbUBAAAAq6jwd9S2bdumSy65pMT6mJgYZWZmBqImVKPcArckKcgmxUU5JR0PaeP6tDOzLAAAAADFVDioxcfHa8eOHWrRooXP+jVr1qhly5aBqqvOOnS0QG9u/K3a9xMX5dT6Sb2qfT8AAAAAKq7CQe2OO+7Qvffeq5deekk2m0379+/XunXrNH78eE2ePLk6aqxTxr61pVr7H3pBUx3NL1KUkwk/AQAAAKuq8Kf1Bx98UB6PR7169VJeXp4uueQShYaGavz48frrX/9aHTXWKTsO5ngfJ8Q4ldQwIqD9j0luG9D+AAAAAARehYOazWbTQw89pAkTJmjHjh3KycnRmWeeqcjIyOqor05bMfZSOezckxwAAACoayp9/VtISIjOPPPMQNaCYuKjnYoI5fJEAAAAoC6qcBK4/PLLZbPZynz+888/r1JBAAAAAFDXVTiodenSxWfZ5XJpy5Yt2rp1q4YPHx6oulBNLpy1UmnZ+YqPZtZHAAAAwKoqHNQef/zxUtdPmzZNOTk5pT4HAAAAAPBfwGaq+POf/6yXXnopUN0BAAAAQJ0VsKC2bt06OZ3OQHUHAAAAAHVWhS99vP76632WDcPQgQMHtHHjRm54DQAAAAABUOGgFhMT47McFBSkdu3a6eGHH1afPn0CVhgAAAAA1FUVCmput1sjRoxQp06dVK9eveqqqU7LyS8yuwQAAAAAJqvQd9Tsdrv69OmjzMzMaiqnbvvxQLaOFhDUAAAAgLquwpOJnHXWWfr111+ro5Y6b9W2w97H2fkuEysBAAAAYKYKB7VHHnlE48eP18cff6wDBw4oOzvb5weV5zEM7+P7ktuaWAkAAAAAM/n9HbWHH35Y48aNU//+/SVJ1157rWw2m/d5wzBks9nkdrsDX2Ud1Couolr6fXxIFxW6PQqxB+zODAAAAAACzO+gNn36dP3f//2fvvjii+qsB9Wse6sGZpcAAAAAoBx+BzXjf5flXXrppdVWDAAAAACggt9RK36pIwAAAACgelToPmpt27YtN6wdOXKkSgWheq1LzfB+R43LIAEAAABrqlBQmz59umJiYqqrFtSA+97corTsfMVHO7V+Ui+zywEAAABQigoFtaFDhyouLq66agEAAAAAqALfUeP7aQAAAABQM/wOakaxmzEDAAAAAKqP35c+ejye6qwDkp5fs8vsEgAAAABYQIWm50f1ySuS8grd3uUwR4W+PggAAACgFiGoWUSxjCZJuiCpvjmFAAAAADAdQc2CrjwrXvYgJm8BAAAA6iqCGgAAAABYDF+EqgU++c8BzUvZptwCd6nPD72gqcYkt63hqgAAAABUFkGtFpiXsk2ph3LLfP5ofpH38fpJvWqiJAAAAABVQFCrBU6cSQuySXFRzhLPRzl5mQEAAIDTCZ/ga5G4KCdnzAAAAIBagKBWC3RrWV9HcgtVPyLE7FIAAAAABABBrRb459BzzC4BAAAAQAAxPT8AAAAAWAxBzSIOF5hdAQAAAACrIKhZxKIddu9jm83EQgAAAACYju+oWUTeH7c60+Xt4irUdthz63U4p0ANI0O1eNSFAa4MAAAAQE0jqFmMw27Tn85rWqE2Ow/nKi073+fG1gAAAABOX1z6aDFN64ebXQIAAAAAkxHULIavpwEAAAAgqFmEYXYBAAAAACyDoGYxNqZ8BAAAAOo8gppFcEYNAAAAwAkENYvhfBoAAAAAghoAAAAAWAxBzSq49hEAAADA/3DDa4upzFwi9/Rqo7zCIoWH8HICAAAAtQGf7C2iKifUbuzWLGB1AAAAADAflz5ajI3pRAAAAIA6j6AGAAAAABbDpY8WU5nvqB3MzpfbMGS32RQX7Qx8UQAAAABqFEGtFrj2qbVKy85XfLRT6yf1MrscAAAAAFXEpY8Wwez8AAAAAE4gqAEAAACAxRDUrIJTagAAAAD+h6BmMbbKzCYCAAAAoFYhqFkEJ9QAAAAAnEBQsxjOpwEAAACwdFBzu92aPHmykpKSFBYWplatWmnGjBkyjD/OP9lstlJ/5s6dW2a/06ZNK7F9+/bta+KQysQZNQAAAAAnWPo+ao8++qiefvppLVy4UB07dtTGjRs1YsQIxcTE6J577pEkHThwwKfNp59+qttvv12DBg06Zd8dO3bUihUrvMvBwdYYCr6iBgAAAMAa6aQMX3/9tQYMGKCrrrpKktSiRQstXrxY33zzjXeb+Ph4nzYffPCBLr/8crVs2fKUfQcHB5doCwAAAABWYOmg1qNHDz333HP65Zdf1LZtW33//fdas2aN5s2bV+r26enp+uSTT7Rw4cJy+96+fbsSExPldDrVvXt3zZ49W82aNStz+4KCAhUUFHiXs7OzJUkul0sul6uCR+areHvDMHyWP92apvkrU5VbWFSi3f192urazglaeGtXuT2G7EG2KtdSV5wYJ8arZjHu5mDczcG4m4NxNwfjbg7G3Rw1Nd42o/gXvizG4/Fo0qRJmjNnjux2u9xut2bOnKmJEyeWuv2cOXP097//Xfv375fT6Syz308//VQ5OTlq166dDhw4oOnTp2vfvn3aunWroqKiSm0zbdo0TZ8+vcT6RYsWKTw8vHIHWMzY9Xa5DZvOiDA04Wy3d/2sLXalHyv9esihLd3q3tiyLx8AAABQ6+Tl5enGG29UVlaWoqOjq20/lg5qS5Ys0YQJEzR37lx17NhRW7Zs0ZgxYzRv3jwNHz68xPbt27dX79699eSTT1ZoP5mZmWrevLnmzZun22+/vdRtSjuj1rRpUx0+fLjKL5DL5dJZMz6Xx7CpY2KU3v9Ld+9zPeeuVnp2gYJsUqOoUJ92J86ooeJcLpdSUlLUu3dvORwOs8upMxh3czDu5mDczcG4m4NxNwfjbo6MjAwlJCRUe1Cz9KWPEyZM0IMPPqihQ4dKkjp16qTdu3dr9uzZJYLaV199pW3btunNN9+s8H5iY2PVtm1b7dixo8xtQkNDFRoaWmK9w+EI6D+MIFuQT3+2/03YHxfl1PpJvQK2HxwX6NcP/mHczcG4m4NxNwfjbg7G3RyMe82qqbG29PT8eXl5CgryLdFut8vj8ZTY9sUXX1TXrl3VuXPnCu8nJydHqampSkgw8eyUZc9rAgAAAKhplg5q11xzjWbOnKlPPvlEu3bt0nvvvad58+Zp4MCBPttlZ2fr7bff1siRI0vtp1evXnrqqae8y+PHj9fq1au1a9cuff311xo4cKDsdruGDRtWrcfjj5On528UFar4aGeJyx4BAAAA1F6WvvTxySef1OTJk3XXXXfp4MGDSkxM1J133qkpU6b4bLdkyRIZhlFm0EpNTdXhw4e9y3v37tWwYcOUkZGhRo0aqWfPnlq/fr0aNWpUrcdTGR/9tafZJQAAAACoYZYOalFRUZo/f77mz59/yu1GjRqlUaNGlfn8rl27fJaXLFkSgOoCiysfAQAAAJxg6Usf66LSJ+IHAAAAUJcQ1AAAAADAYix96WNdYpw4l3bSbCIT3/1BWccKFRMWotnXdzKhMgAAAAA1jaBmAYeO/nEjbfdJtx744ueDSsvOV3y0s6bLAgAAAGASLn20gPTsP4Ja6sFcEysBAAAAYAUENYv503lnmF0CAAAAAJMR1AAAAADAYghqAAAAAGAxBDUAAAAAsBiCGgAAAABYDEENAAAAACyGoAYAAAAAFsMNry3u2i6JyspzKSbcYXYpAAAAAGoIQc3iJvXvYHYJAAAAAGoYlz4CAAAAgMUQ1AAAAADAYghqAAAAAGAxfEfN4q54bJUOZhcoLjpUn4+7zOxyAAAAANQAzqhZXF6BWzkFRcorcJtdCgAAAIAaQlADAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGIIaAAAAAFgMQQ0AAAAALIagZjEfbNmnH/ZmmV0GAAAAABMFm10AfGUdK9Kvh3PU6YwYSdLMgWcp3+WR00GmBgAAAOoKgpoF3btkiwZ0aSJJ6tWhscnVAAAAAKhpnKaxoPhop9klAAAAADARQQ0AAAAALIagBgAAAAAWQ1ADAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGG54bTFDz2+qv119ptllAAAAADARZ9QsJiQ4SJGh5GcAAACgLiOoAQAAAIDFENQAAAAAwGIIahaz5bdMvfDVr2aXAQAAAMBEBDWL+c/eLL3w1U6zywAAAABgIoIaAAAAAFgMQQ0AAAAALIagBgAAAAAWQ1ADAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGIKaxTSMDNFZTaLNLgMAAACAiQhqFrA/65j3cf9OCXph+PkmVgMAAADAbAQ1Cygo8ngf/3Ykz8RKAAAAAFgBQc1iLmrd0OwSAAAAAJiMoGYx9iCb2SUAAAAAMBlBzWJeXLNTIxd+a3YZAAAAAEwUbHYBkAzjj8d7fz+mIrdR9sYAAAAAaj3OqAEAAACAxRDUAAAAAMBiCGoWFBFqN7sEAAAAACYiqFnQuD7tzC4BAAAAgIkIahZQfOqQKGew+ndKMK0WAAAAAOYjqFkMd1EDAAAAQFADAAAAAIshqFlM91YNzC4BAAAAgMkIahbTo1VDs0sAAAAAYDKCmhUYRvnbAAAAAKgzCGoWY2M2EQAAAKDOI6hZTIHLbXYJAAAAAExGULOYJz/fYXYJAAAAAExGUAMAAAAAiyGoWQBTiQAAAAAojqAGAAAAABZDUAMAAAAAiyGoAQAAAIDFENQsIN/lMbsEAAAAABZCULOA3UfyvI+ZWAQAAAAAQc0CGkSEmF0CAAAAAAshqFmAUew8mt1mM7ESAAAAAFZAULOY/7u0ldklAAAAADAZQc0CjGJfTGveINy8QgAAAABYAkHNAooHNa58BAAAAGDpoOZ2uzV58mQlJSUpLCxMrVq10owZM2QUSza33nqrbDabz0+/fv3K7XvBggVq0aKFnE6nunXrpm+++aY6D6UCSGoAAABAXRdsdgGn8uijj+rpp5/WwoUL1bFjR23cuFEjRoxQTEyM7rnnHu92/fr108svv+xdDg0NPWW/b775psaOHatnnnlG3bp10/z589W3b19t27ZNcXFx1XY8/vjP3kz1Oyve1BoAAAAAmMvSZ9S+/vprDRgwQFdddZVatGihG264QX369Clx9is0NFTx8fHen3r16p2y33nz5umOO+7QiBEjdOaZZ+qZZ55ReHi4Xnrppeo8HL8s+maP2SUAAAAAMJmlz6j16NFDzz33nH755Re1bdtW33//vdasWaN58+b5bLdq1SrFxcWpXr16uuKKK/TII4+oQYMGpfZZWFioTZs2aeLEid51QUFBSk5O1rp168qspaCgQAUFBd7l7OxsSZLL5ZLL5arKYarI7f5jwVCV+0P5TowxY12zGHdzMO7mYNzNwbibg3E3B+Nujpoab5tR/AtfFuPxeDRp0iTNmTNHdrtdbrdbM2fO9AlZS5YsUXh4uJKSkpSamqpJkyYpMjJS69atk91uL9Hn/v371aRJE3399dfq3r27d/3999+v1atXa8OGDaXWMm3aNE2fPr3E+kWLFik8vGozNabss+njPcdrDQ82NPt8dzktAAAAAJghLy9PN954o7KyshQdHV1t+7H0GbW33npLb7zxhhYtWqSOHTtqy5YtGjNmjBITEzV8+HBJ0tChQ73bd+rUSWeffbZatWqlVatWqVevXgGrZeLEiRo7dqx3OTs7W02bNlWfPn2q/AL9+vl2ac9OSVKII0T9+19epf5QPpfLpZSUFPXu3VsOh8PscuoMxt0cjLs5GHdzMO7mYNzNwbibIyMjo0b2Y+mgNmHCBD344IPeMNapUyft3r1bs2fP9ga1k7Vs2VINGzbUjh07Sg1qDRs2lN1uV3p6us/69PR0xceXPYlHaGhoqZOUOByOKv/DCAoqdubPJv6h1aBAvH6oOMbdHIy7ORh3czDu5mDczcG416yaGmtLTyaSl5enoCDfEu12uzweT5lt9u7dq4yMDCUkJJT6fEhIiLp27aqVK1d613k8Hq1cudLnUkizMDk/AAAAAEsHtWuuuUYzZ87UJ598ol27dum9997TvHnzNHDgQElSTk6OJkyYoPXr12vXrl1auXKlBgwYoNatW6tv377efnr16qWnnnrKuzx27Fg9//zzWrhwoX766Sf95S9/UW5urkaMGFHjxyhJlv2SIAAAAABTWPrSxyeffFKTJ0/WXXfdpYMHDyoxMVF33nmnpkyZIun42bX//Oc/WrhwoTIzM5WYmKg+ffpoxowZPpcppqam6vDhw97lIUOG6NChQ5oyZYrS0tLUpUsXLVu2TI0bN67xYwQAAACAk1k6qEVFRWn+/PmaP39+qc+HhYVp+fLl5faza9euEutGjx6t0aNHV7HCwLDwxJsAAAAATGDpSx/rotDgkrcUAAAAAFC3ENQsoPj5tNnXdzKtDgAAAADWQFADAAAAAIshqFlB8VNqzM8PAAAA1HkENQswiiU1choAAAAAgprFLPn2N7NLAAAAAGAygpoFFJ+df8OvGeYVAgAAAMASCGoAAAAAYDEENQvgdtcAAAAAiiOoAQAAAIDFENQswOCUGgAAAIBiCGoWYHDxIwAAAIBiCGoAAAAAYDEENSvghBoAAACAYghqFmMzuwAAAAAApiOoWUDxE2pdmtUzrQ4AAAAA1kBQs5iRFyeZXQIAAAAAkxHULIDp+QEAAAAUR1CzgOLT89v4lhoAAABQ5xHULGBfZr73sY2cBgAAANR5BDULOJxT4H088d0fTKwEAAAAgBUQ1CygcZTT+/hovsvESgAAAABYAUENAAAAACyGoGYBvpOJAAAAAKjrCGoW4Ds9P1ENAAAAqOsIagAAAABgMQQ1C+B+1wAAAACKI6hZAUkNAAAAQDEENQswSGoAAAAAiiGoAQAAAIDFENQsoPisj3dd3sq8QgAAAABYAkHNAopf+Ni3Y7xpdQAAAACwBoKaBRjFTqnZuI0aAAAAUOcR1AAAAADAYghqFlD80sc9GXmm1QEAAADAGghqFlB8MpHRi78zrxAAAAAAlkBQAwAAAACLIahZDHOJAAAAACCoWUDxWR8BAAAAgKBmAcQ0AAAAAMUR1AAAAADAYghqFsCVjwAAAACKI6hZgMHFjwAAAACKIahZAGfUAAAAABRHUAMAAAAAiyGoWUDxE2qv397NtDoAAAAAWANBzQqKJbVGUaHm1QEAAADAEghqFlB8MhGbzcRCAAAAAFgCQc1ibCKpAQAAAHUdQc0Cis/6+O9Ne80rBAAAAIAlENQsoPhkIs9+mWpaHQAAAACsgaAGAAAAABZDULMAbngNAAAAoDiCmgUYIqkBAAAA+ANBzQqK5TSm5wcAAABAUAMAAAAAiyGoWcD3e7PMLgEAAACAhRDULCAyNNjsEgAAAABYCEHNAhpGhXgf28SX1AAAAIC6jqBmMUkNI8wuAQAAAIDJCGoWcOI+ag67TYtHXWhuMQAAAABMR1ADAAAAAIshqAEAAACAxRDULMAwyt8GAAAAQN1BULOQIo+he5d8Z3YZAAAAAExGULOAEyfUDEPa8OsRU2sBAAAAYD6CGgAAAABYDEENAAAAACyGoGYFzCYCAAAAoBiCGgAAAABYDEENAAAAACyGoGYBXPgIAAAAoDiCGgAAAABYDEENAAAAACyGoGYBJyZ9tAdJQy9oam4xAAAAAExHULOQEHuQxiS3NbsMAAAAACYjqFmIzWYzuwQAAAAAFkBQswCDeR8BAAAAFENQAwAAAACLIahZwInJRPIK3bpw1kpziwEAAABgOoIaAAAAAFgMQQ0AAAAALIagZgFMJQIAAACgOEsHNbfbrcmTJyspKUlhYWFq1aqVZsyYIeN/X+pyuVx64IEH1KlTJ0VERCgxMVG33HKL9u/ff8p+p02bJpvN5vPTvn37mjgkAAAAAChXsNkFnMqjjz6qp59+WgsXLlTHjh21ceNGjRgxQjExMbrnnnuUl5enzZs3a/LkyercubN+//133Xvvvbr22mu1cePGU/bdsWNHrVixwrscHGzpoQAAAABQh1g6nXz99dcaMGCArrrqKklSixYttHjxYn3zzTeSpJiYGKWkpPi0eeqpp3TBBRdoz549atasWZl9BwcHKz4+vvqKrwCDax8BAAAAFGPpoNajRw8999xz+uWXX9S2bVt9//33WrNmjebNm1dmm6ysLNlsNsXGxp6y7+3btysxMVFOp1Pdu3fX7NmzTxnsCgoKVFBQ4F3Ozs6WdPzyS5fLVbEDK8Eo9sgIQH8oz4kxZqxrFuNuDsbdHIy7ORh3czDu5mDczVFT420zDOuez/F4PJo0aZLmzJkju90ut9utmTNnauLEiaVun5+fr4suukjt27fXG2+8UWa/n376qXJyctSuXTsdOHBA06dP1759+7R161ZFRUWV2mbatGmaPn16ifWLFi1SeHh45Q7wf2ZtsSv9mE2SFBNi6OGu7ir1BwAAAKB65OXl6cYbb1RWVpaio6OrbT+WDmpLlizRhAkTNHfuXHXs2FFbtmzRmDFjNG/ePA0fPtxnW5fLpUGDBmnv3r1atWpVhQYtMzNTzZs317x583T77beXuk1pZ9SaNm2qw4cPV/kF6jN/jXZm5EmSGkeHas2ES6vUH8rncrmUkpKi3r17y+FwmF1OncG4m4NxNwfjbg7G3RyMuzkYd3NkZGQoISGh2oOapS99nDBhgh588EENHTpUktSpUyft3r1bs2fP9glqLpdLgwcP1u7du/X5559XeMBiY2PVtm1b7dixo8xtQkNDFRoaWmK9w+Go8j8M2/GTaQpzBGn+kHP4h1aDAvH6oeIYd3Mw7uZg3M3BuJuDcTcH416zamqsLT09f15enoKCfEu02+3yeDze5RMhbfv27VqxYoUaNGhQ4f3k5OQoNTVVCQkJVa65Khz2IHVvVfH6AQAAANQulg5q11xzjWbOnKlPPvlEu3bt0nvvvad58+Zp4MCBko6HtBtuuEEbN27UG2+8IbfbrbS0NKWlpamwsNDbT69evfTUU095l8ePH6/Vq1dr165d+vrrrzVw4EDZ7XYNGzasxo9RYtZHAAAAAL4sfenjk08+qcmTJ+uuu+7SwYMHlZiYqDvvvFNTpkyRJO3bt08ffvihJKlLly4+bb/44gtddtllkqTU1FQdPnzY+9zevXs1bNgwZWRkqFGjRurZs6fWr1+vRo0a1chxAQAAAMCpWDqoRUVFaf78+Zo/f36pz7do0UL+zIWya9cun+UlS5YEoLrAOXEERR6P1qVmcPkjAAAAUMdZ+tLHuiav0KP73txidhkAAAAATEZQAwAAAACLIahZAJOJAAAAACiOoAYAAAAAFkNQAwAAAACLIahZQG5hkdklAAAAALAQgpoFZOa5vI8jQu0mVgIAAADACghqFlD8XnDj+rQzsRIAAAAAVkBQsxCbTerfKcHsMgAAAACYjKBmAYmxYZKk2DCHyZUAAAAAsAKCGgAAAABYDEHNAk58Q81mM7UMAAAAABZBUAMAAAAAiyGoWUD2sePT8x8rdJtcCQAAAAArIKhZQG7B8RteFxR5TK4EAAAAgBUQ1AAAAADAYghqFmCUvwkAAACAOoSgBgAAAAAWQ1ADAAAAAIshqAEAAACAxRDUAAAAAMBiCGoAAAAAYDEENQAAAACwGIKaBTjsQT7/BQAAAFC3kQwsoKDII0mKCXOYXAkAAAAAKyComSy3oMj7+ODRAhMrAQAAAGAVBDWTGcUet2wYblodAAAAAKyDoGYhCTFhZpcAAAAAwAIIaiYzjD/OqW3dl2ViJQAAAACsgqBmIcdcHrNLAAAAAGABBDUAAAAAsBiCmsmM8jcBAAAAUMcQ1AAAAADAYoLNLqCuMzilBgAAEHBut1sul8vsMqqVy+VScHCw8vPz5Xa7zS6n1nA4HLLb7WaXQVCzFJvZBQAAAJzeDMNQWlqaMjMzzS6l2hmGofj4eP3222+y2fggGUixsbGKj483dVwJagAAAKg1ToS0uLg4hYeH1+oA4/F4lJOTo8jISAUF8Y2mQDAMQ3l5eTp48KAkKSEhwbRaCGpm49JHAACAgHC73d6Q1qBBA7PLqXYej0eFhYVyOp0EtQAKCwuTJB08eFBxcXGmXQbJKwoAAIBa4cR30sLDw02uBKe7E+8hM7/nSFCzkOb1+aUCAABQVbX5ckfUDCu8hwhqJjOKXfuYGOM0sRIAAAAAVkFQsxLzgzsAAABqiRYtWmj+/PlmlxEQNptN77//viRp165dstls2rJli6k1VTeCGgAAAGAim812yp9p06ZVqt9vv/1Wo0aNqlJtl112mU8tjRs31p/+9Cft3r27Sv1WRdOmTXXgwAGdddZZptVQEwhqJit+w+uCIo95hQAAAMAUBw4c8P7Mnz9f0dHRPuvGjx/v3dYwDBUVFfnVb6NGjQIyscodd9yhAwcOaP/+/frggw/022+/6c9//nOV+60su92u+Ph4BQfX7gnsCWoWsnlPptklAAAAoIbFx8d7f2JiYmSz2bzLP//8s6KiovTpp5+qa9euCg0N1Zo1a5SamqrrrrtObdu2VXR0tM4//3ytWLHCp9+TL3202Wx64YUXNHDgQIWHh6tNmzb68MMPy60vPDxc8fHxSkhI0IUXXqjRo0dr8+bN3ufdbrduv/12JSUlKSwsTO3atdM///lPnz5WrVqlCy64QBEREYqNjdVFF13kc1bugw8+0Lnnniun06mWLVtq+vTpZQbSky99XLVqlWw2m1auXKnzzjtP4eHh6tGjh7Zt2+bTriL7sILaHUMBAABQ513z5BodOlpQo/tsFBWqj/7aM2D9Pfjgg/rHP/6hli1bql69evrtt9905ZVX6sEHH1SDBg30+uuv65prrtG2bdvUrFmzMvuZPn265syZo7lz5+rJJ5/UTTfdpN27d6t+/fp+1XHkyBG99dZb6tatm3edx+PRGWecobffflsNGjTQ119/rVGjRikhIUGDBw9WUVGRrrvuOt1xxx1avHixCgsL9c0333hnVvzqq690yy236IknntDFF1+s1NRU7yWbU6dO9XuMHnroIT322GNq1KiR/u///k+33Xab1q5dG9B91CSCmsm43zUAAED1OnS0QGnZ+WaXUSUPP/ywevfu7V2uX7++OnXqpOzsbEVHR2vGjBl677339OGHH2r06NFl9nPrrbdq2LBhkqRZs2bpiSee0DfffKN+/fqV2eZf//qXXnjhBRmGoby8PLVt21bLly/3Pu9wODR9+nTvclJSktatW6e33npLgwcPVnZ2trKysnT11VerVatWkqQOHTp4t58+fboefPBBDR8+XJLUsmVLzZgxQ/fff3+FQtTMmTN16aWXSjoebK+66irl5+fL6XQGbB81iaBmIUz6CAAAEHiNokJP+32ed955Pss5OTmaOnWqPv74Y6Wnp6uoqEjHjh3Tnj17TtnP2Wef7X0cERGh6OhoHTx48JRtbrrpJj300EOSpPT0dM2aNUt9+vTRpk2bFBUVJUlasGCBXnrpJe3Zs0fHjh1TYWGhunTpIul4qLz11lvVt29f9e7dW8nJyRo8eLASEhIkSd9//73Wrl2rmTNnevfpdruVn5+vvLw8v79nV/zYTvR98OBBNWvWLGD7qEkENQAAANRqgbwE0SwRERE+y+PHj1dKSoqmT5+uTp06KSIiQjfccIMKCwtP2Y/D4fBZttls8nhOPaFdTEyMWrduLUlq3bq1XnzxRSUkJOjNN9/UyJEjtWTJEo0fP16PPfaYunfvrqioKM2dO1cbNmzw9vHyyy/rnnvu0bJly/Tmm2/qb3/7m1JSUnThhRcqJydH06dP1/XXX19i306n//cZLn5sJy6rPHFsgdpHTSKomcwwuPgRAAAAFbN27VoNHz5cV199taKjo5WXl6ddu3bVyL7tdrsk6dixY95aevToobvuusu7TWpqaol255xzjs455xxNnDhR3bt316JFi3ThhRfq3HPP1bZt27xhsDrUxD4CjaAGAAAAnGbatGmj9957T5dffrkiIyM1derUcs+MVVZeXp7S0tIkHb/0ccaMGXI6nerTp4+3lldffVXLly9XUlKSXnvtNX377bdKSkqSJO3cuVPPPfecrr32WiUmJmrbtm3avn27brnlFknSlClTdPXVV6tZs2a64YYbFBQUpO+//15bt27VI488EpBjqIl9BBrT8wMAAACnmXnz5qlevXrq27evBgwYoL59++rcc8+tln09//zzSkhIUEJCgi6//HIdPnxYS5cuVbt27SRJd955p66//noNGTJE3bp1U0ZGhs/ZtfDwcP38888aNGiQ2rZtq1GjRunuu+/WnXfeKUnq27evPv74Y3322Wc6//zzdeGFF+rxxx9X8+bNA3YMNbGPQLMZXHtXKdnZ2YqJiVFWVpaio6Mr3c/hnAKd98jxe16EBgdp2yNXBqpEnILL5dLSpUvVv3//Etdqo/ow7uZg3M3BuJuDcTeHVcY9Pz9fO3fuVFJSkmW/dxRIHo/HO+tjUBDnXwLpVO+ljIwMNWzYsMo5oDy8ogAAAABgMQQ1CzmnaYzZJQAAAACwAIKayYpfeBoRytwuAAAAAAhqlsINrwEAAABIBDUAAAAAsByCmskM/XHtY1pWvomVAAAAALAKgpqFbD+Ua3YJAAAAACyAoAYAAAAAFkNQMxu3GwcAAAC0atUq2Ww2ZWZmSpJeeeUVxcbGmlqTmQhqAAAAgIlsNtspf6ZNm1alvt9///0K1RAcHKxmzZpp7NixKigoqPS+q2rIkCH65ZdfTNu/2bhxFwAAAGCiAwcOeB+/+eabmjJlirZt2+ZdFxkZWSN1vPzyy+rXr59cLpe+//57jRgxQhEREZoxY0aN7P9kYWFhCgsLM2XfVsAZNZNx5SMAAEDdFh8f7/2JiYmRzWbzWbdkyRJ16NBBTqdT7du317/+9S9v28LCQv31r39VQkKCnE6nmjdvrtmzZ0uSWrRoIUkaOHCgbDabd7kssbGxio+PV9OmTXX11VdrwIAB2rx5s/f51NRUDRgwQI0bN1ZkZKTOP/98rVixwqePf/3rX2rTpo2cTqcaN26sG264wfucx+PR7NmzlZSUpLCwMHXu3Fn//ve/y6zn5Esfp02bpi5duui1115TixYtFBMTo6FDh+ro0aOV3oeVcUYNAAAAsKg33nhDU6ZM0VNPPaVzzjlH3333ne644w5FRETo5ptv1rPPPquPPvpIb731lpo1a6bffvtNv/32myTp22+/VVxcnPdMmd1u93u/v/zyiz7//HPdeuut3nU5OTnq37+/Zs6cqdDQUL366qu65pprtG3bNjVr1kwbN27UPffco9dee009evTQkSNH9NVXX3nbz549W6+//rqeeeYZtWnTRl9++aX+/Oc/q1GjRrr00kv9qis1NVXvv/++Pv74Y/3+++8aPHiw/v73v2vmzJkB24dVENQAAABQ673w1a964aud5W53VpNovTD8fJ91Ixd+q637ssttO/LiJI28uGWlayzN1KlT9dhjj+n666+XJCUlJenHH3/Us88+q5tvvll79+5VmzZt1LNnT9lsNjVv3tzbtlGjRpL+OFNWnmHDhslut6uoqEgFBQW6+uqrNXHiRO/znTt3VufOnb3LM2bM0HvvvacPP/xQo0eP1p49exQREaGrr75aUVFRat68uc455xxJUkFBgWbNmqUVK1aoe/fukqSWLVtqzZo1evbZZ/0OUR6PR6+88oqioqIkSTfffLNWrlypmTNnBmwfVkFQM5nBtY8AAADV7mh+kdKy88vdLiHWWWJdRm6hX22P5hdVqray5ObmKjU1VbfffrvuuOMO7/qioiLFxMRIkm688UZdf/31ateunfr166err75affr0qdT+Hn/8cSUnJ8vtdmvHjh0aO3asbr75Zi1ZskTS8TNq06ZN0yeffKIDBw6oqKhIx44d0549eyRJvXv3VvPmzdWyZUv169dP/fr108CBAxUeHq4dO3YoLy9PvXv39tlnYWGhN8z5o0WLFt6QJkkJCQk6ePCgJAVsH1ZBULOQ0GC+MggAAFAdopzBio8uGcJO1iAipNR1/rSNcgb2o3VOTo4k6fnnn1e3bt18njtxGWPnzp2Vmpqq5cuXa8WKFRo8eLCSk5Mr9b2s+Ph4tW7dWpLUrl07HT16VMOGDdMjjzyi1q1ba/z48UpJSdE//vEPtW7dWmFhYbrhhhtUWFgoSYqKitLmzZu1atUqffbZZ5oyZYqmTZumb7/91nssn3zyiZo0aeKz39DQUL9rdDgcPss2m00ej0eSArYPqyCoWUj3lvXNLgEAAKBWGnlxy0pflnjypZA1pXHjxkpMTNSvv/6qm266qcTzJwJKdHS0hgwZoiFDhuiGG25Qv379dOTIEdWvX18Oh0Nut7tS+z8RBo8dOyZJWrt2rW699VYNHDhQ0vFgtGvXLp82wcHBSk5OVnJysqZOnarY2Fh9/vnn6t27t0JDQ7Vnz55quwTxzDPPrPZ91CSCmskM5n0EAABAGaZPn6577rlHMTEx6tevnwoKCrRx40b9/vvvGjNmjBYsWKAWLVqoa9euCgoK0ttvv634+HjvbIktWrTQypUrddFFFyk0NFT16tUrc1+ZmZlKS0uTx+PR9u3b9fDDD6tt27bq0KGDJKlNmzZ69913dc0118hms2ny5MnesChJH3/8sX799VddcsklqlevnpYuXSqPx6N27dopKipK48eP13333SePx6OePXsqKytLa9euVXR0tIYPH17lsaqJfdQkgpqF2Gw2s0sAAACAhYwcOVLh4eGaO3euJkyYoIiICHXq1EljxoyRdPwea//4xz+0fft22e12nX/++Vq6dKmCgo5/peaxxx7T2LFj9fzzz6tJkyYlzoAVN2LECEny3h7gkksu0axZsxQcfDwyzJs3T7fddpt69Oihhg0b6oEHHlB29h+TrMTGxurdd9/VtGnTlJ+frzZt2mjx4sXq2LGjpOOTjzRq1EizZ8/Wr7/+qtjYWJ177rmaNGlSwMarJvZRU2yGwXQWlZGdna2YmBhlZWUpOjq60v0cyDqm7rM/lyT169hYz9x8XqBKxCm4XC4tXbpU/fv3L3GtM6oP424Oxt0cjLs5GHdzWGXc8/PztXPnTiUlJcnpLP87Zac7j8ej7OxsRUdHe4MZAuNU76WMjAw1bNiwyjmgPLyiJisek7fuL3/aVwAAAAC1H0HNQg4dLTC7BAAAAAAWQFAzGdedAgAAADgZQc1CmEoEAAAAgERQAwAAAADLIaiZjEk3AQAAAovPV6gqK7yHCGoAAACoFU7cGiAvL8/kSnC6O/EeMvN2E9zwGgAAALWC3W5XbGysDh48KEkKDw+XzVZ7ZwHweDwqLCxUfn4+91ELEMMwlJeXp4MHDyo2NlZ2u920WghqJrPAWVUAAIBaIz4+XpK8Ya02MwxDx44dU1hYWK0OpGaIjY31vpfMQlADAABArWGz2ZSQkKC4uDi5XC6zy6lWLpdLX375pS655BJTL9GrbRwOh6ln0k4gqFlIYqzT7BIAAABqBbvdbokP29XJbrerqKhITqeToFYLWfpiVrfbrcmTJyspKUlhYWFq1aqVZsyY4TMLi2EYmjJlihISEhQWFqbk5GRt37693L4XLFigFi1ayOl0qlu3bvrmm2+q81D80iEh2uwSAAAAAFiApYPao48+qqefflpPPfWUfvrpJz366KOaM2eOnnzySe82c+bM0RNPPKFnnnlGGzZsUEREhPr27av8/Pwy+33zzTc1duxYTZ06VZs3b1bnzp3Vt29f069l5spiAAAAAJLFg9rXX3+tAQMG6KqrrlKLFi10ww03qE+fPt6zX4ZhaP78+frb3/6mAQMG6Oyzz9arr76q/fv36/333y+z33nz5umOO+7QiBEjdOaZZ+qZZ55ReHi4XnrppRo6MgAAAAAom6W/o9ajRw8999xz+uWXX9S2bVt9//33WrNmjebNmydJ2rlzp9LS0pScnOxtExMTo27dumndunUaOnRoiT4LCwu1adMmTZw40bsuKChIycnJWrduXZm1FBQUqKCgwLuclZUlSTpy5EiVvqi6Y2+WPAXH79OQczRLGRkZle4L/nO5XMrLy1NGRgbXdNcgxt0cjLs5GHdzMO7mYNzNwbib48iRI5Kq/6bYlg5qDz74oLKzs9W+fXvZ7Xa53W7NnDlTN910kyQpLS1NktS4cWOfdo0bN/Y+d7LDhw/L7XaX2ubnn38us5bZs2dr+vTpJdYnJSVV6JhOZaGkhX8JWHcAAAAAqklGRoZiYmKqrX9LB7W33npLb7zxhhYtWqSOHTtqy5YtGjNmjBITEzV8+PAarWXixIkaO3asd9nj8ejIkSNq0KBBle9bkZ2draZNm+q3335TdDQTitQExtwcjLs5GHdzMO7mYNzNwbibg3E3R1ZWlpo1a6b69etX634sHdQmTJigBx980HsJY6dOnbR7927Nnj1bw4cP996ELj09XQkJCd526enp6tKlS6l9NmzYUHa7Xenp6T7r09PTT3lTu9DQUIWGhvqsi42NrcRRlS06Opp/ZDWMMTcH424Oxt0cjLs5GHdzMO7mYNzNERRUvdN9WHoykby8vBIDYLfb5fF4JB2/7DA+Pl4rV670Pp+dna0NGzaoe/fupfYZEhKirl27+rTxeDxauXJlmW0AAAAAoCZZ+ozaNddco5kzZ6pZs2bq2LGjvvvuO82bN0+33XabpON3nh8zZoweeeQRtWnTRklJSZo8ebISExN13XXXefvp1auXBg4cqNGjR0uSxo4dq+HDh+u8887TBRdcoPnz5ys3N1cjRoww4zABAAAAwIelg9qTTz6pyZMn66677tLBgweVmJioO++8U1OmTPFuc//99ys3N1ejRo1SZmamevbsqWXLlsnpdHq3SU1N1eHDh73LQ4YM0aFDhzRlyhSlpaWpS5cuWrZsWYkJRmpKaGiopk6dWuLSSlQfxtwcjLs5GHdzMO7mYNzNwbibg3E3R02Nu82o7nklAQAAAAAVYunvqAEAAABAXURQAwAAAACLIagBAAAAgMUQ1AAAAADAYghqNWDBggVq0aKFnE6nunXrpm+++eaU27/99ttq3769nE6nOnXqpKVLl9ZQpbXH7Nmzdf755ysqKkpxcXG67rrrtG3btlO2eeWVV2Sz2Xx+is8eilObNm1aifFr3779KdvwXq+6Fi1alBh3m82mu+++u9TteZ9XzpdffqlrrrlGiYmJstlsev/9932eNwxDU6ZMUUJCgsLCwpScnKzt27eX229F/z7UNacad5fLpQceeECdOnVSRESEEhMTdcstt2j//v2n7LMyv6vqmvLe77feemuJMezXr1+5/fJ+P7Xyxr203/U2m01z584ts0/e76fmz+fF/Px83X333WrQoIEiIyM1aNAgpaenn7Lfyv5NOBlBrZq9+eabGjt2rKZOnarNmzerc+fO6tu3rw4ePFjq9l9//bWGDRum22+/Xd99952uu+46XXfdddq6dWsNV356W716te6++26tX79eKSkpcrlc6tOnj3Jzc0/ZLjo6WgcOHPD+7N69u4Yqrh06duzoM35r1qwpc1ve64Hx7bff+ox5SkqKJOlPf/pTmW14n1dcbm6uOnfurAULFpT6/Jw5c/TEE0/omWee0YYNGxQREaG+ffsqPz+/zD4r+vehLjrVuOfl5Wnz5s2aPHmyNm/erHfffVfbtm3TtddeW26/FfldVReV936XpH79+vmM4eLFi0/ZJ+/38pU37sXH+8CBA3rppZdks9k0aNCgU/bL+71s/nxevO+++/TRRx/p7bff1urVq7V//35df/31p+y3Mn8TSmWgWl1wwQXG3Xff7V12u91GYmKiMXv27FK3Hzx4sHHVVVf5rOvWrZtx5513Vmudtd3BgwcNScbq1avL3Obll182YmJiaq6oWmbq1KlG586d/d6e93r1uPfee41WrVoZHo+n1Od5n1edJOO9997zLns8HiM+Pt6YO3eud11mZqYRGhpqLF68uMx+Kvr3oa47edxL88033xiSjN27d5e5TUV/V9V1pY378OHDjQEDBlSoH97vFePP+33AgAHGFVdcccpteL9XzMmfFzMzMw2Hw2G8/fbb3m1++uknQ5Kxbt26Uvuo7N+E0nBGrRoVFhZq06ZNSk5O9q4LCgpScnKy1q1bV2qbdevW+WwvSX379i1ze/gnKytLklS/fv1TbpeTk6PmzZuradOmGjBggP773//WRHm1xvbt25WYmKiWLVvqpptu0p49e8rclvd64BUWFur111/XbbfdJpvNVuZ2vM8Da+fOnUpLS/N5P8fExKhbt25lvp8r8/cB5cvKypLNZlNsbOwpt6vI7yqUbtWqVYqLi1O7du30l7/8RRkZGWVuy/s98NLT0/XJJ5/o9ttvL3db3u/+O/nz4qZNm+RyuXzeu+3bt1ezZs3KfO9W5m9CWQhq1ejw4cNyu91q3Lixz/rGjRsrLS2t1DZpaWkV2h7l83g8GjNmjC666CKdddZZZW7Xrl07vfTSS/rggw/0+uuvy+PxqEePHtq7d28NVnv66tatm1555RUtW7ZMTz/9tHbu3KmLL75YR48eLXV73uuB9/777yszM1O33nprmdvwPg+8E+/ZiryfK/P3AaeWn5+vBx54QMOGDVN0dHSZ21X0dxVK6tevn1599VWtXLlSjz76qFavXq0rr7xSbre71O15vwfewoULFRUVVe4leLzf/Vfa58W0tDSFhISU+J8/5X2WP7GNv23KElyhrYHT0N13362tW7eWe0129+7d1b17d+9yjx491KFDBz377LOaMWNGdZd52rvyyiu9j88++2x169ZNzZs311tvveXX//FD1b344ou68sorlZiYWOY2vM9RG7lcLg0ePFiGYejpp58+5bb8rqq6oUOHeh936tRJZ599tlq1aqVVq1apV69eJlZWd7z00ku66aabyp0Mive7//z9vFiTOKNWjRo2bCi73V5iZpj09HTFx8eX2iY+Pr5C2+PURo8erY8//lhffPGFzjjjjAq1dTgcOuecc7Rjx45qqq52i42NVdu2bcscP97rgbV7926tWLFCI0eOrFA73udVd+I9W5H3c2X+PqB0J0La7t27lZKScsqzaaUp73cVyteyZUs1bNiwzDHk/R5YX331lbZt21bh3/cS7/eylPV5MT4+XoWFhcrMzPTZvrzP8ie28bdNWQhq1SgkJERdu3bVypUrves8Ho9Wrlzp83+0i+vevbvP9pKUkpJS5vYonWEYGj16tN577z19/vnnSkpKqnAfbrdbP/zwgxISEqqhwtovJydHqampZY4f7/XAevnllxUXF6errrqqQu14n1ddUlKS4uPjfd7P2dnZ2rBhQ5nv58r8fUBJJ0La9u3btWLFCjVo0KDCfZT3uwrl27t3rzIyMsocQ97vgfXiiy+qa9eu6ty5c4Xb8n73Vd7nxa5du8rhcPi8d7dt26Y9e/aU+d6tzN+EUxWIarRkyRIjNDTUeOWVV4wff/zRGDVqlBEbG2ukpaUZhmEYN998s/Hggw96t1+7dq0RHBxs/OMf/zB++uknY+rUqYbD4TB++OEHsw7htPSXv/zFiImJMVatWmUcOHDA+5OXl+fd5uSxnz59urF8+XIjNTXV2LRpkzF06FDD6XQa//3vf804hNPOuHHjjFWrVhk7d+401q5dayQnJxsNGzY0Dh48aBgG7/Xq5Ha7jWbNmhkPPPBAied4nwfG0aNHje+++8747rvvDEnGvHnzjO+++847u+Df//53IzY21vjggw+M//znP8aAAQOMpKQk49ixY94+rrjiCuPJJ5/0Lpf39wGnHvfCwkLj2muvNc444wxjy5YtPr/rCwoKvH2cPO7l/a7Cqcf96NGjxvjx441169YZO3fuNFasWGGce+65Rps2bYz8/HxvH7zfK6683zOGYRhZWVlGeHi48fTTT5faB+/3ivHn8+L//d//Gc2aNTM+//xzY+PGjUb37t2N7t27+/TTrl0749133/Uu+/M3wR8EtRrw5JNPGs2aNTNCQkKMCy64wFi/fr33uUsvvdQYPny4z/ZvvfWW0bZtWyMkJMTo2LGj8cknn9Rwxac/SaX+vPzyy95tTh77MWPGeF+nxo0bG/379zc2b95c88WfpoYMGWIkJCQYISEhRpMmTYwhQ4YYO3bs8D7Pe736LF++3JBkbNu2rcRzvM8D44svvij1d8qJsfV4PMbkyZONxo0bG6GhoUavXr1KvB7Nmzc3pk6d6rPuVH8fcOpx37lzZ5m/67/44gtvHyePe3m/q3Dqcc/LyzP69OljNGrUyHA4HEbz5s2NO+64o0Tg4v1eceX9njEMw3j22WeNsLAwIzMzs9Q+eL9XjD+fF48dO2bcddddRr169Yzw8HBj4MCBxoEDB0r0U7yNP38T/GH7X+cAAAAAAIvgO2oAAAAAYDEENQAAAACwGIIaAAAAAFgMQQ0AAAAALIagBgAAAAAWQ1ADAAAAAIshqAEAAACAxRDUAPx/e3cf09TVxwH82xZGKy8aFAOV4pAODIpgN90LgeFLAk46JlaYYwPDUDPGugyj22KiBWXoImziZsZiQs1kySC8TLZJnHEuwGImSBHBNcVANmPjElhgzkZde54/zG7ss4KPiMD2fD/JDTn3nPPr79z+wy/39F6iSWc2mzFr1qypTuOByGQyNDY2jjlm06ZNeOGFFyYlH09MJhNkMhlkMhk+/PDDB4qVlJQkxbJYLBOSHxERjY6FGhERjcumTZukf9zvPvr6+qY6tUlht9uxZs0aAMDAwIDHAubgwYMwm82Tn9xdFi1aBLvdji1btkjnCgsLERgYCI1Gg+rqarfxtbW10Ov1f4tTX1+PH3/88aHnS0REd3hNdQJERPTPlZKSgqqqKrdzQUFBU5TN5AoODr7nmJkzZ05CJmPz8vJyy7WpqQmff/45Tp48CZvNhtzcXCQnJ2POnDkYHh7Gzp07cerUqb/FCQwMxMjIyGSmTkT0f4131IiIaNx8fHwQHBzsdigUCpSXlyMmJga+vr7QaDTIz8/H9evXR43T1dWFFStWwN/fHwEBAXj88cfR3t4u9be2tiIhIQEqlQoajQZGoxF//PHHqPFMJhPi4uJQWVkJjUaDGTNmICMjA8PDw9IYl8uF4uJihIaGwsfHB3FxcWhubpb6b926hYKCAoSEhECpVGL+/PkoLS2V+u/e+hgeHg4AWLp0KWQyGZKSkgC4b3389NNPoVar4XK53HJNS0tDbm6u1P7yyy+h0+mgVCqxYMECFBUV4c8//wQACCFgMpkQFhYGHx8fqNVqGI3GUa+DJ5cuXUJSUhKeeOIJbNy4EQEBAejv7wcA7NixA6+99hrCwsLuKyYREU08FmpERDTh5HI5Kioq0NPTg6NHj+L06dPYsWPHqOOzsrIQGhqKc+fOoaOjA++88w68vb0BAJcvX0ZKSgrWr1+PCxcu4IsvvkBraysKCgrGzKGvrw81NTVoampCc3MzOjs7kZ+fL/UfPHgQZWVlOHDgAC5cuIDk5GQ8//zzsNlsAICKigocP34cNTU1sFqtqK6uxqOPPurxs/7aEnjq1CnY7XbU19f/bcyGDRswODiI7777Tjo3NDSE5uZmZGVlAQBaWlqQnZ2NN998E729vaisrITZbEZJSQkAoK6uDh988AEqKyths9nQ2NiImJiYMa/Df4uNjUV7ezt+++03dHR0wOFwQKvVorW1FefPn7/vwo+IiB4SQURENA45OTlCoVAIX19f6TAYDB7H1tbWitmzZ0vtqqoqMXPmTKnt7+8vzGazx7mvvvqq2LJli9u5lpYWIZfLhcPh8Dhn9+7dQqFQiCtXrkjnTpw4IeRyubDb7UIIIdRqtSgpKXGbt2zZMpGfny+EEOKNN94QK1euFC6Xy+NnABANDQ1CCCH6+/sFANHZ2ek2JicnR6SlpUnttLQ0kZubK7UrKyuFWq0WTqdTCCHEqlWrxHvvvecW47PPPhMhISFCCCHKyspEZGSkuHXrlsecPF2H2NhYj+cjIiLE4sWLRX19vbh586ZYvHixaG9vF4cOHRKRkZHimWeeERcvXnSbN9o6iYho4vGOGhERjduKFStgsViko6KiAsCdO0urVq3CvHnz4O/vj1deeQWDg4O4ceOGxziFhYXIy8vD6tWrsW/fPly+fFnq6+rqgtlshp+fn3QkJyfD5XJJW/Y8CQsLw7x586T2008/DZfLBavVipGREVy9ehXx8fFuc+Lj43Hp0iUAd7YtWiwWREVFwWg04uTJk+O+Tn/JyspCXV0dbt68CQCorq7Giy++CLlcLq21uLjYba2bN2+G3W7HjRs3sGHDBjgcDixYsACbN29GQ0ODtC3yfphMJvT19aG7uxvr1q1DaWkpVq9eDW9vb+zduxetra3Iy8tDdnb2A6+ZiIjGh4UaERGNm6+vL7RarXSEhIRgYGAAqampWLJkCerq6tDR0YGPP/4YwJ3ffXliMpnQ09ODtWvX4vTp04iOjkZDQwMA4Pr169i6datbQdjV1QWbzYaIiIiHtjadTof+/n7s2bMHDocDGRkZMBgMDxRTr9dDCIGvv/4av/zyC1paWqRtj8CdtRYVFbmttbu7GzabDUqlEhqNBlarFYcPH4ZKpUJ+fj4SExNx+/btcef0008/4dixY9izZw/OnDmDxMREBAUFISMjA+fPn8fvv//+QGsmIqLx4VMfiYhoQnV0dMDlcqGsrEy6U1RTU3PPeZGRkYiMjMRbb72FjRs3oqqqCuvWrYNOp0Nvby+0Wu195fHzzz/j6tWrUKvVAICzZ89CLpcjKioKAQEBUKvVaGtrw7PPPivNaWtrw/Lly6V2QEAAMjMzkZmZCYPBgJSUFAwNDSEwMNDtsx555BEAgNPpHDMnpVKJ9PR0VFdXo6+vD1FRUdDpdFK/TqeD1Wodc60qlQp6vR56vR6vv/46Fi5ciO7ubrc4/yshBLZu3Yry8nL4+fnB6XRKRd9ff++1JiIiejhYqBER0YTSarW4ffs2Dh06BL1ej7a2NnzyySejjnc4HNi+fTsMBgPCw8Nx5coVnDt3DuvXrwcAvP3223jqqadQUFCAvLw8+Pr6ore3F99++y0++uijUeMqlUrk5OTgwIEDGBkZgdFoREZGhvSo+u3bt2P37t2IiIhAXFwcqqqqYLFYpPeKlZeXIyQkBEuXLoVcLkdtbS2Cg4M9vqh77ty5UKlUaG5uRmhoKJRK5aiP5s/KykJqaip6enrw8ssvu/Xt2rULqampCAsLg8FggFwuR1dXFy5evIi9e/fCbDbD6XTiySefxIwZM3Ds2DGoVCrMnz9/zO9kNEeOHEFQUJD03rT4+HiYTCacPXsWJ06cQHR09D/+xeRERP9ULNSIiGhCxcbGory8HPv378e7776LxMRElJaWjvp7J4VCgcHBQWRnZ+PatWuYM2cO0tPTUVRUBABYsmQJvv/+e+zcuRMJCQkQQiAiIgKZmZlj5qHVapGeno7nnnsOQ0NDSE1NxeHDh6V+o9GI4eFhbNu2Db/++iuio6Nx/PhxPPbYYwAAf39/vP/++7DZbFAoFFi2bBm++eYb6S7h3by8vFBRUYHi4mLs2rULCQkJOHPmjMe8Vq5cicDAQFitVrz00ktufcnJyfjqq69QXFyM/fv3w9vbGwsXLkReXh4AYNasWdi3bx8KCwvhdDoRExODpqYmzJ49e8xr4cm1a9dQUlKCH374QTq3fPlybNu2DWvXrsXcuXNx9OjR+45LREQTQyaEEFOdBBER0UQymUxobGyExWKZ6lSm1ERfh4GBAYSHh6OzsxNxcXETEpOIiDzjw0SIiIj+xbq7u+Hn5+d2N3E81qxZg0WLFk1QVkREdC/c+khERPQvZTQapd/BBQUFPVCsI0eOwOFwALjz6gMiInq4uPWRiIiIiIhomuHWRyIiIiIiommGhRoREREREdE0w0KNiIiIiIhommGhRkRERERENM2wUCMiIiIiIppmWKgRERERERFNMyzUiIiIiIiIphkWakRERERERNMMCzUiIiIiIqJp5j/R3YbHQU8r8QAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ], + "id": "DfHHspttKJE0" + }, + "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -2471,25 +1073,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FdQs_PcqEsiL", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "bf226778-45d6-42bf-e8cc-79c0856e3632" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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tkqSPthXpo21Fxmv//cVkjemT0p5lAwAAdFqEo05g3tPr9N53hZKk4welq2+aU1ccP0B90pwmV4b2FnVw+XZflEVRlrpnXbX2vKsBPeIP+9pXe0oVG2MNa43NiY6yqH96vFE/AABAZ0A46mTe+65Q730nldfU6qEfjzG7HESgH47tpQEZ8Sooq5Ek3fP6Rm3dXylJuu2lrzusjskD0vT05cd22PUAAACOFEt5dwLnjOmlmOjAX1VBmcukahDpLBaLRucka8ZRWZpxVJZ6JNhNqePTncU8wBYAAHQqjBx1Aj8c21vTh2cqr7RG03//riTJFk2uRXDuOudo/fOjXar21Lb7tSpdXr28Ya8kyV3r4/44AADQqRCOOokEh03uRquWxVj50ong9O8Rr4WzhnfItTbnlxvhCAAAoLMhHHUiHm/DFKXoKEaOEHlirA39Mj0+Rre++KWJ1QSyyKKpg3to+nAepgwAAJpHOOpEPN6GkaPG0+r+t2GviqvcAW2jLBadPDRDPZNjO6w+YEtBhbFdWOHWPz/aZWI1Tf3z4536eMEpykh0mF0KAACIQISjTiTRYdONM4fI4/VpcGaCcXzpO1v19d6yJu3T4mL00S2nyGZllAkdY1BmvOJirKp0e80upVl+f92Kj6lxMeqb5lT/FpY9BwAA3Q/hqBNJctp09UkDg25/oNKt/eUuRo/QYfqmxWnNLado14Eqs0sxuGp9+tGfPzT2f/ncBkmSxSL95+eTNZaH4gIAgIMIR13AL2cMVmm1x9j/yzvbtDGvXJLksLX/Az+BxhIdNo3olWR2GQZXrbfZ0Sy/X9p1oIpwBAAADISjLuDkoYE3mD/7yffGdizhCN2cPdqqZVdO0jub98vr8+vfa3dpX2ndA3KvW7Ze1y1bb0pdiY5o3fOjkTrt6GxTrg8AAJoiHHVBNbUN/0Ju53lIgEb0SjJGs5Z/uc8IR2Yqq6nVyxv2Eo4AAIgghKMuaEK/VKXFxcjj9SsqyqKCshqVNJp2dzjOGKt6pzgDjm0vrAxYJe9QURYpNy1O0Sz6gE7i5tOGauk7W1XtOXy/bk8FZTVGOPvi+1Jd+uQnYXvv1LgY3TBziFJjGTEGAKAtCEdd0ILThgXsL31nm574YHur500d3ENP/WxCwLHL/v6Jtu6vbPG8oVkJWn7N8YqK4sG0iHwnDsnQiUMyTLv+b/73jfH3cU9JtfaUVIf1/RMcNi04dVBY3xMAgO6CcIQjtjGvXIWVLmUk8OwYoDVTB6fr6bU7VdNOI1ffFZTr6bW79VWeRSVrd8tqjdxRpNy0OB03KN3sMgAAMBCOuoHRfZI125XTartBmU2f+XLaiGztL3c1237Zp7slSdYoixIdtiMrEugmThySofULZ6gqjM+CuuWFL/X613mS6p7j9N53hZKsem77t2G7Rnv52yXH6KSh5o3kAQDQGOGoG/jBqJ76waiebTr3hplDDvvaPeeOlCT5/X5ZLEypA4LlsFnDusx+75TO+yyz0iDuhwQAoKMQjnDECEaAuW48dYgm9Es1gobX69UXX3yhkSNHRuS0unvf2KSCgyPSuelxJlcDAEADwhEAdHL2aKtmHJVl7Hs8HsXmbdDpY3vJZou8Ka83Pv+FsV1U2fy0XQAAzEA4whFZvalANzy3QT5/y+3mTx+snx7b19jPK63R6X94L6hrPHfVJA3o0XA/1H8//153vNL6vRQZCXa9ft3UgGO/en6D3vy2oNVzzxrdU4tmHRVw7Lh73grqPpG7f3h0wBfVDbtLdPXT63TTqUM1q43TG4Gu6o5XvtW9b2xuctweHaXLju+nM0fydwYA0HEIR2iTardXv/jXZ0pxxig2xqrdRS0vR1zjCQwVPr9fRZXuoK7lOyR5uTy+oM5t7gG4Fa7aoM6tcjUNQSVVHlW4als91+NtqNfr8+utjQX6vrhaf/9wB+EIkGSzWoy/J9sLD/+ogHte30g4AgB0KMIR2iQ2xqqjeibp1S/3KcpiUd80Z4vtD13NLjqq9XOMtoc8YDbeER3UuT3i7c0eC+bc1PiYJsdyUp2qcrcejpwxDfd47C6q0kOrvqu7dkLTeoDu6JLJufr32t3NPmDa5/cbwWl3UbX+8dFO/fiYHNl40DQAoAMQjtBmN8wc0uJqdi3JSHTonRtPatO5Z47s2eZ/TV581og2nSdJr117fMjnfFdQYWwPzGi6VDrQHf36jOH69RnDm33tmbW7dPMLXxr7t734lXw+v+ZMzu2g6gAA3RnhCGhHWwhHQEj6pcfJYpH8jWbTbt1foc92FptWU1JstAb0iGdlTgDoBghHQDsiHAGhmdg/Te/eeJJOum+1ag/eb/jUmp16as1OU+u6YcZgzTt5kKk1AADaH+EIaEdbCsqN7d+v3KzROclNvmAt/t/Xyi+rCTiWkeDQ/BmDm9yrBXQHOalOTeiXqg+3HjC7FMO3eeWtNwIAdHqEI6Ad7SlpWMXvzW8LAlayq/fu5v3aur/pil2pcTG65hT+pRrd0z0/GqlnP90d1PL57WXt9iJ9uadUkrTym3yNWrzCtFqORGpcjO49d6TG56aaXQoARDzCEdCOThqSoec++75N5za3FDnQXeSkOvXLGW1b8CVcbnr+CyMcuWt9ctc2XV2vMyit9ug/674nHAFAEAhHQDu697xRuum0ocaSxTHNLEf87yuOlffgvRV/WLVF/167S5I0JCuh4woF0MTFk/tq6/6KoJ/JFmkKK1wqq6l7/MBnO4s1f9n6Ztv5fD7t2ROlt5//UlFRR/aPMtnJDs07aZBiGz3SAAA6E8IR0M7Sm3neUmMZCQ5je2+jaXhDsxLbrSYArTuqZ5Ke//lks8tos6ufXqdXv9gnSdqcX6HN+RUttI7SJ4X7wnLdFGeMLju+f1jeCwA6GuEIiCCbDt70nRRrU2YiD40F0HYT+6Ua4agjfbOvTP/bsLfDrytJGQl2TeiXyrLrANqMcARECFetVzmpsap012pIVoIsFos+2VGkb/eVyev16us8i4o+3iWrtel0leykWE0fnhlw7KX1e1Ra7Wn1uuP6puionknGfqWrVv9ZF9x9Uj8Y1VPJzhhj/7v8cq3Z1voKY7E2q84bnxNwbPWmAu0qqpJUN5o2Y3imoqL4ggO01cWTcnX60dkqPzi17nBqaz1avfodnXjiCYqObtsKmdctW68Nu0skSS+s26MX1u1p0/uEw+9+NFLnH5PTekMAaAbhCIgQ9mirnrtqsvx+vypcdV9mXv8qT4+/v/1gC6ue376x2XOPH5TeJBz96a0t+q6gpWk0dW49Y1hAOCqr8WjhS18HVfOx/dMCwtFnO4uDOjcr0dEkHD376W4t/zLP2L/znBG6cGLfoOoA0Lz0eHurU3s9Ho8yYqXctDjZbG0LR1mJdm1o05nht7OoUlXulgNhZxBlschh494toKMRjoAIY7FYlMDzjVRS1fqoF4DIcM+PRuqkIXmqNGnp9RfWfa+v95ZJkh5+e6sefnurKXWE2w/H9tID5482uwygWyEcARHsrNE9NaJXory1Xq3fsEGjR42SNbrpvyQ2XtSh3k2nDlW5q/WAcXSvpID9pFibfj97VFD1ZSYGXvfY/mlBnRvbzL+GzpmUq4Iylz7dWSxJGpOTHFQNAMyX7IzRjyf0Me36H207YISjruTl9Xt1/3mjuIcK6ECEIyCCjeydrJG9k+XxeGTbu16nj+4Z9LSXaYdMswuWMyZa54zp3aZzc9PjlJse16ZzJ/ZPM+6NsEZZNLpPcpveB0D3c/20wbJaLEH9g1Ckc9f69MmOun8kslikH//1I+WmxWnRD4bLGcPXNqC98bcMQEQorfJoc0Hdan3DsxP5EgAgaMN7JmrpRePMLiMsNuWVa+aD70qSPF6/Pt5epI+3F2ls32TNPsa80Tmgu+DbB4CIsG5Xsfx1z8LVuL4pAa/tPFApV62v1fdIi4tRWqObz2u9Pm0rrAzq+n1SnQE3P5dWe5RfVtPqeVEWiwZmxAcc21da3eoKYZKU4IhWdlJswLEtBRXy1f8gWpCZ6FBSbMMoYo3Ha6z2V+up1b4q6bv8CkXbmv5nvn96nKIbPZD4QIVLB4J40Kk9Okp90wJHBncXVana0/p9JinOGPVIaPjd+Hx+bdnf+oIhfdOcsjczlRToqvqmOTUhN1VrdxQFHP98VwnhCOgAhCMAEeGzg/caSdL43MBwdPXT6/TVntbvJ7hx5hBdfdJAY7+02qMZv383qOv/b95xOrp3w/1Xb23M1/XLWl9/KzUuRutumx5w7Hevb9J/P299KeOzR/fUgz8eE3Bs9l/WBBVUHjh/lH44tmH645aCCp35x/cbtYjW3Rs+bPbcT2+dFrCC2bJPd+t3r29q9ZrDsxO1/NrjA47d+PwGfbSt6DBnNLjqhAG6+bShxn6tzx/U76ZnkkOvXz9ViSxSgm7CYbPq2asmyVXr1SVPfGI8HuGZT3br1BFZOnFIhskVAl0b4QhARHB760aGhmQmaFL/NJOrQaTYW1qj/NIawhG6HXu0VbnpzoBnx23MK1fvlNgWzmrKYrGob6ozYLQYwOERjgBEhF+cOEDWKIuSYm0BU+MkacbwLB2VnXSYMxsMy04I2LfbrJo9PriHQSY7A79890mNC+pcp73plK+J/VIVE8QXkTHNLDpx1uheqnS1PiXv0OltKXExRr0+v0+7d+9WTk6OoixN67BHBx4blpUY1GfNTm66KuJJQzLUN7X1RThG9Q78/UVZ1OI1P9haqKJKtwZlxKt/j/jDtgO6stt/cJSe/fR7eX11U23vfm2j7n6t+efdtWRoVoL+93/HyUZAAlpFOAIQEZKdMbrp1KHNvnbNKYPa9J7x9mjdc+7INp07rm9Kk3ufgvXjCX3avKzxwlnD23Rer+RY47N6PB4tX75Tp59+VFCrG540NEMnDW3bVJ0rTxjQpvOirVFt/t0A3YU92qpj+6fqgy0HWm/cgo155Sqr9jT5hycATRGOAAAAItTiH4zQP9bsCPkBu36/9J9130uqW6wmNS6mPcoDuhzCEQAAQIQamBGvxWeNCPm83UVVRjga2TuJB8kCQSIcAQAixv5yl97eVKA1Ww/onc37W2x74uAeemD26IBjZ/7xPe0taX0J9ptPG6rzG93ztPNApc55pPnV/Q718rwp6p3iNPb/vXaX7n2j9dX+clKdeunqKQHHrvn353p/S2Gr584+JqfJtNNj7nzTuBelJQ/OHq2pg3sY+2u3F+mqf35m7Pv9frndVt2+4e0mX6A/vuWUgPtUHnrzO/19zY5Wrzm+b4r+evH4gGM//usabc5vffn2/zt5oOZO6WfsF1a4gl518unLJ2poVqKx//KGvbr95a9bPS81LkZvzj8h4NiCF77UG1/ntXrumSOz9ZtDwstJ961WaXXrD6S98+wROu3o7FbbtcUX35ca20f3Tm6XawBdEeEIABAxviso16+e/yKotuXNLFxRUuVRURBLoR/63Cyvzx/UeZJ06GOoXB5vUOc2fi5VvQpXbVDnVjXzWYsr3aoNIhx5vIGftdbra+aaFlXWtv5lvsoTXL3NPeertDq4c2s8gfX6/Qr6d3NoWHTXNvdZm4pqZlClMsjfTUVzv5sqt0qqWv951q/SWV7j0ac7izU2J0VJzvCszPjF9yXG9qELogA4PMIRACBijOuborNG99T63SWttm38UNl6vZJjZW3um+4hEuyB//uzWaPUN815mNaBDn3/BIctqHN7NrPaX0aCPahzU5q5X6RPmjOokaNYW+CKinabNeCafr9fVZVVcsY5W516leKMCarezMSmv5ueSQ5VuVtfiTExNvB3Y42yBP27OXSVyHi7Nahzk51Nf77p8cH9btKbWeQgJ8WppNjWw5Ezpu6zfrqjWHOf/ESSdMOMwZp3ctsWoWksYOSoF+EICJbF7w/iUexdSFlZmZKSklRaWqrExMTWT2gndatJLdfpp58e1GpS6N7oLwgF/QWhoL+Y7/4Vm/THt7ZIkv70kzE6c2TPI3o/n8+vUYtXqNxVq6xEhz665ZRwlCmJ/oLQRFJ/CTYDsOA9AACAidbtKja2x/Zp2yMEGtt+oNKYdjqSKXVASJhWBwAAYBKvz6/1u0okSVmJDvVMjj3i92x8v9HKb/M16NfLWz0nK8mhxy4+RkOyElptC3RlhCMAAACTbMorN55h5PH69It/1a0kOO+kQRres2Hqz1d7SvXI6i1BvWfj0Se/X/J4W7+DYndRte545Wv987JjA44v/t/Xyi9rWAHS5/Mrb1+UXivboKiD999ZLBbNGJ6ps0b3Cqo+IJIRjgAAAEzyXUG5sX2g0q3lX9YtH/7jY/oEtCuscBmvtea3Zx+tT3YUaU9JtSRpX0mNDgSx8t4HWw9oR2GlctPjjGPvbt6vrfsrD2kZJRXlBxxZ/uU+nTg4I2yr7QFmIRwBAACYZGTvZCU7bUEt/R2s1LgY/eWihudM/eZ/3+iJD7a3ep7fL923YpP+9JOxIV3vnDG9lOKMUUw0t7Kj8yMcAQAAmKRfepzW3jJNBypdAcdTDlle/Nj+aVqz4OQ2XePaaYN0+dR+zb624us8LXr5G2N/d3G1ln2yy9i/YEKOLLJoQv9Upcfb5fHU6u233tJJJ58sm63ua6Q92qrUZpabBzojwhEAAICJYqKjlJ3U8kIMDpu11TaHkxRra/YhxJI0Z3I/1Xh8WvLaRknSht0l2tDMc8YGZ8ZrxfUnyOPxKNkuZSc5TF+aGWgPjH8CAAB0Y43vMTqczfkV8gXx0GGgs2PkCAAAoBubMTxT/7psonYXVQUcL67y6J7XNxr7J9+/Ws9fOdHY/2BLoVy1Xg3LTlRWokMWi6XDagbaC+EIAACgG7NYLJoyML3J8d1FVQHhaMeBKm1rtHLdn1dv1ftbCiVJa285RRmJjvYvFmhnTKsDAABAEzmpTt15zoiAY7e8+LXu+8Kqsx9ZozXbDkiS0uNj1CPBbkaJQNgRjgAAANCsCyf2Va/khoUgtuyv1O5Ki77eVy7vwXuQhmYlMqUOXQbT6gAAAHBYP5nYR4+8vUWuWp8kyefzyaeGMFTl9uqBlZubPTc7yaHzxvVWtJV/j0fnQDgCAADAYV190kBdfdJASZLH49Gyl5br1k8bvkKu21WsdbuKD3t+rdeniybltneZQFgQjgAAABA0h1WyR0cZI0mteXtjgcbnpsoa1TDalOLkPiVEJsIRAAAAgmaLkrISHdrZaOnve88dqcxGq9XdtfxbbcwrlyS9tWm/3tq0v8n7/OknY3TmyJ7tXzAQAiaAAgAAICRRh6y/MKFfqqYO7mH8SY9vfVTo0x2Hn4oHmIWRIwAAAITkgfNG6oX1++Q+OLUuzh74lfKSyX2VX1aj7woqjGP26Ch5fX7VHlzl7tlPd+v1r/J0yZRcXXXCgI4rHmgB4QgAAAAhGdErUWNy0w77+rThWeqTFqcZv3/XOHboPUpVbq+q3F4tfWcr4QgRg3AEAACAsBvQI14XTOijD7cWyu8PfK3W69Pe0hpJksvj043PbQh4fUyfFF0wIYfnJ6HDEY4AAAAQdtYoi5b88OhmX9t1oEpT731bklTt8eq5z74PeP25z77X8J6JGp2T3N5lAgFYkAEAAAAdZk9Jtc56+P0mizoc6qLHPtZNz3+hdzbvl6vW2zHFodsjHAEAAKDD+Hx+FVd55PO33K7cVatln+7WnCfW6vaXv+6Y4tDtMa0OAAAAHSbaalHfNGezrxWUuVTtaTpKtHV/pTF6FGON4l4ktBvCEQAAADpMdlKs3rnxpGZfO1Dh0qtf7lO126sdB6r077W7JElrtxdpyK2vS5L6pcdp2RXHKqPRQ2eBcGFaHQAAACJCWrxdF0/K1ZUnDNBpI7KabbO9sFKf8ABZtBNGjgAAABBxJg9I06XH9dNXe0olSVsKKnSg0i1Jyk5m1Ajtg3AEAACAiHHvGxv15Z4yvbt5v6YPz1RafIwkaXN+wwoOf169VT8Y1VOzRvU0jtV4vJr/7PqgrjF/+hANzIgPa93oGghHAAAAiBhvfJ2vLQUVkqSV3+Q322blN/kalpUQcKzW59fyL/OCusbPpvSTJHl9fu0vdykjwa6o1tYWR7fAPUcAAACIGCcPzeiwa+0prtaxS1Zp2MLXWS4ckhg5AgAAQAS55fRhuuqEAQEPft2wu0RX/XOdJGnasExdMbWfnDHR2lFYabTx+f169spjW3zvOHu0UuNilBpXN1VvZ1Hd+a5an+zRjBmAcAQAAIAIUx9e6q3etN/YfvPbfL35bfPT7YJx25nDdelxddPqdh6oMo73Ocyzl9C9EJEBAAAQ0bw+f+uNgvTBlkJje1dRQzjqmxoXtmug82LkCAAAABHtvPG9lV9Wo++Lq9t0fkF5jT7YckCS9PG2Azr5vtWSpLyyGqPNjgMVOm5Q+hHXis6NcAQAAICIZo+26pczhrT5/De/yTfCUaXbq22N7lWq96+Pdumnx+a2+RroGghHAAAA6NIm9E/V5AFp+npvmXHM7/errKbW2E9y2swoDRGGcAQAAIAuLdFh09OXB65kV1Beowl3rjL2+6SyIANYkAEAAADd0K5GK9VJUt80FmQA4QgAAADd0M5DwlEOI0dQBISjhx9+WLm5uXI4HJo4caLWrl3bYvsHH3xQQ4YMUWxsrHJycnT99derpqamxXMAAACAxs4a3VOzRvY09vsSjiCTw9GyZcs0f/58LVq0SOvWrdOoUaM0c+ZMFRQUNNv+6aef1s0336xFixbp22+/1eOPP65ly5bplltu6eDKAQAA0JlFW6NUXOU29n/81480f9l6+f3he6YSOh9Tw9EDDzygyy+/XHPnztXw4cO1dOlSOZ1OPfHEE822//DDDzVlyhT95Cc/UW5urmbMmKELLrig1dEmAAAA4FDlrobV6qo9Xr3w+R4VVbpbOANdnWmr1bndbn322WdasGCBcSwqKkrTpk3TmjVrmj1n8uTJ+uc//6m1a9dqwoQJ2rZtm5YvX66LLrqoo8oGAABAF3H9tEH6w6rv9OWeUnm8dSNGC174UtFWS4vnRVksmjWqp2YeldURZaIDmRaOCgsL5fV6lZmZGXA8MzNTGzdubPacn/zkJyosLNRxxx0nv9+v2tpaXXXVVS1Oq3O5XHK5XMZ+WVnd+vYej0cejycMn6Rt6q9tZg3oPOgvCAX9BaGgvyAUXaW/VLpqtfTd7eqTGqtFZw7Vz/6+TgcOjhit+CY/qPd44+s8rb/1FMVEm34Lf8SKpP4SbA2d6jlHq1ev1l133aVHHnlEEydO1JYtW3Tttdfqjjvu0G233dbsOUuWLNHixYubHF+xYoWcTvNvvFu5cqXZJaATob8gFPQXhIL+glB09v7yfaW09Iu6r8ETe/g0KE46UBlayPF4/brzn29oUJJfqfb2qLLriIT+UlVV1XojSRa/SXedud1uOZ1OPf/88zr77LON43PmzFFJSYleeumlJuccf/zxOvbYY3Xvvfcax/75z3/qiiuuUEVFhaKimnbq5kaOcnJyVFhYqMTExPB+qBB4PB6tXLlS06dPl83GE5nRMvoLQkF/QSjoLwhFV+kvr32Vp2uWfSFJuv6UgfrFif11oMIlt7f1r8Vn/OlDldc03KuUFhej92+cqmgrI0iHiqT+UlZWpvT0dJWWlraYAUwbOYqJidG4ceO0atUqIxz5fD6tWrVK8+bNa/acqqqqJgHIarVK0mFXFrHb7bLbm8Z5m81m+i8pkupA50B/QSjoLwgF/QWh6Oz9ZU9pw6IL/TISZLPZlJUS3Ofpk+rU13vLjP0DlW65/VGK7cQ/j/YWCf0l2OubOq1u/vz5mjNnjsaPH68JEybowQcfVGVlpebOnStJuvjii9WrVy8tWbJEkjRr1iw98MADGjNmjDGt7rbbbtOsWbOMkAQAAAC0ZFdRpbGdmxbabRZ/m3uMVm/cr7te+1YlVR7F26OV6OhUd6qgBab+JmfPnq39+/dr4cKFysvL0+jRo/X6668bizTs2rUrYKTo1ltvlcVi0a233qo9e/aoR48emjVrlu68806zPgIAAAA6mZ0HGu4/6ZsaF9K5GQkOnTe+t2596StJUs9khyyWlle3Q+dhesydN2/eYafRrV69OmA/OjpaixYt0qJFizqgMgAAAHRF9eEoKdamJGfo070OVLrlrvVJknomx4a1NpiLO8cAAADQbbhqvdpXWi1J6hvilLp6e0uqjW3CUddCOAIAAEC38X1xtXwH1/Hqk3rk4WhrQYX+sWaHajzecJQHk5k+rQ4AAADoKLsa32/UxpGjfaU1xvbH24v08fYi5Ze5dMPMIUdcH8zFyBEAAAC6jdgYq04Y3EN905zqnx7fpvcYkpnQ5FhxlbuZluhsGDkCAABAt3Fs/zQd2z/tiN5j8sB0rbh+qv7z2ff6y7vbJEm9Urj3qCtg5AgAAAAI0eDMBKXFxxj7vViYoUsgHAEAAABtsKe4YWGG3owcdQmEIwAAAHQLfr9ffr8/bO+3p6RhYQaW9O4auOcIAAAA3cKOA1X6wR/fV7mrVv3S4zR3Sm6z7Y7ulaQxfVKMfY/Xp3+v3dWk3Zd7SiVJ0VEWZSQ42qVmdCzCEQAAALqFHQcqVe6qlSRtL6zUwpe+brbd/508sEk4OlxbSeqRECNrlCW8xcIUTKsDAABAt9ArObZdQkyq0x7294Q5GDkCAABAtzA4M0Gv/N9x2phX1mq7xmKsUfr97FEBxx568zvtOPhA2T7pbXuYLCIP4QgAAADdxrDsRA3LTgzpnGhrlM4Z0zvg2H1vbDa2B6bHhaU2mI9pdQAAAEAI/H6/Citcxn7vVEaOugpGjgAAAIAQzRrZU8+v+16SlBRr0+b88lbPibJYNDAjPuDYvtJqldfULRKR4IhWdhJLgpuJcAQAAACEwGKxyOX1Gfs7Cit11T/XtXpeenyMPr11esCxu1/bqJfW7zX2f336MF0+tX/4ikVImFYHAAAAhGhPcZWxneyMCdv7frC1MGzvhdAxcgQAAACEaE9JtaS60aBBmfGaPT6n1XPiHU2/eh/bP03Vbq9WfJMvSUoNY9BC6AhHAAAAQAjctT4VlNctyNArOVbj+qZqXN/UNr3XBRP6KDctzghH6Qk8M8lMTKsDAAAAQpBXWiO/v267V8qRL6Cwv9HKdz3iCUdmIhwBAAAAIfi+pOF+o17JRx6OCssbhSNGjkxFOAIAAABCsLekxtjuGYZwFDByRDgyFeEIAAAACMGe4mpjOxwjR/sbjRylM63OVCzIAAAAAIRgT+NpdWG45+iHY3tpaFaC9pe7lJ3sOOL3Q9sRjgAAAIAQ1C/jLYVn5GjygHRNHpB+xO+DI8e0OgAAACAE9fccxcVYlRRrM7kahBPhCAAAAAiSz+c37jnqlRIri8VickUIJ6bVAQAAAEHaX+GS2+uTJOWkOI/4/Wo8Xm3bX6lPdhTpw62FskY1DVtxMdG66sQBGtAj/oivh5YRjgAAAIAgfV/csBhD7zAsxrCnpFqn/+G9VtsVV7n12Jxjjvh6aBnT6gAAAIAg7S5qWIwhJ/XIR456p8RqaFZCq+28Pv8RXwutY+QIAAAACFK4R47s0VYtv+Z4FZS75FdgANpdVK2f//MzFVW5lcbzjzoE4QgAAAAI0veNHgD7j492avmXeU3a9EyO1XXTBslhswb1nlFRFmUlNX2+UXZSrD67bbpqvT7jPie0L8IRAAAAEKT8shpj+4MtBw7bLjvJoTmTc8NyzWhrlKKt3A3TEQhHAAAAQJBOGNxDb2/a32q7dbuKA56BNCQrQcOyE9uzNIQB4QgAAAAI0iVT+ukHo3up0lXb5LW/fbBDT3ywXZL00vq9emn9XuM1a5RFr117vAZntr74AsxDOAIAAABCkBoXo9S4mCbHW1p1zuvz6/viqpDC0ZMfbNeXe8qUHh+jK6b2Z1GGDkA4AgAAAMLgvPG9leS0Ka+04b6kF9Z9rw3fl0qSMhObLrrQkve3HNCb3+ZLki49rl/4CsVhEY4AAACAMLBYLJp5VFbAsVUbC4ztnkmhLf19oNJlbKc0M1KF8GPZCwAAAKCd7CupW/rbHh2lZKetldaBDlS4JUnJTptsrFbXIfgpAwAAAO2kfopddpJDFoslpHMPVNSNHKVzr1GHIRwBAAAA7aC8xqPyg6vaebx+/Xn1Vq3fXRLUudVuryrdXklSGlPqOgzhCAAAAGgH+WUN9wztKanWPa9v1PlL16io0t3quYUVDecyctRxCEcAAABAO8hItDe5z8jt9am8xtPquQcaBai0eEaOOgrhCAAAAGgHiQ6b3rhuqh69eLz6pccZx4NZ0ruwvGHkKC2OkaOOQjgCAAAA2klmokPTh2fKXeuTVPcAWYfN2up5jZfxZuSo4/CcIwAAAKAd+Xx+5ZfVrVpXWu3RlLvf0vCeiXpw9mjF2Zv/Ot43LU4/PiZHhRVuDcyI78hyuzXCEQAAANCOXLU+1fr8kiSvz689JdXaU1Ktj7cf0MlDM5s959j+aTq2f1pHlgkxrQ4AAABoV7ExVl11wgBlJznksDV8/XbGME4RaQhHAAAAQDu7+bShWrPgFE0fnmUcywpiYQZ0LMIRAAAA0EHyS2uM7W/2lenrvaXNtqv1+jqqJDTCWB4AAADQQfaVVRvbv/jXOknSwz8ZqzNGZge0O3bJW3LXejW8Z6KeuWJSh9bYnTFyBAAAAHSQ1GaeWbQxryxg3+fzq6jSpbKaWlW6vB1VGkQ4AgAAADrMwz8ZowWnDdW0YRnGsYyEwMBUUu3RwcXtlM4zjjoU4QgAAADoIL1TnLryhAEa1TvZOJZ5yMIMhRUND4BtbqQJ7YdwBAAAAHSwvLKGhRkODUf7yxvCUY8EwlFHIhwBAAAAHSy/rCEAZSUdPhwdOuUO7YtwBAAAAHSw/IMjR1EWKT0+MAAVlDeMKjFy1LEIRwAAAEAHq59W1yPBLmuUJeA1Ro7MQzgCAAAAOpDH6zMWXcg65H4jSSrgniPTEI4AAACADlRY4ZL/4FLdhy7GILEgg5mizS4AAAAA6E7yShvuKTp0MQZJuv0HR2l3UZUKK1yKt/N1vSPx0wYAAAA6UH6jZbyXf7lPn+0s1qlHZen/ThkkSRqcmaDBmQlmldetEY4AAACADlRWU2tsF1a4VVjh1td7y3TRpL5KdsaYWBm45wgAAADoQCcM7qFh2YmyRwd+FbdHW02qCPUYOQIAAAA6UGaiQ69de7wk6YR739bOA1VKdEQrNsaqgrIard1RpIwEh/qlx7EgQwdj5AgAAAAwgd/vNxZnqF+Y4fPdJZr39Oc6/y9rtOyTXWaW1y0RjgAAAAATlFXXylXrk9SwpDfLeJuLcAQAAACYIK/RqnUZCU3DUf0xdBzuOQIAAABM0HhJ7/9t2Ks3v81XtdtrHPu/f38ua5Ql4ByHLUrXnjJYP5nYp8Pq7E4YOQIAAABMUO1pCEJur0+l1R65vT7jWIWrVqXVnoA/+WUuPfHBdjPK7RYYOQIAAABMcNzAdJ1xdLa+3VdmHNtTUm3ch9QvzSmLpW7kyOf3a8eBKkmSM4Ylv9sL4QgAAAAwQZw9Wg9fODbg2OQlq7S3tEbp8TF6+8aTjOMHKlwa99s3JUlpcTwotr0wrQ4AAACIAH6/X/sr6hZkSI8PXKmusMJtbB/6GsKHcAQAAABEgNJqjzxevyQpIzFwpboDFQ2r2KURjtoN4QgAAACIAGXVtcpMtMsaZVGPQ0eOKhuPHDGtrr1wzxEAAAAQAfqkOfXxLdPk8/mNRRnqFTZ6/hHT6toPI0cAAABABImKsij2kBXpDlQ2nlbHyFF7IRwBAAAAEa6wvGFaXVocI0fthXAEAAAARLjGI0fpCYwctRfuOQIAAAAiwB9Wfaet+yuUlejQ1ScPVKLDZrxWv5S3xSKlOglH7YVwBAAAAESA978r1NodRZKk66YNDnit8OBS3inOGEVbmfzVXvjJAgAAABEgr6xGkpToiG66IMPBkaO0OEaN2hPhCAAAADCZ3+83wlFWUuADYKvctar2eCWxUl17IxwBAAAAJiup8sh98NlGmYmB4ajxSnU846h9EY4AAAAAk+WX1xjbWYeGo0oeANtRCEcAAACAyfJKG4WjQ6bV1d9vJHHPUXtjtToAAADAZPllDeGoybS6ioaRo/e3FOpApVujc5J19pheHVZfd0E4AgAAAEyWV9oQgA4NR8VVDSNHH28v0sfb65b77pcep1E5yR1SX3dBOAIAAABMlld2+HuOxuSkKMoi+fyB53ywtVBlNR4d1TNJqUy3CwvCEQAAAGCyKQPTZI2qG0HqmRwYjiYNSNOHN5+ivLIaPfTmZr29ab8k6Xevb5JUt0jDOzeeqDg7X+2PFD9BAAAAwGRnjuypM0f2POzrWUkOZSU5NCgzwQhH9QorXMorq9GAHvHtXWaXRzgCAAAAOolrTxmk7CSHiqs8emHd9/q+uFqSlOpkWl04EI4AAACATiLOHq25U/pJkt7eWKDvi6sVZZGSYm0mV9Y18JwjAAAAwEQer08VrtqQzyuqrFvFLsUZo6goS7jL6pYYOQIAAABMtGF3ic5dukbx9mhdObW//u+UQUGdVx+OWKkufBg5AgAAAEyUX1b3jKMKV61s0cF9Pa92e1Xt8UqS3F6fln2yS698sVfVbm+71dkdMHIEAAAAmKilZxwdTlGjB8PuPFClm/7zpSTpJxP76K5zjg5vgd0II0cAAACAifIbhaPMIMNRitOmBEfTcY6dByrDVld3xMgRAAAAYKK80kYjR0nBhSNnTLT+N+84rd1epILyGt23YrOkusUZ0HaMHAEAAAAmygsYObIHfV5uepzOPyZHxw3qYRxLY3GGI0I4AgAAAExUP60uwREtZ0zoE7uKKxvuP0ohHB0RwhEAAABgEr/fb0yrC3YxhkMVNQpHLOt9ZLjnCAAAADBJabVHrlqfpKb3G/32lW+0t7RakmSzRun88TmaMjC9yXs0Dkfcc3RkCEcAAACASeqDzYR+qfrR2N4Br733XaE25Zcb+x9sOaBPb53W9D2qGDkKF6bVAQAAACbplRKroVkJirFG6ewxvVps6/f7mz1ezMhR2DByBAAAAJjEHm3V8muOV2m1p8lr/7hsgmq9fk393duq9fnVI6H5ley45yh8GDkCAAAATBQVZWl2lbmMBIecMVbV+upGjA4XjoobTatLdtrap8hugnAEAAAARKj95S5ju7WRo7gYqxw2a4fU1VUxrQ4AAACIUAWNwlFGQuBqdv/4aKf8fr/2lhxc0S46Sk+t2dHkPU4akqGcVGe71tlVEI4AAACACFU/cjSqd5L6pgUGnMUvf21MuZOkkiqPFr70dZP3eHxOLOEoSEyrAwAAACJU3zSnLBZpdE6yZo/PMbucLo+RIwAAACBCjemTopeuniK/v27hhsbuO2+U8kprdPfrGyVJ/dKdOmFwD9mjrRqYEa9oa1374T0T5a71KSaacZHWEI4AAACACDayd3Kzx88e00uf7Cgy9rcXVml74U5J0sWT+uo3Z40wXjv2rlUqrnJrQI94Lb/2+HattzMjPgIAAACdVHq8XYcMKEmSNuaVB+yX1XjkqvXJ4/V1UGWdEyNHAAAAQCfVLz1Oy66cpHU7i1VS7dGfV2+VJKU0et6Rx+tTldsrSUqK5TlILWHkCAAAAOjEjslN1ZUnDNCZI7ONY6mNHipbWu0xthMJRy0iHAEAAABdQHFlQwhKcTaEo7JG4YiRo5YRjgAAAIAuoLjKbWw3DkcBI0cO7qppCeEIAAAA6AICwlGjaXVlNbXGNiNHLSMcAQAAAF1A42l1qXENIYh7joJHOAIAAAC6gMYjR9Vun3YUVqrSVRtwzxHhqGVMOgQAAAC6gMbh6Oqn10mSYm1WnT8+xzjOtLqWEY4AAACALsAe3XRSWLXHqxSnTU/9bILKajwa1Tu54wvrRAhHAAAAQBfwyxlD5IyJVmm1R98VlOurPWWSpJxUp6YO7mFydZ0D4QgAAADoAjITHbr9B0dJkh58c7MRjlLimEoXLBZkAAAAALqY4sqG+4+SGz3zCC1j5AgAAADoYoqrGlao21pQofKaWiXH2jQqJ9m8ojoBwhEAAADQxTReue6hVd/p++JqJTttWr9wholVRT6m1QEAAABdTMnBkaMoi1RRUyuJZbyDYXo4evjhh5WbmyuHw6GJEydq7dq1LbYvKSnR1VdfrezsbNntdg0ePFjLly/voGoBAACAyFc/cpQUa1NZjcfYRstMnVa3bNkyzZ8/X0uXLtXEiRP14IMPaubMmdq0aZMyMjKatHe73Zo+fboyMjL0/PPPq1evXtq5c6eSk5M7vngAAAAgQtWPHCXF2oz7jxIdhKPWmBqOHnjgAV1++eWaO3euJGnp0qV69dVX9cQTT+jmm29u0v6JJ55QUVGRPvzwQ9lsdb/c3NzcjiwZAAAAiGjuWp8qXHVT6eIdDV/3GTlqnWnT6txutz777DNNmzatoZioKE2bNk1r1qxp9pyXX35ZkyZN0tVXX63MzEyNGDFCd911l7xeb0eVDQAAAES0kkaLMcTFNISjRMJRq0wbOSosLJTX61VmZmbA8czMTG3cuLHZc7Zt26a33npLF154oZYvX64tW7boF7/4hTwejxYtWtTsOS6XSy6Xy9gvK6t7GJbH45HH42n2nI5Qf20za0DnQX9BKOgvCAX9BaGgv3QO+8uqjG17tMXYjo+J6tDfXST1l2Br6FRLeft8PmVkZOivf/2rrFarxo0bpz179ujee+89bDhasmSJFi9e3OT4ihUr5HQ627vkVq1cudLsEtCJ0F8QCvoLQkF/QSjoL5FtS6lU/zW/uHC/6ieL7du1VcuXb+nweiKhv1RVVbXeSCaGo/T0dFmtVuXn5wccz8/PV1ZWVrPnZGdny2azyWq1GseGDRumvLw8ud1uxcQ0ffrvggULNH/+fGO/rKxMOTk5mjFjhhITE8P0aULn8Xi0cuVKTZ8+3bh/Cjgc+gtCQX9BKOgvCAX9pXN44+t86ZsNkqSe2Vn6srhAknTMqBE6fUJOh9URSf2lfvZYa0wLRzExMRo3bpxWrVqls88+W1LdyNCqVas0b968Zs+ZMmWKnn76afl8PkVF1SXgzZs3Kzs7u9lgJEl2u112u73JcZvNZvovKZLqQOdAf0Eo6C8IBf0FoaC/RLZyt8/Yjo2JVqzNqmqPV+kJsab83iKhvwR7fVOfczR//nw9+uij+vvf/65vv/1WP//5z1VZWWmsXnfxxRdrwYIFRvuf//znKioq0rXXXqvNmzfr1Vdf1V133aWrr77arI8AAAAARJSiyoYFGU4dkaVv7zhVG+84VdOHZ7ZwFiST7zmaPXu29u/fr4ULFyovL0+jR4/W66+/bizSsGvXLmOESJJycnL0xhtv6Prrr9fIkSPVq1cvXXvttbrpppvM+ggAAABARGm8Wl2Ks252lcNmPVxzNGL6ggzz5s077DS61atXNzk2adIkffTRR+1cFQAAANA51T/0VZJS4mJU4/FqV1HrCxLE26PVMzm2PUuLeKaHIwAAAADh03jkKNlp09b9FTrjD+8Hde710wbr2mmD2qu0iGfqPUcAAAAAwqvxyFFybPOLljUnN82pj7YdaI+SOg1GjgAAAIAupPjgyFG8PVox0VFKdsZo9viWl/C2WKRrThnEtDqzCwAAAAAQPiUHR46SnXXLV/dKjtU95440s6ROg2l1AAAAQBfh8/mNe45S44KfUoc6hCMAAACgiyir8cjnr9tOdhKOQsW0OgAAAKCLCFjG++C0utZszi/XbS9+pQRHtE4dka1zx/Vur/IiHuEIAAAA6CKKm3kAbGv2l7v08fYiSdLgzIR2qauzYFodAAAA0EUUVTSEo2DvOapw1Rrb8Y7uPXZCOAIAAAC6iKLGI0fBhqOaRuHITjgCAAAA0AUUVTaEo7Qgw1GluyEcxcUQjgAAAAB0AY3DUbDT6sprmFZXj3AEAAAAdBGNw1FSrE1en19+v7/FcypdTKur170/PQAAANCFNA5Hpz30niQpwRGtP1wwRicNyWj2nMYLMsR183DEyBEAAADQRfiaGSUqr6nV/9bvPew5FYwcGbr3pwcAAAC6kOunDZbfXzdVrsJVq4155ZIkp9162HNYra5B9/70AAAAQBcyKidZf//ZBEnSO5v3a84TayVJqS08EPa0o7OUmx6n8ppaJcZ273jQvT89AAAA0EUVN7r/KLmFcHTOmN46Z0xHVBT5uOcIAAAA6IKKq0Jf1ru7IxwBAAAAXVDgyJHNxEo6D8IRAAAA0AUVV3mM7ZZGjsprPPL6Wn4WUnfBPUcAAABAF1TUaFpdymHuOfL7/Rq5eIX8fmlCv1Q9e+WkjiovIjFyBAAAAHRBJY3D0WFGjqrcXtU/Gik6ytIRZUU0whEAAADQBRVV1k2ri7FGKS6m+eccVfIA2ACEIwAAAKALqh85SnbaZLE0PypUTjgKQDgCAAAAuhi/36+ig6vVtbQYQ+ORozjCEQsyAAAAAF1NtccrV63P2P942wFJUu9Up3olxxrHKxqPHDmIBvwEAAAAgC6m8TLeG/PKNfuvH0mqW3ThPz+frFE5yZKkihqm1TXGtDoAAACgi4mLsTa7+lytz68v95Qa+5Xu2oBzujviIQAAANDFJDtj9NSlE/TO5v2SX/p8d4nWbi+SJCXG2ox2FS6vsR3vsDV5n+6GcAQAAAB0QZMHpGvygHRJ0n1vbDLCUYqzUTgKmFbHyBHhCAAAAOjiSqobHgibHNuwet2PxvbS+NwUVbhqdVTPRDNKiyiEIwAAAKCLK2m0QENyo5GjjESHMhIdZpQUkViQAQAAAOjiDheOEIhwBAAAAHRx9dPqrFEWluxuAT8ZAAAAoIurHzlKdESr/OCDX+3RUfpw6wG5PD4lOqI1aUCaLJamy393J4QjAAAAoIsrPRiOiqs8Gnn7Ckl1D31Ndtr0fXG1bFaLNv/2NDNLjAiEIwAAAKCLi2rmgbAVrlrV+nySpESHrduPGknccwQAAAB0efeeO1KnDM3Q1ME9dExuinHc5akLRxWuWl2/bL3ySmvMKjEiMHIEAAAAdHEzjsrSjKOyJEnbCyt10n2rJUn+g6+7an367+d71DslVr+cMcScIiMAI0cAAABAN5Kb5tRpI7Kafa3K7e3gaiIL4QgAAADoRiwWi/7803F671cnNXmtuy/zTTgCAAAAuqFKd22TYwkOwhEAAACAbqa8hnB0KMIRAAAA0A2VVXuaHIu320yoJHIQjgAAAIBuyOvzK8VpU+NHIDFyBAAAAKDbmXFUlj5fOEMXHdvXOBbfzcNR0J/+iy++CPpNR44c2aZiAAAAAHSsykbLdyd089Xqgv70o0ePlsVikd/vb/b1+tcsFou83u69PjoAAADQWVQ0WpghwdG97zkKOhxt3769PesAAAAAYIJyV8PCDEyrC1Lfvn1bbwQAAACgU3j03W36Nq9Mm/LKJUkWixQXYzW5KnMFHY5efvnloN/0Bz/4QZuKAQAAANAx3t9SqHc27zf24+3RslgsLZzR9QUdjs4+++yg2nHPEQAAABD5ymoCn3NUXlOr3JtflSTFWKN06fH9dNOpQ80ozTRBL+Xt8/mC+kMwAgAAACJf+cGFGJobK3J7fXr03W3yeH0dW5TJuvcdVwAAAEA3VX5w5CjZadPQrERVeeoGOTbllanG41Otz68r//GZLKq7H+mUYZm6YEIfEytuf20OR5WVlXrnnXe0a9cuud3ugNeuueaaIy4MAAAAQPspq64bOUqPt+vfVxxrHD/9off0zb4ySdJbGwuM429+W6DjBqYrJ9XZsYV2oDaFo88//1ynn366qqqqVFlZqdTUVBUWFsrpdCojI4NwBAAAAEQwj9en6oMjRYmxgc82mn1Mju589Vu5m5lSV1rtUU6HVGiOoO85auz666/XrFmzVFxcrNjYWH300UfauXOnxo0bp/vuuy/cNQIAAAAIo/KAB78GjpfMmZyr9Yuma91tdX/OHJltvOawde2lvtsUjtavX69f/vKXioqKktVqlcvlUk5Ojn73u9/plltuCXeNAAAAAMKotLphpbrkQ0aOJMkZE63UuBilxsXI7284HtvFn4PUpnBks9kUFVV3akZGhnbt2iVJSkpK0u7du8NXHQAAAICwK6lqWDMg2RnTYtv66XeS5OziI0dtuudozJgx+uSTTzRo0CCdcMIJWrhwoQoLC/WPf/xDI0aMCHeNAAAAAMIoMdamHx+To9Jqj0b0SmqxbbW7IRx19ZGjNoWju+66S+Xl5ZKkO++8UxdffLF+/vOfa9CgQXr88cfDWiAAAACA8BrQI153/2hkUG2rGo0c2aPbNPGs02hTOBo/fryxnZGRoddffz1sBQEAAACIHDUHR45ibVZZLM09MrbraFP02759u7777rsmx7/77jvt2LHjSGsCAAAAECHq7znq6lPqpDaGo0suuUQffvhhk+Mff/yxLrnkkiOtCQAAAEA78jdegq4VRjjq4osxSG0MR59//rmmTJnS5Pixxx6r9evXH2lNAAAAANrRDc99oaMXvaEpd7+l3UVVLbatn1bnsHXt+42kNt5zZLFYjAUZGistLZXX623mDAAAAACRoqTKrXJXrcpdta1Ol6sfOXLGtCk6dCptin9Tp07VkiVLAoKQ1+vVkiVLdNxxx4WtOAAAAADh1/ghsEnNPAS2nrvWp1pf3RS87jCtrk3x75577tHUqVM1ZMgQHX/88ZKk9957T2VlZXrrrbfCWiAAAACA8Co5GI7iYqyyWQ8/XtL4AbAOFmRo3vDhw/XFF1/o/PPPV0FBgcrLy3XxxRdr48aNPAQWAAAAiHAlVXXhKNkZ02K7mkbhKJZ7jg6vZ8+euuuuu8JZCwAAAIB25vf7VVrtliQltjClTpKq3Y3DESNHh/Xee+/ppz/9qSZPnqw9e/ZIkv7xj3/o/fffD1txAAAAAMKr2uOVx1t3H1Fya+Go8cgR0+qa95///EczZ85UbGys1q1bJ5fLJalutTpGkwAAAIDIVT+lTpKSncGHIwcjR8377W9/q6VLl+rRRx+VzdbwA50yZYrWrVsXtuIAAAAAhFfjcNTSSnVS4LQ6JyNHzdu0aZOmTp3a5HhSUpJKSkqOtCYAAAAA7aTk4P1GkpTU2shRN7vnqE0LMmRlZWnLli3Kzc0NOP7++++rf//+4agLAAAAQDsYlpWoJy4Zr9Jqjwb0iG+xbXebVtemcHT55Zfr2muv1RNPPCGLxaK9e/dqzZo1+uUvf6mFCxeGu0YAAAAAYZISF6OTh2YG1ba7LcjQpnB08803y+fz6ZRTTlFVVZWmTp0qu92uG2+8UZdddlm4awQAAABggsDnHHX9cNSme44sFot+/etfq6ioSF999ZU++ugj7d+/X0lJSerXr1+4awQAAABggu52z1FI4cjlcmnBggUaP368pkyZouXLl2v48OH6+uuvNWTIED300EO6/vrr26tWAAAAAEfoqz2l+nBrob7ZWyZ3ra/FtlWNwpGDaXWBFi5cqL/85S+aNm2aPvzwQ5133nmaO3euPvroI91///0677zzZLV2/R8aAAAA0Fn9efVWvfrlPknSBzefrF7JsYdt23hanbMbjByFFI6ee+45PfXUU/rBD36gr776SiNHjlRtba02bNggi8XSXjUCAAAACJOApbxbe85RN1uQIaRpdd9//73GjRsnSRoxYoTsdruuv/56ghEAAADQSRRX1j0ENjrKorhWAg/3HLXA6/UqJibG2I+OjlZ8fMtrowMAAACIHMVVdSNHKXExrQ5y8JyjFvj9fl1yySWy2+2SpJqaGl111VWKi4sLaPfCCy+Er0IAAAAAYeH3+1VUWReO0uJiWml9yFLe3WBaXUjhaM6cOQH7P/3pT8NaDAAAAID2U+X2ynVwhboUZ+vhqKqbTasLKRz97W9/a686AAAAALSz+lEjSUoNYuSou02ra9NDYAEAAAB0PiGHo4MjRzHRUbJGdf1F2AhHAAAAQDdRVNUQjlJCGDlqbVW7roJwBAAAAHQTVS6vbNa6EaBgFmSodNWFI2dMSHfjdFrd41MCAAAA0Bkjs3X60VmqcNUGNU2uyl0rSYqzd4+RI8IRAAAA0I1YLBYlOGyttvP5/MZqdbHdZOSIaXUAAAAAmmi8Uh33HAEAAADotioPTqmTuOcIAAAAQBfz0JvfqbjKrdS4GP3fyQNlsRz+vqMqV6ORI+45AgAAANCVvLRhj7btr1S8PVrXnDKoxbb19xtJ3WfkiGl1AAAAQDdRfPAhsClxrS/IUNVoWh33HAEAAADoMrw+v0qqPZKk1Dh7q+0rG48c2Rk5AgAAANBFlFS55ffXbac6gxg5cjFyBAAAAKALKjo4pU5qw8gR4QgAAABAVxEYjkK754gFGQAAAAB0GSGPHHXDpbwJRwAAAEA3UFQV2shRNSNHAAAAALqiooq233PUXUaOukcEBAAAALq5ARnxOuPobBVVutUz2dFq++54z1H3+JQAAABAN3f60dk6/ejsoNsH3HNEOAIAAADQFV3290+1bldxi23KazzGdmw3WcqbcAQAAAB0M+U1noDV61oz4/fvKMpikSRFWy26eFKurj5pYHuVZxrCEQAAANDNZCU51DfN2WKb/LIa1Xh8kqTiKk/Aaw++uVmXHtdPDlvXGlEiHAEAAADdzEM/HtNqm4+3HdBdr21UcaMRpryyGrlrffJ4/e1ZnmlYyhsAAABAExP7p+nKqf01PjdFJw7poScuGa9hWQmSJItFskd3vSjByBEAAACAZn3xfaleWLdHknTaiGxjmp0j2irLwXuQupKuF/cAAAAAhEXjZx3F2a2qqa1b3tth65oxomt+KgAAAABHrPGzjpwx0ap21+3HdrGFGOoRjgAAAAA0q8nIkad+5IhwBAAAAKAbqXQHjhzV1Nbdc2QnHAEAAADoTqpcDSNH9ugouQ+Go1juOQIAAADQndSPHMVER8nnb3i2UVedVsdS3gAAAACaVX/PUa3XZyzjLUm1Pr+eWrOjxXO9Xq+KyyS/v/M8MJZwBAAAAKBZ9avVTR+eqWpPw/1HVa5aLXzp6yDeIVoTdxZryqDMdqowvJhWBwAAAKBZo3onSZJunDnUWKlOkqKtwcWI3nF+5ZW62qW29sDIEQAAAIBmPXzhWK3etF8ZiXbtLqoyjvdOidWcyX1bPNceZZF7+6c6Y1R2e5cZNoQjAAAAAM1y2Kw6dUSWJAWMHGUkOHTOmN4tnuvxeLR8R3tWF35MqwMAAADQqsYLMsTGdM0Y0TU/FQAAAICwajxy5IhmKW8AAAAA3VTj1epae87Rh1sK9fs3N6vogFWWPnn6wZic9i4vLAhHAAAAAFrVeFqdI6blcHSg0q1PdhRLsiivrPOsVse0OgAAAACtCpxW1zVjRNf8VAAAAADCqiaEaXWdVUSEo4cffli5ublyOByaOHGi1q5dG9R5zzzzjCwWi84+++z2LRAAAADo5hqHo1jCUftYtmyZ5s+fr0WLFmndunUaNWqUZs6cqYKCghbP27Fjh2644QYdf/zxHVQpAAAA0H0F3HNEOGofDzzwgC6//HLNnTtXw4cP19KlS+V0OvXEE08c9hyv16sLL7xQixcvVv/+/TuwWgAAAKB7ClytzvQY0S5MXa3O7Xbrs88+04IFC4xjUVFRmjZtmtasWXPY837zm98oIyNDl156qd57772OKBUAAADo1hpPq3tg5WYlO23GflqcXVdM7a+cVKcZpYWNqeGosLBQXq9XmZmZAcczMzO1cePGZs95//339fjjj2v9+vVBXcPlcsnlalg+sKysTJLk8Xjk8XjaVngY1F/bzBrQedBfEAr6C0JBf0Eo6C/dW623IRx9uPVAk9fLq92699yjJdXN9Krn83pN7zPBXr9TPeeovLxcF110kR599FGlp6cHdc6SJUu0ePHiJsdXrFghp9P8ZLty5UqzS0AnQn9BKOgvCAX9BaGgv3RPqZUW2aOi5PJZmn39mx17tHz5bknS54UWSXX3JW3avEnLy5sf+OgoVVVVQbUzNRylp6fLarUqPz8/4Hh+fr6ysrKatN+6dat27NihWbNmGcd8vrobw6Kjo7Vp0yYNGDAg4JwFCxZo/vz5xn5ZWZlycnI0Y8YMJSYmhvPjhMTj8WjlypWaPn26bDZb6yegW6O/IBT0F4SC/oJQ0F+6t9MlXeWuVWl1rXGsrNqjMx+uux0mOzNDp58+VpLUP69cMRl7tH3HDv3oxPGaOKCHGSUb6mePtcbUcBQTE6Nx48Zp1apVxnLcPp9Pq1at0rx585q0Hzp0qL788suAY7feeqvKy8v10EMPKScnp8k5drtddru9yXGbzRYRf6kjpQ50DvQXhIL+glDQXxAK+kv3lWSzKSmuYb+gvKbRqxaVu/2SpF6p8brs+P760LtNEwf0ML2/BHt906fVzZ8/X3PmzNH48eM1YcIEPfjgg6qsrNTcuXMlSRdffLF69eqlJUuWyOFwaMSIEQHnJycnS1KT4wAAAADaV63Xb2y/s3m/xt4ROOUyJcaqccfVqE965wjTpoej2bNna//+/Vq4cKHy8vI0evRovf7668YiDbt27VJUVNdcKhAAAADozOId0YqxRsnt9TX7erHbolUbCzT3uIQOrqxtTA9HkjRv3rxmp9FJ0urVq1s898knnwx/QQAAAABaleiw6YHZo/Ti53vl8zeMIuWVVuubfeWSJJen+eAUiSIiHAEAAADonM4c2VNnjuwZcOz2l782wtGnO4vNKKtNmK8GAAAAACIcAQAAAIAkwhEAAAAASCIcAQAAAIAkwhEAAAAASCIcAQAAAIAkwhEAAAAASCIcAQAAAIAkwhEAAAAASCIcAQAAAAizwZnxxvbQrAQTKwkN4QgAAABAWCU4bMZ2YqythZaRhXAEAAAAACIcAQAAAIAkwhEAAACAMCuucjdsV7pbaBlZCEcAAAAAwmrb/kpj+7uCChMrCQ3hCAAAAABEOAIAAAAASYQjAAAAAJBEOAIAAAAASYQjAAAAAJBEOAIAAAAASYQjAAAAAJBEOAIAAAAASYQjAAAAAGEWZWnYtlgsh28YYQhHAAAAAMJqTJ8UY/uY3JQWWkYWwhEAAAAAiHAEAAAAAJIIRwAAAAAgiXAEAAAAIMx2HagytrcXVppYSWgIRwAAAADCan+Fq2G73NVCy8hCOAIAAAAAEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAECYJTttxnaKM8bESkJDOAIAAAAQVgN6xBvbgzLjW2gZWQhHAAAAACDCEQAAAABIIhwBAAAAgCTCEQAAAIAw+2pPqbG9YXdpCy0jC+EIAAAAQFi5an2Ntr0mVhIawhEAAAAAiHAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAADCrE+q09jOTXO20DKyEI4AAAAAhFWPBLuxnZHoMLGS0BCOAAAAAECEIwAAAACQRDgCAAAAEGY1Hq+xXd1oO9IRjgAAAACE1dd7y4ztL78vNbGS0BCOAAAAAECEIwAAAACQRDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAABhNqJnorE9sneSiZWEhnAEAAAAIKzsNqux7Wi0HekIRwAAAAAgwhEAAAAASCIcAQAAAAizgvIaYzu/rKaFlpGFcAQAAAAgrHYXVRvbOw9UmVhJaAhHAAAAACDCEQAAAABIIhwBAAAAgCTCEQAAAABIIhwBAAAAgCTCEQAAAABIIhwBAAAAgCTCEQAAAABIIhwBAAAACDN7dEPMcNisJlYSGsIRAAAAgLAa0SvJ2B7ZO6mFlpGFcAQAAAAAIhwBAAAAgCTCEQAAAABIIhwBAAAACLMtBRXG9ub8chMrCQ3hCAAAAEBYlVZ7jO2SKk8LLSML4QgAAAAARDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAACQRDgCAAAAAEmEIwAAAABh1iPBbmxnJDpMrCQ0hCMAAAAAYdUn1Wls56Y5W2gZWQhHAAAAACDCEQAAAABIIhwBAAAAgCTCEQAAAIAw+3xXsbH9yY7iFlpGFsIRAAAAgLDy+Ru2/X7/4RtGGMIRAAAAAIhwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAwmxAjzhje1BmvImVhIZwBAAAACCskp0xxnZKo+1IRzgCAAAAABGOAAAAAEAS4QgAAABAmJVVe5rdjnSEIwAAAABh9V1BhbG9Ma/cxEpCQzgCAAAAABGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAITZmJxkY3t8bop5hYQoIsLRww8/rNzcXDkcDk2cOFFr1649bNtHH31Uxx9/vFJSUpSSkqJp06a12B4AAABAx4qKsjRsWywttIwspoejZcuWaf78+Vq0aJHWrVunUaNGaebMmSooKGi2/erVq3XBBRfo7bff1po1a5STk6MZM2Zoz549HVw5AAAAgK7E9HD0wAMP6PLLL9fcuXM1fPhwLV26VE6nU0888USz7f/1r3/pF7/4hUaPHq2hQ4fqsccek8/n06pVqzq4cgAAAABdianhyO1267PPPtO0adOMY1FRUZo2bZrWrFkT1HtUVVXJ4/EoNTW1vcoEAAAAEILvi6uM7d1FVS20jCzRZl68sLBQXq9XmZmZAcczMzO1cePGoN7jpptuUs+ePQMCVmMul0sul8vYLysrkyR5PB55PJ42Vn7k6q9tZg3oPOgvCAX9BaGgvyAU9BcEa19JtbG9t6Ta9D4T7PVNDUdH6u6779Yzzzyj1atXy+FwNNtmyZIlWrx4cZPjK1askNPpbO8SW7Vy5UqzS0AnQn9BKOgvCAX9BaGgv6A1O3ZEqX6SWkFBgZYvX25qPVVVwY1emRqO0tPTZbValZ+fH3A8Pz9fWVlZLZ5733336e6779abb76pkSNHHrbdggULNH/+fGO/rKzMWMQhMTHxyD7AEfB4PFq5cqWmT58um81mWh3oHOgvCAX9BaGgvyAU9BcE67NXN+rdvF2SpIyMDJ1++lhT66mfPdYaU8NRTEyMxo0bp1WrVunss8+WJGNxhXnz5h32vN/97ne688479cYbb2j8+PEtXsNut8tutzc5brPZIuIvdaTUgc6B/oJQ0F8QCvoLQkF/QWuiohqWNrBYLKb3l2Cvb/q0uvnz52vOnDkaP368JkyYoAcffFCVlZWaO3euJOniiy9Wr169tGTJEknSPffco4ULF+rpp59Wbm6u8vLyJEnx8fGKj4837XMAAAAA6NxMD0ezZ8/W/v37tXDhQuXl5Wn06NF6/fXXjUUadu3aFZA8//znP8vtduvcc88NeJ9Fixbp9ttv78jSAQAAAHQhpocjSZo3b95hp9GtXr06YH/Hjh3tXxAAAACAbsf0h8ACAAAAQCQgHAEAAACACEcAAAAAwizBEd1ou/OsbEg4AgAAABBWgzMTjO1h2QkttIwshCMAAAAAEOEIAAAAACQRjgAAAABAEuEIAAAAQJh9u6/M2P5qT1kLLSML4QgAAABAWFW5vY22a02sJDSEIwAAAAAQ4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAIRZr+RYY7t3SmwLLSML4QgAAABAWGUlOYztnsmEIwAAAADoVAhHAAAAACDCEQAAAIAw83h9xra71tdCy8hCOAIAAAAQVl98X2psr99dYl4hISIcAQAAAIAIRwAAAAAgiXAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAAAgiXAEAAAAAJIIRwAAAADCbFh2grF9VM9EEysJDeEIAAAAQFg5Y6KN7Th7dAstIwvhCAAAAABEOAIAAAAASYQjAAAAAGF2oMJlbO8vd7XQMrIQjgAAAACE1Y4DVcb29sJKEysJDeEIAAAAAEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAYRZttRjbNmvniRydp1IAAAAAncKo3snG9pg+yYdtF2kIRwAAAAAgwhEAAAAASCIcAQAAAIAkwhEAAACAMNteWGlsb91fYWIloSEcAQAAAAirokq3sX2gwt1Cy8hCOAIAAAAAEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAECYpcbFGNvp8XYTKwkN4QgAAABAWPVLjzO2+/eIa6FlZCEcAQAAAIAIRwAAAAAgiXAEAAAAAJIIRwAAAADCbMPuEmN73a6Sw7aLNIQjAAAAAGFV6/M3bHt9JlYSGsIRAAAAAIhwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAAAAwqxfepyx3b9HXAstIwvhCAAAAEBYpcbFGNvp8XYTKwkN4QgAAAAARDgCAAAAAEmEIwAAAABhVumqNbYrGm1HOsIRAAAAgLDamFdubH+zt8zESkJDOAIAAAAAEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAQBLhCAAAAAAkEY4AAAAAhNnI3knG9pg+yeYVEiLCEQAAAICwslmjmt2OdJ2nUgAAAABoR4QjAAAAABDhCAAAAECY7SutNrb3llS30DKyEI4AAAAAhNXekhpj+/tiwhEAAAAAdCqEIwAAAAAQ4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAABAmDljrMZ2nD3axEpCQzgCAAAAEFbDshON7aN6JrbQMrIQjgAAAABAhCMAAAAAkEQ4AgAAAABJhCMAAAAAYbYpr9zY/nZfeQstIwvhCAAAAEBYVbhqje3yGo+JlYSGcAQAAAAAIhwBAAAAgCTCEQAAAABIkjrP42oBAADQpXm9Xnk8nef+FBxefLRPvRKskqRku1RTU9Ou17PZbLJarUf8PoQjAAAAmMrv9ysvL08lJSVml4IwmZLp16iTMiRJsbYobd++vd2vmZycrKysLFkslja/B+EIAAAApqoPRhkZGXI6nUf05RaRoaCsRsVVbklSvD1avVKc7XYtv9+vqqoqFRQUSJKys7Pb/F6EIwAAAJjG6/UawSgtLc3schAm0TV+Weqykaw2mxwOR7teLzY2VpJUUFCgjIyMNk+xY0EGAAAAmKb+HiOns/1GFtA91PehI7lvjXAEAAAA0zGVrmuJtjb8Pm3WjvndhqMPEY4AAAAAhFWMtSFmxER3nsjReSoFAAAAurjc3Fw9+OCDZpcRFqNyUvTW669Kknbs2CGLxaL169ebW1QrCEcAAABAiCwWS4t/br/99ja97yeffKIrrrjiiGo78cQTA2rJzMzUeeedp507dx7R+x6JnJwc7du3TyNGjDCthmAQjgAAAIAQ7du3z/jz4IMPKjExMeDYDTfcYLT1+/2qra0N6n179OgRlsUpLr/8cu3bt0979+7VSy+9pN27d+unP/3pEb9vW/j9ktVqVVZWlqKjI3uxbMIRAAAAEKKsrCzjT1JSkiwWi7G/ceNGJSQk6LXXXtO4ceNkt9v1/vvva+vWrTrrrLOUmZmp+Ph4HXPMMXrzzTcD3vfQaXUWi0WPPfaYzjnnHDmdTg0aNEgvv/xyq/U5nU5lZWUpOztbxx57rObNm6d169YZr3u9Xl166aXq16+fYmNjNWTIED300EMB77F69WpNmDBBcXFxSk5O1pQpUwJGn1566SWNHTtWDodD/fv31+LFi40QWOX2Gu2q3N4m0+pWr14ti8WiVatWafz48XI6nZo8ebI2bdoUUENL12gPkR3dAAAA0C3N+uP72l/u6vDr9kiw63//d1xY3uvmm2/Wfffdp/79+yslJUW7d+/W6aefrjvvvFN2u11PPfWUZs2apU2bNqlPnz6HfZ/Fixfrd7/7ne6991798Y9/1IUXXqidO3cqNTU1qDqKior07LPPauLEicYxn8+n3r1767nnnlNaWpo+/PBDXXHFFcrOztb555+v2tpanX322br88sv173//W263W2vXrjVWhHvvvfd08cUX6w9/+IOOP/54bd261ZgOuGjRoqB/Rr/+9a91//33q0ePHrrqqqv0s5/9TB988EFYrxEKwhEAAAAizv5yl/LKaswu44j85je/0fTp04391NRUjRo1yti/44479N///lcvv/yy5s2bd9j3ueSSS3TBBRdIku666y794Q9/0Nq1a3Xqqace9pxHHnlEjz32mPx+v6qqqjR48GC98cYbxus2m02LFy829vv166c1a9bo2Wef1fnnn6+ysjKVlpbqzDPP1IABAyRJw4YNM9ovXrxYN998s+bMmSNJ6t+/v+644w796le/Cim43HnnnTrhhBMk1YXJM844QzU1NXI4HGG7RigIRwAAAIg4PRLsnf6648ePD9ivqKjQ7bffrldffVX79u1TbW2tqqurtWvXrhbfZ+TIkcZ2XFycEhMTVVBQ0OI5F154oX79619LkvLz83XXXXdpxowZ+uyzz5SQkCBJevjhh/XEE09o165dqq6ultvt1ujRoyXVBblLLrlEM2fO1PTp0zVt2jSdf/75ys7OliRt2LBBH3zwge68807jml6vVzU1NaqqqpIU3DOHGn+2+vcuKChQnz59Wr1Gezw4OCLC0cMPP6x7771XeXl5GjVqlP74xz9qwoQJh23/3HPP6bbbbtOOHTs0aNAg3XPPPTr99NM7sGIAAAC0p3BNbTNTXFxcwP4NN9yglStX6r777tPAgQMVGxurc889V263u8X3sdlsAfsWi0U+n6/Fc5KSkjRw4EBJ0sCBA/X4448rOztby5Yt02WXXaZnnnlGN9xwg+6//35NmjRJCQkJuvfee/Xxxx8b7/G3v/1N11xzjV5//XUtW7ZMt956q1auXKljjz1WFRUVWrx4sX74wx82ubbD4ZDcwU2JbPzZ6qfs1X+2Vq/RDkwPR8uWLdP8+fO1dOlSTZw4UQ8++KBmzpypTZs2KSMjo0n7Dz/8UBdccIGWLFmiM888U08//bTOPvtsrVu3LuKXBgQAAED39cEHH+iSSy7ROeecI6nuy/+OHTs65NpWq1WSVF1dbdQyefJk/eIXvzDabN26tcl5Y8aM0ZgxY7RgwQJNmjRJTz/9tI499liNHTtWmzZtMgJYe+iIaxzK9NXqHnjgAV1++eWaO3euhg8frqVLl8rpdOqJJ55otv1DDz2kU089VTfeeKOGDRumO+64Q2PHjtWf/vSnDq4cAAAACN6gQYP0wgsvaP369dqwYYN+8pOftDoC1FZVVVXKy8tTXl6eNmzYoJ///OdyOByaMWOGUcunn36qN954Q5s3b9Ztt92mTz75xDh/+/btWrBggdasWaOdO3dqxYoV+u6774z7jhYuXKinnnpKixcv1tdff61vv/1WzzzzjG699dawfYaOuMahTA1Hbrdbn332maZNm2Yci4qK0rRp07RmzZpmz1mzZk1Ae0maOXPmYdsDAAAAkeCBBx5QSkqKJk+erFmzZmnmzJkaO3Zsu1zr0UcfVXZ2trKzs3XSSSepsLBQy5cv15AhQyRJV155pX74wx9q9uzZmjhxog4cOBAwiuR0OrVx40b96Ec/0uDBg3XFFVfo6quv1pVXXimp7vv3K6+8ohUrVuiYY47Rscceq9///vfq27dv2D5DR1zjUKZOqyssLJTX61VmZmbA8czMTG3cuLHZc/Ly8pptn5eX12x7l8sll6thzmNZWZkkyePxyOPxHEn5R6T+2mbWgM6D/oJQ0F8QCvoLQtEe/cXj8cjv98vn87XbKEp7u/jii3XxxRcb9U+dOlVeb91zfhp/pj59+jR5rtHPf/7zgHbbtm0L2G/ufYqKipoca+ytt946bK3159hsNj3++ON6/PHHA16/88475fP51KNHD/3nP/9p8T2mT58esBpf49ejLNKG3cWSJKtF6tWnT8Bnae5nNHLkyCbHWrpGc8f8fr88Ho8xjbBesH3W9HuO2tuSJUsClimst2LFinZZ4SJUK1euNLsEdCL0F4SC/oJQ0F8QinD2l+joaGVlZamioqLVhQnQeXg9gTtlZe3/DzBut1vV1dV69913mzwotm4FvdaZGo7S09NltVqVn58fcDw/P19ZWVnNnpOVlRVS+wULFmj+/PnGfllZmXJycjRjxgwlJiYe4SdoO4/Ho5UrV2r69OlNViABDkV/QSjoLwgF/QWhaI/+UlNTo927dys+Pr7dViBDx0vwS+k+n8rLK5SUmCBrVHBLex+JmpoaxcbGaurUqU36Uv3ssdaYGo5iYmI0btw4rVq1SmeffbakuuGwVatWHfZBWJMmTdKqVat03XXXGcdWrlypSZMmNdvebrfLbm+6Xr3NZouI/wlESh3oHOgvCAX9BaGgvyAU4ewvXq9XFotFUVFRiooyfa0whJHFIkVHSdYoS4f8bqOiomSxWJrtn8H2V9On1c2fP19z5szR+PHjNWHCBD344IOqrKzU3LlzJdXN4ezVq5eWLFkiSbr22mt1wgkn6P7779cZZ5yhZ555Rp9++qn++te/mvkxAAAAAHRypoej2bNna//+/Vq4cKHy8vI0evRovf7668aiC7t27QpImpMnT9bTTz+tW2+9VbfccosGDRqkF198kWccAQAAADgipocjSZo3b95hp9GtXr26ybHzzjtP5513XjtXBQAAAKA7YWInAAAAAIhwBAAAAACSCEcAAAAA2mj16tWyWCwqKSmRJD355JNKTk42taYjQTgCAAAAQmSxWFr8c/vttx/Re7/44osh1RAdHa0+ffpo/vz5crlcbb72kZo9e7Y2b95s2vWPVEQsyAAAAAB0Jvv27TO2ly1bpoULF2rTpk3Gsfj4+A6p429/+5tOPfVUeTwebdiwQXPnzlVcXJzuuOOODrn+oWJjYxUbG2vKtcOBkSMAAAAgRFlZWcafpKQkWSyWgGPPPPOMhg0bJofDoaFDh+qRRx4xznW73Zo3b56ys7PlcDjUt29f45meubm5kqRzzjlHFovF2D+c5ORkZWVlKScnR2eeeabOOussrVu3znh969atOuuss5SZman4+Hgdc8wxevPNNwPe45FHHtGgQYPkcDiUmZmpc88913jN5/NpyZIl6tevn2JjYzVq1Cg9//zzh63n0Gl1d999t8aOHat//OMfys3NVVJSkn784x+rvLy8zddoT4wcAQAAAGH0r3/9SwsXLtSf/vQnjRkzRp9//rkuv/xyxcXFac6cOfrDH/6gl19+Wc8++6z69Omj3bt3a/fu3ZKkTz75RBkZGcaIkNVqDfq6mzdv1ltvvaVLLrnEOFZRUaHTTz9dd955p+x2u5566inNmjVLmzZtUp8+ffTpp5/qmmuu0T/+8Q9NnjxZRUVFeu+994zzlyxZon/+859aunSpBg0apHfffVc//elP1aNHD51wwglB1bV161a9+OKLeuWVV1RcXKzzzz9fd999t+68886wXSNcCEcAAACISI+9t02Pvbe91XYjeiXqsTnHBBy77O+f6Ks9Za2ee9nx/XTZ8f3bXGNzFi1apPvvv18//OEPJUn9+vXTN998o7/85S+aM2eOdu3apUGDBum4446TxWJR3759jXN79OghqWFEqDUXXHCBrFaramtr5XK5dOaZZ2rBggXG66NGjdKoUaOM/TvuuEP//e9/9fLLL2vevHnatWuX4uLidOaZZyohIUF9+/bVmDFjJEkul0t33XWX3nzzTU2aNEmS1L9/f73//vv6y1/+EnRw8fl8evLJJ5WQkCBJuuiii7Rq1SrdeeedYbtGuBCOAAAAEJHKa2qVV1bTarvsZEeTYwcq3UGdW15T26baDqeyslJbt27VpZdeqssvv9w4Xltbq6SkJEnSJZdcounTp2vIkCE69dRTdeaZZ2rGjBltut7vf/97TZs2TV6vV1u2bNH8+fN10UUX6ZlnnpFUN3J0++2369VXX9W+fftUW1ur6upq7dq1S5I0ffp09e3bV/3799epp56qU089Veecc46cTqe2bNmiqqoqTZ8+PeCabrfbCFDByM3NNYKRJGVnZ6ugoECSwnaNcCEcAQAAICIlOKKVldg0+BwqLS6m2WPBnJvgCO/X4YqKCknSo48+qokTJwa8Vj9FbuzYsdq+fbtee+01vfnmmzr//PM1bdq0Nt1nk5WVpYEDB0qShgwZovLycl1wwQX67W9/q4EDB+qGG27QypUrdd9992ngwIGKjY3VueeeK7fbLUlKSEjQunXrtHr1aq1YsUILFy7U7bffrk8++cT4LK+++qp69eoVcF273R50jTabLWDfYrHI5/NJUtiuES6EIwAAAESky47v3+Ypb4dOs+somZmZ6tmzp7Zt26YLL7zwsO0SExM1e/ZszZ49W+eee65OPfVUFRUVKTU1VTabTV6vt03Xrw9g1dXVkqQPPvhAl1xyic455xxJdWFkx44dAedER0dr2rRpmjZtmhYtWqTk5GS99dZbmj59uux2u3bt2tVu09uGDx/e7tcIBeEIAAAACKPFixfrmmuuUVJSkk499VS5XC59+umnKi4u1vz58/XAAw8oOztbY8aMUVRUlJ577jllZWUZq7zl5uZq1apVmjJliux2u1JSUg57rZKSEuXl5cnn8+m7777Tb37zGw0ePFjDhg2TJA0aNEgvvPCCZs2aJYvFottuu80YtZGkV155Rdu2bdPUqVOVkpKi5cuXy+fzaciQIUpISNANN9yg66+/Xj6fT8cdd5xKS0v1wQcfKDExUXPmzDnin1VHXCMUhCMAAAAgjC677DI5nU7de++9uvHGGxUXF6ejjz5a1113naS6QPC73/1O3333naxWq4455hgtX75cUVF1T9m5//77NX/+fD366KPq1atXk5GexubOnStJxlLiU6dO1V133aXo6Lqv+Q888IB+9rOfafLkyUpPT9dNN92ksrKGhSqSk5P1wgsv6Pbbb1dNTY0GDRqkf//73zrqqKMk1S3g0KNHDy1ZskTbtm1TcnKyxo4dq1tuuSVsP6+OuEawLH6/39/hVzVRWVmZkpKSVFpaqsTERNPq8Hg8Wr58uU4//fQm8zCBQ9FfEAr6C0JBf0Eo2qO/1NTUaPv27erXr58cjtbvEULn4fP5VFZWpsTERCP4taeW+lKwGYCHwAIAAACACEcAAAAAIIlwBAAAAACSCEcAAAAAIIlwBAAAAACSCEcAAACIAN1sAWW0g3D0IcIRAAAATFO/JHhVVZXJlaCzq+9DR7LMPA+BBQAAgGmsVquSk5NVUFAgSXI6nbJYLCZXhXDw+Xxyu92qqalp1+cc+f1+VVVVqaCgQMnJybJarW1+L8IRAAAATJWVlSVJRkBC1+D3+1VdXa3Y2NgOCbzJyclGX2orwhEAAABMZbFYlJ2drYyMDHk8HrPLQZh4PB69++67mjp16hFNdQuGzWY7ohGjeoQjAAAARASr1RqWL7iIDFarVbW1tXI4HO0ejsKFBRkAAAAAQIQjAAAAAJBEOAIAAAAASd3wnqP6h0OVlZWZWofH41FVVZXKyso6zRxMmIf+glDQXxAK+gtCQX9BKCKpv9R/92/tQbHdLhyVl5dLknJyckyuBAAAAEBHKi8vV1JS0mFft/hbi09djM/n0969e5WQkGDqA8bKysqUk5Oj3bt3KzEx0bQ60DnQXxAK+gtCQX9BKOgvCEUk9Re/36/y8nL17NmzxQfSdruRo6ioKPXu3dvsMgyJiYmmdxZ0HvQXhIL+glDQXxAK+gtCESn9paURo3osyAAAAAAAIhwBAAAAgCTCkWnsdrsWLVoku91udinoBOgvCAX9BaGgvyAU9BeEojP2l263IAMAAAAANIeRIwAAAAAQ4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGO2tXDDz+s3NxcORwOTZw4UWvXrm2x/XPPPaehQ4fK4XDo6KOP1vLlyzuoUkSCUPrLo48+quOPP14pKSlKSUnRtGnTWu1f6FpC/e9LvWeeeUYWi0Vnn312+xaIiBJqfykpKdHVV1+t7Oxs2e12DR48mP8ndSOh9pcHH3xQQ4YMUWxsrHJycnT99derpqamg6qFmd59913NmjVLPXv2lMVi0YsvvtjqOatXr9bYsWNlt9s1cOBAPfnkk+1eZygIR+1k2bJlmj9/vhYtWqR169Zp1KhRmjlzpgoKCppt/+GHH+qCCy7QpZdeqs8//1xnn322zj77bH311VcdXDnMEGp/Wb16tS644AK9/fbbWrNmjXJycjRjxgzt2bOngyuHGULtL/V27NihG264Qccff3wHVYpIEGp/cbvdmj59unbs2KHnn39emzZt0qOPPqpevXp1cOUwQ6j95emnn9bNN9+sRYsW6dtvv9Xjjz+uZcuW6ZZbbungymGGyspKjRo1Sg8//HBQ7bdv364zzjhDJ510ktavX6/rrrtOl112md544412rjQEfrSLCRMm+K+++mpj3+v1+nv27OlfsmRJs+3PP/98/xlnnBFwbOLEif4rr7yyXetEZAi1vxyqtrbWn5CQ4P/73//eXiUigrSlv9TW1vonT57sf+yxx/xz5szxn3XWWR1QKSJBqP3lz3/+s79///5+t9vdUSUigoTaX66++mr/ySefHHBs/vz5/ilTprRrnYg8kvz//e9/W2zzq1/9yn/UUUcFHJs9e7Z/5syZ7VhZaBg5agdut1ufffaZpk2bZhyLiorStGnTtGbNmmbPWbNmTUB7SZo5c+Zh26PraEt/OVRVVZU8Ho9SU1Pbq0xEiLb2l9/85jfKyMjQpZde2hFlIkK0pb+8/PLLmjRpkq6++mplZmZqxIgRuuuuu+T1ejuqbJikLf1l8uTJ+uyzz4ypd9u2bdPy5ct1+umnd0jN6Fw6w/fdaLML6IoKCwvl9XqVmZkZcDwzM1MbN25s9py8vLxm2+fl5bVbnYgMbekvh7rpppvUs2fPJv/BQdfTlv7y/vvv6/HHH9f69es7oEJEkrb0l23btumtt97ShRdeqOXLl2vLli36xS9+IY/Ho0WLFnVE2TBJW/rLT37yExUWFuq4446T3+9XbW2trrrqKqbVoVmH+75bVlam6upqxcbGmlRZA0aOgE7u7rvv1jPPPKP//ve/cjgcZpeDCFNeXq6LLrpIjz76qNLT080uB52Az+dTRkaG/vrXv2rcuHGaPXu2fv3rX2vp0qVml4YItHr1at1111165JFHtG7dOr3wwgt69dVXdccdd5hdGtAmjBy1g/T0dFmtVuXn5wccz8/PV1ZWVrPnZGVlhdQeXUdb+ku9++67T3fffbfefPNNjRw5sj3LRIQItb9s3bpVO3bs0KxZs4xjPp9PkhQdHa1NmzZpwIAB7Vs0TNOW/75kZ2fLZrPJarUax4YNG6a8vDy53W7FxMS0a80wT1v6y2233aaLLrpIl112mSTp6P9v595Cmv7/OI6/prkt3cS0gxPKyMpiRFFZREF0oJSOROhFyIRoUFpdRFB0oXSgAxWhljeRKzpaFyFmdhhGYQRBGotsZeeoKKiLMkxjn99V++Uv+5P9c+vwfMAutr33+X4+Xz5s39c+3+931Ci1trbK6/Vqw4YNionhf3j861vHu4mJib/EqpHEylGPsFqtGjdunPx+f/i1UCgkv9+vSZMmdfmZSZMmdaqXpAsXLnyzHn+OH5kvkrRjxw5t2rRJdXV1Gj9+fCS6il9Ad+fLiBEjFAgE1NTUFH7Mnz8/fKeggQMHRrL7iLAf+X6ZPHmyWlpawiFaku7evSuXy0Uw+sP9yHz58OHDVwHoc7A2xvRcZ/Fb+i2Od6N9R4g/1fHjx43NZjM+n8/cvn3beL1ek5SUZF6+fGmMMSY/P9+sW7cuXN/Q0GB69epldu7caZqbm01xcbGJi4szgUAgWkNABHV3vmzbts1YrVZz6tQp8+LFi/Dj3bt30RoCIqi78+W/uFvd36W78+XJkyfG6XSaoqIiEwwGTU1Njenfv7/ZvHlztIaACOrufCkuLjZOp9McO3bMPHjwwJw/f95kZGSY3NzcaA0BEfTu3TvT2NhoGhsbjSSze/du09jYaB4/fmyMMWbdunUmPz8/XP/gwQMTHx9v1q5da5qbm83evXtNbGysqauri9YQvkI46kFlZWVm0KBBxmq1mgkTJphr166F35s6darxeDyd6quqqszw4cON1Wo1brfbnDlzJsI9RjR1Z76kp6cbSV89iouLI99xREV3v1++RDj6+3R3vly9etVMnDjR2Gw2M2TIELNlyxbz6dOnCPca0dKd+dLR0WFKSkpMRkaGsdvtZuDAgWbFihXm7du3ke84Iq6+vr7L45HPc8Tj8ZipU6d+9ZkxY8YYq9VqhgwZYiorKyPe7//FYgxrngAAAADANUcAAAAAIMIRAAAAAEgiHAEAAACAJMIRAAAAAEgiHAEAAACAJMIRAAAAAEgiHAEAAACAJMIRAOAvYrFYdPr06Z9eCwD4MxCOAABRUVBQIIvFIovFIqvVqqFDh2rjxo369OlTj23zxYsXysnJ+em1AIA/Q69odwAA8PfKzs5WZWWlPn78qNraWhUWFiouLk7r16/vVNfe3i6r1fp/by81NbVHagEAfwZWjgAAUWOz2ZSamqr09HQtX75cM2fOVHV1tQoKCrRw4UJt2bJFaWlpyszMlCQ9ffpUubm5SkpKUnJyshYsWKBHjx51avPAgQNyu92y2WxyuVwqKioKv/flqXLt7e0qKiqSy+WS3W5Xenq6tm7d2mWtJAUCAU2fPl29e/dWSkqKvF6v3r9/H37/c5937twpl8ullJQUFRYWqqOj4+fvOABAjyAcAQB+Gb1791Z7e7skye/3KxgM6sKFC6qpqVFHR4dmz54tp9OpK1euqKGhQQ6HQ9nZ2eHPVFRUqLCwUF6vV4FAQNXV1Ro6dGiX2yotLVV1dbWqqqoUDAZ15MgRDR48uMva1tZWzZ49W3369NH169d18uRJXbx4sVPwkqT6+nrdv39f9fX1OnjwoHw+n3w+30/bPwCAnsVpdQCAqDPGyO/369y5c1q5cqVev36thIQE7d+/P3w63eHDhxUKhbR//35ZLBZJUmVlpZKSknTp0iXNmjVLmzdv1po1a7R69epw21lZWV1u88mTJxo2bJimTJkii8Wi9PT0b/bv6NGjamtr06FDh5SQkCBJKi8v17x587R9+3YNGDBAktSnTx+Vl5crNjZWI0aM0Jw5c+T3+7Vs2bKfsp8AAD2LlSMAQNTU1NTI4XDIbrcrJydHeXl5KikpkSSNGjWq03VGN2/eVEtLi5xOpxwOhxwOh5KTk9XW1qb79+/r1atXev78uWbMmPFd2y4oKFBTU5MyMzO1atUqnT9//pu1zc3NGj16dDgYSdLkyZMVCoUUDAbDr7ndbsXGxoafu1wuvXr16nt3BwAgylg5AgBEzbRp01RRUSGr1aq0tDT16vXvz9KXQUSS3r9/r3HjxunIkSNftdOvXz/FxHTv/76xY8fq4cOHOnv2rC5evKjc3FzNnDlTp06d+rHBSIqLi+v03GKxKBQK/XB7AIDIIhwBAKImISHhm9cE/dfYsWN14sQJ9e/fX4mJiV3WDB48WH6/X9OmTfuuNhMTE5WXl6e8vDwtXrxY2dnZevPmjZKTkzvVjRw5Uj6fT62treHQ1tDQoJiYmPDNIgAAvz9OqwMA/BaWLFmivn37asGCBbpy5YoePnyoS5cuadWqVXr27JkkqaSkRLt27VJpaanu3bunGzduqKysrMv2du/erWPHjunOnTu6e/euTp48qdTUVCUlJXW5bbvdLo/Ho1u3bqm+vl4rV65Ufn5++HojAMDvj3AEAPgtxMfH6/Llyxo0aJAWLVqkkSNHaunSpWprawuvJHk8Hu3Zs0f79u2T2+3W3Llzde/evS7bczqd2rFjh8aPH6+srCw9evRItbW1XZ6eFx8fr3PnzunNmzfKysrS4sWLNWPGDJWXl/fomAEAkWUxxphodwIAAAAAoo2VIwAAAAAQ4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJEn/AI01Y6vMsRL6AAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ], + "id": "FdQs_PcqEsiL" + }, + "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -2529,22 +1115,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "qjGWErngGny7", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "112a112e-7d3f-47b8-c3cd-58fef7f0cef0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Weight for class 0: 0.50\n", - "Weight for class 1: 289.44\n" - ] - } - ], + "id": "qjGWErngGny7" + }, + "outputs": [], "source": [ "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", "# The sum of the weights of all examples stays the same.\n", @@ -2574,54 +1147,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "UJ589fn8ST3x", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8ab120c7-9c19-4f27-f369-95f681a8a395" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 18ms/step - Brier score: 0.0020 - accuracy: 0.9978 - auc: 0.8179 - cross entropy: 0.0129 - fn: 151.4066 - fp: 224.7363 - loss: 2.6473 - prc: 0.3597 - precision: 0.4048 - recall: 0.4473 - tn: 150619.6094 - tp: 106.8242 - val_Brier score: 0.0013 - val_accuracy: 0.9986 - val_auc: 0.9300 - val_cross entropy: 0.0104 - val_fn: 41.0000 - val_fp: 21.0000 - val_loss: 0.0104 - val_prc: 0.4581 - val_precision: 0.6182 - val_recall: 0.4533 - val_tn: 45473.0000 - val_tp: 34.0000\n", - "Epoch 2/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0060 - accuracy: 0.9931 - auc: 0.8712 - cross entropy: 0.0292 - fn: 75.2308 - fp: 612.5604 - loss: 0.9810 - prc: 0.2220 - precision: 0.1202 - recall: 0.4986 - tn: 93372.8906 - tp: 79.8901 - val_Brier score: 0.0022 - val_accuracy: 0.9978 - val_auc: 0.9428 - val_cross entropy: 0.0155 - val_fn: 16.0000 - val_fp: 85.0000 - val_loss: 0.0155 - val_prc: 0.6574 - val_precision: 0.4097 - val_recall: 0.7867 - val_tn: 45409.0000 - val_tp: 59.0000\n", - "Epoch 3/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0095 - accuracy: 0.9888 - auc: 0.9128 - cross entropy: 0.0447 - fn: 50.1978 - fp: 1033.1758 - loss: 0.6856 - prc: 0.2480 - precision: 0.0889 - recall: 0.6417 - tn: 92952.0547 - tp: 105.1429 - val_Brier score: 0.0043 - val_accuracy: 0.9943 - val_auc: 0.9476 - val_cross entropy: 0.0237 - val_fn: 16.0000 - val_fp: 244.0000 - val_loss: 0.0237 - val_prc: 0.6520 - val_precision: 0.1947 - val_recall: 0.7867 - val_tn: 45250.0000 - val_tp: 59.0000\n", - "Epoch 4/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - Brier score: 0.0132 - accuracy: 0.9843 - auc: 0.9118 - cross entropy: 0.0619 - fn: 43.1209 - fp: 1458.9890 - loss: 0.5465 - prc: 0.2216 - precision: 0.0719 - recall: 0.7105 - tn: 92524.0469 - tp: 114.4176 - val_Brier score: 0.0062 - val_accuracy: 0.9917 - val_auc: 0.9483 - val_cross entropy: 0.0315 - val_fn: 16.0000 - val_fp: 364.0000 - val_loss: 0.0315 - val_prc: 0.6518 - val_precision: 0.1395 - val_recall: 0.7867 - val_tn: 45130.0000 - val_tp: 59.0000\n", - "Epoch 5/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0166 - accuracy: 0.9802 - auc: 0.9420 - cross entropy: 0.0767 - fn: 32.6044 - fp: 1842.9670 - loss: 0.4640 - prc: 0.2562 - precision: 0.0749 - recall: 0.8069 - tn: 92124.6719 - tp: 140.3297 - val_Brier score: 0.0084 - val_accuracy: 0.9901 - val_auc: 0.9534 - val_cross entropy: 0.0410 - val_fn: 13.0000 - val_fp: 440.0000 - val_loss: 0.0410 - val_prc: 0.6102 - val_precision: 0.1235 - val_recall: 0.8267 - val_tn: 45054.0000 - val_tp: 62.0000\n", - "Epoch 6/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0196 - accuracy: 0.9761 - auc: 0.9495 - cross entropy: 0.0900 - fn: 30.9890 - fp: 2289.6375 - loss: 0.3925 - prc: 0.2116 - precision: 0.0563 - recall: 0.8074 - tn: 91690.8438 - tp: 129.0989 - val_Brier score: 0.0104 - val_accuracy: 0.9879 - val_auc: 0.9562 - val_cross entropy: 0.0501 - val_fn: 12.0000 - val_fp: 541.0000 - val_loss: 0.0501 - val_prc: 0.5908 - val_precision: 0.1043 - val_recall: 0.8400 - val_tn: 44953.0000 - val_tp: 63.0000\n", - "Epoch 7/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0233 - accuracy: 0.9720 - auc: 0.9509 - cross entropy: 0.1045 - fn: 24.0659 - fp: 2611.6045 - loss: 0.3303 - prc: 0.2385 - precision: 0.0542 - recall: 0.8642 - tn: 91360.3438 - tp: 144.5604 - val_Brier score: 0.0112 - val_accuracy: 0.9873 - val_auc: 0.9590 - val_cross entropy: 0.0543 - val_fn: 12.0000 - val_fp: 569.0000 - val_loss: 0.0543 - val_prc: 0.5775 - val_precision: 0.0997 - val_recall: 0.8400 - val_tn: 44925.0000 - val_tp: 63.0000\n", - "Epoch 8/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Brier score: 0.0241 - accuracy: 0.9707 - auc: 0.9557 - cross entropy: 0.1079 - fn: 25.4835 - fp: 2768.0989 - loss: 0.2904 - prc: 0.2209 - precision: 0.0436 - recall: 0.8428 - tn: 91219.0312 - tp: 127.9560 - val_Brier score: 0.0127 - val_accuracy: 0.9863 - val_auc: 0.9603 - val_cross entropy: 0.0614 - val_fn: 12.0000 - val_fp: 613.0000 - val_loss: 0.0614 - val_prc: 0.5583 - val_precision: 0.0932 - val_recall: 0.8400 - val_tn: 44881.0000 - val_tp: 63.0000\n", - "Epoch 9/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Brier score: 0.0268 - accuracy: 0.9672 - auc: 0.9618 - cross entropy: 0.1191 - fn: 19.7143 - fp: 3080.3845 - loss: 0.2417 - prc: 0.1898 - precision: 0.0384 - recall: 0.8885 - tn: 90914.2891 - tp: 126.1868 - val_Brier score: 0.0135 - val_accuracy: 0.9853 - val_auc: 0.9625 - val_cross entropy: 0.0658 - val_fn: 12.0000 - val_fp: 658.0000 - val_loss: 0.0658 - val_prc: 0.5371 - val_precision: 0.0874 - val_recall: 0.8400 - val_tn: 44836.0000 - val_tp: 63.0000\n", - "Epoch 10/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - Brier score: 0.0298 - accuracy: 0.9634 - auc: 0.9549 - cross entropy: 0.1327 - fn: 21.3516 - fp: 3426.9670 - loss: 0.2952 - prc: 0.2044 - precision: 0.0405 - recall: 0.8664 - tn: 90547.7500 - tp: 144.5055 - val_Brier score: 0.0141 - val_accuracy: 0.9847 - val_auc: 0.9628 - val_cross entropy: 0.0685 - val_fn: 12.0000 - val_fp: 684.0000 - val_loss: 0.0685 - val_prc: 0.5282 - val_precision: 0.0843 - val_recall: 0.8400 - val_tn: 44810.0000 - val_tp: 63.0000\n", - "Epoch 11/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - Brier score: 0.0307 - accuracy: 0.9620 - auc: 0.9655 - cross entropy: 0.1353 - fn: 21.6703 - fp: 3583.5056 - loss: 0.2558 - prc: 0.1959 - precision: 0.0387 - recall: 0.8808 - tn: 90397.5859 - tp: 137.8132 - val_Brier score: 0.0154 - val_accuracy: 0.9835 - val_auc: 0.9641 - val_cross entropy: 0.0745 - val_fn: 12.0000 - val_fp: 742.0000 - val_loss: 0.0745 - val_prc: 0.5155 - val_precision: 0.0783 - val_recall: 0.8400 - val_tn: 44752.0000 - val_tp: 63.0000\n", - "Epoch 12/100\n", - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - Brier score: 0.0326 - accuracy: 0.9591 - auc: 0.9397 - cross entropy: 0.1407 - fn: 26.8242 - fp: 3843.8682 - loss: 0.3581 - prc: 0.1931 - precision: 0.0348 - recall: 0.8298 - tn: 90134.2344 - tp: 135.6483 - val_Brier score: 0.0153 - val_accuracy: 0.9835 - val_auc: 0.9654 - val_cross entropy: 0.0743 - val_fn: 12.0000 - val_fp: 741.0000 - val_loss: 0.0743 - val_prc: 0.5055 - val_precision: 0.0784 - val_recall: 0.8400 - val_tn: 44753.0000 - val_tp: 63.0000\n", - "Epoch 12: early stopping\n", - "Restoring model weights from the end of the best epoch: 2.\n" - ] - } - ], + "id": "UJ589fn8ST3x" + }, + "outputs": [], "source": [ "weighted_model = make_model()\n", "weighted_model.load_weights(initial_weights)\n", @@ -2650,25 +1178,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "BBe9FMO5ucTC", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 855 - }, - "outputId": "be1f7941-652c-4e8e-b044-f8a544d9b8b6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "BBe9FMO5ucTC" + }, + "outputs": [], "source": [ "plot_metrics(weighted_history)" ] @@ -2686,22 +1198,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "nifqscPGw-5w", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8ac89a0c-110e-4b9e-ed2b-fca440be5853" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", - "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n" - ] - } - ], + "id": "nifqscPGw-5w" + }, + "outputs": [], "source": [ "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)" @@ -2711,39 +1210,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "owKL2vdMBJr6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 623 - }, - "outputId": "fe3f19be-a3ac-4dd8-9f00-3e43e8440e3b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "loss : 0.016584360972046852\n", - "compile_metrics : 0.016584360972046852\n", - "\n", - "Legitimate Transactions Detected (True Negatives): 56747\n", - "Legitimate Transactions Incorrectly Detected (False Positives): 103\n", - "Fraudulent Transactions Missed (False Negatives): 22\n", - "Fraudulent Transactions Detected (True Positives): 90\n", - "Total Fraudulent Transactions: 112\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "owKL2vdMBJr6" + }, + "outputs": [], "source": [ "weighted_results = weighted_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -2778,25 +1247,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "3hzScIVZS1Xm", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "54f23c34-db79-49ff-b16f-ce4d77ab6a12" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "3hzScIVZS1Xm" + }, + "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -2821,25 +1274,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7jHnmVebOWOC", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "ebab4ec2-bd58-4c4b-9191-2a446f2ebeda" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "7jHnmVebOWOC" + }, + "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -2902,24 +1339,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "BUzGjSkwqT88", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "10efae2b-a1f2-4e47-be0b-2c5a1475a2f8" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(181971, 29)" - ] - }, - "metadata": {}, - "execution_count": 47 - } - ], + "id": "BUzGjSkwqT88" + }, + "outputs": [], "source": [ "ids = np.arange(len(pos_features))\n", "choices = np.random.choice(ids, len(neg_features))\n", @@ -2934,24 +1356,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7ie_FFet6cep", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "6b2652b4-2772-449c-d11e-e51bff6b25f2" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(363942, 29)" - ] - }, - "metadata": {}, - "execution_count": 48 - } - ], + "id": "7ie_FFet6cep" + }, + "outputs": [], "source": [ "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n", "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n", @@ -3014,28 +1421,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "llXc9rNH7Fbz", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "16a5bf80-80b9-4edb-99e8-109b0d3a6570" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Features:\n", - " [-1.71909383 2.38497281 -4.59481652 1.17323447 -0.78699302 -2.69341776\n", - " -2.93305552 1.60763628 -2.78266045 -5. 2.92735061 -5.\n", - " -0.16521428 -5. 0.04789472 -4.72027875 -5. -2.35087136\n", - " 0.53308929 -0.00701804 1.40398131 0.35945914 -0.75014734 -0.53770716\n", - " -0.29864578 0.23727469 0.86211054 0.8456621 -1.93388031]\n", - "\n", - "Label: [1]\n" - ] - } - ], + "id": "llXc9rNH7Fbz" + }, + "outputs": [], "source": [ "for features, label in pos_ds.take(1):\n", " print(\"Features:\\n\", features.numpy())\n", @@ -3068,21 +1456,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "EWXARdTdAuQK", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fe92c1d9-fba5-49c9-fbb0-c85018f6d929" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0.5009765625\n" - ] - } - ], + "id": "EWXARdTdAuQK" + }, + "outputs": [], "source": [ "for features, label in resampled_ds.take(1):\n", " print(label.numpy().mean())" @@ -3103,24 +1479,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xH-7K46AAxpq", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ca89ac53-dd21-4ea2-bd20-3feae503c065" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "278" - ] - }, - "metadata": {}, - "execution_count": 53 - } - ], + "id": "xH-7K46AAxpq" + }, + "outputs": [], "source": [ "resampled_steps_per_epoch = int(np.ceil(2.0*neg/BATCH_SIZE))\n", "resampled_steps_per_epoch" @@ -3143,62 +1504,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "soRQ89JYqd6b", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7105bd8e-365d-41e1-9e6e-7159499f5228" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/100\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 87ms/step - Brier score: 0.1800 - accuracy: 0.7457 - auc: 0.8137 - cross entropy: 0.6766 - fn: 31326.3906 - fp: 54831.5156 - loss: 0.9703 - prc: 0.7956 - precision: 0.6252 - recall: 0.7266 - tn: 145406.4531 - tp: 112110.3047 - val_Brier score: 0.0533 - val_accuracy: 0.9647 - val_auc: 0.9642 - val_cross entropy: 0.2261 - val_fn: 10.0000 - val_fp: 1598.0000 - val_loss: 0.2261 - val_prc: 0.7297 - val_precision: 0.0391 - val_recall: 0.8667 - val_tn: 43896.0000 - val_tp: 65.0000\n", - "Epoch 2/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 78ms/step - Brier score: 0.0652 - accuracy: 0.9160 - auc: 0.9698 - cross entropy: 0.2180 - fn: 12836.7168 - fp: 10300.5518 - loss: 0.2180 - prc: 0.9767 - precision: 0.9217 - recall: 0.9095 - tn: 132932.8281 - tp: 130642.5703 - val_Brier score: 0.0228 - val_accuracy: 0.9833 - val_auc: 0.9727 - val_cross entropy: 0.1132 - val_fn: 11.0000 - val_fp: 751.0000 - val_loss: 0.1132 - val_prc: 0.7277 - val_precision: 0.0785 - val_recall: 0.8533 - val_tn: 44743.0000 - val_tp: 64.0000\n", - "Epoch 3/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 75ms/step - Brier score: 0.0464 - accuracy: 0.9404 - auc: 0.9837 - cross entropy: 0.1586 - fn: 10936.4404 - fp: 5890.3799 - loss: 0.1586 - prc: 0.9864 - precision: 0.9565 - recall: 0.9230 - tn: 137368.5000 - tp: 132517.3438 - val_Brier score: 0.0164 - val_accuracy: 0.9854 - val_auc: 0.9744 - val_cross entropy: 0.0801 - val_fn: 11.0000 - val_fp: 654.0000 - val_loss: 0.0801 - val_prc: 0.7347 - val_precision: 0.0891 - val_recall: 0.8533 - val_tn: 44840.0000 - val_tp: 64.0000\n", - "Epoch 4/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 70ms/step - Brier score: 0.0385 - accuracy: 0.9500 - auc: 0.9891 - cross entropy: 0.1321 - fn: 9469.4658 - fp: 4698.1758 - loss: 0.1321 - prc: 0.9904 - precision: 0.9659 - recall: 0.9330 - tn: 138342.9375 - tp: 134202.0781 - val_Brier score: 0.0139 - val_accuracy: 0.9863 - val_auc: 0.9741 - val_cross entropy: 0.0655 - val_fn: 12.0000 - val_fp: 613.0000 - val_loss: 0.0655 - val_prc: 0.7255 - val_precision: 0.0932 - val_recall: 0.8400 - val_tn: 44881.0000 - val_tp: 63.0000\n", - "Epoch 5/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 80ms/step - Brier score: 0.0332 - accuracy: 0.9569 - auc: 0.9922 - cross entropy: 0.1141 - fn: 8225.2656 - fp: 4017.6809 - loss: 0.1141 - prc: 0.9929 - precision: 0.9710 - recall: 0.9422 - tn: 138940.4531 - tp: 135529.2500 - val_Brier score: 0.0123 - val_accuracy: 0.9875 - val_auc: 0.9733 - val_cross entropy: 0.0566 - val_fn: 12.0000 - val_fp: 559.0000 - val_loss: 0.0566 - val_prc: 0.7203 - val_precision: 0.1013 - val_recall: 0.8400 - val_tn: 44935.0000 - val_tp: 63.0000\n", - "Epoch 6/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 70ms/step - Brier score: 0.0304 - accuracy: 0.9608 - auc: 0.9937 - cross entropy: 0.1047 - fn: 7469.0322 - fp: 3682.0574 - loss: 0.1047 - prc: 0.9939 - precision: 0.9731 - recall: 0.9476 - tn: 140014.3906 - tp: 135547.1875 - val_Brier score: 0.0112 - val_accuracy: 0.9880 - val_auc: 0.9705 - val_cross entropy: 0.0502 - val_fn: 12.0000 - val_fp: 536.0000 - val_loss: 0.0502 - val_prc: 0.7182 - val_precision: 0.1052 - val_recall: 0.8400 - val_tn: 44958.0000 - val_tp: 63.0000\n", - "Epoch 7/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 88ms/step - Brier score: 0.0282 - accuracy: 0.9629 - auc: 0.9948 - cross entropy: 0.0960 - fn: 7075.3188 - fp: 3527.6309 - loss: 0.0960 - prc: 0.9949 - precision: 0.9747 - recall: 0.9507 - tn: 139543.1719 - tp: 136566.5469 - val_Brier score: 0.0102 - val_accuracy: 0.9888 - val_auc: 0.9706 - val_cross entropy: 0.0447 - val_fn: 12.0000 - val_fp: 500.0000 - val_loss: 0.0447 - val_prc: 0.7181 - val_precision: 0.1119 - val_recall: 0.8400 - val_tn: 44994.0000 - val_tp: 63.0000\n", - "Epoch 8/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 90ms/step - Brier score: 0.0266 - accuracy: 0.9640 - auc: 0.9956 - cross entropy: 0.0899 - fn: 6878.3228 - fp: 3399.3513 - loss: 0.0899 - prc: 0.9954 - precision: 0.9757 - recall: 0.9517 - tn: 140075.2500 - tp: 136359.7344 - val_Brier score: 0.0092 - val_accuracy: 0.9895 - val_auc: 0.9707 - val_cross entropy: 0.0398 - val_fn: 12.0000 - val_fp: 467.0000 - val_loss: 0.0398 - val_prc: 0.7185 - val_precision: 0.1189 - val_recall: 0.8400 - val_tn: 45027.0000 - val_tp: 63.0000\n", - "Epoch 9/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 83ms/step - Brier score: 0.0248 - accuracy: 0.9657 - auc: 0.9963 - cross entropy: 0.0839 - fn: 6540.0322 - fp: 3233.0896 - loss: 0.0839 - prc: 0.9961 - precision: 0.9767 - recall: 0.9540 - tn: 140536.8125 - tp: 136402.7188 - val_Brier score: 0.0084 - val_accuracy: 0.9903 - val_auc: 0.9708 - val_cross entropy: 0.0356 - val_fn: 12.0000 - val_fp: 432.0000 - val_loss: 0.0356 - val_prc: 0.7087 - val_precision: 0.1273 - val_recall: 0.8400 - val_tn: 45062.0000 - val_tp: 63.0000\n", - "Epoch 10/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 82ms/step - Brier score: 0.0235 - accuracy: 0.9674 - auc: 0.9967 - cross entropy: 0.0795 - fn: 6132.1685 - fp: 3185.6416 - loss: 0.0795 - prc: 0.9965 - precision: 0.9773 - recall: 0.9572 - tn: 139754.8438 - tp: 137640.0000 - val_Brier score: 0.0074 - val_accuracy: 0.9912 - val_auc: 0.9661 - val_cross entropy: 0.0314 - val_fn: 12.0000 - val_fp: 389.0000 - val_loss: 0.0314 - val_prc: 0.7116 - val_precision: 0.1394 - val_recall: 0.8400 - val_tn: 45105.0000 - val_tp: 63.0000\n", - "Epoch 11/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 77ms/step - Brier score: 0.0225 - accuracy: 0.9683 - auc: 0.9970 - cross entropy: 0.0761 - fn: 5892.4697 - fp: 3121.7490 - loss: 0.0761 - prc: 0.9967 - precision: 0.9778 - recall: 0.9584 - tn: 140245.8906 - tp: 137452.5625 - val_Brier score: 0.0070 - val_accuracy: 0.9914 - val_auc: 0.9671 - val_cross entropy: 0.0292 - val_fn: 12.0000 - val_fp: 382.0000 - val_loss: 0.0292 - val_prc: 0.7027 - val_precision: 0.1416 - val_recall: 0.8400 - val_tn: 45112.0000 - val_tp: 63.0000\n", - "Epoch 12/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 79ms/step - Brier score: 0.0215 - accuracy: 0.9703 - auc: 0.9972 - cross entropy: 0.0730 - fn: 5490.4980 - fp: 3039.1074 - loss: 0.0730 - prc: 0.9968 - precision: 0.9784 - recall: 0.9617 - tn: 140386.8594 - tp: 137796.2031 - val_Brier score: 0.0068 - val_accuracy: 0.9915 - val_auc: 0.9620 - val_cross entropy: 0.0279 - val_fn: 12.0000 - val_fp: 375.0000 - val_loss: 0.0279 - val_prc: 0.6917 - val_precision: 0.1438 - val_recall: 0.8400 - val_tn: 45119.0000 - val_tp: 63.0000\n", - "Epoch 13/100\n", - "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 83ms/step - Brier score: 0.0210 - accuracy: 0.9701 - auc: 0.9973 - cross entropy: 0.0710 - fn: 5511.9858 - fp: 3034.5950 - loss: 0.0710 - prc: 0.9970 - precision: 0.9786 - recall: 0.9615 - tn: 139996.2500 - tp: 138169.8281 - val_Brier score: 0.0065 - val_accuracy: 0.9919 - val_auc: 0.9510 - val_cross entropy: 0.0263 - val_fn: 12.0000 - val_fp: 357.0000 - val_loss: 0.0263 - val_prc: 0.6914 - val_precision: 0.1500 - val_recall: 0.8400 - val_tn: 45137.0000 - val_tp: 63.0000\n", - "Epoch 13: early stopping\n", - "Restoring model weights from the end of the best epoch: 3.\n" - ] - } - ], + "id": "soRQ89JYqd6b" + }, + "outputs": [], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", @@ -3246,25 +1554,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "YoUGfr1vuivl", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 855 - }, - "outputId": "900013c8-0a75-4071-f972-96c7a74721ea" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], + "id": "YoUGfr1vuivl" + }, + "outputs": [], "source": [ "plot_metrics(resampled_history)" ] @@ -3293,66 +1585,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "e_yn9I26qAHU", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a0c08ef9-9288-482c-d791-30ba9ee88f14" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 133ms/step - Brier score: 0.1260 - accuracy: 0.8303 - auc: 0.7891 - cross entropy: 0.5845 - fn: 5591.7144 - fp: 6751.8096 - loss: 1.8949 - prc: 0.5380 - precision: 0.4449 - recall: 0.4974 - tn: 49988.6172 - tp: 5667.3335 - val_Brier score: 0.3379 - val_accuracy: 0.3906 - val_auc: 0.5873 - val_cross entropy: 0.9111 - val_fn: 24.0000 - val_fp: 27746.0000 - val_loss: 0.9111 - val_prc: 0.0134 - val_precision: 0.0018 - val_recall: 0.6800 - val_tn: 17748.0000 - val_tp: 51.0000\n", - "Epoch 2/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 172ms/step - Brier score: 0.3219 - accuracy: 0.5424 - auc: 0.6023 - cross entropy: 1.2477 - fn: 3986.5239 - fp: 6108.9048 - loss: 1.2477 - prc: 0.7228 - precision: 0.5391 - recall: 0.6340 - tn: 5025.7617 - tp: 7309.2856 - val_Brier score: 0.3100 - val_accuracy: 0.4563 - val_auc: 0.9047 - val_cross entropy: 0.8457 - val_fn: 5.0000 - val_fp: 24773.0000 - val_loss: 0.8457 - val_prc: 0.1116 - val_precision: 0.0028 - val_recall: 0.9333 - val_tn: 20721.0000 - val_tp: 70.0000\n", - "Epoch 3/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 117ms/step - Brier score: 0.2662 - accuracy: 0.6107 - auc: 0.7148 - cross entropy: 0.9069 - fn: 2916.3809 - fp: 5682.6191 - loss: 0.9069 - prc: 0.8052 - precision: 0.5887 - recall: 0.7345 - tn: 5535.5713 - tp: 8295.9043 - val_Brier score: 0.2728 - val_accuracy: 0.5428 - val_auc: 0.9333 - val_cross entropy: 0.7601 - val_fn: 6.0000 - val_fp: 20826.0000 - val_loss: 0.7601 - val_prc: 0.4609 - val_precision: 0.0033 - val_recall: 0.9200 - val_tn: 24668.0000 - val_tp: 69.0000\n", - "Epoch 4/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - Brier score: 0.2149 - accuracy: 0.6830 - auc: 0.7974 - cross entropy: 0.6877 - fn: 2166.2856 - fp: 4835.0000 - loss: 0.6877 - prc: 0.8606 - precision: 0.6462 - recall: 0.8009 - tn: 6444.3809 - tp: 8984.8096 - val_Brier score: 0.2349 - val_accuracy: 0.6326 - val_auc: 0.9392 - val_cross entropy: 0.6739 - val_fn: 6.0000 - val_fp: 16738.0000 - val_loss: 0.6739 - val_prc: 0.5582 - val_precision: 0.0041 - val_recall: 0.9200 - val_tn: 28756.0000 - val_tp: 69.0000\n", - "Epoch 5/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - Brier score: 0.1844 - accuracy: 0.7266 - auc: 0.8463 - cross entropy: 0.5815 - fn: 1737.0952 - fp: 4309.8096 - loss: 0.5815 - prc: 0.8937 - precision: 0.6842 - recall: 0.8418 - tn: 6921.1431 - tp: 9462.4287 - val_Brier score: 0.2002 - val_accuracy: 0.7173 - val_auc: 0.9430 - val_cross entropy: 0.5943 - val_fn: 6.0000 - val_fp: 12878.0000 - val_loss: 0.5943 - val_prc: 0.6364 - val_precision: 0.0053 - val_recall: 0.9200 - val_tn: 32616.0000 - val_tp: 69.0000\n", - "Epoch 6/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 85ms/step - Brier score: 0.1574 - accuracy: 0.7700 - auc: 0.8854 - cross entropy: 0.4912 - fn: 1416.6190 - fp: 3682.4761 - loss: 0.4912 - prc: 0.9206 - precision: 0.7248 - recall: 0.8722 - tn: 7523.4761 - tp: 9807.9043 - val_Brier score: 0.1690 - val_accuracy: 0.7844 - val_auc: 0.9462 - val_cross entropy: 0.5219 - val_fn: 7.0000 - val_fp: 9818.0000 - val_loss: 0.5219 - val_prc: 0.6896 - val_precision: 0.0069 - val_recall: 0.9067 - val_tn: 35676.0000 - val_tp: 68.0000\n", - "Epoch 7/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 80ms/step - Brier score: 0.1382 - accuracy: 0.8019 - auc: 0.9053 - cross entropy: 0.4331 - fn: 1326.8572 - fp: 3077.5239 - loss: 0.4331 - prc: 0.9332 - precision: 0.7591 - recall: 0.8801 - tn: 8216.4287 - tp: 9809.6670 - val_Brier score: 0.1427 - val_accuracy: 0.8369 - val_auc: 0.9494 - val_cross entropy: 0.4599 - val_fn: 7.0000 - val_fp: 7427.0000 - val_loss: 0.4599 - val_prc: 0.7112 - val_precision: 0.0091 - val_recall: 0.9067 - val_tn: 38067.0000 - val_tp: 68.0000\n", - "Epoch 8/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - Brier score: 0.1266 - accuracy: 0.8178 - auc: 0.9158 - cross entropy: 0.3961 - fn: 1301.3810 - fp: 2720.4761 - loss: 0.3961 - prc: 0.9405 - precision: 0.7809 - recall: 0.8815 - tn: 8511.5713 - tp: 9897.0479 - val_Brier score: 0.1216 - val_accuracy: 0.8722 - val_auc: 0.9523 - val_cross entropy: 0.4087 - val_fn: 7.0000 - val_fp: 5817.0000 - val_loss: 0.4087 - val_prc: 0.7195 - val_precision: 0.0116 - val_recall: 0.9067 - val_tn: 39677.0000 - val_tp: 68.0000\n", - "Epoch 9/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 90ms/step - Brier score: 0.1146 - accuracy: 0.8401 - auc: 0.9286 - cross entropy: 0.3630 - fn: 1216.6190 - fp: 2340.0476 - loss: 0.3630 - prc: 0.9486 - precision: 0.8092 - recall: 0.8907 - tn: 8866.9521 - tp: 10006.8574 - val_Brier score: 0.1034 - val_accuracy: 0.9024 - val_auc: 0.9556 - val_cross entropy: 0.3636 - val_fn: 8.0000 - val_fp: 4441.0000 - val_loss: 0.3636 - val_prc: 0.7246 - val_precision: 0.0149 - val_recall: 0.8933 - val_tn: 41053.0000 - val_tp: 67.0000\n", - "Epoch 10/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 141ms/step - Brier score: 0.1019 - accuracy: 0.8630 - auc: 0.9407 - cross entropy: 0.3283 - fn: 1137.5238 - fp: 1933.7142 - loss: 0.3283 - prc: 0.9574 - precision: 0.8400 - recall: 0.8985 - tn: 9235.6670 - tp: 10123.5713 - val_Brier score: 0.0886 - val_accuracy: 0.9234 - val_auc: 0.9583 - val_cross entropy: 0.3257 - val_fn: 8.0000 - val_fp: 3483.0000 - val_loss: 0.3257 - val_prc: 0.7331 - val_precision: 0.0189 - val_recall: 0.8933 - val_tn: 42011.0000 - val_tp: 67.0000\n", - "Epoch 11/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 87ms/step - Brier score: 0.0954 - accuracy: 0.8722 - auc: 0.9462 - cross entropy: 0.3064 - fn: 1127.0952 - fp: 1727.5714 - loss: 0.3064 - prc: 0.9611 - precision: 0.8538 - recall: 0.9002 - tn: 9409.7617 - tp: 10166.0479 - val_Brier score: 0.0767 - val_accuracy: 0.9397 - val_auc: 0.9607 - val_cross entropy: 0.2939 - val_fn: 9.0000 - val_fp: 2737.0000 - val_loss: 0.2939 - val_prc: 0.7362 - val_precision: 0.0235 - val_recall: 0.8800 - val_tn: 42757.0000 - val_tp: 66.0000\n", - "Epoch 12/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - Brier score: 0.0889 - accuracy: 0.8828 - auc: 0.9506 - cross entropy: 0.2921 - fn: 1129.1904 - fp: 1491.3334 - loss: 0.2921 - prc: 0.9634 - precision: 0.8706 - recall: 0.8988 - tn: 9768.9521 - tp: 10041.0000 - val_Brier score: 0.0671 - val_accuracy: 0.9512 - val_auc: 0.9625 - val_cross entropy: 0.2669 - val_fn: 9.0000 - val_fp: 2215.0000 - val_loss: 0.2669 - val_prc: 0.7375 - val_precision: 0.0289 - val_recall: 0.8800 - val_tn: 43279.0000 - val_tp: 66.0000\n", - "Epoch 13/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0832 - accuracy: 0.8917 - auc: 0.9556 - cross entropy: 0.2738 - fn: 1092.8572 - fp: 1334.7620 - loss: 0.2738 - prc: 0.9666 - precision: 0.8821 - recall: 0.9028 - tn: 9941.6670 - tp: 10061.1904 - val_Brier score: 0.0592 - val_accuracy: 0.9591 - val_auc: 0.9644 - val_cross entropy: 0.2439 - val_fn: 9.0000 - val_fp: 1854.0000 - val_loss: 0.2439 - val_prc: 0.7288 - val_precision: 0.0344 - val_recall: 0.8800 - val_tn: 43640.0000 - val_tp: 66.0000\n", - "Epoch 14/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 79ms/step - Brier score: 0.0784 - accuracy: 0.8973 - auc: 0.9592 - cross entropy: 0.2589 - fn: 1079.2858 - fp: 1219.2380 - loss: 0.2589 - prc: 0.9692 - precision: 0.8923 - recall: 0.9037 - tn: 9997.1904 - tp: 10134.7617 - val_Brier score: 0.0531 - val_accuracy: 0.9647 - val_auc: 0.9660 - val_cross entropy: 0.2253 - val_fn: 10.0000 - val_fp: 1599.0000 - val_loss: 0.2253 - val_prc: 0.7295 - val_precision: 0.0391 - val_recall: 0.8667 - val_tn: 43895.0000 - val_tp: 65.0000\n", - "Epoch 15/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 77ms/step - Brier score: 0.0740 - accuracy: 0.9025 - auc: 0.9634 - cross entropy: 0.2437 - fn: 1043.6666 - fp: 1119.0952 - loss: 0.2437 - prc: 0.9724 - precision: 0.9003 - recall: 0.9070 - tn: 10041.3330 - tp: 10226.3809 - val_Brier score: 0.0479 - val_accuracy: 0.9687 - val_auc: 0.9674 - val_cross entropy: 0.2090 - val_fn: 10.0000 - val_fp: 1418.0000 - val_loss: 0.2090 - val_prc: 0.7313 - val_precision: 0.0438 - val_recall: 0.8667 - val_tn: 44076.0000 - val_tp: 65.0000\n", - "Epoch 16/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - Brier score: 0.0700 - accuracy: 0.9100 - auc: 0.9666 - cross entropy: 0.2328 - fn: 995.7619 - fp: 1030.0952 - loss: 0.2328 - prc: 0.9742 - precision: 0.9069 - recall: 0.9115 - tn: 10276.5713 - tp: 10128.0479 - val_Brier score: 0.0435 - val_accuracy: 0.9717 - val_auc: 0.9683 - val_cross entropy: 0.1946 - val_fn: 11.0000 - val_fp: 1278.0000 - val_loss: 0.1946 - val_prc: 0.7319 - val_precision: 0.0477 - val_recall: 0.8533 - val_tn: 44216.0000 - val_tp: 64.0000\n", - "Epoch 17/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 140ms/step - Brier score: 0.0674 - accuracy: 0.9139 - auc: 0.9682 - cross entropy: 0.2265 - fn: 1035.0476 - fp: 884.1429 - loss: 0.2265 - prc: 0.9756 - precision: 0.9201 - recall: 0.9080 - tn: 10257.5234 - tp: 10253.7617 - val_Brier score: 0.0400 - val_accuracy: 0.9740 - val_auc: 0.9694 - val_cross entropy: 0.1825 - val_fn: 11.0000 - val_fp: 1176.0000 - val_loss: 0.1825 - val_prc: 0.7334 - val_precision: 0.0516 - val_recall: 0.8533 - val_tn: 44318.0000 - val_tp: 64.0000\n", - "Epoch 18/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - Brier score: 0.0632 - accuracy: 0.9185 - auc: 0.9712 - cross entropy: 0.2126 - fn: 998.7143 - fp: 832.3333 - loss: 0.2126 - prc: 0.9776 - precision: 0.9244 - recall: 0.9117 - tn: 10379.8096 - tp: 10219.6191 - val_Brier score: 0.0369 - val_accuracy: 0.9761 - val_auc: 0.9702 - val_cross entropy: 0.1717 - val_fn: 11.0000 - val_fp: 1078.0000 - val_loss: 0.1717 - val_prc: 0.7340 - val_precision: 0.0560 - val_recall: 0.8533 - val_tn: 44416.0000 - val_tp: 64.0000\n", - "Epoch 19/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 76ms/step - Brier score: 0.0625 - accuracy: 0.9207 - auc: 0.9717 - cross entropy: 0.2095 - fn: 1008.9524 - fp: 757.5714 - loss: 0.2095 - prc: 0.9781 - precision: 0.9303 - recall: 0.9115 - tn: 10366.4287 - tp: 10297.5234 - val_Brier score: 0.0345 - val_accuracy: 0.9777 - val_auc: 0.9703 - val_cross entropy: 0.1624 - val_fn: 11.0000 - val_fp: 1005.0000 - val_loss: 0.1624 - val_prc: 0.7342 - val_precision: 0.0599 - val_recall: 0.8533 - val_tn: 44489.0000 - val_tp: 64.0000\n", - "Epoch 20/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0601 - accuracy: 0.9228 - auc: 0.9737 - cross entropy: 0.2039 - fn: 996.7143 - fp: 726.3333 - loss: 0.2039 - prc: 0.9792 - precision: 0.9335 - recall: 0.9111 - tn: 10425.5713 - tp: 10281.8574 - val_Brier score: 0.0324 - val_accuracy: 0.9788 - val_auc: 0.9705 - val_cross entropy: 0.1543 - val_fn: 11.0000 - val_fp: 956.0000 - val_loss: 0.1543 - val_prc: 0.7349 - val_precision: 0.0627 - val_recall: 0.8533 - val_tn: 44538.0000 - val_tp: 64.0000\n", - "Epoch 21/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - Brier score: 0.0578 - accuracy: 0.9262 - auc: 0.9753 - cross entropy: 0.1954 - fn: 978.0952 - fp: 676.4762 - loss: 0.1954 - prc: 0.9805 - precision: 0.9380 - recall: 0.9123 - tn: 10584.3809 - tp: 10191.5234 - val_Brier score: 0.0306 - val_accuracy: 0.9794 - val_auc: 0.9708 - val_cross entropy: 0.1471 - val_fn: 11.0000 - val_fp: 926.0000 - val_loss: 0.1471 - val_prc: 0.7357 - val_precision: 0.0646 - val_recall: 0.8533 - val_tn: 44568.0000 - val_tp: 64.0000\n", - "Epoch 22/1000\n", - "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 86ms/step - Brier score: 0.0563 - accuracy: 0.9297 - auc: 0.9764 - cross entropy: 0.1900 - fn: 960.0476 - fp: 637.4762 - loss: 0.1900 - prc: 0.9812 - precision: 0.9422 - recall: 0.9154 - tn: 10543.0000 - tp: 10289.9521 - val_Brier score: 0.0290 - val_accuracy: 0.9804 - val_auc: 0.9709 - val_cross entropy: 0.1406 - val_fn: 11.0000 - val_fp: 880.0000 - val_loss: 0.1406 - val_prc: 0.7359 - val_precision: 0.0678 - val_recall: 0.8533 - val_tn: 44614.0000 - val_tp: 64.0000\n", - "Epoch 22: early stopping\n", - "Restoring model weights from the end of the best epoch: 12.\n" - ] - } - ], + "id": "e_yn9I26qAHU" + }, + "outputs": [], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", @@ -3383,25 +1618,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FMycrpJwn39w", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 855 - }, - "outputId": "a09cbb77-4d2f-4861-ec09-5c4cf599fe7b" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "FMycrpJwn39w" + }, + "outputs": [], "source": [ "plot_metrics(resampled_history)" ] @@ -3419,22 +1638,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "C0fmHSgXxFdW", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "18daf060-f084-4bec-d167-b3adca61ed9b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", - "\u001b[1m28/28\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n" - ] - } - ], + "id": "C0fmHSgXxFdW" + }, + "outputs": [], "source": [ "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)" @@ -3444,39 +1650,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "FO0mMOYUDWFk", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 623 - }, - "outputId": "c48eb61f-fb8b-4154-a6d4-b6f6fcf4d25e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "loss : 0.26978760957717896\n", - "compile_metrics : 0.26978760957717896\n", - "\n", - "Legitimate Transactions Detected (True Negatives): 53944\n", - "Legitimate Transactions Incorrectly Detected (False Positives): 2906\n", - "Fraudulent Transactions Missed (False Negatives): 9\n", - "Fraudulent Transactions Detected (True Positives): 103\n", - "Total Fraudulent Transactions: 112\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "FO0mMOYUDWFk" + }, + "outputs": [], "source": [ "resampled_results = resampled_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", @@ -3499,25 +1675,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "fye_CiuYrZ1U", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "a4d4cbc5-5cad-41ee-989d-1f633bc9e2f9" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], + "id": "fye_CiuYrZ1U" + }, + "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", @@ -3541,25 +1701,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "wgWXQ8aeOhCZ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "outputId": "e27d71e0-0901-442d-d6fe-3e969f3cf51c" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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mu4saiU9trbFPSX0JL+94mUhLJBNSJzAve153RSyddMMJIyitaaS6ITApqrO7OFhRD9CpRWdFRERE5OgoOepNRp4Ex94Kn/3BeyjS7BtyNyV1DhtKV/k1OTP3TCKtRjnvwtpCnlz3JADfGPsNJUe90KzcFF676dhWz729oYBbnjcWBXa4PBRU1DMoKbonwxMREREZ0FTKu7cZt8hv19Zs3aPjs0/DavbPZy8YfQEJEQkANLoavce1/lHfM2Vwkt/+8m1FVDU4whOMiIiIyACk5Ki3yZkNc7/n3XWbfH9Fpw09hxWXruD03NO9x7677Ls43cYQLb/kyKrkqK8ZmhpDamyEd/+e1zdx83/WhDEiERERkYFFyVFvFBHr2/Z4/wer2URiZCLTM6b7XV5Sb8xPUc9R39dyGN3WQ9V4WpR6FxEREZHuoeSoN2rWW5ToKuWryO8RQwMPLdkOGPOJTPiG2z345YOAf3L0l/V/6aFgpSv9/ZpZ3HzSSO9+SU0jp/zuI8rr7O20EhEREZGuoOSoN4rP9NtNN1Vx37h8BiVF0eBwYTFbOH/U+d7zH+R/QIOzgShLlPeY3W2noqGihwKWrpKREMV50wb7HdtTXMvWQ9VhikhERERk4FC1ut5oymWwfQnsWuY7lFjLRWdP8O5fP+V6/rfrf979Lw99yTGDjvG7jcvj6v5YpcuNzojjp2eO4zfvbvMe+9ErG4kzWfjXwVWYTKZ2WvukxUVyz6IJDFbFOxEREZGQKDnqjSLj4ZuvwLPnQN4nAIx27va7JCc+h9lZs/nq8FcAPPL1I7x1wVvMy57HF4e+MG6jeUd9kslk4sYFI3lnwyE2HqwEoLjGTjEm9lZXdOheQ1NjuOus8d0QpYiIiEj/o+SoN5v5LW9yxOZXIWuSsZ0yHMadw2VjL/MmR3lVefxt498oqCnwNlfFur7tgumD2VFYTaPTHfziNmw4UMGTK3YBMD47gZPGZnRVeCIiIiL9jpKj3iw+239/+b2+7UV/4PSZ3+I3q37jrVb3hzXG4rGLRizipmk3YTXpr7cv+/Zxw/nW/Fw8gMPh4N133+XMM8/EZrO12+7Rpdt5coXR0/jFnjK+2FPmPfe/m+YzfWhyd4YtIiIi0mfp03NvljUZIhOgsSrgVFX+JhJmwqzMWSzJW+J3bnflbobED+mpKKUbmY8sAuw2mzCbwGI2YTG3P+doZHpcm+c2HawkOsLSpTG2xmo2MSItzhu/iIiISF+g5Kg3i0qAW9fjfGg0Vpx+pxLW/RXOf4QHj3+Qi8ZcREl9CXd+cicAW0q3kFeZR25ibhiClnC7cMZgRmbEUVTVAMBvl2xjd3EtAPe8sbnH4pg/MpXnr5/XY88TEREROVoq5d3bxaRgMgf+NRVajCF3VrOVednzOH3Y6X7nb/ngFm5efjNFdUU9Eqb0HiaTiWk5SSycmMXCiVmkx4dn7tnX+8q1gK2IiIj0Keo56gMst66jfvenRL/5Xe8xu9n/A6/NYuOCURd4y3vvq9rHvqp9uNwq5z3QPXDBZJ77Yj/1Dmfwi49SbaOLN9cbRUHsTnfIZcdFREREegMlR31B4mCi41P9DnlMgfNG5mTP8Vv7CFSxTmBEehw/XzQh+IVdYEdhtTc5EhEREelrlBz1FW6H3+5Q++6AS04ccmLAsT+s+QM2s391s8yYTC4deymJkYldGqJIhMU3BDQtLoK7X98Yxmj8mTBxwph0TpuQGe5QREREpJdSctRXuByBx+orIDqJt9YXUF5nB+DY1Cv4rPR57yWv7Xyt1dvVOeu4dcat3RGpDGC7imq82yU1dp77Yn8Yown03Jf7+PLOU8hIiAp3KCIiItILKTnqK9ytJEfrnofUUXy9bBObSz2s9ozBFJVETC4Em+rx8o6XmZ4xvd1rJqdNJjlKa+JI6EZnxhEbYaHW3jvnunk88MnOElJiIxiWGsOIdsqei4iIyMCj5KivGDQdMIHZ6kuU3jNKd98LEAkvOE/kpw3fxVE5k4ik1QG3uG/+fdzz+T0AVDZWcvPym9t9ZKQlkvcueo/U6NR2rxNpMiw1lpV3ncL+0rpwh+LV6HRz0Z8/9+7f8fJ6wPgC4dXvzWeGFsUVERGRI5Qc9RUpI+D7q2H5r2DL661eck7CbiJPm8pTH8WwK38yJnM9p80uJCU6gSHxQzgx50RsZhuO1nqhWtHoamRz6WZOGHJCF/4g0t8lRNmYNLj3zGdrdLpa7c3yeGB/aZ2SIxEREfFSctSXpI6E8/8Mo0+DqmYVwT56CNwO4uryueDNyaRHncg3a64D4HcLziA6wlfZ7rmznuOTA5/goe31Zz45+AkbijcAcPPym3nohIc4c/iZ3fMziXSzSKuFF284ho92FONye/jvqv0cqjQWyP3hi+v44YvrwhJXQpSV3140hTMnZ4fl+SIiIhJIyVFfExED07/pf+zrf0D1kWTJ4+a4+g8YYjqPA550Iq3+C8hOSJ3AhNT2yzo73U5vcgTwwrYXlBxJnzZpcKK3N2vxxkPe5CicqhqcvLm+QMmRiIhIL6LkqC8pWAf/OBMcdTDmDDjzt5CcC2c/AiufhMJN0FABwItxv+ODuHMwm89me3EB+ZXFQW8/JCGLcRmZXDzmYnaU7+DD/A8B2FC8gZX79pAalR7QxmyC3NRYrBZzwDmR3uinZ47jqY92U+9wh+X5RVUN3uRsw4FKvvPsV11275TYCH50+lhSogPXQRMREZHglBz1JS67kRgB7FhivGZfbyRH486GV74Nm14FYLBjH1eVP0H+3oU8ta6GpWX3Y7bWtHNzGOr+Du9c+0OyYrO479j7OO6F4wBwepxc/+H5NBadgaNsQUC7cVnxLP7B8ZjNQUrkifQCJ47N4MSxGWF7/q/e2sIzn+0F4GBFPQcr6rv0/vFRNu48Y3SX3lNERGSgUHLUl8RnAyZoPl9o82tGcgQw+VLYsRTs1d7TN336E/LcdZg7+DfdcoFYk8lDRPIX4I4MuHZ3A/xjYz7xUbYWbUzMzZrL0IShHXu4SD92wpg0nl+1j4Zu6rnaWVTN86vy2XTYRMWqfCyW3tuLlJsay3Gj08IdhoiIiJeSo74kKQcu/RfsWgYbXzF6kUzNPviMPQN+vAsW/wjW/huAh/N381zGGFZHzwx6+7kZvm+bPR4PFiy48FX4MkeUE5X9eqttH1vX+j0TIhJYcekKbBZb6xeIDDAnjs1g3c8XUteFa0Hd9dpGlmw+DBjrOH2yswSw8PLerV32jO7yj2/N5qRx4evJExERaU7JUV8z4VzjteM9IzmytujJsUXBsPne5Gic3cGvi/bDXZ+3crO2mUwmJqRNYGPJxqMKt8peRVF9EYPjBh/VfUT6kyibhShb1/XoDEmO7rJ79bTK+tCWFhAREekJSo76Kpfd+P/WemSmXm4UavjHkQpz9mrYucwoAd4Bf134V17c9iK/X/N7AGZmzOT80eeH1PaJdU9wuNb4Jvus187isRMf46ShJ3Xo+SISmh+fMZY5w1O8iYbL5WLDhg1MmTKlVw6re/i97RRVNwKQmxYb5mhERER8lBz1Vc6m5ChwDhAmk9F7FJkIjZXGsc3/63ByFGuLZUr6FO/+5PTJnD/q/JDavrLjFW9y5Pa4+eTgJ0qORLpJpNXCwolZ3n2Hw0H04fWcNWMwNlvvG9L641d8SwWU1TaGMRIRERF/So76qsh4MFsgMs537NXrYPcHvn2P07e97j9GdTuzFWZcAyf/LKTHVDcr7rA0byl3zLrDu19QU8BFb1xKTaOztaZAFFiMksUv73jZb+0kh8vNnpJa7765YTTmikWYCCwJ/vKNxzAy3fdz/m/tAe57O/hcioz4SJb88AS/Y//3ynre31oUtO150wbxi0UT/Y4d99sPQpon8psLJ/t9UF2fX8HNz6/hJ2eMY9HUQUHbiwwk9729lYff2xFwPNJq5rrjh3POFP2bERGRnqPkqK/60fbAY43VUFfadpumc588AvNvgajEtq89wmL2Dck5YYgv0ai3u/jRy2upMVdCG6N2PK4Imhf33l7eIuYI36Y7ooDqkum4G7Noye32+O03OtyU1dqDxt5yAVyAmkZnSG3rGgOToIo6RzuJoI/D5YvX5fbwwbYiDpTX88/P85QciQA2i8n772Rvsy9JWvrtkm1KjkREpEcpOepP4jIhebj/sfK9/vtTvgEjFrQ+HK8V87LnsWjEItYVr2Ny+mTv8egIC2Mzk9h8OM2vsjiA21IKJg9morDYB+O0HcBqNmFptg6SxwN2lxtwgsm4QWZKAxH2mIAYWi4wGxdlZVhq4HUtpccF/ozpcZEhtU2Jiwg4lpMSQ509eHIUE+HLFvPL6vjD8p3Gs+ND+zMX6e++NT+X/67Kx+EKLGfu9ni8iVN+WT3//mIf35idg00LTYuISA9QctSfnPvHwGNv/gDW/NO331AB064I+ZYRlggeOP6BVs/94qxj+QUf+h3zeDzMeG4GTreTMWnZvHLuK+3e//ql1/PFoS8ASMlZzDsX3hw0pnOmDOr0t8n3njepU+0A3r31+A632VnkW3h3VEZcO1eKDBw/O3sCPzt7QqvnXli1n5++5quSec/rm3C7PVwzP7eHohMRkYFMyVF/N+JE/+RoxxKj28ZkarPJ0ah31mMz23C6nSRFJQW9fmLqRG9ytL96P+uK1rV6ndlkZnTyaKKtfatk8S4lRyIdMjwtFpPJ+DXVZHdxDav3lYctpsRoKyPT4zB10+9NERHpPZQc9XeTLoTsqfD4DN+xt38Ii/7QLY+LscWw6spV1DvraXQGr0J149Qb+fumv3v3r3r3qjavzU3I5fXzXvebB9XbKTkS6Zi5I1L5+McncdIjK3AemW/4r5X7+NfKfWGN60cLx3DLyaODXygiIn2akqOBIGWEse5ReZ6xv/Y/MO6cDpf27ohoazRlDWX8asWv2rzGhIl5g+YRbY2m3lkf9J55VXlU2atIjkruylC71a4iX7W/3y/bwbScpIAPWPe+tZnCqga/YxnxUdy+cAwJUb2vDLNId8tJiWHO8BQ+391OgZketvVwdfCLRESkz1NyNBCYTHD5C/DkPGPf7YD/XAwpI+Gc3xsFGrpBjb2GZfuWtXvN0n1L+f2Jv2d98XpcntbLZL+x6w2q7FUAnP3a2SEPbbGarVw4+kJunXFrxwLvQgcrfEnf+1uL/CrZNfl4RzG7iwMrdqXERvCDU/RNtQxMv71oCi99nR9S+fzusmpvGRsPGmvFLdtSyNR7l4YtlqOREhvBwxdPYVZuSrhDERHp9ZQcDRQZ48EWA44637Gy3fDFn7stOQrV+NTxnDrs1DbPf5T/kTc5qnZ07NvbZzc9y/enfx+zKTyVrk4am8HLqw90qm1rpchFBoqclBjuWDg2rDH85JUN3uTI7nRjdwZW1+sLKusdvLrmgJIjEZEQKDkaSBb9Ed64CVzN1vnZ8S68doOxbbbC5Itg5Mld8rgRiSNYdnHrPUe//PyXrDy0ErfHTWpUarv3uWnaTTyz6RnsruDrEzXZV7UPDx6cHid3f3o3OfE5XDf5OmyWnh2m9vAlU/nJmeO8JYsjWilH/N/vzsN1ZG7FH5fv4r+r9gMwNiu+5wIVkQBXzx/G7uKakNZG641KahqpajCWH1i9r5zbX1zX6nVut5uDB818+MpGzOaj+1ImOymKW04aTXRE35kbKiLSnJKjgWTKJfDJo1C81f/4hhd821tehx/thIjgawEFY7PYyIoNXNQV4KnTnsLldlHRWEGUNard+5w94mzOHnF2yM/1eDzM+PcMnB7jQ8Fbe94CIDsum/NHnR/yfbpKWivrLTWXEe/7+QuaDcMbl5XQbTGJSHATByXyyvfmhzuMTrv5+TW8s+EQADsKa9hRWNPO1Wa+KjnUJc9NjonguuNHdMm9RER6mpKjgcZ+ZG6L2WbMPQo4XwPr/gMxIQy/yJwM6WM6HYrFbCE1uv1eo84wmYxCD58e/NTv+JK9S4iyHElETDA1bSrZcdld/vyjsf3IpO/EaBuZCVo0VkQ6b+7wFG9y1JO2HKrirfUFPf5cgIz4SOYMT1HZdRHpNCVHA439yDeHbgdctxyij1R+e+VaOLTe2F78o9DuZYmAm76A1JGdCmV/1X4+K/gs6HVWs5VLxlzid2zVoVXsrtzdZpsTBp/AlLQprCla411H6bOCz/yeF2WJ4pbptxBhiWj1HrMzZzMqeZR3v7KxksV7FweNNzEikVOGnUKkpWPJTaPTRU5KNLV2J2Oz4jGZTHyVV8bWQ1W4XC42HzZR9uV+LJbA4SrZidGcNiHT79gb6w5SWd9KAtzCzGHJTByU6N2vbXTy6prQ5kmdO3UQSTG+P7+dhdWs3BO8wli0zcIls3L8jq3YXsT+MmNOXEZ8FAsnZGI26wOOSGddfUwuZ03OpvrI0Lq2OJ0OVqz4iBNPXIDV2rmhxz98cR3r8ysAeG3NQV5bc7BT9+kKD100hUtn5wS/UESkFUqOBhp7i6poTYnNoOm+5ChULjuU7+10crStbBsPfPlA0OtirDEBydHivYt5deerQduOTmq72luDq4FHvn6kzfO/OOYXfslRaX1pSPECfLv829w287aQrm0SabXw8o3z8Xg81DQaH2aWbDrM3z/de+QKC6/s3dZq2+NHpwUkR3/6YBc7i9obRmO4++zxfslRVYODn7+xOaSY541I9UuOVu8rD6ltVkJUQHL00tf5LN542Lt//wWTuHLusJDiEJHWpcVFBh3a63A4yIiG3NRYbLbOJUdZCZF08L8g3WZfWS119vYTwr7AbDIRZdPcLZGepuRooBk8A/avDDx++oNGQYav/hb8HiYLNJXd/vxxyJpi9ED1cLGDUAxNGMp3Jn+HsoYy77FHvnoEN91XdWp08miq7Z1fE8VkMhGv9Y2oqAve6yUivcNvL5rCSWMPUxum0uuvrTnA5gKjqukTH+7miQ/bHlnQl1w4YzC/u3RauMMQGVCUHA0033wVdr0PjgZIHu47HhED838AQ+YEv8eGF2H3cmN7zwp4ZLRR4e6q/3UolElpk3jguOA9MVZz4Nv0gtEXMDNzZtC22bHZzMqa5Xfsn5v/SWFdoXf/+snXMzxxeMumTEmf4refHpMeUrwn5pxIfETXVJo7b9ogJg1OwOV0sW79eqZNnYrFGvhNYvOiDk1+csY4qhuDJxiTByf67SdG2/j9ZVNDii8zwf+580akhtQ2upVvQ685Jpeiqka+3lcOwPScpJBiEJHwS4qJ4Btzhobt+V/sKfUmR/3Jm+sKePSSqZpDJdKDlBwNNBGxMOG81s8lDzNeweTMgT9O8z92aCP8I/SKcgCDjry8cS34CQwJnvAATE2fytT00D7At/Tr437N9Uuv9+7/b9f/yE3IDbjutZ2vBRyzmCycNeIsLhx9Yaee3VFThiQxZUgSDocDW8E6zpo2KORhL6e2GGYXqpgIKxdMH9KptrlpseSmxXaq7dwRqd65ERaziWlDkzp1HxEZeG47dQwWkymkL4R6O7vTzVd5xpdEJhN84+kvyE2N5RfnTiAmQh/bRLqb/pVJx8W0UmGurhj2FR/dfZ0NcM2bR3ePEMzLnsecrDmsOrwKgJL6EkrqS0Ju/1XhV5wz4pw2CzlI51TWOdhRZAxHnJCdoA8BIhKyCYMSeOqq0L5c6+22H67m9Mc+BsDh8vDl3jK+3FvGjGFJXDY7fL1zIgOFPn1Ix0UlGL1PW97o2vvu/QiW3wdRR4Z5JQ6GiRdCyU7fHKf2xGdDdJJv31EP5XmtXnpm2jTWF62lsbVy5kG4PW6e2/ocZswBx6Ot0czMmhlwrrmkqCTSotP82u2p2NPuM51OJ4WuQuqd9X49RzX2Gg7XHm6npcFkMjEyyb9wRlFdEVWNwYehxNpiA0qe51Xm4XQHn/CcHpNOYqRv2J7dZWd/1f5Wr121twyTrRATMHqIBYfL4V20d19pLSV15ZQ3tl8JLynGRnpcLMMSjB5Qp8vNnpJaiuoP0eCsb7dtdmIUGbEppMekA1BZ76CwqoH91e3/3ZhNJoamxJAdl02szeg1O1RZT1FNFcX17f/dxEZaSI+LYmTSSO+wmV1FNZQ2lFBtr2y3bWpcJBlx8QyOGwxAg8PF/rI6DtUeoMFez6aaQqJ2b2x1GGZOcjQZsekkRxnVKktrGimsrqOgtvW/myYRVjODEqPJScjxVmPML6ujuK6csob2v2BIiLaRHhftHcLqdnvYVVxDSX0hdc7aNtsNSooiwmIhPiKezFj/3tA9lXtwu33zBxMiE8iIyWg3DpHeblhqDHNyU1iVV+Z3fO3+CiVHIj1AyZF0zqX/gjX/hjdvCTwXkwq3t1ho9vWbYNMrwe/7SYvqcU47LL0b6kLo2bngLzD1G779kh3wlxNavfRi4HzwlWW4bQvEpfsu+OwP8MGvvbtuk4ljhg3BeeQD7O9X/z54PG24fvL1/GDGD7z7ja5GLnjzgpDaTi2bytzBc737Xx76kh+u+GHQdlGWKL765ld+x55a/xQv73g5aNuTc07mDyf/we/YDctuoKA2+Dom98y7h0vHXurdP1hzsN2fNfZI/rasCu6oX+L94H/z82vYXvcuUVlvBX1mbkIub11gXFdZ72Dh7z8mOucfWOO2B217xbgruHPunQB8sK2Q215cT9y4uzCZghfweOKUJzhhiPF+e2jJdt7c/hExw0IocAKsvWotVpPx6/iyv6ykJvZ1IlI/DtpubtZc/na68YxdRTWc8/inxAx/DEuUkZS98GXbbW+beRvfnvRtAF78Op+Hl31N3Jj7Q4r3lUWvMDZlLAA/fmU9X5cuJXpQ8H/fqVGprLhsBQBOt4eFv/+YqEH/xZYYvM7ZuSPP5f7j/OO7avFVVNn9E/yfzvkpV46/MqSfQ6Q3irJZeOnGY2h0uvjWM195l0d44at8zpiUxYlj9QWASHdSciSdF5XQ9jlri9Kx5k6WI937cWi9Rp3g9+a3RBivJqYW8Xo8DHU42ROhKnIivdn64vVKjqRfiLRayE2L8Vs7btvhaoYkR3foPiaTiWEpMVgtbY9oEBEfJUfSeePOgdPuM3pomouIC7x22LH+yUdzjjqoPdIzlDoS7HWw4QVjf/3zbT/fEmHEEHGkAEDKCP/z0Skw/argPwcEJnOZk/zblu3hX/krWR0ViWvihTDJv/fj7d1v80H+B9799go2TEyd6P9jmCxBCzy43W7y8/NJjfKf75Udlx1ScQibOTCpm54xHVcIiee4lHEBx04ffjqVje0P+wICqgDGR8S3GW+j08W2Q9VEWM2Mz04gxhrjPbdwQhaZJZMocLZfIn1IcjSTs3zFJCJtFi6blcNexxyq3dnttDTmLDQv8jE0JZbLZuWwsXEBHjxttrNaTMwYmkxmjG/I19zhKdhNY9jjOLHdZ6bGRjAyIw4TvkpU500bzK7aaRS72v8gMyItltmDfX83ybERXDYrhx32+TS4S6mprSUuNtaY0d3C9JwkxiSP8e6Pz0rggmm5bLW3H29MhIVJgxP9hkqeNDaD+MMTOOBsv212YhTjMn29s2YTXDYrh/2OWVS4kwOuL6xqoNHpJjHKyqkTMpmeMT3gmnNGnEODq4H1Reu9i0K3dp1IX/XLcyfy0tcHcLmN30G/eXcbv3m39fXu2jMuK563vn8cNiVIIkGZPB5P2//V74eqqqpITEyksrKShIR2ej66mcPhYPHixZx11lmdXnSv31r1V1j8o9Cu/cZ/YdxZ3RsPwCe/g+X3GtsX/R0mX+x3+ol1T/DU+qe8+2uvWttqCfLO0vtFOmKgvV+uefca1hStAWDxhYvJic8J0kKaG2jvl77myr99wWe72p9vGYrVd59KapAFgUOh94t0RG96v4SaA6jnSHqfGVcbPUmV+a2f3/c5lO81tl+4HJJzA69JGGzMQUrqog9Jpbt826mjAk5fPeFqv+Ro0f8WYTaZOW3Yadw641atUSHSTSobK1lXvA4w5pspMZL+5t5zJ/HvlXkdXmDX44FX1xwAjF7qlFhVWBUJhZIj6X2skXDSnW2ff/U6X3IErVekK8+DjS/B8Xd0TUxDZkFDpZEktZIctSzrfaDG+A/S3zf9nWsnXes3DElEus7KQytxe4yCGccNPi7M0Yh0vVEZcdx73qQOt8svq/MmR1OGJOpLOpEQKTmSvmfmt6BgHdSXB55z1IPjSFngDx+AmqL275U+zrhfsP9ozPq28QJwu+Ah//lNkcA1cRG8E2XBbTJRbgLPkXs+vvZxrGYrOfE5fMOWheW16wnKZIYf7/I7NL7gZay/vy142+EL4JJ/+B/726lQ1n5JagBOvgdmXevbrzzQZsW/AN9ZZswZa7Luv7D0Z/7XxGfDxc9A+tjQ7ikDTnF1Ix9uL2Ll7lI+2tH+2mknjkln3rQqEiISqLJXcfyQ4znn8U/Id72DO2FFu21jIixMz5rMU6caPb77Smu54MnPcab/BU/EgXbbJkTbuG7yt7l2kvFv5b+r9vPQ0vU4B7Vf7c9sNpEYZePPp/6ZiWnG3MMf/HctHx9cgSvlxXbbRlrNpMUmsOSiJd5js+9/H3via7hj1rTbNi7Symm5J/OrY38FGGXzb3xuNY6sh8FiVPvzeODufz8Y0DYpJoKfzPk/zhlxDgB/eH8nz371Jc7MP7X7TKvFRHykjVfOfcVbXv0bT69ka+17uBOXtNs2JsLKolFn8LO5P8NkMlFS08jC3wev3gjw/PVzGZflGy7z5voCfvnm5qDtUmIjeP/2BX7H7nxtI+9tDr5UwjlTsvlVi+TlpEdWUFkffKmI+8+fxJmT258P2VkbDvjmhU4ektQtzxDpj5QcSd+Texx8/+vWz634Daw48h94txO+fKr165pLHAKjT+tYDHWB479/VAc/AjzAzNwcmv6z+OJ234eepFFXcnYrbQOYAifNWtyNmEJp29hK0YL6ilZjDuBs8N/3uENrB0bS2PJeLdvWlcLGl+Hku0O7pww4O4uq+b9XNoR0bXWjk0vHXsqFoy9kY8lGJqZOpKLuU+otDURa2l47CaDOBdV2378Vl9tDWa2dmPQ6LEHaVtmhodm/lUaHi/I6O/FB2rmB8kZwenxrhNU0OqlpbCA6SNtGD1Q2+k8RLq+1Y4mrJyJI2xon1Dp81zhdbspq7cSaajEfadvW10MVjbXYXXbvfp3DSWW9ndggz3Ri/KxNvXoAlfVO6hwNRIXwd/Pi9he5ZdotJEUl4fFAWa293TZNmgoXNLE73SG1NbfyB1Db6AypbU1j4Jpv5XV2KuqCJ0d2l/HnU93g4Ot95czISSYxpmvmZWw4UOHdnjpEoxdEQqXkSPqXIbM63mb7u60mI34yJ0J8lm8/eXibl5qAyS4Xa1r51/Vx5Q6S03ODx2QywcHPvbtOl5OCCBeH03Pb/BDjFWXzawtAQgqYQ3iuvdTbNsoaxeTIVGzt/Kx+LC1+4Mh4359TXSk0LTgb6v1kQJo5LJnzpg1iXX5F0GvT443J5Vaz1VulbnBSNA5HIg3OtPaakhRj86swaLOYGZYaQ7UpBVeQBYMzE6NIiPT1TsRH2RiaEkNlkGdaLSYyE6KIMPuG4WbER5IeF09dkLaxkVYGxfl/wB2aGkO1LQl7kLapcRGkRvsqXUbaLAxLjaHKk4bbacTi9rgxt/J7cFBSNDE2X+XI5JgIBiXFUh3kmZFWM2nxkViaLYswKDGK8uoE6ttp67aWkGBNZ1LGaO+fscVsYlhqTJttmotoUY0tLtISUtukmMD5OGlxkSG1TWulyEFOcgyJ0cGTo5gI4/fm13nlXPussRbdjxaO4ZaTRwdtG4xfz9FgJUcioVK1ujDpTdU7+p3Kg1AdZCjEh/fD7uWh3zMiDm75ChIGhXS5w+1gV/kunG4nT65/kk8Pfhr6s3qRc0acw4PHBw616bD/XAI7lxrb31/jP/xOupx+v0hH6P0Sfo8u3c7jHxhDqf90xXTOmRLaf2va4nZ7mHrvUqobnWQlRPHFXad0RZiA3i/SMb3p/aJqdTJwJQ42Xu1JHdmx5MheAxX5ISdHNrON8anjARgaPzT05/Qyuyt2H/1N3G7IX2Vsx6QFrkclIjLArdnvm0M7Y2jgul8dtbe0luojw/2maEidSIcoOZKB6eR7jOFdDRXtX7fueV9J8WcWQiuLqQaIToYLnoJRxjd135/+fXLic6i0B180tS0ul4tdO3cxavQoLBZL8AZHYV/VPt7d+y4AW8u2Mv3fR7mopscDWfFAvDF88bkZRx9kHzQ+ZTx/PvXPqlwoIn5cbg/r9lcAkJUQxaCk6KO+Z/P5Rsu2FjL6Z4uDtslKjOJvV89mbFb8UT9fpC9TciQDU1QCHHNT8Ot2f+C/3pI7+BhyaouMogNHkqO4iDi+OeGbnQzU4HA4WHxgMWdN6f5u6aV5S73JEYDTHTjZuMO81QA9RqGMAWhjyUbWv3Y1J1jaSI5y5gW+J9/8vlFCPpjZ18HwZlUFK/Yza+/jWF59pfWZ5k1MFph0EYw/J/gzRLpRnaOON3e/ye6K3ZQ2tF8E5vxR53PCkBCraPYB2w9Xe9cwcrjc3PSf1QDcctJoJgzyDf3ZdLCSJ1fsavUeLTXvffJ4wOEKPoMiv6ye+97ezHPXzfM7fu9bmyms8hUgcbs9HD5k5t2q9ZiP/H4xmUwsnJDJedOCjNoQ6QOUHIm056S74KOHfeXB21NXDpX7je31/229alwnWdxu5hQWYnn5BTCbjR6YyZfAhHO77BlNThhyAueOPJed5Tu79sbORjBbwGw15oTVFAZvExEbuK5UyQ6jZHsw8VkQ55twj9sFhZtCizVtNDSbhE59BVTsC97ObIFM/5K+O8u24cT4YPKPyi286na31hLse6F+u/+xw8uNP7dg1tXA3le9u56GSkwRe6FsbzuNjljxCRxaStv1yqS3sllsXDzmYuZlzwt+cS+3s2In93/Zfin0Jk3FN/qLnUW+/1aU1tpZvNGYM/uN2f5DsktqGr3ngvn1+ZP5Kq+MgxXG78pDFQ2UhlB577PdpeSV1JKbFus99vGOYnYXt/xvoBnK/H+HL954iBPHZHRZtT2RcFFyJNKekScbr1Cs+iss/pFvf9vbXRaGGcgGaN6JsP1duLvQ+EDehaKsUdx/XGgfUjpt+X2w45Hg1w09Bha95H/s6ROhIIQP/SddBwt+7NuvK4OHQqyUd/bzMHimb3/jK/Dqd4K3i0qC6/zjnfXPqd7k6OvoqLbbuiog/wP/Y5EWiAyhSlfVTuPVXGxo1b0AyP8w9GulV1lfvJ5lFy8LdxhHLTchl8FxgzlYczCk690eN2/seoPZWbMZEj+km6PrXlOGJJEUYwup9HeoUmIj+MtVvuqtv3prC898Fvz3pscDjyzdzp+u6Njw5wumDyY5JoIIa5DKryJ9gKrVhUlvqt4hXaRwCzxzBjR2fm5Rhy36ozFkzWyFESeGXDAi7BoqobEm+HWWCIhL9z9WUwSuED5ERMYbwyebuN1QfSi0+GLTwdqstK+9rvVFh1symQL+Du5Y/n2WHlgR2nNFOiglKoVbZ9x6VPdwuVxs2LCBKVOmhDSnMTEikeOHHE+EJbD89VHF4XZRXN/+wr8A8RHx7K/az6VvXwrARaMv4pfzf9mlsfQ0u9NNaa1/L3FyTARRNt/fR8OR9bRCkZ3oP2+pst5Bnb31Ic1LNx/mF29u8e5PzUniijk53v3qBgcmTMwZkUJaXCQOh5MPP/iAk04+GZvN+I490mohJbZr3w/SP/Smz7uqVifS0zInwI93hfYhuoMcTgfLly/nlPkzsT19rO/EWz/wbSfnwq3ru/zZ3SIq0Xh1RlxG59qZzcGrGLYlIsZ4dcKjpzxOeUM5Lo8r+MVdxOE48n455ZSw/8dIut7SvKU8uMoosV/WUMYvPv9Fl9z39S9fD/nab0/6NrfNvK1LntvEYraQFZsV/EJg1eFV3u0xyWO6NI5wiLCaAxKalqJslqDXtCUx2kZidOu/C66ZP5wGh5sH390GwPr8Cta3ss7YmMw4lt62AIfDQVIkZCdG6feL9EtKjkS6kjUC4jODX9dRDgeNtiRIyjGGbrVWZa+q4Mj6Ti3mjsSmdfnQO+mY5KijL83bEQ6rg3hzPGnRaa1/ePF4mhXJkL5mWMKwcIdAQU0BJfUlXXa/+Ih4Ii2Bi6m25YtDX3i352bP7bI4Bqrmc4zasqOwBrd7QA02kgFKyZFIX2KLge9+CHmfgccNjVWw9G7jnMsOj44NbJMwGK7/sHuSNul7qgrg7wuNghjjzoZhx7Z+XdZkGNpsor/bDV//PbRnjD0TEpvNA6nYDzuXwciTtM5VFzh28LE8c/oz7K/a3yX3c7lcbNy4kcmTJ7c7rO6dve/w1eGvAFiSt4QleUu65PkA0dZo/nLaX0IqtuBwOVhdaFR0s5qtfHnoS29P0oWjL/RLsjaXbKbKXsUxg47pslj7o4UTMvnPdXPJL6vzO15e5+C3S7Z5909+dAWv3OBLRj/bVUKj08X47ASyEqIw6UsX6QeUHIn0NSkjfB8wqw75kqO2VB2EwxuVHIlh7ye+8vSb/2e8WjP/+/7JER7/giPtSR3lnxzt+Qjeud1YBPiO7WDRf3qO1uys2czOmt0l93I4HETuiOSsUe3PCdhQssGbHHW1emc9qw6tCik52la2jXqnUYXN6XZ6hxgCnDX8LG9yVNFQwacHP+VP6/7E06c9rQSpHSaTiWNHpQUczy+r80uO8krr2NOsct2fV+zm011GD+Kqu04hI6GdojMifYT+CyXSlyVkG0UZdi4NPJf3qW/43Xt3wgf3de2zLREw57sw5ZKuva90r5YFLrqbxwNv3mJsN1YDGpbTV90w5QbASDq6yoaSDd7heZtKQyu1nxSZhNVsDboG27Obn/X2blXbu25phYEkJyWG+y+YxM/+5/u7uev1zTTWWXg6byXbCo3COmlxEaTHhz4sUqQ3U3Ik0tfNvMZ4tfSn2b7kqGRH9zy7fK+So75m5MnwnfehbE/716W3HKJpggueDu0ZGeN9283Xlhp+PFg0gbuvGhQ3iHvn39ul97xy8ZXe5GhF/grcHjdmU/vloHMScnhl0StsKd0ScC7a6itYsOrwKm9p8BmZHStNLT5Xzh3Gkx/u9q6ZtKu4FjBBrS/hHJeVoCF10m8oORLpr2ZcDSt+C84QFkztCI8Hmiqvuezw4QNde3/pvNh0mP5Ngv5qz5ltvDrCbIapl3U8ph3v+bZHn97x9tKvnTX8LDYUbwDAZrbx5LonMZlMWEwWTh16KqOSR7XabmTSSEYmjWzzvtX2ajaXbgZgVNIo0qIDh4xJ6K6YO5QnP9xFo9NYxNrtduNuVvynzu7id8ta/xIuOzGKS2YOwWrRGkjSNyg5Eumv5n/feHW1fZ/DP840thsq4aPfdv0zpPPqyuDY28MdhU/z5GjMwvDFIb3S2GRfD6XD7eAvG/7i3X95+8ssv3R5p+67unA1bo/xQV7V7I7ezSeN4uaTjETV4XDw4huLuftr30fINfvLWbO/7WUsnC43Vx2T291hinQJJUci0jEJg42qeY664NdKzyvfi6lgDUm1ezAVrAFrN/6aj05uv/pcbSkcOLIeTUS8sRaXSDOZsZlEWiJpdDUGnKtx1LCpJLR5SC29s+cd73Z2bDYFNQUMivNfoHlf1T4cISwonRadRlJUknff4XKwr2pfSHEMTRja5Yvl9gZRFoi0mr09ScF8uK2IWbkpWMy+3qbkGM1Tkt5JyZGIdEzyMPj+GigKHO8vYbLiQThwpIrY+v9iXf9fFgB001QzP6c/AMfc3Pq53c2+9bdXw8cPwwk/7oGgpK/Iic/hnQveYXfFbgC2lW/j96t/D0Cds47L37n8qJ/xyNePsPLQSp469Sm/49//4PvsrdwbtP3/zf4/rppwlXe/pL6EC968IKRnv3HeG4xI6n/l621myEqIYl+z0t8PXzyFzGbV6h5YvJVth415SR9sL+aD7cUB9/nTFdM5Z8qggOMi4aTkSEQ6LiHbeEnv8PUz4Xv2oQ1tn7PX+O/nd08ZaOnbMmMzyYw1lhqw9dOCHV8d/optZduYkTmDscljsZr7/scvc4v6C3OGpzAs1beY7NMf7wHarxL4dV65kiPpdfr+v04RkYHuzN9C+rgjpbLB5Xazb18ew4blYjF3wyTovE98PYdb3oBd77d+nccD1mhwNYLZCvs+g4eOTKK3RcOC/zMKh4gcMStzFr+a/yu2lW0LfnE7luxdQlljGQBWk5W1hWtZ8OICv2vqHHV+C8a25c/r/szfNv7Nu+/2uENqB3DNkmvIjs0mJz6HpfuMJReeOf2ZLlujKpx+d8kUXlt3CPuRoXWxkf4fKb81fxiFVQ3sLPJ9SRJpNeNye3C6jZL+L32dz5JNh/nWsbncuKDtAhsiPUnJkYhIX5c4BE65x7vrdjjYuHgxOaefhaWdRT077aVrfMmRsz60ioguu/Fq3pv0+eNKjsSPyWTigtGhDVlrz9qitd7kyOlx4nQ6qXN2bp5ko6sRgk9NarNtRWMFh2oPAWA1W5mcNrlzN+tlJg1OYHpuapvnT52QxdDUWBb+/mPvsZZzlOrsLursLp76aLeSI+k1lByJiEjHzL0RyvN862h1RGMN1Bnr2pA5sSujEvG6bvJ1/Hn9n2lwNoQthrKGMm9CVtFYAUBCRAL3f3l/p++ZE5/DtZOuxWbuG8MPR6bHcfmcoXy+uwRPi/WfnS43BZXG30+jw82PX17vd3760GQun5Oj9ZOkxyk5EhGRjhl2DNzwUefaLr0HPv+jsT3mzK6LSaSZhbkLWZgb3tLxNy67kc8KPvM7VtZQxuu7Xj+q+2bGZHLeqPOO6h49xWI28eCFrfeU7S+t44SHPwSg3uHi5dUH/M6/vPoAEwYlMC0nqbvDFPGjFblERKR7fPEUvPcz+GUi/H0h1BTBjiW+845aKNwcvvhEutH0jOndct+d5Tu75b496WBFPec98WlAUYeWrvrbl/zklQ18tKOYRqerZ4KTAU89RyIi0j02vgwHvza287+Edf+Bkmb1xd++zfj/b78HQ+f1fHwi3eiGqTdw3qjzuPvTu/ny8Jd+56wmK6+e+6rfsb9s+AuL9y4Oet9/bvknMzJncPLQk7s03p7kdnsorws+kau60cmLX+fz4tf5XD4nhwcvnNID0clAp+RIRES6x/G3wwtX+PbTx4HZBu4WH4oq9sOgdr5lt0SA5h1IH5QVm8UNU2+g2lFNtd1X1tpqtgasfzQsYRg58TkB9yiuK6bB5T93an/Vfuwue9Dn99YFaK0WE8NSY1o9V1TVSL0jsJdod3Gtt/cowmLWXCTpNkqORESke4w82Sjl7ayHuEwYfTp8e4lR0vujh3yV61673ni1JWUkXLsY4rN6Jm6RLjQ7azYvnvNi0OtumnYTN027KeD4Lctv4aMD/nP8Hl39KI+ufjToPaelT+PphU8TbY0OPeAekJ0YzUc/PqnVc6U1jbyz8RD1dhd5pXX8d9V+AFbtLWPs3caw3OFpsbz43XlkNFt0VqSraM6RiIh0j70f+8p8j14IZjMMmQXH3tqx+5TtNobliQxAfzrlT3z6jU+Zlj6tw23XFa9jf9X+rg+qG6XGRXL1MbncsGAkZ05q/QuRvSW1fJVX3sORyUChniMREeke29/1bY9tUZlu0R9g7b/B5Wy7ff6XviF4n/4evn7Gd85sg+nfhInnd1m4Ir1VYmQid869kz+v+zM1jpqg168rXofTbfzbeuDLB1odXmcxWThz+Jm9uvLd/JGpfOe44Ww6WAnArqIaSmuN4YTZSeo1ku6h5EhERLqexwM73vPtjzjR//yOJRCVCGljYMFPwNJi3Ra3C36V4tsvWBv4jPwvlRzJgPD3jX9nc+lmbBYbyZbkNq+bP2g+F4+5mBn/nuE9tqZoTZvXf1bwGWcNPwtby39/Yfbwe9vYeLCKj3cUc9qETFLjjORuR6FvsaQ/r9jNuVMHsWjqIO+xBoeL219aF9Izbj9tLKMy4ro0bukflByJiEjXq8yH6gLffkSs//mNL/u2M8bDpIv8z5vMMPFC2Pxa289IGNT2OZF+ZF3xOlbkrwh6XUqU8YXCmcPP5M3db4Z076c2PEVSZJLfscFxgzkp56SwFT14b3Mhu4qMHrJlWwpbvWbZlkLGZ8X7HXO6PSzeeDikZ3z72OEAuNweiqsbyYiPxBystrgMCEqORESk68VmQNZkOLwRZlzT/rUxaYHHTCa45B9w3hPgcfuOr/8vLP6RsT32rK6LV6Qfuf+4+7ln3j24PW6Of+F47O62K9s9veHpVo8/uuDRsC2ke/K4DG9y1N0OltdzwsMfEmk1c/mcofzy3Ik98lzpvZQciYhI17NFwQ2fQPUhsLWolOVqVso7MgGGzW/7PhEtyv3uWeHbzpgApbsD28SmQ1RCh0MW6a1+Nf9XNLoag17XvCpdlNWYk/POhe/4XVNjr+GCNy8Ieq/dFbtbLebgcDqoclcFbX807jprPDcuGOm38Ov6/ApufM4YInjq+Ey+e8JwYiKs5JXUeq9xezy8dEP7a6bFRlpJiY0gJdYYqrevzGjf6HQTaVWdMlFyJCIi3cVkan3o2/4vfNujTwucb9QWRwPs/sC3/9p1rV9ntsHVr0PucSGHKtKbJUe1Pc8omKxY/4pvux27MZvMuD1upqdP54rxvrXI/rXlX2ws2QjAk+uf5Mn1T7Z53/yv87nrmLs6HVcwTclLkxXbi73b728t5P2trQ+3C8U950zgO8cZw+r2ldZ5jw9tY+0lGViUHImISM/yq2LXgaFxzgZwBv/2HLcDDnyl5EikFSOTRvLFFV+wrWwbkZZIJqRO8J5bum+pNzkK5qvCr7orxFa53J7gF4Xos10l3uRof5kvORqWEttWExlAlByJiEjP8Xhg+2LfftKw0NtGJ8Elzxrtm89DarJzGdSXGdtf/BnWPtex2GIz4JzfQ8a4jrUT6WOirdFMz5jOyzteZn3xeu/x0UmjKasvCxjClx6TTqwtlrKGMj4v+ByAvKo8Fry4IOiz4m3xrRZ2WDBkAXfMuiPkog+XzBpCYVUDB8rrQ7q+paLqBj7bVQrAl3tKOfmRFQAcrmrwXpNXWsNxo1uZAykDipIjERHpOeV5UL7Xt//cRXD7FogMsaTuhHONV2sem+JLjmoKjVdHlO4y1lI666GOtRPpox7+6mHqncGTjUcnGcUZPjnwiTc5cnqclDWUBW3b1jV5W/K4YvwVDIoLrepkpNXCHQvHhnRta97fUuhNjmrtLvY0m6vU5D9f7Oeb83I7/QzpH5QciYhIz7HFGC+PBy79lzFUrqvKBc+7yVgs1tkQ/Nrm7LW+xWYL1sJ7PwveZsgsmBh8UrtIfzI9Yzpzs+ay9vBa7LRdAa+5lj1H1fZqPHgwYQooId6d5oxIYf7IVDYX+IpJeDweqhp8C1EnxvSu9Z4kPJQciYhIz4nPhBs/hQNfGxXlhnZhqeB5NxqvjnrmTNhvfBvOgVXGKxQpIyF7SsefJ9JL3Dv/XpxuZ9DrJqVNAiAuIo4/n/xnXnr7JWInxmKxWIK2PWP4GTy+5nFsFhtjk8dy16d30ehqZGjCUGJsPVcAISHKxvPX+1eyK6puYM79y737Q1NUkEGUHImISE9LHWm8eouM8b7kqCOskV0fi0gPOnP4mZ1qF2eO46zhZ2GzBe9pcbldvLD9Beqd9WTFZHnnMyVGJvLloS+915kwMTZlLImRiZ2KqTP2N6tUBzAsVQUZRMmRiIj0NLcLzMG/ce4xZz0Cs75tDK9rT2M1/Oci335rRSFExE9+db53XlNaTBqH6w4DsKF4A9ct9S/HnxadxjsXvNNjPUr7WiRHOeo5EiDsq1098cQT5ObmEhUVxdy5c1m1qv3hDI899hhjx44lOjqanJwcbrvtNhoaOji+XEREwqOmCH6bC38+DlY+AUVbW3+1LNldVwaVB7snJrMZsibB0LntvwbP8G9XEbhApoj4216+3bs9LL796pQl9SUhFXnoKudNG8SiKb6CEMOUHAlh7jl68cUXuf3223nqqaeYO3cujz32GKeffjrbt28nIyMj4Prnn3+en/70pzzzzDPMnz+fHTt28K1vfQuTycTvfve7MPwEIiLSITveg8YqKNwI77WznsotqyFtlLHt8cCGl2DJT+CUX8Dxt/dMrC1Ft1iI88WrjCp7p94LM64KT0wivdz2Ml9ytHDYQo4dfCx7K/f6XfOfrf+hzmn04pz/xvmYCF6kJcoaxfenf59Lx17a6disFjPldb7CEt94+gvOnJTFo5dODbnEuPQ/YU2Ofve733H99ddz7bXXAvDUU0/xzjvv8Mwzz/DTn/404PrPP/+cY489liuuMFZzzs3N5fLLL+fLL78MuFZERHqhxuqOtyneDsvuMbYPru7aeDqiZRU8VyPUNcLqZ5UcibShec/R2NSxDI4bHHDNSztegiN1IVqusdSWBlcDL+94+aiSI4DqRl9BinqHi9fWHuRnZ48nNU5zCgeqsCVHdrud1atXc+edd3qPmc1mTj31VFauXNlqm/nz5/Pcc8+xatUq5syZw549e1i8eDFXXaX/KImI9AmzrjV6jioPtH9dZLxve9vb4Dry7e6wY7svtmBs0XDafbDxZf91lCoPwJK74KQ7/eMWEW/PUbwtnkGxra9pdMfMO3hx+4s4mkrqt6PR1ci+qn0AHK49zO0rjJ7klKgUbpx6I2nRHVvE9bZTR/PH5TvZeLASh8sDwJ2vbcRqab/nyGwysWjqIE6fmNWh50nvF7bkqKSkBJfLRWZmpt/xzMxMtm3b1mqbK664gpKSEo477jg8Hg9Op5Mbb7yRu+66q83nNDY20tjo+xaiqsqob+9wOHA4gv8j7C5Nzw5nDNJ36P0iHdG73y9WOPaO0C49Er9l2zveCbKOUQu9x8NizvdgzvcwL70Ly1dPG8dqDsMXT+CKTcc975bwxdZJvfv9Ir1NR94vFY0VFNYZXyKMTh6N09l62fBzcs/hnNxzQnr+2qK1fOf973jvv2zfMu85K1bumBna75faRidPfbyXoSnR/OKccXz7n2sorTW+hFm6JbQFpN/bfJh1d59ChDXsU/h7rd70+yXUGPpUtboVK1bwwAMP8OSTTzJ37lx27drFrbfeyn333cc999zTapsHH3yQe++9N+D40qVLiYkJ/8S7ZcuWBb9I5Ai9X6Qj+sP7JcpexukFawCojB7Kis83A5vDGxSQXRHFLJMFs8flPVawZinFOw/iMtsojp+Mw9q3ygL3h/eL9JxQ3i+7Hbu925EVkSxevPion1vnriPJnESFuyLg3P68/SwuDO0ZB2rhqQ3Gx+C56W5Gx0JpbceSHIfLw/3PvcfoRA8pGoXXrt7w+6Wuri74RYDJ4/F4ujmWVtntdmJiYnjllVc4//zzvcevueYaKioqeOONNwLaHH/88cybN4+HH37Ye+y5557ju9/9LjU1NZjNgW/q1nqOcnJyKCkpISEhoWt/qA5wOBwsW7aM0047LaR1AmRg0/tFOqI/vV/Mq5/BsuT/AHAd/2PcJ/wkzBE1Y6/B+reTMJXvDTjlHnYcrm++3vMxdUJ/er9I9+vI++W5rc/xu7VGwayfz/055488v0ticLldlDSUAPDartf466a/AvDLeb/k3BHnhnSPdzcd5gcvbgDgtlNGcdOJIyitacTuCv6x+Ow/fU51g68XLDU2gk9/fAJWi3qQWupNv1+qqqpIS0ujsrKy3RwgbD1HERERzJw5k+XLl3uTI7fbzfLly7nlltaHJdTV1QUkQE2rM7eV40VGRhIZGZjO22y2sP8l9aY4pG/Q+0U6ol+8X3Yu8W5avnwKS8Y4mHyx73zhFvhnaMNxuOkLiGtWCfXLv8BHvw3eLn08XPuO/7EXroT9K6GhstUm5v2fY/79WJjzXTixWYEhlxMeHRNavJf+G3KbzbHatRxeuz54O5MFfrwztGc00y/eL9JjQnm/TMqYxOXjLmd72XamZEzBZrPx7fe+za7yXUHvf8PUG7hy/JXe/dL6Ui544wLvfqwtlp/M+QkV9grvsfTYdGpcNZhNZhIiEtqtOHew0lelbnhGPDabjazk0N7/Q1Ni2FxQ5Yut1o7dYyZa/37a1Bt+v4T6/LAOq7v99tu55pprmDVrFnPmzOGxxx6jtrbWW73u6quvZvDgwTz44IMALFq0iN/97ndMnz7dO6zunnvuYdGiRd4kSURE+pGiZnNQ7dW+wgxNPC6oKw3tXi2/RHPUh9a2oSLwWGNV+209buN8awvLhhpvy8npLkdobU3676H0DrOzZjM7a7bfsarGKsoby4O2bWhRHdKDx69deWM5L2x/we+6m5ff7N2ekTGDZ05/BksbC07vL/P928xN7dg0i39cO5sV24p54N2tVNQ5iIu0khDVp2aqSDvC+jd52WWXUVxczM9//nMOHz7MtGnTWLJkibdIw/79+/16iu6++25MJhN33303Bw8eJD09nUWLFnH//feH60cQEZHudNKdxmKxTYvCRsT5n7dEQPLw0O7V8kNSVGJobRMCSw8Tnx3YtqEC6pt96LNEQExKYNtQ47VG++/bokNr28aHQZHeICs2y7umUXviI/wrP1pMFnLic2hwNlBcXwxAenQ6uyt2t9acNUVrKG8sb7N63b5SXwzDUjo2PzAjPopLZg3h7jc2ATAoKUrrIvUjYU9zb7nlljaH0a1YscJv32q18otf/IJf/OIXPRCZiIiE3YyrjVdb0sfCres6d+9Z1xqvzrjw6cBj9eXwh6m+oXbWaNj6tvFqLiY1tGe810ol1jFnwOn3t58Ale+DTa/CqNMgKnxza0Va86dT/tSpdslRySy+cDFv7X6Luz41/m2MTBzJxWMu5l+b/0WDy+hB+qLgC+xuo4f5jhV3cPWEqzll2CkB92tKjhKjbSTGdHy4V2mtHbvTDcCgpOggV0tfEvbkSEREpF+ITobpV8HKIx/+Givh4Ndd+4yDX8OE82DYMW1fs+lVWH4vmG1w0d9g4vldG4NIGDXvKRqRNIKp6VN59MRHvcfm/3c+druRHK0pWsPh2sMByVGj08WhynoAhnVwSF2Tgop677aSo/5FZTVERES6yvhFEJcZ/LqjkTik/fPbj5Qydjsge2r3xiLSw3ZX+pKjkUkjA85fPOZibGZfT1DL4XkAB8rrcR+Zgjg05eiTo91FNfx7ZR4NDlc7LaSvUM+RiIhIVxk6D360o2vvWVsCDzf7ELj7A5h5TevXVhfCgSO9VWYbLP8VTLwAJoRW3likt9tTsQeAaGs02bHZ3uNPrX+KHeXGv73ZWbP5vOBzwFgo9ukNT3Pd5Oswm4w+gf3N5xt1sufoUKWvEMSXe8v4cm8ZhVWN/Oj0sZ26n/QeSo5ERER6M2uU/77b2fp1AAdWAUe+Enc7YPNrsPVNuKsArFqlUvo2j8dDQU0BIxNHcsHoC7zJDsCawjWsPLQyoE1hXSGPr32ciakTOXawURo/OsLCgjHp5JXWMiItLqBNKMZmBvZIldfZW7lS+holRyIiIr1ZZBwkDIGqA8b+mNPbvjZrilHwoXnJ76gkoxdJpI8zmUwcP+R48qvzuWZiG72nbWg+1G7eiFTmjQixMEob5o9KY+ltJ/Dq6gP85WOjN2twsuYe9QdKjkRERHqziv2+xGjQ9PbnHCUPgzu2w7r/wFu3GsfGnglmTTGW/uEPJ/2BysbAxZd/c8JvsB9ZB+2hrx5i2b5lfueHJ4ZYQr8DxmTGkxoX4d0frMIM/YKSIxERkd5s22LfdtE2eHRc8Db2Gt/2uHO6PiaRMDGZTCRFJQUcT4nyrSl2uPaw37k4W1yb6x0drYPlvsIMQ9Rz1C8oORIREenNijb7tp31UF3f9rUtZU6GEQu6PiaRXsrj8bC3ci8AmTGZ/Gr+r6horPAu0urxGHPyumrR1oMVvsIMKundPyg5EhER6c1mXguH1kNtafBrwViE1l5tbDvqjMVkx58LI0/qvhhFeomS+hJqHEbP6ajkUcwfPN/vfF5pHec+/inVjU6Gp8Vy7bG5rd5n8uBEpg9N9u47XG7+u2p/wHUbDxpD/KxmExnxUQHnpe9RciQiItKbDZ4BN3wc+vWvfRc2vGhsl+02Xuueh5/mgzWi/bYifVxTrxHA8ITAeUZ5pbVUNxoVH/eW1PLzNzYHXAPw/ZNHBSRHbV0LkB4fgcXcNb1REl5KjkRERPoLj8co2tCUHDVxO2Hne2CyBDQxuZxkVa7BtMMEllY+FmRNgqSh3RSwSNfyS45aKcIwOCkai9mEq2kV2C6SEqNS+f2FkiMREZH+omgLfPywsfBrwmBY+SfjuNsJL36z1SZWYC7AnjbuaY2Cm1cZlfBEerm9Vb7kKL86n7VFa5mcNhmr2fjIOyYznre/fxzbDle1e58xLdYxirCY+f1lU/2O/eH9neQdWVB2aFrnFpOV3kfJkYiISH+x7R1jjaPN/4O5N3bNPZ0NUFui5Ej6hD0Vviz/2c3P8q8t/+KrK7/yu2Z8dgLjsxM6dF+rxcwF0/3L6D/y3g7v9qi02E5EK72RkiMREZH+Ytvbvu3534cRJxq9Se1wudxs376dsWPHYrEcWQ/JaYePfmNsRydD9tS2byDSizTvOQIYEjeECEvXz7XzeDyU1DT6npOinqP+QsmRiIhIf1CRb1S1A0gZAc5GGHOGsQhsO9wOBzsrFzP62LOw2GzGwZ3v+y4Yc0brc5FEepk6R13AGkfdsfhrk0VTBvHKGmOB5sRoGzsKq4O2MZtMjMqI8zt2qLKe6gajSER8lJXsRJUEDyf9thMREekPtr/r2y7bA4/PgIkXwiX/6Pi9mvdAHVwNz10UvM2IE43eKpEwyavKCziWm5DbLc8ymUw0uty+Z5fUcuNza4K2S4uL4Ou7T/M79pt3t/HGugLv/s/OGs/1J4zoumClQ5QciYiI9Af2Vr61PvB15+7VvF3JDuMVzK73YcyZkDaqc88UOUrNK9U16c6eo4Pldd7tpJiuG7r32e4SJUdhpORIRESkP5jzXXA0wN6PIf8L49jw4zt3r/HnGHOVPK6Otdv8GkQlhX69xQZjToeEQR17jkgrejw5qqgHjN6g0ZlxXDYrJ2ibuKjAj97zRqRSb3exdEshACldmGhJxyk5EhER6Q8i4+Hkn0Hd94xhcVvfhgnnd+5eJ/4U5v8AXPb2ryvfC0+f6Nv/8P6OPytzEnzvs463E2lhT2VgPfruSo7sTjdF1UZBhsFJ0cwclsLMYSmdutflc4aSmxrrTY7S4rVmUjgpORIREelPYlJgxtXG62hExABBKnA5B4EtFhy1nX9ObQnUlkJsaufvIUJgz1FiZCLJUcnd8qzDlQ14jqwjOzj56AsoFDerfJcep+QonJQciYiISOfEZ8KNnxhFGzpi/0r4+hlju+YwPDwCTvkFHH9718coA4LL7WJf1T6/Y8MTum9I3YEK33yjwUlHnxyVVDdLjtRzFFZKjkRERPqTsr3wt1NCu/b6DyGu2Xyf1f+E5fcGXpecC5f+GxIHB55LHWm8OsLZCDzjf2zvx0qOpNMO1BzA4XYAkBqVSrQ1mpFJHXxfdkBBRYN3e1AXJEd+PUdKjsJKyZGIiEh/4nFDXWno1zbnbGy9bV2pMY9p7g1HHx/AlEuhPM+4Z/E249jBNfDEXLBGwrG3wqQQyoeLHLGrYpd3++IxF3PL9FvwNI176wYHy+u9213Rc1TcrOcoTcPqwkrJkYiISH9itkJyiMOJzC0+BkQl+LetPABHvo0na3LXxAdGAnTKPVBX4kuOGiuhuNLYXn6fkiPpkD0VvmIMTT1GJpOp2553sPmwui6Yc3ThjMGMy4qnuLqR7KSoo76fdJ6SIxERkf4keRjcui706x0O3/bUbxgvAHsdPDTCSI5i0iBnbpeGCcCUy2DPCqMgg9sBziNDlcr3gsthlPoWCUHznqPuHE7XpKmMN3RNz9H8kWnMH5l21PeRo2cOdwAiIiLSC+35EJxHPgCOOwvMlq5/xrD5cOt6uOsAnHy3/7l1/+n650m/tbtiNwBmzOQm5Hb785rmHMVGWEiMVhLfnyg5EhERkUC1xRCVaGyPW9T9z0sb679v0kcUCY3L7fKW8fbg4ap3r+LJdU922/Pcbo93ztHg5OhuHb4nPU/D6kRERCTQzG/BtCsh71OITYeX2lg3yRIB06+CEQuO7nljFkJEPNirjf3RC4/ufjJg5FfnY3cbCxZ78LCldAsjE7tvaF1xTSN2l1HMJCc5yFpgIWhwuNhTXMtXeWV8vrsEizkw2YqNsHLjiSMZmR531M+T9ik5EhERkdZZbDDyJMhfBVveaPu6vE/hjm1H96yK/b7EaNAMiM86uvvJgNE0pK65EUkjuu15B8p9xRiGdEExhoMV9Zz1x0+CXldeZ+dv18w+6udJ+9RnLSIiIkcnpgsmkm9f4tsuWAM1RUd/TxkQmhdjaNKdPUf5Zb5iDDkpR99zNCQ5mnFZ8UGvc7m7rzS5+KjnSERERNqXPRVu2+J/bO2/YcWDxva4s4/+GaUtPuDuWg7TLj/6+0q/11rP0aikUd32vK7uOYq0Wlj8g+Mpqm7Eg38ClF9Wz/eeW01ZnZ1UrX/UI5QciYiISPuskZA42P+YyQy2GHDUwfhzjv4Zs78Dq/7i21/3H6gtgvk/AE14l3bsrvRPjiItkQyKG9RtzzvQbAHYf3+xj8UbDwdcMygpmh+eOpooW2hVHs1mE1mJgesbZSdGs/qe03C63N55TtK9lByJiIhIxy34PzjmFtj3GWROOvr7pY8Fa7SvfHjeJ8YreyqMOPHo7y/9ktPt9FaqazI8cTiW7ig9f0RhVYN3+7NdpW1el50YxTXzc7vkmVaLGatFs2F6gpIjERER6RxrJJTnwVd/8z8enwVjO7E20ogFsGNJi4PqNZK25Vfn43A7/I6NSOy+YgwAC8ak8+H24qDXrdlf7rcG0tiseMZnJ3RnaNIFlByJiIhI57idsPhHrZ876xGYc33H7nf5C1C+Fx6fBR6Xcez9X8J3PzyqMKX/am2+0cik7ivGAPCtY4dz7rTB1DY6A87947M8nvnM6Ml6Y10Bb6wr8J6zmE28e+vxjMkMXnxBwkfJkYiIiHQ9e23H25hMkDDYWDupaXhdVUH7bWRAa7VSXTcnRwApsRGkxEYEHG+v6pzL7eFAeV2HkqNnP9vLxoNVpMVF8N0TRqgoQw9QciQiIiKdY7bCBU/79j+4Dyrzje0xZ3TuntZIOPluWPozY7/mMOz+0FhvSaSF5j1Hd8+7mzpHHZNSu2AOXCddMmsIiTE2Dlf65iW9tuYA6w9UApCZEFh0oT2f7irl/a2FAHznuOFdF6i0ScmRiIiIdI7ZAlMvM7ZriuB/NxjbqaONAgudZWtRHnnX+0qOpFVNPUdWk5ULR1+IzWwL0qJ7mUwmTp/ov4Dx8m2+NbsGJXas9HdpbaN3O7mVnirpekqORERE5Ohtewea1miZcO7Rld+echm8c7tvf+ubcOBr3/6g6bDw12DRx5iBzOl2kleVB8CwhGFhT4zacqjCGCIaaTWTFNOxGEtr7AAkxdiwqVpdj9CfsoiIiBy9hMEw4iQwWWD8oqO7V2SckQA1qdgP+V/4Xl/+GfZ9enTPkD5vf/V+nG6jKEJPzDPqrKYhdtmJUZg6+KVBaY3Rc5SmuUY9RsmRiIiIHL0xC+Hq1+HHuyB72tHfb8plYG1nfoYrsFKYDCzN5xs53U42l2ym3lnfToueV93goPpIVTuHy8OfV+xmXX5FSG3r7S5q7UbVxlQNqesxSo5ERESk68SkHN2Quibzvgd3HYKflxmvn+7Hb82j5sPuZEBqXqnug/wP+MY73+DTg72rR7Gwyjdn6GBFPb9dso1Ln1pJWa09aNuSGl9b9Rz1HCVHIiIi0juZzUbRB7PF6EWKTvKda6iEom3gdoUtPAmvneU7A46NThodhkjalpEQGTDPyO5yU93gaKOFT2mzBCo1Tj1HPUXJkYiIiHReYw3s/aT7kxRrpFGEoUlDBTw5F/56shKkAaplchRliSInPidM0bQuIcrGez88gb9ePYvhabHe46GU9C6p9vUcpcaq56inKDkSERGRztu1DP55DjwyBja81L3Pam0O0qH14Nb8o4Gm0dXI/ur9fsdGJI3AYraEKaK2ZSZEcdqETOxON2AsIBtlCx5n8zLe6jnqOUqOREREpPO2vGn8f10JxKZ377MmnA+n3gtjzvQ//vgsePMH4HZ37/Ol19hTsQe3x//vu7cNqWvO7fZQWGVUrausd3Dsbz7g+n99TW1j24n9sNRYvjE7h1PHZzIqI66nQh3wtECAiIiIdI6jAXYuNbajkyH3uO59nsUKx/0Qtr4NO949ctADlfthzT/hmFsgfUz3xiC9ws6KVuYbJffe5KjR6cbpNtYBc7k9HKyo52BFPV/uLeXkcZmttpk3IpV5I1J7MkxBPUciIiLSWXs+BHuNsT32LLD00CKcIxbAqFMhNsP/eGNVzzxfwq4vFGNoLjrCwo0LRpKdGEWUzffxOyZC/RS9jZIjERER6Zytb/m2x5/bc8+NjIdvvgpXv+F//I2bey4GCau8qryAY3Z38PLY4fTTM8ex8s5TOG1ClvdYVgiFGaRnKV0VERGRjnM5YNs7xnZEHIw4sedjiM/y32+sgR1LW7/WYoWcuRAR2/p56VOmpU9jRf4Kv2PxtviwxNJRhZUN3u0th6qotTuZOCgx4Dqny43Von6MnqbkSERERDou71OjnDbAmNPBFoZvwGNS4PQH4b07jf2qA/D8JW1fP2Q2XPd+z8Qm3eo7k7/DSTkncfk7l1PnrANgTErfmG92qKreu33Tf9YA8MQVMzh7SrbfdfMe/AC708WEQQm88N1jejTGgUzpqIiIiHTc1jd92z05pK6lyA5U8SrP67YwpOcNTxyOyWQCYFDsIOIj+kbPUUoraxZtO+w/X87t9lBW20hVg5PaRq3j1ZPUcyQiIiId43YZFeMSBkNdqVEcASB/Faz8k7GdORmOvwPM3fw97LQrwWyFygOtny/aApv/Z2zXFsMvA4cvYYuBk++BY27qvjilyxXUFlDrqAVgTHLf6DUCeOKK6byz4RBf5ZXx/tYiADLi/ROminoHR4rbkaY1jnqUkiMRERHpmLI9UFsElz0Hu5b7em+qCmDLkSIJW96AQdNg9GndG4vZAtOuaPv86n/6kqO2OOpg3fNKjvqYHWU7vNu9uYx3S0OSY7hhwUjsTrc3OcpsUZihpMa3AGxrPU3SfZQciYiISMfEZ0P6OKOU9tmPtn1ddy8KG4pJF0LeJ0ZC11JNsbFGEkB1ATx/mbFtizHWTBoys+filA7bXrbdu91X5hs1d7jKV5ihZXJUXO1LjtLjlRz1JCVHIiIi0jGRcfC9leB2Gj03TYbM8m0nDYPsqT0fW0uR8XDR31o/98H98PFDxnZdKexY4jtXeQCuW9b98UmnuD1u/rbJ9/fal4bVNSms8iVAWYltJ0cth9xJ91JBBhEREek4sxmsLeZC7P7Atz3hPDgyWb7XGrEAbG2U9j6wCux1PRuPhOxgzUEaXUYCYcbM0PihYY6o4wqP9ByZTZAW558AFVX7epXUc9SzlByJiIhI19jSbFHWCeeHLYyQ5R4HP9kLP95jvOb/wP/8sp+HJy4JanPJZu92clQyVnPfGwzVNKwuPT4Si9n/iwT1HIVP33sniYiISO9TXw57Vvj2B8/wbVcdgm1v+19vthrFGhKH9Eh4bbJGGi+A5GH+5yoPQOVBY7HZ5sMHJey+OvyVd3t44vAwRtI5DpfbW3QhKyFwjbAizTkKGyVHIiIicvR2f2DMQQJIGuo/pK5sDyz+UWCblBHwg7U9E18oZn4bVj8Lhzca+zveNV4pI+C7KyCqlTLgEhZbSrd4tyenTQ5jJJ1TUtOI50ip7pbFGEAFGcJJyZGIiIgcvZg03/Zxt4XWprGme2LpLLMZsqf5kqMmZXuMBWR7Q4EJAWB/9X7v9pzsOWGMpHMOV/rmFLUsxgDwy3Mnkl9WR0lNI3GR+rjek/SnLSIiIkdvxAK4bjmU7oZh8/3PpY2GC542tvd9Cmv+ZWyPPbNnYwzFgp8YQ/5qi/2HAr72XTDbjO3UEXDu4+pJCpM6Rx1V9irv/oTUCWGMpnMKm5XxXrzxEKv3lXPGxCy+f4qxXtOYzHjGZMaHK7wBTcmRiIiIdI0hs/zLeTeJy4CpR9YQal4ue+L5PRJWhyTlwKLHjJ6i5slR8TbfduFGGHkKzLymp6MTYEe5b/HXSEskKVEpYYymc6oanN7tkho7JTV2NhdUcdUxw0iKiWinpXQ3VasTERGRnuGohx3vGdvRyZB7fHjjaU/CEKMcuTUarFHGq7kDq/BOGpEetbpwtXfbZrbx1w1/pbC2MIwRddyCMemMz04g0ur/UTzSqsIf4aaeIxEREekZJTvAYgMHMO5s8LihaGv7bVJG+KrJ9SSLFS79l/+xR8ZAzZEP4Wufg3Hn9M6hgf3c2iJfEY8aRw1/XPtH1hat5clTnwxjVB2TmRDFu7caXw4sePhD9pXWkRBlJTrCQlFVA6vyysiIj2J4WqwKMvQwJUciIiLSM7Knwo93wd6PjaF2Ffnw5Lz228QPgps+N3qawi11tC85AijcBDGp/tck5kBCds/GNcDcMOUGVhasxO62e481LQjb13g8Hm9xhqbCDGvzK7jleSMB/NHCMdxy8uiwxTcQKTkSERGRnmOxwahTjO2SXcGvry4w1knqDcnRVa/BrzN8+x/8Gvh1i4tMcO27MOyYnoxsQJmcPpn3Ln6P13e9zh/W/AGAMcljwhxV51TVO2l0ugFfSW+V8Q4vJUciIiISHpHxMP2qwOOOetj0irEdmwHpY3s2rrZYIyE+G6oPtXORxyjeoOSoW6VFp2Frqh4IjE3pJe+RDjrcrGpdRnxgctR0THqOkiMREREJj/hMOO9PrZ878aew5Q2jp8nciyapX/4CbH4NXE7/4+v/C/VlxnbucT0f1wC0vWy7d3tsct9MjpqX9H5rfQHvby2k3u7yHvv+f9diMZv82kTZzNx6yhiumDu0x+IcSJQciYiISO+TNhpO+FG4owg0aJrxaq6hElb9xdhOHg6po3o6qgHj4wMfU9lYyZjkMWwrM8qrW01WRiaNDHNknVPv8CVCdpcbe73b73xNo7NlEyrr4ZnP9io56iZKjkRERESOxu4PwH3kQ+yYM8Bkav966bTntz3PZwc/A8BiMnoUcxNzibD0zbWBjhuVxtmTs9l6yLeo7cGKeu88pOGpMZiOvJ/cHg95pXUAxET0ot7UfkbJkYiIiPQujTXwhymhXXvFS60vPNuTdiz1bY9ZGL44BoCmoXRxtjhqHDVA351vBBAbaeWJK2f4HZv/4HIKKhtIi4vgwx+f5D1eWtPIzF+/D0BqbN9MBvsCJUciIiLS+9SVhnadO3DYUY9yu2FnU3JkgupC2PRqKxeaIGcOJA7pyej6lZL6EkrqSwDIiMmgptJIjhwuB18f/pqZmTO9vSx9lcfjobjGKMiQFudfqa6kxle6vOU56TpKjkRERKR3MZmMuTuhCMcCsc0d3gB1JUd2PPD6jW1fG5UEP9wAUYk9EVm/07wAQ7Q12ru9dN9Slu5byn3H3sf5o84PQ2Rdp7LegcPlASAjwb9SXWmNr4pdqpKjbqPkSERERHqXiFi4dV3w6xqrISKu28NplzUKTGbwuINf21ABdWVgDvLxyxoNZnOXhNefNBVgABgcN5jNpZv9zh+oPtDTIXW5qnonmQmRlNTYSW/Zc1TbvOdIw+q6i5IjERER6Zte+Q6U7oIJ58IJ/wcRMT0fQ8Y4Y9HXg2taP19fDh8/5Nv/47Tg90wYAte+A8m5XRFhv9G85+g7k7/DyUNP5n87/8eXh78EYHTy6HCF1mWGpsbw5V2n4nZ7vEUZmpQ0W/9Iw+q6j5IjERER6XsaqmDPh+Cyw/oX4eSfhy+WofOMV2uKtvonR6GoOgD7Vio5amFbudFzZDPbGJ08mgmpE3h/3/ve8311raPWmM0moltUpCutbT6sTj1H3UXJkYiIiPQ9O94zEiMAPPDKt/zPz70Rhs337Zftgfd/Gdq9z3sCIuO7IEggfRyc8nPY81Hwaw98DY5aY/vzx2Htc75zaaPgjN+ALbr1tv1cnaOOvMo8AEYljcJmtgH+Q+1+ufKXLBiygGsnXRuOELtdSbVvWF1qrHqOuouSIxEREel79n3m264+BFve8D8/bpH/fn1F4DVtOeexo4nMn8kEx99hvIJ5aIQvOSryn0/Dvk9h2LEw5dKui60P2VWxCw9GoYKm0t1uj5uC2gLvNasLV7O6cDULcxcyOG5wWOLsTs17jtLi1XPUXTTbT0RERPqe4ccHL2zQ10y5DEztLO65aznYa3sunl7m2MHHkhKVwoTUCQCYTWbOHXkuJvzLd8dHdFGvXxj8cflObn1hLQ8u3kpVg8PvXFMpb5MJUmKUHHWXfvZbRURERAaESRfB6IXG3KPWRCf772dOgtu2hHbvcJXaPuNBOO1X/pXv/jgdqg4a2xtegJhUOOOB8MQXRlPSp/DUqU/h8XhweVze4/cdex/3zLuH4144jnpnPYPjBpMQkRDGSI/OpztLWJVXBsAPTx3jd67kSCnv5JgIrBb1b3QXJUciIiLSN0XGhz43yBoBiX1gqJXF5r+fmONLjsA37G6AMplMWE3+H18Lagqod9YDMC5lXDjC6jKHqxoASIiyBhZkONJzlBqrXqPupLRTREREZPeHcHgTeDzhjsTflS9Dykjf/ogTwxZKb9VUxQ5885H6Io/H402OshL9F4Ctszupdxg9ZqpU173UcyQiIiIDm8cD79xuVLRLHQXf+xysvaQaWFSCrzfJZB6QyZHdZcdqtmI2tf6dfvP1j8Yl992eo4o6B/YjaxtlJvgnR80r1WmNo+6l5EhEREQGtqKtRmIEEJ/dexIjgMoDUOzrGeHpE/3P58yD8/4UOByvH3l156s8tvoxxqWM49YZtzIjc4bf+eblvMenju/p8LpMYXWDdzurZXJUqwVge4qSIxERERnYtr3t2x53TvjiaE3zxMjjhvI8//PleTDr2zB0bk9G1aO2lG6hzlnHmqI1WMyB1fyakqPEyEQyYzJ7Orwuc7iyWXLUYlhd03wj0Jyj7qbkSERERAa2rW/5tsedHb44WpMzD0adBgVr/Y/XlRzZMEHqyIBm/cnmUmPNJ4vJwthk/zlFJfUllNQbfxbjksdhMpkC2vcVhVW+5ChgWF2Nr+fo010llNbamZaTxPnT+0CRkT5GyZGIiIgMXOX74PAGYzt7GiTlhDWcAJFx8M1X/I9VF8KjR8o8Z0+F2LSej6uHNDgb2FNhDHkckTSCKKt/0tB8vlFfLsYAcLjSlwC1TI7K63w9R1/uLePLvUa57+FpsUzNSeqR+AYKJUciIiIycG17x7c9vpcNqWvLng9924lDjMVhWzJbYPDM0Eud91Lby7d71zWamDox4Hzz+Ub9pYw3BM45mp6TjNkE7hbFFD/bXUJVg4OJgxJJ0XC7LqHkSERERAYuv/lGi/zPle4Gl502xWcFLjbbE/as8G1ve9v/Z2gubQzcvAr68FCzLaW+hXsnpE4IOO9Xqa6PJ0fHjkrFYjZ6kAYl+SdHx4xM5fOfnsLhqgb+8P4OPtxeDMBDS4yfPy0uko9+fCKxkfpof7T0JygiIiIDU20p7F/p209vMSzrhSv8CyK0ZLbCt96BofO6J742nxtYlKBVZXuNIg6mEK/vhYIlR1vLtgIQYY4gNzG3p8LqFudMGcQ5Uwa1eT4rMYqsxChGZ8Z7k6MmJTWNHK5qYGR6XHeH2e8pORIREZGByV5jJA8AkQkd72FxO+HQ+p5Pjk67DzImQENl4Lnqw7Dmn0fic8CfZsEVL0Ha6J6NsYu0V4yhzlHHvqp9AIxKHoXN3H/LmTd36ymjyU6MorzOwWtrDnCgvB6AlBgNq+sKSo5ERERkYEoeBhc/YwxTS29lSNb4c2HI7MDja//t2849rtvCa1NMChxzc+vn8j71JUdgrN+0Y0mfTI6aF2MYmTQyoBjDzoqdeDAm4YxP6bvrG3VUbKSVa48dDsCH24o4UF6P2QSJ0QMjOexuSo5ERERk4Jp0kfFqzck/a/34cbcZ83wOrjZ6cHqTnHkw+zr46m++Yx8/AlmTYcSJYQurM3aU7/AWYwg236ivV6pzuNw0Ot3EdXDOUFmtMScuOSYCs7nvzi3rTZQciYiIiHRE6kg49tZwR9E6ixXOfhTyv4TDG41jDRXw+k1w+5Z2m/Y2k9Mm8+6F77KldAsZMRkB55vmG0HfL8awPr+Ci59aSVyklRtOGMH3Twmtp68pOVKluq6j5EhERESkvxm90JccAUSnhC+WTjKZTAyJH8KQ+CGtnm/qOTJhYkzymJ4MrcsVVhlrHNU0OrFZzSG1qbe7qHcYPWt2l5sXv9pPbKSVU8ZlEh3Rd4twhJuSIxEREZGOWPx/sKnFwqzp4+Cy54z5QL3BKT+HxBx4+4fG/siTwhpOV3O6news3wnA0IShxNpiwxzR0WlvjaO2lDVbGHZfaR0/edVIhq+YO5QHLpjctQEOIKGlpiIiIiJisNdAXan/a99nsPuDcEfm7+Bq33Y/S47yKvNocBkJRcsqdn1RYbPkKDPE5Cg5xkZ8VGA/x77S2i6LayBSz5GIiIhIR8SmQbJRLYyKfb5y4Fm96Nt6jwd2f2hsW6Ng6DHhjaeDtpVt483dbzIlbQqzsmaRFp3md775fKPWijX0NYcrm/UcJYaWHMVEWHnrluNYtbeMouoGHlm6AzCKM0jnqedIREREpCNO+xXcug6+s8x3LHV04CKy4VSyE6oOGNvD5oMtOrzxdNDKgpX8e8u/+fHHP+aj/I8CzjdfHHZ8at8v433Yr+coMuR2uWmxXDo7h+NGp3uPpao4w1FRciQiIiLSGTve9fUajT8nvLG0tOdD3/buD2D5r4zepD5iY4mvmMTk9MAeuebJ0cNfPcxdn9xFvbO+R2LrDk3D6uKjrMREdHxgV3mtb/5RspKjo6LkSERERKQztr7t2x7Xy5Kjwk3++588CqW7whNLJ2wo3gBAjDWGkYkjA84frDno3d5VsYu39rzVag9TX+DxeLzD6kItxtBSWbPkSGW9j47mHImIiIh0RtroI+WyPTBoBuxcBmv/DWljYMFPwGILX2yTLoIdS6HmsLFvtkF8dvji6YCiuiIK6woBmJg2EYs5sCz1xWMu5plNz/j1FqVGp/ZYjF2pst5Bo9PogWw53+jXb2+hoNL4GW0WM5fOyuHYUWkB92ieHGnO0dFRz5GIiIhIZ5x+v7Gw6nXvg9kMG16CLW/Axw/D1jfDG9uIE+Hb7/r2h86DyLiwhdMRG4ubDalLa73IxY1Tb2TVlauYkj7Fe2xsSi+a89UBTYnNnOEpXDTDf02nT3aWsHjjYRZvPMwb6wq49YV1rd+jTj1HXUXJkYiIiEhnmUyQeOQD7caXfMdj01u/viftWu7b7kOlvJvPN5qSNqXN65xuJzvKjAptOfE5JEQkdHts3WFwcjTjsuKJsJg5f/rgdq/1tDFvrFw9R11Gw+pEREREjpbDV22MqCQYOj9soXjtblaUYeQp4Yujg5onR5PSJrV5XfO1jvpyOe9Iq4XFPzieynpHwLl/XzcHp8vDCQ99iNPtIT2+9Up2mnPUddRzJCIiInK09jYrBjD2LLCE+ftnlwP2fmxsx6RBVts9ML2Jy+1iU4lRTCIjJoPM2Mw2r91S1qycd0rfLudtNptarTKXER9FTIQFp9voMWorOSpvNqwuKSaMc936ASVHIiIiIkdr61u+7fGLwhdHkwNfgb3a2K4rgWcWQvXh8MYUgj2Ve6hz1gHtD6kD2FrqWwj2rxv/yrmvn8vaorXdGl84FFc3ereD9RzFRliIsgUWsJDQaVidiIiIyNFwu2D7Yt9+yvDwxdKkZdnuA1/Btndg9nfCE0+IYmwxXDvpWjaVbGJ21ux2r22qaAdQ66hlb+VeXtv5GtMzpnd3mD2qqFlylBHvX83u31/sw+PxUFBxpKKd1cy/VuYF3OOksRnkpMR0a5z9hZIjERERkaNRsA7qSn37JTshI8zDvMadA5tfh93NijKEO6YQDI4bzO0zbw/p2ivHX8mB6gMcqj1ERWMFANmxfaNceUc09RxNHZLIsFT/BOfeNzd7h9wBVNQ5+PkbmwPu8fdropUchUjD6kRERESORlQimHvZ980xKXDlyxCdYuxHJsCQ9nti+pqZmTN5adFLXDD6Au+xiakTwxhR9xiWGoPJBNNykrhsVk64w+n3etm/ZBEREZE+Jm0U3PgpHNpg7A+aDi9eZSQo4xcZleJMpp6Pq2Ad1JcZ2yMWhHdR2m60pcRXmKEvV61ry/Shybxx87F4PEbhhuYeuWQqhysb+M2SbQAMT4thwZh0Iq0WRmXEYbUY108YlIDd6SbCqn6RYJQciYiIiBytjPG+YWvVhUcKNHhg30q4ZVV4Ymo+pG7UqeGJoQNK6kuoc9SRE5+DKcRk0uPxeKvWpUenkx7TC9aX6gZThiS1evz86YP5Kq/Mu7+3pI69JfsAuPqYYfzqPF8p9HkPLKe8zs7I9DgW33p8t8bblyl9FBEREelK298BjswDCWflul3v+7b7wDpHb+1+i7P/dzYnvHgCnx38LKQ2B6oPUH2kKl9/7DUKRVpcJOZWcslth6v99qsaHDQ63Thc7h6KrG9Sz5GIiIhIV9rypm97wrnhiaG+3KhQB5A2FpJ6/1yVpsVfKxoryIjJCKnN5jJf8YGBmhwNT4vlxRuOYc2+cirqHfx5xW4Akputd+RwuamzuwBIjO6fwyu7inqORERERLpKXRnkfWJsJw0N3+Kre1aA50gPQR8YUgewodiYsxVjjWFE4oiQ2mwp7d/zjUI1OzeFGxaM5Jwpvmp9Kc0Wla2sd3i3E5QctUs9RyIiIiJdZcd74HYa2+PPDU8hBvAfUrfrffhXi/LOiTlw+v1Gpb1e4HDtYe+6RZPSJmExh7aQqZIjf+W1viQoOcaXHFU1S47Uc9Q+JUciIiIiXWVrsyF148M0pA6gYL1vu2S78WopYwIcc1PPxdSO9cW+eKemTw2pjcfj8SZH6dHpIQ/F68/K6+ze7ebJkV/PUZQ+/rdHfzoiIiIiXaGxBnYdqRAXlxnedYUmngfFW329WK2Jz+y5eIJYV7TOuz0tY1pIbZoXY4i2RvPvLf/2nou1xXLqsFNJiEjoyjB7Pb/kqNmwuqoG3/tAPUftU3IkIiIi0hV2LQNXo7E97hwwh3Fq9wk/hvk/AJfD//jTJ0LpTjCZYcRJYQmtNU3zjQCmpIU2T2t7ua83bH/1fh766iG/86sOr+I3x/+mawLsI5oPq0uJ9SVBmnMUOiVHIiIiIl1h9we+7XCW8G5ijTReTSoPGIkRGBXs6suNV3uikiA2tdtCBGh0NXrXKspNyCUpKimkdtmx2Zgw4Wkqm95CZWNlV4XYZzTvOaq3u8krqSU9PtJvzpGSo/YpORIRERHpCuc8BlOvgB3vQu5x4Y4m0O4PfdvFW+HxGSE0MsGix2Dmt7opKKOogvPI8L/mQ+qcbicnv3QyADkJOTx24mN+i7xOTJvIy4teZm/lXu+xtUVreX7b8wCMTxnfbTH3Vs2To5ufXwNAtM3CpbN8pdw1rK59So5EREREuoLZAsOOMV69UWN18GsCeGDf592aHBXUFABwyZhLOD33dL9z5Y1Gz1Z5cTkrDqzgkjGX+J0fmzKWsSljvfs7ynd4twdi9bpIa+BQznqHi+QYG//69hyqGhxMHZLU84H1IUqORERERAaCmd+C2mKoOhj82kProXibsT14VreGdcKQE1gwZAGD4wYzN3tum9flxAdfyHZr2Vbv9vjUgddzdMfCscREWKmsd7CzqJpNB6sAyEmJ4YQx6UFaCyg5EhERERkYImLg1F+Edu2/L/AlRyNO7LaQAOIj4vnTKX8KOG41WxmXMo5tZdswm8xMTpvc7n2al/ZOjExkUOygbom3N8tMiOKX504E4LH3d3iTo+RYDaULlZIjERERkaOx/V34/HGYeIHxik0Ld0RHx9FgDKUDMFlg9bPexWxNOfN7LIxaR613mNzopNHE2mLbvb6wrpCyhjIAbGYbj3z9iPec1Wzl9NzTB9RQu/Ja3/yjpGZrHkn7lByJiIiIHI1Nr8K+z4xXci6MPi3cER2dwxvB2WBse1zwxRPeU5aVTxA98dEeCWNTySbcHjcQ2sKwuyt2e7dL6kv415Z/+Z1/a/dbfHDpBy2b9Vvldb4KdbuLaqhucJIUbWNqTlL4guoDlByJiIiIdJaj3ug5AohKhOELwhtPV0gcApGJ0EopbBMeUmu2Y8r7GCzNPkZao2DQdLB2XQ9FRxeGHZYwjGhrNPXO+lbPuzyuLoqsb2heue4Py3dyoLyepBgb636+MIxR9X5KjkREREQ6a9dysNcY2+MWdWlyEDYJ2fDDDVB8ZJFVew08d6H39Mx9f4F9fwlsN/YsuPy/XRbG+uL13u1Qeo6GxA9h6UVLyavK8x4rqivijo/uAAZeae+KIz1HZhPUNBil0lXGO7gwLt1seOKJJ8jNzSUqKoq5c+eyatWqdq+vqKjg5ptvJjs7m8jISMaMGcPixYt7KFoRERGRZjb/z7c98YLwxdHVopNg6FzjNWQ2WCKDNvEWcOgCbo/bmxylRKWEVKkOICkqiWkZ07wvm9mXDAy06nVNPUeJ0TaqGhzebWlfWHuOXnzxRW6//Xaeeuop5s6dy2OPPcbpp5/O9u3bycjICLjebrdz2mmnkZGRwSuvvMLgwYPZt28fSUlJPR+8iIiIDGx+Q+qSYEQ/GFLXmqgEuOZN2PU+LqeT3bt3MXLkKCwWM5Tugi1vGNeV7YVfZwW2t0XDSXfBnOtDfqTH4+E3x/+GdcXrMJvMmI4UhOio5qW9x6WM69Q9+qqmnqPEaJt3/lFClJKjYMKaHP3ud7/j+uuv59prrwXgqaee4p133uGZZ57hpz/9acD1zzzzDGVlZXz++efYbMZfbm5ubk+GLCIiImLYuQwctcb2+HPA0o8/eA6dB0Pn4XY42Fq/mOEnnYXFZoP1L/qSIzzQ2nwfZ71R8a4DyZHFbOH4Icdz/JDjjyrs5snRhJSBU6nO7nRT02gMpYuL8n3cV89RcGEbVme321m9ejWnnnqqLxizmVNPPZWVK1e22ubNN9/kmGOO4eabbyYzM5NJkybxwAMP4HINrAl2IiIi0gv01yF1HTHuLONnz5wU+Eoc6ruu+jD870YoWNuj4W0t9SVHv1v9O3676rfUNiW0/VhFs2IMsRG+5ChByVFQYes5KikpweVykZmZ6Xc8MzOTbdtaH7O6Z88ePvjgA6688koWL17Mrl27uOmmm3A4HPziF60vatbY2EhjY6N3v6rKWAzL4XDgcDhabdMTmp4dzhik79D7RTpC7xfpCL1fOslRh3XHEkyAJzoZ55D5MAD+DAPeL+YoOP+vrV5rWvNPrO8axRCoK4H1/8VdvB3XtUt7IlTqnfUU1hV699/f/z4AqZGpXDPhmh6JIVyKq+q825FW35DEuAhzj/5b702/X0KNoU9Vq3O73WRkZPD0009jsViYOXMmBw8e5OGHH24zOXrwwQe59957A44vXbqUmJiY7g45qGXLloU7BOlD9H6RjtD7RTpC75eOSa3eyrEOYwjZvpgprH9vYP35hfJ+ia9v5HhzNDa3b6hdXckBtv7nFxTFT8JpbX1R1wZPA5vsmxhqHUqaOQ2zqXMDnTweD8Otw9nr3Ot3fPnm5RTsKGCkdSRx5rhO3bu321UJTR/zy0uKaRosdmj/bhYv3tXj8fSG3y91dXXBLyKMyVFaWhoWi4XCwkK/44WFhWRltTKZD8jOzsZms2GxWLzHxo8fz+HDh7Hb7UREBJbPvPPOO7n99tu9+1VVVeTk5LBw4UISEhK66KfpOIfDwbJlyzjttNO886dE2qL3i3SE3i/SEXq/dNZZOGu+iXnrmwwZNIPBg2eGO6Ae0eH3i/NqXF//Hcty40vsOHsRs/OewD3iFFyXv9hqk88KPuPXK34NwFXjr+K26bd1Ot6zPGdR0lDCY2se4919RvGMTY5NbHJsYlTSKF4666VO37s3e29zIWwxqv0Nys5iY3kRALOnTuKsOaFV/usKven3S9PosWDClhxFREQwc+ZMli9fzvnnnw8YPUPLly/nlltuabXNsccey/PPP4/b7cZsNjLgHTt2kJ2d3WpiBBAZGUlkZGD5SZvNFva/pN4Uh/QNer9IR+j9Ih2h90snJA+B+TeFO4qwCPn9YrNBZmAJbXNtEeY22m8q2+TdnpI+5ajfl4MiBjEsaRjs8z9+uPYwNa4a4mxx2PpZMY1qu9u7HR1hJdpmod7hIi0+Oiz/znvD75dQnx/WdY5uv/12/vrXv/LPf/6TrVu38r3vfY/a2lpv9bqrr76aO++803v99773PcrKyrj11lvZsWMH77zzDg888AA333xzuH4EEREREWnPmNPhqtdhxtW+Y0Pntnn5uuJ13u1QFn8NxXcnf5dHFzzKD6b/wHusxlHDCS+ewIIXF7CldEuXPKe3KKv1FWQ4Y1IWW+87g233ncFpEzLbaSUQ5jlHl112GcXFxfz85z/n8OHDTJs2jSVLlniLNOzfv9/bQwSQk5PDe++9x2233caUKVMYPHgwt956Kz/5yU/C9SOIiIjIQOPxQCfX3RmwRp4Ee1b49nOPa/Uyl9vFxuKNAGTEZJAV2/pUi46yWWwszF3IgeoD/HHtH/3OVTuqWXVoFRNS+0+p7+bV6pJjjNFVUTZLW5dLM2EvyHDLLbe0OYxuxYoVAceOOeYYvvjii26OSkRERKQVtaXw52MgfRxMuwKym/VsxKZDbFr4Yuvt8j71bQ87ttVLdlXsos5pTJyfmj6104u/tmVI/BDumnsXnx38jB3lOzhUewiAnISem4fTE5oWfQVIjo2gweFif1nwggRxkVYGJUV3Z2i9XtiTIxEREZE+Y9vbUFNovPZ+5H/OZIZL/wXjF4Untt6ssca3xlHaWIjLaPWy9cXrvdvT0qd1SyiXj7ucy8ddzvfe/543ORqfEjgvqi9r3nOUFGNjd3ENZ//x03Za+Nx26hhuPXV0d4XW64V1zpGIiIhIn9JQ0fY5j7vHFzntM/K/BI/L2G5jSB3AmqI13u2pGV0z36gt28qMdTUTIhLIjs3u1mf1tOY9R0nRrRcta01uagxf7CntjpD6DPUciYiIiIRq9nVgr4Oqg75j296G+nJje+Qp4Ymrt2s+pC639SF1AGsKjeQoyhLFhJTumwNUUl9CSX0JYPQadfXwvXArP9JzFBdpJcJqJikmgstmtT900GSCH5wyWsPqwh2AiIiISJ8REQsn+Srp4nZBeR7kfQIJg2HoMWELrVfb95lvOzIB7LXGn2Uzh2oOeYe5TU2f2q3ltbeWbvVuj0sZ123PCZeKIz1HSTHGn+HgpGh+e/GUcIbUZyg5EhEREeksswW+9TZU7DeSJLNmLASw18HB1b79/1wMMalw0xd+c4/qnfWcOORE1hStYUbmjG4NqWlIHcD41P4138jt9njnHKXEhj6kTgxKjkRERESOVtJQ4yWB3I7AY3WlUJHvlxyNSBrB46c8jtvjxu6yB7bpQlvLfD1H/a0YQ1WDA7fH2E6KUXLUUfp6Q0RERES6T1QiXP4izL4eLEc+rFsiILP1OUVmk5koa1S3htTUcxRtjWZYwrBufVZP8yvjHRPa0MQdhdVc9peVXPfPr3hl9YHuCq1PUM+RiIiISGdUFxprG2koXXCjT4WMcfDVX439IXPAFp6J/9X2avKr842wkkdjMfevxVHLW1kANpji6ka+3FsGwJjM+G6Jq69QciQiIiLSGc9dBA2VMPkiOPnnSpKCaV6xrq4E3vyBd7cqNpXoY3+ILSqx28PYXrbdu93fhtQBlNX4kqNQ5xzVNDq923FRAzs9GNg/vYiIiEhnFG2Fwo3Gdt6nSoxC0XwNqOJtxuuIp1KSeOXg60zJnMnd8+4mNzG328JoXoyhP1aqK2vecxRqctTQLDmKHNjpgf4li4iIiHTUxld825MvCV8cfcngWW2eWhMVSb3HyZeHvyQpMqlbw+jPxRgAymp9yVFqiMlRrd2XHMVGDOzkaGD/9CIiIiId5fHAxpeNbZMZJl4Q3nj6iimXwPATjCF1Td65g9r8L9gaYXyIH5U0iqSopG4No6nnyGKyMCp5VLc+KxyaJ0ehDqurbtCwuiYD+6cXERER6agDX0PFPmN7+AK/ctQSRHym8QJwu6FoK+ujInGbTADMzJzZrY+3u+zsqdgDGKXDIy2R3fq8cGieHCVG23C5PZhNYDryZ9ya2kYNq2sysH96ERERkY7a+JJvW0PqOq9wEzRU8HWyrwjDjIzuXfx1Z8VOnB4jEeiPQ+rAPzk68w+fABAfZeWPl0/npLGtJ/LNCzLEDvDkSHOORERERELlcsCmV41taxSMPye88fRlecYH96+jfL033d1ztLXUN9+oPxZjAHB7PAHHqhucvLWuoM02Neo58hrYP72IiIhIR+x6H+pKje2xZxkLnErn5H1KncnExkgjOcpNyCUzNrNbH7mpZJN3e2LqxG59VrjcduoYPB5jqFxNo5Nth6sBiIlsez0nVavzGdg/vYiIiEhHrH/Btz31G+GLo69zu2DfZ6yLjMR5ZC7MrKy2q9l1lc2lm4H/Z+++46Oq8j6Of2Ymk96BJPSEDtJ7RxQUUVTsigKuqLsudtey+4jYdS1rWV27rh0LsqiAdERAOqL0HloILb1Ne/64MJOQHmYyKd/388qLc++ce89vwrjP/Djn/g6YTeY6O3PUrXk0//1TXwCWbD/KhA9WARBbxoawF3VJILFhGJl5diJD6nd6UL/fvYiIiEhlhMdDUBRYrND6PH9HU3ul/A556UWeN+qb0NenQ+bZ89h5cicAraJaEWoN9el4NcHJQs8fRZeRHI3t0YyxPaojoppPyZGIiIhIRY3+J4x8AvYtg7Ufld+/cXdo3sfXUdU+e38x/rBa3ad6x/t25mjbyW3uYgydG3b26Vg1xcmcypf1ru+UHImIiIhUhjUYgqNh1gPl9zWZ4S/LIa5uVkarslPJ0cupxzh88w/8QR6NQhv5dMhNxza523X1eaMzFZ05spbRU05TciQiIiLiKy4n2PP8HUXN4nTAvuVGO7QBjVsMpnEZe/B4y+nnjaA+zRzZ3O2yZo4y82yEBgZgMfv+76GmU3IkIiIiUlkxiTD2nbL7mC1gMhlL68QjZSPkpxvtxMHG76ganK5UF2AOoF1Mu2oZ099OFFpWF1PKM0cul4uuj8/F5YK+SbF8dfuA6gqvRlJyJCIiIlJZYQ2g27X+jqJ2OrWkDoA9S+HtoTDobuh8pc+GzLZlsyd9DwDtYtoRaKkfz9+kFU6OSpk5yilwcHprpADNHGkTWBERERGpRke3kW0ycW2TeJ4PcbL65FZY8KRPh9x8fDMujAygc4P6saQO4ES2sawu0GImLLDkfY6ytQFsEfoNiIiIiHhLzgkICILAMH9HUnN1u551+5ewOQg2n9rnqE9Uok+H3Hx8s7t9TsP6UYwBPDNH0aFWTKUsX8xUclSEZo5EREREvGXpS/BCW5h+O6Tt93c0NVPiIFb3v9l92Cc3D5KG+nTI088bQf2pVOdyuThxqlpdWcUYCs8chSk50syRiIiIiFc4HfD7N2DLhj++hVHP+juiGmtlykp3u3dePlhDYe8yT4eoZhDT0mvjnU6Ogi3BtI5u7bX71mS5Ngf5dqf7eOXu4wA0iw2laXSI+3xW4ZmjYKUG+g2IiIiIeMPuxZCVYrTbXQihsX4Np6ZKy0tjy/EtAHTILyDW6YQ5DxXvOP5/0Orcsx4vPT+dA1kHjPFiOxBgrh9ffwuX8d6aksm17/wKGEUXvv3LQLo1jwYgK0/L6grTsjoRERERb9g4zdPuqkp2pVmVsspdHKF/bhl7QKVu9cp4hTd/rS/7GwGEBVpKrD5nd7r4/WC6+zi7wF7kmvpO6aGIiIjI2crPgi3fG+3gaGPmSEr06+Ff3e3+bS4GawPPi5tnQFqy0W7WxyvjFd78tVODTl65Z20QHRrIx7f0Zcn2o+CC9fvTWLXnBACRIVZ3v6x8h7sdHmwtdp/6RsmRiIiIyNna8j3Ycoz2OWONinVSohWHVgBgNVvpccGLxvNGAC4X/Pal0Q6MgMbdvDLe78d+d7fr08wRwMDWDRnYuiEAL/60zZ0cxYQWSo6KLKvTzJGSIxEREZGztfFLT3vbLBjzStHXZz8Mv39V9Jw1FM59GHrc6PPwaor9mfvdz/90j+tO6OnECODYdshONdotB4Dl7L+mulwufjv6GwCRgZG0jPRekYfaJi3XsyFsdIinet2VPZvSOzGGrHw75zSJ9EdoNYqSIxEREZGzleKZnaAgp/jrBVmQc/yMk8dhxRv1KjlaedhTpa5/4/5FX9zzs6edOMQr4x3IOsCJPGO2pEujLphN9fdx+7RCBRqiC80cxUUGExcZ7I+QaiQlRyIiIiJn67z/g1/fAkcBBIYXfz2sIcQkGe3ck5CXZrSb9qq2EGuC0UmjSQhL4NdDvzKs2bCiL+5d6mnnpcP2uUa1uoDS9+gpz8ajG93tbo28s0yvtiotOZKilByJiIiInK3efzJ+SjNiqvED8NnVsGOu0a5nVe1CraEMbjqYwU0HF39x3wpPe+mLxp+D7oaRT1R5vNNL6gC6NaznydGpZXUWs0klu8ug34yIiIhIdclKhZ0LjHZUc2g5yL/x1CQh0Z5njk47uQ/yMyEookq3PD1zZMJEl0ZdzjLA2u30zFFkcACZpzZ+DQows3zXcfJtTiKDAxjQugEmU/Hy3/WJkiMRERGR6rL1R3CdKp3c5Wow199nYIq56TtjRm3L97BroXFu8wzjJ2ko3PhdpYo05Nnz2HZiGwCtoloREVi1BKuuSD+VHJ3MsdF1qjFzGR4UQHSolQMnc7FaTGx/6iJ/hlgjKDkSERERqS69JkKjDsaGsd2ug3lT4ORe47WAYOP1lgP9GKDvvPf7e8QGx9K/cX+ahDcp3iGqmbE08fguT3J02p6fIfcEhMdVeLzNxzdjdxkzJN3i6veSOgBzCRvCZuXbsTudAEQGW+v9rBEoORIRERGpPiaTUaa65QDjePcSOLzB8/qB1XDXer+E5kv5jnze/u1t8hx5xIfGM++qeaV/ER8w2ajsl3HQU8EuPB7CGlVqzCLPG9XzYgwAL1zVlWmr92NzusgtsLN670kA8m1GcpSVb+feaRt4aFQHEqLqb/U6JUciIiIiNUVEY39H4BNrj6wlz5EHwIAmA8qeoYhsDGPfguSVsOcC41zSUCOxrITCleq6Nuxa6ZjrmgvOSeCCcxIA2HMsm+EvLgbAder1fLuT79YfpFlMCPdf0N4/QdYAWugqIiIi4i/jvoau13mO62j1uuUHl7vbg5pUsAjF3qrve1R489dwazitoltV6vq6LrFBKBd1TijxtZwCRzVHU7MoORIRERHxl5AY2DnPaFuC4JzL/RqOryw7tAwwqsYV2/y1NHsK7XuUVLnkKCU7haO5RwHo0rB+b/5aEpPJxH9u7MXSB4cXe62+l/nWJ0VERETEX3bON56vAehwMQRH+TceHziSfYSdaTsB6NywM9HB0eVfZM+H/SuNdlRzzwa6FVTkeSMVYyhVdoG92LmIYCVHIiIiIuIPv33haXe73n9x+NDyQ54ldUGWIPak7yny+vHc40zbOo19Gfs8Jw+sBrvxjBKJQyr9vFHh5EjPG5UuM0/J0ZmUHImIiIj4g70A9q0w2mGNoPV5/o3HR9YcWVOkven4piKvbzmxhadWPsW4WePId+QbJ4ssqRta6TGLFGNopOSoNBm5tmLnwoOsfoik5qjfqaGIiIiIvwQEwj2/w/Y5kJ9ZqQ1Oa5MGIQ3KfP2vC/4KQHZBNi7XqdppewoVY6jk80b5jnw2n9jsPr76+6sJCQhhco/JjGw5slL3quscThcxoVbSc204T/3q6/vMUf1+9yIiIiL+ZA2us0UYTruz+530ju9Nen46AN0bdXe/djDrIE6Xsc9O10ZdCQ4IhoIcY1kdGM8aRTWr1HhHc45id3qWix3OPgzAR398pOToDKfLez/2vz/47wpjWWO4kqOK2bhxY/mdTunaVdOXIiIiIgJWi5WhzUpeGlf4eaSBTQYajf0rwXlquZfLCT/cB017QY9xFRqvSXgTrmx7JUsPLCXPkUdGQQYATcObVv1N1HHZhcp3R9TzanUVfvfdu3fHZDJ5pjvPcPo1k8mEw1G/66OLiIiIlMlhr7PL6CpjxaEV7rY7OTq41tMhbR+sed/4adQemvUu955mk5mpA6cC8OnmT3l+9fMAdI/r7q2w65ysQoUZIoL1zFGF7Nmzp/xOIiIiIlI2pxPe7AdxnaD7OGg/yt8R+YXdaefXw78CEBUURacGnYwXmnQHTMAZ/yBvCaz0GOtT17vbPeJ6VC3QeiAz31OYQcvqKqhly5a+jENERESkfkheDsd3Gj8F2fU2Odp0fBOZBZkA9G/cH4vZYrzQZgTcvw0yDsAHF4EjH0JiIb5zpe7vcrnYkLoBgNCAUNrGtPVm+HXCuz/vZktKBttSjL8HkwnCAi1+jsq/KpwczZw5s8I3vfTSS6sUjIiIiEidt/5TT7uO7m1UEcsPlvC80WkR8ZC+30iMAFoNA3PldqA5lH2I1NxUwCj2EGCu3zMiJfll5zGWbD/qPg4PCsBUyT2l6poKf0ouv/zyCvXTM0ciIiIipchLh00zjHZQFHS8xK/h+FOJxRgK273I0251bqXvryV15cvIK7rPUWaencSHfwQg0GLmliFJPDSqgz9C85sKp+BOp7NCP0qMRERERErxx7dgzzXaXa8Ga4h/4/ETh9NBgDkAi8lCq6hWJIQlFO+0e4mnXYXk6PSSOlAxhtJknirEUNJcUYHDybs/78bmcFZvUH6m+UURERGR6rLuE0+7x03+i8PPLGYLH476kIyCDA5nHS7eoSDHKOkNEN0SYhIrPcbpmSOzyUy3Rt3OItq6K/PUzFF0qJUOCZHk2IxJjm0pGeTZnNidLm7/ZC0mjOeRzu8Yz/V9W/gxYt+rcnKUnZ3NkiVLSE5OpqCgoMhrd91111kHJiIiIlKnHNkEh9YZ7YSup6qy1W+RgZFExkYWfyF5BThOfb+swqxRZkEmO07uAKBdTDvCrGFnEWXdlZFrzBw1DA/ii9v6u8+PfnUpmw8b+0Mt3JrqPj9/SyqD2zSkeWxo9QZajaqUHK1fv57Ro0eTk5NDdnY2sbGxHDt2jNDQUOLi4pQciYiIiJyp8KxRz/H+i6M22L3Y065CcrTx6EZcp0qBd2/U3Ssh1TU2h5PcUzNFkSFF9za6tk9znv5xCwUlLKlLz7XRvFoi9I/Klf045d5772XMmDGcPHmSkJAQfv31V/bt20evXr148cUXvR2jiIiISO1mz4eNXxptSxB0ucq/8fhRgaMAp6uc51gKJ0dJwyo9hooxlC+zyMavRedLJgxMZMNjI1n3qPFzSdfG7teCrXW71HeVkqMNGzZw//33YzabsVgs5Ofn07x5c/75z3/y97//3dsxioiIiNRuGYcg+tSzGp0uhZAY/8bjR9O2TeP8r8/n0WWPsid9T/EO2cch5XejndAVwhpUeozCxRiUHJUsPddTqS76jJkjgNDAAGLDAokNC8RVaD/ekDq+D1KVkiOr1Yr5VK35uLg4kpOTAYiKimL//v3ei05ERESkLohNgtt/htuXwtC/+Tsav/r5wM8cyz3GjJ0zSp5B2vsznFoSR6vKzxrZnXY2HtsIQHxoPI3DG5dzRf2UluOpGRAdGlhm39PL7wBC6/jMUZWeOerRowerV6+mbdu2DBs2jClTpnDs2DE++eQTOneu3O7FIiIiIvVG467+jsCvcmw5rDmyBoAmYU1oFdWqeKezfN5o64mt5J4ql65Zo9JFhli5rk9z0nNtdG4aVWbf3AJPclTXZ46qlBw988wzZGZmAvD0008zfvx4/vKXv9C2bVvef/99rwYoIiIiInXDisMrsDuNZ12GNBuCyVTCDjun9zeyBEKLAZUeY03KGne7d3zvKsVZH7RuFM5zV1YsWc8pNHMUFFClhWe1RpWSo969PR+0uLg45syZ47WAREREROoMpxMOb4ADq2HfshI6mKD1edBrQnVH5hdLDyx1t4c2G1q8w8m9cPLUc0jN+kJg5Utwn56ZAuidoOTIG/JOzRyFWC0lJ7R1SJWSoz179mC322nbtm2R8zt27MBqtZKYmOiN2ERERERqt2Pb4d3hEBgBBZkl99k8w3i2pgobndYmLpeLpQeN5CjIEkSfhD7FO+1c4Gm3Hl7pMRxOB+uOGHtJxQbHlrxsTyrt9DNHdX1JHVSxIMPEiRNZvnx5sfMrV65k4sSJZxuTiIiISN0Q1RQatC09MQIICIGQ2OqLyU+2n9xOao6xoWifhD6EBIQU77Rroafd+rwqjZFpM37XveJ71flZjrPhKlyCrhzu5KiOF2OAs9gEdtCgQcXO9+/fn8mTJ591UCIiIiJ1QlAE/HUVZB4uen7TdJj7f0a78xUQHFn9sVWznw/87G4PaTqkeAeHDfac6hMQAo4COL4LGrSu8BiFl9T1iu9V5Vjrgwe+3sjcTSlEhlj58rb+NI8NLbXv6WV1wda6/bwRVDE5MplM7oIMhaWnp+NwOEq4QkRERKSeMpuNGaTC+twK4fGw9iPoWU+eNzroed5oSLMSkqOD6yA/w2jbc+GDC432Ja9A75srNIaKMVRcWk4Bmfl2MvPt5S6XOz1zFBpYpdShVqlS+jd06FCeffbZIomQw+Hg2WefZfDgwV4LTkRERKROsgZD12vg5lnQop+/o/G5fEc++zONvTCTopJoHtG8eKdTVeyKSd1coTGcLidrU9cCEBUURduYtuVcUb8V3gQ2qoRNYE8rsDuxO40leFpWV4rnn3+eoUOH0r59e4YMMTL/pUuXkpGRwcKFC8u5WkRERETqkyBLEPOvms+m45vILO35q5YDYezbcHgj7FoAR7ca55tXLHncmbaT9Px0AHrG9cRsqvtLwM5G2qnkKCzQgtVS+u+q8AawwSrIULJOnTqxceNGrrnmGlJTU8nMzGT8+PFs3bpVm8CKiIiISDEWs4WujboyqGnx59YBMJmg23Uw6hkwnf4SbqpwYQYtqauctBwjOYoODSyzX16h5ChEzxyVrkmTJjzzzDPejEVERESkbvvjW9jyPfSaCIlDjeeRpKjMFEjdZLStITDnEQiJgUF3Q2TjUi/T/kYV53K5SM8tACCyjCV1ALkFhZMjzRyVaunSpdx4440MHDiQgwcPAvDJJ5/wyy+/eC04ERERkTpl1Xuw6Tv4+DI4uNbf0VSLypSMBuCAJ8nBlgMbv4SV/4HFz5Y5xtojxu8zwhpB+5j2VQm13si1ObA5jL+X6PKSo8IzR1pWV7Jvv/2WCy+8kJCQENatW0d+fj5gVKvTbJKIiIhICY5ug+RT+0Q2bA/N6sfsxktrXuLmOTfz8aaPS3/eqLD4cyA4qvj5oIhSL9mTvocTeScA6BHfA4u57n+JPxunl9QBRIdWPDkK1sxRyZ566ineeust3n33XaxWzy900KBBrFu3zmvBiYiIiNQZa//rafeaaDxjU8e5XC7mJ89nzZE1/Gvtv3BRgVmk2CS4fxvcuQ7aX+w5X8azR0WW1Ol5o3IVTo7KqlQHRZfVhWrmqGTbtm1j6NChxc5HRUWRlpZ2tjGJiIiI1C22PPjtc6NtCTIKD9QD209u52CW8fhF74TeRAZWcLNbawjEtoJD641jSyA07g72ghK7r0pZ5W4rOSpfWq7n9xhV3sxRPXvmqEoFGRISEti5cyeJiYlFzv/yyy+0atXKG3GJiIiI1B1bvofck0a702UQGuvfeKrJov2L3O3hzYdX7uKj2yDzkNF2FMALrYwk6YKnod9t7m5Ol5PVKasB43mjjg06nnXcdV3HhEg+mNib9FwbrRuFl9m3vi2rq1JydOutt3L33XfzwQcfYDKZOHToECtWrOD+++9nypQp3o5RREREpHZb876n3Wui38KobmeVHGWlFD/nKIAtM4skRzvTdrqfN+qV0IsAc5WLMdcbMWGBnNchvkJ961tBhip9eh5++GGcTifnn38+OTk5DB06lKCgIP72t78xadIkb8coIiIiUnul/AHJK4x2ow7GZqf1QEp2CpuPbwagY2xHGoeXXoa7RIlDYNA9xtK69ANwYpdxvnG3It1WHfYsqdt6Yis3z7mZhiENub/3/SSEJZzNWxDO3Oeo7idHVXrmyGQy8Y9//IMTJ07wxx9/8Ouvv3L06FGioqJISkrydowiIiIitVfhWaM+k+pFIQaAxfsXu9uVnjUCMFtg5OMwYSa0GOA53+b8It22ndzmbqdkp7DmyBrm7J3D51s/r/yYUkx9e+aoUslRfn4+jzzyCL1792bQoEHMmjWLTp06sWnTJtq3b8+rr77Kvffe66tYRURERGoXlwuObjfageHQ9Vr/xlONiiypa1GF5Og0lwt2LTDaASHQoujM2/ktzifcWvy5mbiQuKqPWcf9cTCd5buOsflQBgV2Z5l9cwolR8FaVlfUlClTePvttxkxYgTLly/n6quv5uabb+bXX3/lpZde4uqrr8Ziqfu/NBEREZEKMZlg4g/Ghq/HdkBwBau11XKZBZnuCnJNwpqc3aasqZsh87DRThwM1uAiL5/b/Fx+ue4XHC4Hdy28i2WHlgHQr3G/qo9Zx/1n8S5+/N34nS57+DyaRoeU2rfwsrrQejBzVKnk6Ouvv+bjjz/m0ksv5Y8//qBr167Y7XZ+++03TPVkilhERESkUkwmY8PX2Q/B3H8UfS0kFsa8Ynzpr0M2Ht2Iw2l8qT63+bln9z1x5wJP+4wldadZzBacTifrU43S3w2CG9Amuk3Vx6zjipTyLm+fIxVkKN2BAwfo1asXAJ07dyYoKIh7771XiZGIiIhIefLSIOd40XM5x2Hdx3UuORrUdBALr1nI4v2LOafBOWd3s12FkqPWJSdHAH8c+4Mcew5gzBrp+2npTmYbm8AGmE2ElZPw1LdnjiqVHDkcDgIDAz0XBwQQHl52bXQRERERAaKawanZFDIOGmWpAVr0919MPtQwpCFXtbvq7G5SkAP7TlX6i2oBDduW2vXXw7+62/0b183fqbeczDE+ezFhgeUmkdrnqAwul4uJEycSFBQEQF5eHn/+858JCwsr0m/69Onei1BERESkLhj/P+PP7OPw8qmNSoOi6lWRhkrbtwwc+UY7OxVe6w7BUXDhs5A4qEjXlYdXutt63qh0LpeLE9lGctQgLLCc3meU8tayuqImTJhQ5PjGG2/0ajAiIiIidd76jz1f+HvcCIFhZfevz1K3eNr2PDi512iv/E+R5CjHlsNvR38DoHlEc5qEN6nGIGuXnAIH+acq1MWElp8c5WhZXek+/PBDX8UhIiIiUj+cXiYG0OcW/8XhAwWOAq7+/mr6JvRldKvR9IjrcXY37HQZbJoOafuhIBvsucb5hu2KdFuXug670w5oSV15Ts8aAcRWYOZIy+pERERExHdumAb7lsP+X6FBa39H41UrDq1gd/pudqfvJseec/bJUUxLuG2x0f56Imz6zmi3GVmkm5bUVVylk6NTM0eBAWYs5rpf5ELJkYiIiEh1MpmMJWFnPDNTF8zdN9fdvqDlBd67scMOuxYa7eAoaNanyMuFk6O+CX29N24ddCLHkxzFVGLmqLyqdnWF2d8BiIiIiEjtZ3PYWLR/EQBh1jAGNBngvZsfXAN56Ua71XCweP59Py0vja0ntgLQIbYDMcEx3hu3DsrJd2C1GDNAFSnIkJ1vJEehgfVjTqV+vEsRERERf8s4BJF1t1DAypSVZBZkAsbGr4GW8r94V9iOeZ5226IzUqtSVuHCBUC/BC2pK8/FXRszuksCWfn2Ci2TyykwnuUKC9LMkYiIiIh4Q14G/LsvvDfC89xMHTNvnyeBGdlyZBk9q2BnoeSozYgiLxVeUte/iYoxVITJZCIi2FrubJDT6XJXqwupJzNHSo5EREREfG3jNCjIhAOrYfdif0fjdTanjQXJCwAICQhhUBMvPk+VeQQOG2W6SegKEfFFXl5x2Kj+F2AOoGdcT++NK0Uq1emZIxERERE5ey4XrHrHc9znVv/F4iNrUtaQnm88EzS02VCCA4K9d/Od8z3ttkVnpJIzktmfuR+AnnE9CbWGem9cIfvUkjrQM0ciIiIi4g27F8Gx7Ua75SBI6OzfeHygcJU6ny6pO+N5o18O/uJuD2wy0Lvj1lGvzt/ByZwCYsMCufO8NphMpT93lJNfaOaonjxzpORIRERExJdWvOlp9617s0Yul4sNqRsAY0ndkKZDvHfzwiW8TWZI3exJNIFle791twc3Hey9ceuw//12kN1HswkPCuCu89uW2ff080agmSMREREROVupWz0zH9EtoMMY/8bjAyaTiW/GfMPqI6tJzkj27tK2lN88JbxdTvjhXvdLBcDqls3AbKahOYh2Me28N24ddvLUJrAxYdZy++YUWlZXX545UnIkIiIi4iu/vuFp9/tLkf156hKL2UL/xv3p39jL1eKCIsFkAZej2EvrgoPINRuPzw8yhWHKSi1+fXAkWEO8G1Mt5nC6SMu1ARAbFlRu/+zCM0dBdfOze6b68S5FREREqlvWUfhtmtEOioQeN/o3ntqoYVu4bREc2lDspWW/vwNkADD48HZ4qYSZo4AQGPcVJA31bZy1RFpOAS5jSyhiQyswc5SvmSMRERER8YbV74Ej32j3mmDMYtQxDqcDi9nHX5obdzN+zvDL5v+AE8wuF/1z80q+1p4Le35WcnTKiVNL6qAKM0dKjkRERESkylqfBykbYcc86Hu7v6PxifuX3E+2LZvRSaO5pNUlWC3lz0Z4Q0p2Cjud2QB0NocR3W500Q4H10LmYaPdpEe1xFQbFE2OKvfMkQoyiIiIiEjVtegHLb6ArFQIj/N3NF6Xnp/OkgNLsDvt7E7bzaWtL622sZcfWu5uD+o6AbrfUbTDv/sYyZHJAomqYndapWeOVMpbRERERLyqDiZGAPP3zcfuNGYWRiWN8v3yukIK7280qOmgoi+mH/SU+w6OghVvACZjaV3iGX3rmRM5lZs5ytXMkYiIiIhI+WbtmeVuj04aXUZP77I5bfx66FcAIgMj6dzgjE11k1d42rknYMnzRnvpS3Df5jqbrFbEiayqP3NUX2aOzP4OQERERKROmf0w/PGtsYFpHZWak8rqlNUAtIxsSacGnapt7A2pG8i0ZQIwsMnA4jNWsUnGhrFnctpKPl+PtI4L5+IujRnQqgFNooPL7a9njkRERESk6g5vhJX/MX6ShsGEmf6OyCfmJs/FhVET+qKkizCZTNU29pL9S9ztYc2HFe/QtBfcuQ5O7Ibck/DtLcb5+M4Q1rCaoqyZRndpzOgujSvcv8gzR0qORERERKRSfn3T005eAf9sVfT15v3g+i+KnvvoEkjdXP69hzwAA+4ov181mLN3jrt9UdJF1Tr2kgNGcmQ2mRnSdEjJnWKTjJ8/pnvOtR5eDdHVHpP+u4Z1ySfL7JOZZ3O3Q1TKW0REREQqZf8qT9tRADnHi76el1H8mry04v1KYs89q9C85ajjKJvTjGSuY2xHWkW1KucK79mbvpe9GXsB6N6oO1FBUWVfsGuhp91KyVFhmXm2ItXrynPBv5ZgPjVDGGAxMX5AIn8d3sZX4fmNkiMRERERbzn3YfjlX2ArJZGJLGFJU2QzyM8q/97B0WcVmresL1jvbl/S6pJqHfv0rBHAuc3PLbuzywW7FxttSxC0HOizuGqjhKhgWjYILbPPkYw88mxOAE7m2Iq89sr87dwyOIlga92aUVJyJCIiIuItXa8xfirjhi/L71OQA4Flf5GtLhlOY/YrwBTAxa0urtaxfz7ws7s9rFkJzxsVdnwnpO832i0HgDXEh5HVPq9eV/7muCt3H+eZ2Vs5WWiGKSUjjwK7E5vD5cvw/KZ+l+wQERERqekyDsNL7WHmnXCkAs8m+dhVYVfxw2U/8MyQZ2gQ0qDaxs0oyGDdkXUANI9oTlJUUtkXFF5S1/o8H0ZWd/Vr1YDbh7aid2IM57ZvxAcTe9MxIQIAkwmCAupeKqGZIxEREZGabM0HkJ8B6z6GsDiIr76y2aVpEtaEltEtq3XM5QeXY3cZpaWHNRtWfoW8XYs8bSVHVbbxQDrT1x0E4KLOjd3L7IIDLNVapbC6KDkSERERqalseUZydNr+lfDVeM9xkx4w+N6i18z6G2QdKf/ePcZD2xGe46xU2PI99Lnl7GL2kcUHFrvbJZbwLsxhg71LjXZYI4g7x3eB1XGF9zoKC7KQZzfKewdb696sESg5EhEREam5DqyGnGOe49Nf+E+zl1BtbMc8OLmn/HsnFiqDvek7iO8CP95nnG/Urlj3Y7nHCDeHVzBw77I77Sw9YLz3cGs4veJ6lX3BgdVQcKrIRavhYK6bX+SrQ+G9jkIDA8gtMI5D6lghhtOUHImIiIjUVI3aQ0QTyDzkuzHs+fDj/dDpMrAElrpR6n2L7+NQ1iE6ODow0jESq9Xqu5jO8NvR38goMApBDGwyEKulnLGLPG+kEt5no9jMke30zJGSIxERERGpTuFxcM/vpS+TCwgufu5Pc8DpKH7+TMGn9gja+qOxz9KaD6DzlRAaW6zrvox9rE81Sni7zC4CzNX7FXJRsuf5oXKX1AHsnO9pa3+js5JdUHTmKM9uPHMUpORIRERERKqdJQCimla8f0RC5e6/9iNPu9fEErvM3DXT3e4Z2LNaH8R3uVwsSF4AgMVkKb+Ed1YqHDq1F1N8l5L3lpIKy8n3zBwFBZgpOJUchdTRZ47q5rsSERERkfId3wV7Tm2sGtuq6HNIpzhdTndyZDFZ6BbYrTojZPvJ7RzIOgBA7/jeRAVFlX3BjnmedtuRPoysfjg9cxQYYMbp8uxtpGV1IiIiIlK3rPvY045qZhz3mlCky6p175CSnQLAgMYDiMiJqM4I3bNGAOe3PL/8C3bM9bTbXeiDiOqX088c2R1OdxlvALvTxccr9pZ5rcPh4GSGMftXWyg5EhEREamv/vjW097zM6T8Xiw5+t9v78GpSYIxSZdg22SnOhVOjs5rXs5+RQ6bpxhDcDQ07e27wOqJ09XqRnaKJ9fmef4oJ9/OlP9tqsAdAui37ySD2sb7KELv0rI6ERERkfoqOLrMl9PT9jHXlAdApNPF0PKe9/Gy/Zn72X5yOwBdGnYhPqycL9j7Vxob5gK0GWE8ryVnpVszYxnj3y7s4K5UBxBgqVga0SzMRUp6vk9i8wV9YkRERETqqwkzYfdiY8YF4IwS2d+veJ4Cs1F84dKwJIIsQdUa3sJkT0nu81qUM2sERZfUtb3ABxHVP2+M68nibUeJiwxi/4kc9/lmMSFMGNiyzGuDzCYK9qzh4m61pyiGkiMRERGR+io0FjpfUeJLLqeTb1KWudcZXd3rzmoMzFDkeaMWFXjeaPvp5MhkzBzJWQu2WhjV2aiAWHjmKC4imLE9mpV5rc1mY9ZeX0bnfUqORERERKSY/AMr6ZeZTmp4GG1NQbRqfQE2m63axj+We4wNqRvcx3ctvAuAtjFteXrw04QEhBS9IC0Zjm4x2s16Q1iDaoq0/ihckCEksG4+naPkSERERESKCd7wBY+cOMk9J9M4NuTeoi867ZC603Mc3RICQ706/vYT23HhqXK2N2Ov+8+LW11cfCZJS+p8rvDMUXCASnmLiIiISH1gy3NXsgtxuWjuPKMUc14GvNnfcxwcDX9ZZpQD95Lucd0Z0HgAm44bFdEyCjLcr7WJblP8giL7Gyk58oXC1erK2+do+c5j/Gv+dk4ct2BqkcKlPZr7OjyvUHIkIiIiIkXZcsCW6zkODCu7f14anNjt1eQo1BrKOxe8A0BKdgojvzE2dG0b05aWkWcUArDlwu5Tm9mGx0NCV6/FIR6Fl9UFB5adHB3PLmD13pOAiZQMVasTERERkVrI5XIx/cAizrvibWL2LAWXs3iyERAIHS+FLTON45AYaN7PZzEVLswwssXI4h12Lwb7qWSu7QVgrpvPw/hb0WV1dfN3XDfflYiIiIhUyfrU9UxdMZXzNzzLO4md4bJ/Q7sLi3YKDIeWgzzH3a6HAN+V+Z671/M80YiWJVSh2/qDp93hEp/FUd/lVWJZXW1VI5KjN954g8TERIKDg+nXrx+rVq2q0HVffvklJpOJyy+/3LcBioiIiNQT32z/BgCb00bjsFL2p3G5YN1/Pcc9x/ssnmO5x1ifuh6AxMjE4s8bOR2wbY7RtoZCq+rdqLY+KZwchSg58o1p06Zx33338dhjj7Fu3Tq6devGhRdeSGpqapnX7d27lwceeIAhQ4ZUU6QiIiIidVt6fjo/7f0JgMjASEa2LGEJG2A6tA5SNxsHzfpCXEefxbQweaG7at3IliMxmUxFO+xfBTnHjHab88F6Rolv8ZoizxwpOfKNl19+mVtvvZWbb76ZTp068dZbbxEaGsoHH3xQ6jUOh4Nx48bx+OOP06pVq2qMVkRERKTu+n7X9xQ4CwC4tPWlBAcEl9jPvP5jz0GvCT6Nad4+TxW6cpfUtb/Yp7HUd0Wr1fk9jfAJvxZkKCgoYO3atTzyyCPuc2azmREjRrBixYpSr3viiSeIi4vjlltuYenSpdURqoiIiEid5nQ5mbZtmvv4qnZXldrXZQ4ASxBYAuGcsT6LKS0vjdUpqwEwYeK9398r3il5Hg0axPDntCwanPlslHhV4WV1L8/bTnSo1X3cICyI24a2onmsd/e7qm5+TY6OHTuGw+EgPj6+yPn4+Hi2bt1a4jW//PIL77//Phs2bKjQGPn5+eTne8oHZmQYNfJtNlu17vJ8ptNj+zMGqT30eZHK0OdFKkOfFzlt+aHl7o1We8X1okVYi2Kfi9PH+SOfwzr8/zAd+R2XKRB89PlZm7IWh8v4Qu7CVWQWyS0QCIwgOKIJd1kjfBaLgN3hSY6W7zpe7PXM3AJeuKoLYKz0Os3pcPj9f2MqOn6tKuWdmZnJTTfdxLvvvkvDhg0rdM2zzz7L448/Xuz83LlzCQ31f2Y7b14J/5GLlEKfF6kMfV6kMvR5kU+yPnG322W1Y9asWaX2LfJ52Vx6v7OV5kwjzBRGtiu73L4FjoZlxixnLzbbRJDZTL7TVOLrm/ceZNas/QCsP2YCjOeStm3fxqzMkic+qktOTk6F+plcLper/G6+UVBQQGhoKN98802RinMTJkwgLS2N//3vf0X6b9iwgR49emCxeB4AczqNB8PMZjPbtm2jdevWRa4paeaoefPmHDt2jMjISB+8q4qx2WzMmzePkSNHYrVay79A6jV9XqQy9HmRytDnRQCSM5MZ+/1YXLhICE1g5qUzCTAX/zd0f3xebE4bJ/JOlPja5OmXsMtszFDMPPctmjXpWy0x1Wc5BXbSc+3u44xcG5e8YTwOM6xdQ967qScAW1MymbnhIHv27mXiyF70a93IL/GelpGRQcOGDUlPTy8zB/DrzFFgYCC9evViwYIF7uTI6XSyYMECJk+eXKx/hw4d+P3334uc+7//+z8yMzN59dVXad68ebFrgoKCCAoqXnffarXWiP8nUFPikNpBnxepDH1epDL0eanfZuye4a4Id33H6wkJKrnim+ngWizO/Gr9vFixEhpUfLXPocPr3IlRB6eZpML7LonPRFmtRIV5jlMz8wq9aiKzwPgcNY0NZ9KQVix37KZf60Z+/9+Xio7v92V19913HxMmTKB379707duXV155hezsbG6++WYAxo8fT9OmTXn22WcJDg6mc+fORa6Pjo4GKHZeRERERCrmz93+TOOwxkzfMZ0r2lxRcidbLpYvr+HCggLMwevhomerN8gzzN/gKc4wIkbfA/3F7vAsQluy/Sg9nyy6RDcm0EKvwXm0aFg7/vHF78nRtddey9GjR5kyZQopKSl0796dOXPmuIs0JCcnYzbXzVKBIiIiIjVBmDWMGzrewA0dbyi906YZmPLSsQLOnOIP41e3+amr3O2R54zzYyT1W3hwAIEWMwUOZ4mvnywwsWBrKjcPjqjmyKrG78kRwOTJk0tcRgewePHiMq/96KOPvB+QiIiIiBS16h1309ljvF83yzx6bDsbXHlgMtHKAa3aXOTHaOq3yGArL1/bjRnrD+EsVMogJT2XzYczAci3lZw41UQ1IjkSERERkerncrkwmUquPFbEgTVwaB0AaSEtCWvm38IHC9b/B9epuEdEtYeKvAfxmUu6NuGSrk2KnJs6c5M7OVqz76Q/wqoSJUciIiIi9dT9S+4nMjCSGzreQLuYdqV3LDRrtKfRCDr7ORmZf2i5uz3SGQRL/gkWK3S8FBq0LuNKkbIpORIRERGph/Zl7HNvqrr80HLmXDkHs6mExXJZqbDpOwBcITEciBmAP8sfnMw+yhpXNphMNLPZaL/pB9j0g/Hihs9h8mo/Rie1nSodiIiIiNRDX2790t2+rsN1JSdGAOv+C44CAJzdb8RpDqyO8Eq16MASHKdmrkZm51JkDstVe55tkZpJyZGIiIhIPZNVkMWMnTMACLYEc2XbK0vu6LDB6g9OHZhw9ry5WuIry7z9C9ztEYP/AT1u8rzY4RI/RCR1iZIjERERkXpm+o7pZNmyALi41cVEBUWV3HHrj5B5yGi3vwiiW1RThCVLy0vj10O/AhAfGk/nnpMgLdnT4ZzL/ROY1BlKjkRERETqEZvTxqdbPnUfjz9nfOmdWw6E4f+AiMbQ97ZqiK5sc/fNxe6yA3BR0kWYc07A3qXGi9EtoXF3/wUndYIKMoiIiIjUI/P2zuNw9mEAhjUbRquoVqV3Do+DYQ/C4HvBHAB2ezVFWbLZe2a72xclXQRbvvc8Z3TO5SrpLWdNM0ciIiIi9YTL5eKjTR+5jyecM6FiF1qsfk88UrJTWHtkLQCJkYl0jO0Im2d4OnS63C9xSd2imSMRERGRemJ1ymq2nNgCQKcGnegd35v0/HSO5hwt1tdsMtMysiUWs6W6wyzRT3t/woULgNFJozHlnIA9p5fUtYAmPfwYndQVSo5ERERE6ol8Rz4tIlqQnJnMxHMmYjKZmLdvHo+veLzE/m0jE5l26XSsFms1R1rcrD2z3O2Lki6Crd+Dy2GcOGes32e2pKh28eHudoeECD9GUjlKjkRERETqiSHNhjCwyUB+PvAzQ5oNKbf/joy95KftxdqgbTVEV7q96XvZfHwzYMx4JUYlwqYZng5aUlfjRAR7EurIEP8n1xWl5EhERESkHrGYLQxvMdx9nBiZyBVtr3Afbzmwgi25RsGGIaYwwv2cGEHRQgyjk0ZD9nHY87NxQkvqxIuUHImIiIjUY70TetM7oTcALqeTa//b012y6+Yut/oxMoPL5XIvqTNh4sLEC2HrD54ldZ0u15I68RpVqxMRERERALJ2ziU2z9gctpPTQu9uN/s5IthyYgt7M/YC0Cu+FwlhCfDHt54O2vi1RjqZU+BpZxeU0bNm0cyRiIiIiAAQsepd3jpylG1WK3nn/x8ms///Hb3IkrpWoyHjsGdJXUwSNOnpp8ikLLuPZrvbO1Kz/BhJ5Sg5EhERERE4shl2zgegfVhj6DPZzwGBw+lwJ0cBpgBGthgJ6z6FUyW96XqNltSJV/n/nwNERERExP+Wv+5p9/8rWPz/b+irUlZxJOcIAIOaDiI6OBo2TvN06HKNfwKTOkvJkYiIiEg9l3rkdxbsnIkTIDgaetzo54gM3+/63t2+tPWlcHQbpGw0TjTpAQ3b+CkyqauUHImIiIjUc5/+8QH3xMVyabPG/N5tLASFl3+Rj+XYcpifbCzzC7YE0yamDQfXfUj66eegNGskPuD/+VIRERER8Zusgiy+PvIrAIcCg2nc+3Y/R2RYkLyAXHsuAHmOPC6bcRkAphZN+fuJNK7rfKU/w5M6SjNHIiIiIvXYtzu+JctmVBO7tM3lNGzU0c8RGY7nHi/xvMtkYm2D5hARX80RSX2gmSMRERGResrmtPHJ5k/cx+M7jfdjNEVd3f5qTuSdICU7BYBV+xZw3GXslzOk+bl+jEzqMiVHIiIiIvXUj6tec1eDO7fZubSKbuXniDzCrGHc1/s+AHLy0jlv9ywwmwhzOhnR9x7/Bid1lpbViYiIiNRDdlse727+yH38p441o0JdSeavfoVss7Gf0ajAOELDtaSupjMX2n7KVIv2olJyJCIiIlIPzf7lKZItRrsfwfRo0s+/AZXhu1MbwQJc3nGcHyORiurRIsbd7pMYU0bPmkXJkYiIiEg947AX8M6eme7j27v9xY/RlG3/kd9Y48oGINHuolu3if4NSOo0JUciIiIi9cxvq//NPrMTgN6uIPp0/5OfIyrdjNX/crfHxnTGZNEj8+I7So5ERERE6hOnk57rpjHj4GEuzsrmz11v83dEpXI47Pzv6DoALC4XY3rf7eeIpK5T6i0iIiJSn2yZCUe30gp4Lrgt9LjV3xGVauUfn3LE7AJgsCuYRi0G+Dkiqajk4znu9p5j2X6MpHI0cyQiIiJSXzidsOSfnuNhD0INriT23aaP3e2xSaP9GIlU1tGsfE87M7+MnjWLkiMRERGReuLQxk+xpW4yDpr1gdbn+TegMqRnHGRBQSoAMQ4nQ/vd5+eIpD5QciQiIiJSD7hcLu7a+DpjmjXh2/AwHEP/VqNnjWat+Ce2U/FdEpaINSTavwFJvaDkSERERKQeWLx/MdtceRy0BvB1fHPMbUb6O6QyfXdoibs9tsef/RiJ1CdKjkRERETqOJfLxVsb33If//nc5zGZa+7XwG3bv2eL2QHAOU4LbduN8XNEUl/U3P8qRERERMQrlh5cyubjmwHoENuBYc2G+Tmiss1Y70nkxjYZ6sdIpL5RciQiIiJShzkddv69+gX38e1db8dUg581KshN44fsvQAEuVxcNOBB/wYk9YqSIxEREZE6bN7yZ9mSsReAjlGtOK9Fza1QBzD/1xdJsxhfUc8LjCMyspmfI5L6RJvAioiIiNRRdlse/97xNViM47ubjsRsqtn/Nj5t32w4NbF19ckT8P6FxkF0Cxj9TwiJ8V9wUucpORIRERGpo75f8ih7LS4AermCGNjrDj9HVLZtxzaxzlQAQOuCAnofTQH2GC/u/xWa9oT+f/FfgFJh0aFWdzsmNNCPkVROzf6nAxERERGpEqctl/eTZ7uP7+pxV42uUAfw1Y5v3e1rM7Io9mRUow7VGo9UXetG4e522/jwMnrWLDX7vxARERERqRLz2v/yzqFDjM3M4lxTOD27jfd3SGXKKsjih90/AhASEMKYv26Gv672dIhtBUk1u8qe1H5aViciIiJS1+RlwM//pIndwRPHTuC4Yrq/IyrXD7t/IMeeA0C/hH4cyj0CK18nxmKmkcMJvW6GGj7zJbWfkiMRERGRumbZq5Bz3Gh3vhJLkx7+jacCvtn+jbu9+MBiFh9YbBy0aMaTx9O5vMeN/glM6hWl3yIiIiJ1SNrRrWT/+qZxYLbCeY/6N6AKOpl3stTXtiS0h9DYaoxGztYfB9Pd7d/2p5fRs2bRzJGIiIhIHfKvn//O4sYx3HHSzBWdbsQam+TvkCrk5eEv88OuH7A5bQD8uP1b8k5VZBjRuWY/LyXF5dudhdoOP0ZSOUqOREREROqInSd3MiN7F06LhVcbNODCfrcT7e+gKqhbo250a9QNgE3bZvCtyahc19Zppnfncf4MTeoRLasTERERqSNeWfcKTpfxL/a39Lqb6NhWfo6oar5c96a7fX3T4TW+BLnUHfqkiYiIiNQBKw+vZMmBJQDEh8YzrmPtnG05mb6PWfmHAIhwOrl40N/9HJHUJ1pWJyIiIlLLOewFPL/sMffxnT3uJDgg2I8RVd30X56iwGQ8bDQ230zowQ3GC4Gh0LwfWKz+C07qPCVHIiIiIrXc9IUPsSP7IACdolozpvUYP0dUNXannWmpK8EMJpeLa48dhs+v9nTocg1c+a7/ApQ6T8vqRERERGqxzIyD/PvgPPfxQ0lXYDbVzq94Sw4s4bDZBcDg3Dxa2O1FO2Qc8kNUUp9o5khERESkFnt33p2cMBvL0C60xNCzW+0te/3Fli/c7RsSL4ZOTWHR054O+36BqVFG22SGLlfD2Lfh1DI8kbNVO/9ZQURERETITd3CzPStAAS6XNw7/EU/R1R1209uZ2XKSgBaRLRg4Kh/wbAHoWnvki9wOWHjNCjIqsYopa7TzJGIiIhILRWy8Cm+O3CYN2OiiGral6ZN+/o7pCr7ZPMn7nZyZjJ3LbwLgKAmTbku0EyfglMbiWYfhbRko920FwRFVHeoUgEtYkPd7cQGoWX0rFmUHImIiIjURjvnw9YfiAH+kR8IF7/v74jOyuL9i4scny5LDrArqjUzLp9hHEy/zZMc9ZlULbFJ5TWKCHK34yJrT+VELasTERERqW3s+TDrQc/xyCdr/QzKeS3OK/W15pHNjUb2Mdj03amzJsg9CavfN362/ggOe6n3EKkIzRyJiIiI1DJLFzxCp7Q9NABoMQC6XuPvkM7a4wMf54HeD2B32nHhYtLcSew4uQOAmzreZHTa9B04Ck5d4YKfztgg9sJnYcAd1Re01DmaORIRERGpRVKyU7g/ZQFjmjXhi8gIGP1CnanWFhEYQUxwDHvT97oTow6xHeiT0MfoEBxV9g3suT6OUCoqz+Zwt3MLtWs6zRyJiIiI1CIvrXmJXJxgMbM7aQAkdPF3SF738eaP3e3xncZjOp38dbkaIhIgbb9x7LTBjw8Yf5rMxiaxUiNsOpThbv9+IN2PkVSOkiMRERGRWmLl4ZXM2TsHgJigGCaPfs/PEXnf/oz9LExeCECjkEaMShzledFkgqShnuNN3xmJEUD70RDdvBojlbpIy+pEREREaoECRwFP/fqU+/junncTFVTOMrNa6NMtn+LCBcANHW/AarGW3nlVoeRQlevEC5QciYiIiNQC78/+M3sz9gLQrVE3xrYd69+AfCA9P53vdhrV6EICQri63dWldz6yGfb9YrQbtIVW5/o+QKnzlByJiIiI1HB79i7m3WOrAOOZiCn9HsVsqntf477d8S25p4oqXNr60rJnxlYXmjXqe2udKUoh/lX3/qsSERERqUNcTidPL/4btlNf/m8Kb0e7Bu39HJX3FTgK+GzLZwCYMHFTp5tK75yXDr99abStYdDtumqIUOoDJUciIiIiNdgPi/+PlaY8AJo64M8XvePniHxj5q6ZpOakAjC8+XBaRrYsvfNvX4It22h3u7b8Et8iFaRqdSIiIiI1VV46ob9/TYNwK8cDLPz9+ElC/90HAsPgnt+L9p39EPz+dfn3bH8RXPaG59iWB++eByExcO0nEBrr3fdQAXannfd/f999PKlLGcUVnM6iS+r63OrDyKS+UXIkIiIiUlMd3sj5J1Ppk25iXmgoQ7NPzZbYC4r3LciCnOPl3zM/q+jx3H9A6iajvWshdLnq7GKugjl753Ag6wAA/Rv3p0ujMvZu2rUAjm032i0HQXynaohQ6gslRyIiIiI1VfN+0OVqIg+s4UqAmFPnA8OL9w1rBDFJ5d8zPM7TTvkd1nxgtK2hkDj4LAOuPKfLyXsbPTNBt3W9rewLVvzb0+5/h4+ikvpKyZGIiIhIDWNz2ggwBWAKCIQrK7jR64ipxk9FuVww+2FwOY3joX+DiITKhnrWFu1fxK70XQB0b9Sd3vG9S++c8jvsXmy0Y1sZSwSlRurcJNLd7tqs9jwTpoIMIiIiIjXM86ue555F93A056jvBtn0nWefoJgkGPBX341VCpfLxbsb33Uf39r1VkxlleRe8aan3f8OMFt8GJ2cjSCr5+8m2Fp7/p40cyQiIiJSg6xJWcO0bdMA+OP4H8y+YjaBlkDvDlKQDXMf9RyPehYCgrw7RgWsOLSCTceN5506xHZgSNMhpXfOOOwpOBEcDd1v8H2AUu9o5khERESkhsiz5zF1xVT38Z86/8n7iRHAL69AhlEAgTYjoN0o749RAe/87ilLPqnLpLJnjVa/C06b0e79J6Nin4iXKTkSERERqSFeW/8a+zL2AdCtUTeua++DzU2P74JlrxptcwCMeg7KSkp8ZO2Rtaw9shaAxMhERrQYUXrn/CxP4QizFfqWU7RB/C41M8/dPpKRV0bPmkXJkYiIiEgNsDplNZ9u/hSAQHMgTwx8AosvnqlZ8Dg48o12/79Aw7beH6McLpeL19e/7j6e1GVS2e917UeQe9Jod7kKIhv7NkA5a/tP5Lrb+47n+DGSytEzRyIiIiJ+lm3L5tFlj+LCBcBdPe+iVXQr3ww2+kUICIZ9y2HYw74ZoxwrDq8oMmt0cauLS+9sy4PlnkSKQff4Njip15QciYiIiPjZC6tf4GDWQQB6xffipk43+W6w8Di44h1jJiaohP2SfMzlcvHv9Z69iu7ofgcB5jK+kv72OWSlGO2OYyCug48jlPpMy+pERERE/OjnAz/z7Y5vAQgJCOHJQU9iNlXDV7SQmPL7+MDi/Yv5/djvALSNacuFiReW3tlhN4pHnDbkfp/GJqKZIxERERE/WnFohbv9tz5/o3lEc+8PcnyXMVN0aH35fXvdDBbffEV0upz8e4Nn1mhy98llJ4J/fANpRoEKWp8PTXr4JC6R05QciYiIiPjRQ30fontcdxbtX8RVba/yzSDzpsDO+WCvQNWwHjcZyZEtD5a+aGwO66VZprl757L95HYAujTswvDmw0vv7HTC0pc9x0Mf8EoMImXRsjoRERERP7sw8UKeG/Jc2fv8nA1raMUSo8KWvw4/vwCv94Id8846BLvTzhsb3nAfT+4xuez3u/UHOLbNaLcYAC0HnnUMIuXRzJGIiIhINXO5XL5LhEpy6WtwzljIzyy/r8UKJ/cas0YAuWkQ2eSsQ/hh9w/szdgLQJvoNsSHxrM7bTcACWEJhFpDPZ1dLlj6kue40+VwdJvnOCYJAnywOa7Ue0qORERERKqRzWnjr/P/yti2Y7ko6aLqGdQaAh1GV7z/7Ic9M039/wLx55x1CO9sfMfd3pm2k8v/d7n7OCQghC8v/tJTvnzvL3B4g+fiOQ8VvVl4AtyxAkJjzzoukcK0rE5ERESkGr254U1WHF7Bgz8/yGvrXvN3OMVtmw3bZxvt8AQY9lDZ/SvA5rRxLPdYqa/n2nPZkbbDcyLjUNk3zEqB9ANnHZf4TlCAJ80ItvpgM2Mf0cyRiIiISDVZnbKa939/H4AAUwDntTjPzxGdoSAHZj/oOb7waQiOPOvbWs1WXhn+CnP3zsXutAOwP3M/61LXAdAguAFDmg7xXND5Csg4YFTZO+3Ydjiw2mjHdYL4zmcdl/hO56ZR7nbXZlFl9KxZlByJiIiIVIO0vDQeWfoILlwA/LXHX+ncsIZ9wV/6EqQlG+2kodD5Sq/demCTgQxs4imqcNvc29zt27vdXvSZI4u1+J5GH13iaQ+5H8xaACXep+RIRERExMecLif/WPYPjuQcAaBvQl9uPudmP0d1hiObYdkrRttshdEvgY+KRqw8vJIVh439nZqGNy2/hPm+FbB3qdE2mSH5V88sEhiV7Dpd5pNYpX5RciQiIiLiY//d9F9+PvAzADFBMTwz+Bks5hr0HIbTAd/fBaeWvDH4HmjUzmfDTds2zd2+qdNNWC3Wsi/47XNP2+WE1e8WfX3lWzB5DTRs68UopT5SciQiIiLiQxtSN/DqulcBMGHi2SHPEh8W7+eozmAyQ88JcGwHhDWCIb7dcLVFRAt3+8utX3JZ68sIDwwv/YJGHcu/6YE1niWBAJFNIa7DWUQpZ2Nnapa7vf1IBUrI1xBKjkRERER85GTeSR5Y8gAOlwOASV0mMajpID9HVQKTCXreBO0uhKxUsAb7dLg/d/szyw4tY+uJrezN2MuU5VN4adhLpe/9NOAOaH8R5JzwnFv9XtEZpRl/Ln7dle9Dl3KW7IlPpOfa3O20HFsZPWsWPckmIiIi4iOHsg+5q7P1iu/FHd3v8HNE5QiPgwTfF4kIDgjm5WEvE2GNAGDevnl8uuXTsi+KTYJmvTw/cRWYTVK5b6kkzRyJiIiI+Mg5Dc7hm0u/4blVz5Gckcx5X5VfuvvGTjdyW9fbyu3nFVlHIbxR9Yx1huaRzXlq8FPcvehuAF5e8zK943vTsUEFkh6AvrcZZcYL74lUkA0r/u05nv8YLHzKc2y2QOer4LJ/+6zYhNRuSo5EREREfKhhSENeHPYiE2ZP4GT+yXL759nzqiEqjMTozf7QZgRc9ByExFTPuIWc1+I8RrQYwfzk+dhddpYdWlbx5MgaDL0mFj2XuqVocgTgtBVtb/jUeL9BEWcVu9RNSo5EREREqkF8aDzNI5qX2y8qqBo2zHS54Mf7IOcYbPwSAgLh0td9P+4ZjuYcZdmhZQBYTBbOb3H+2d2wUQcYdDfsXlz0fFYqZB422s37KTGSUik5EhEREakG/xz2T3+H4LFpOmyZabRDYuG8R/0Sxr83/Jtcey4AV7e7mqSopLO7ockEI58ofv6jSzzJ0cC7zm4MqdNUkEFERETEjz7d/Cnz9s2rvgEzj8CP93uOL3nZKMRQzbad2MZ3O74DINwazl+6/8U3Ax1c59lAtkEbaD/aN+NInaCZIxERERE/WZOyhhfWvIDT5eTGjjfyYJ8HSy9n7Q0uF/xwL+SeevbpnLHGjx+8vPZlXLgAuLXrrcQGx/pmoOWvedoDJoNZcwNSOiVHIiIiIn5wIu8ED/78IE6XE4Afdv/AkZwj7te7NOzCzZ1vLnLNU78+xbHcYwSYAxjRYgSjkkZVbtCNX8G2H412WCMY/dJZvYeq+uXgLyw/tByAJmFNGNdxnG8GOrEHNv/PaIc1gm7X+2YcqTOUHImIiIj4wQ+7fuBo7lH3cVp+WpHldQ6no9g1i5IXkZqbCsD8ffMZ0GRAxQs4pCXDrL95ji/5F4Q1qFrwZ8HutPPSGk9Sdk+vewiyBPlmsF/fhFPJJ31v9/nmtuLRKMLzdxoXWXt+75pXFBEREfGDbnHdCLeGV+qa04kRQNPwpkQGRlbsQqcDpt8O+enGcddroeOYSo3tLV9u/ZKdaTuNMBp2ZVRiJWe/KirzCKz72GhbQ6HPLb4ZR0rUIjbU3U5sEFpGz5pFM0ciIiIiftCtUTcWXbOItPy0El8/czZlwb4FRY7/OfSfFX8+qSDbU746qgWMfqGy4XrFsdxjvLHhDffxQ30f8t0zVstfg9N7RvX+E4T66JkmqVOUHImIiIj4SXBAMAkBCeX225+5n0eXecpt/73f3zmn4TmVGCgSbpgGq9+D+M4QXA17KZXgX2v/RZYtC4Ar2l5B10ZdfTNQ9jFY84HRDgiGgXf6Zhypc5QciYiIiNRg+Y587l98P5m2TAAuTLyQ69pfV/kbmUzQ91YvR1dxG1I3MHOXsbdSRGAEd/e823eDrXgDbDlGu+cEiCg/ARUBPXMkIiIiUqO9sPoFtpzYAkDLyJZMHTC14kvR8rN8GFnFOZwOnl75tPv4zh53+q50d84JWPWO0bYEwiAfJmFSqvXJJ93t1XtPltGzZlFyJCIiIlKDDWoyiIjACIIsQbw07CXCAytYxOGPb+H1XrBroW8DrICvt3/N1hNbAWgU0ohODTrxx7E/+OPYH2w6tomc07M83rDyLSg4lRR2HwdRTb13b6kwp8vTdrlcpXesYbSsTkRERKQGG95iOF/Hfs22E9toH9u+Yhcd3wUz7zKShE/Gwq0LoWkv3wZaCpvDxuvrX3cfH809yo2zbizSJzQglMvaXMb1Ha4nKSqp6oPlpsGvbxltcwAMvrfq95J6ScmRiIiISA3XNLwpTcMrOANiy4WvJnhmT7peC016+i64cmTZssixlz0zlGPP4YutX/DF1i8Y1GQQN3S8gcFNB2M2VXKR0/LXC5Urvw5iWlYxaqmvlByJiIiI1CC59lzm7JnD5W0ur1qZ6zkPw5HfjXbDdnDxy0YxBj+JCY7hjfPeYOnBpThPb8haSEZBBvP3zSfPYZTdXnZoGcsOLaN1VGveGvkWCWEVLKaQlQq//sdom60w7EFvvQWpR5QciYiIiNQQLpeLKcumMGfvHFalrGLqwKnF9jsq08avYe1HRjsgBK7+LwRVbqNZXxjYdCADmw4s9fWH+z7MjJ0z+GLrFxzMOgjArvRdLNm/hGs7XFuxQZa+BLZso937Zs0aSZWoIIOIiIhIDfHu7+8yZ+8cABYmL3QnChVybAf8cI/n+OIXIb6TdwP0kaigKCacM4HXz/M8mxRoDmRQ00EVu0FasmdfI2soDHnAB1FKfaDkSERERKQGWJC8wF24wISJ54c+T6uoVhW7uCC76HNG3W6AHjeWfU0N43K5eH7V8+7jW7rcQrOIZhW7eMnz4Cgw2v3+DBHxPohQ6gMtqxMRERHxs20ntvHI0kfcx3f1vItzm59b8RvMvBNSNxntRh2MWaNa5qd9P7EyZSVgFKD4U+c/VezCo9thw+enDkwQkwib/+e9wEIbQov+YLZ4755SYyk5EhEREfGjE3knuGvhXeTacwEYnTSaWzrfUrmbdLoctv8EmOCaTyAwzOtx+lKOLYcXVr/gPn6oz0MEBwRX7OIV/wZ3oQcXfH+X9wM8fwoMud/795UaR8mRiIiIiJ/YHDbuXXQvh7IPAdC5QWceH/h45avUdbrUqEyXcQAatfNBpL719sa3Sc1JBWBI0yGVmzVzOnwTVGHZx30/Rh3TupEnQW8b7/+iIBWl5EhERETED1wuF0+vfJp1qesAaBTSiFeGv1LxGZMzxXUwfmqZ/Rn7+Xjzx4BRhOGRvo9ULjkc9Qw07wt5ad4LymmHRc8YfwL0GOe9e9cT0aGB7nZMoXZNp+RIRERExA8yCjJYe2QtYCQFrw5/lfiwChYSKMiGbbOhy1U+jLB6/HbsN+ynkpBzm59L88jmlbtBcBT0muDdoP741pMYtRkB8ed49/5SYyk5EhEREfGDqKAoPh39KQ/+/CBjWo+hS6MuFb941oOw4VP49hYIjICAUv5lfvIaCI31HK94w9gP6LQWA+Dqj8BirdJ78IaBTQYSaA6kwFnAskPLyCrIIjzQj8uwXC5Y9lqhAH3wDJPUWCrlLSIiIuInUUFRvDXiLS5pdUnFL3K5YMtMz3FBJuQcL/nnTLacoq9v/QEO/3b2b+QsxAbHMqb1GACybdl8t/M7v8bD3qVweIPRbtwNkob6NZzaKiPXVmK7ptPMkYiIiEg1OZx1mKigKEKtoe5zlS6+YDIZ1dNWvQtZKRASW3bfwoKjix436gAJXSs3vg/c2PFGvt3xLQCfbfmMGzrcgMVfpbMLzxpFNoNf/1P5e5hM0HKgkVzVUztSs9ztrSmZfoykcpQciYiIiFSD47nHmTR3EhGBEbxx/hs0CGlQ9Zv1vdX4qSxbrqcdFAnXflb6krxq1CamDQObDGT5oeUczDrI4v2LOb/l+dUfSMZh2DnPc7ztR+OnKgKC4Z7fITzOO7FJtdCyOhEREREfy7HlMHnBZJIzk9l0fBNTl0+t/iB2L4H5j3mOx74NDdtUfxyluKnTTe726ep11c5iBW8972TPB5O+atc2mjkSERER8SGb08Z9i+/jj+N/ABAXGsc/+v+jeoNI2w/f3OzZLHXog9BhdPXGUI5BTQbRKqoVu9N3sy51HZuOb+KcBtVcJS6sIdzxKxxYDbgqf/2epbD2Q6PdbpRxP6lVlByJiIiI+IjT5WTq8qksO7QMgIjACN4e8TYJYQnVG4g1FBK6wO7F0GYkBEXAV+PLvy5pKPSZZLSdDqMYhI+YTCZu7HQjT6x4AoBPNn/Cc0Oe89l4pYpubvxUxfpPPe0+t3gnHqlWSo5EREREfMDlcvH8queZucuoLBdoDuT1816nTYwflrKFNYAbp8Py16DXRPj+Htj8v/KvC4rwtOdPxXJsJwFBY3wVJWNajeG1da+Rlp/GT3t+4t6e91Z87yd/O74Ldi002tEtobUfnpmSs6aFkCIiIiJe5nK5eHXdq3y+9XMAzCYzzw99nl7xvfwXlNkCg++FkJjKX7vle1j+Gubtsxi640lwFHg/PiA4IJir210NgN1l58ttX/pkHJ9Y84Gn3ecWMOtrdm2kmSMRERERL3v393d5/4/33cdPDnqSES1H+DGiM1zyL7jwmfL7WUOMGZEZd7hP7Wl4Hh0tvqtwd12H6/hw04fYnXa+3v41t3W9jZCAEJ+N5xW2XM+SOksQdL/Rv/FIlSmlFREREfEip8vJjpM73Mf/1+//uLT1pX6MqAShsRDVtPyfgGCYdhPkZwDg7DSWPQ1H+jS0uNA4RiWOAiA9P53vd33v0/G8YtN3kJdmtM8ZayxjlFpJyZGIiIiIF5lNZp4b8hyXt7mc+3vdz7UdrvV3SFXjcsGP90HqJuO4YXscF/+r+MayPlC4rPcnmz/BebrKXk21+j1PW4UYajUtqxMRERHxMovZwhMDn8BUDYmEz6z9CH77wnN87SdF9wBa8yE47eXfp/V50KB1pYbu1KATveJ7sfbIWvZm7GXpgaUMaz6sUveoNofWw8G1RjuhCzTr4994aogezaP56FS7d2IVnnPzkxqRHL3xxhu88MILpKSk0K1bN15//XX69u1bYt93332Xjz/+mD/+MPYK6NWrF88880yp/UVERER87ae9P9EqqhVtY9q6z9XqxMiWCz/93XNsDYNG7cFm85yb+ygUZJZ/r6s/qnRyBDC+03jWHjGSjjd/e5OhzYbWzN/pijc97d63VMvMWm1gNnt+D+Za9Dvx+7K6adOmcd999/HYY4+xbt06unXrxoUXXkhqamqJ/RcvXsz111/PokWLWLFiBc2bN+eCCy7g4MGD1Ry5iIiICHy/63se/PlBJs2dxK60Xf4OxztcTuN5o9M6j636vdKSIeNQpS87t/m5dIjtAMDm45tZuH9h1WPwlfSDsGm60Q6Jha61dAmluPl95ujll1/m1ltv5eabbwbgrbfe4scff+SDDz7g4YcfLtb/s88+K3L83nvv8e2337JgwQLGj6/AZmYiIiIiXjJj5wymLJuCCxcn8k7w/a7vuafXPf4O6+wFhsEdK2DPUiNRik0q3ufSV8FRzrI6RwHMnwrLXoXL34J2F1Q4BLPJzOTuk5m8cDIA/17/b4Y3H47Z5Pd/2/dY+ZZnaWGfSRAY6t945Kz5NTkqKChg7dq1PPLII+5zZrOZESNGsGLFigrdIycnB5vNRmxsrK/CFBERESnm2+3f8viKx3HhAuC69tdxd8+7/RyVF0UkQNerS3+985Xl32PO3yHnmNH+6RFoPRws1gqHMLTZULo27MrGYxvZmbaTn/b+xEVJF1X4ep/Kz4S1//Uc7/8VvirlH+qDo2HwPRDbqjoiqxEOnMxxt/efyCmjZ83i1+To2LFjOBwO4uOL7nwcHx/P1q1bK3SPhx56iCZNmjBiRMl7B+Tn55Ofn+8+zsgwSlHabDZshdfNVrPTY/szBqk99HmRytDnRSpDn5eq+WbHNzyz2rNP0PXtr+eBng9gt1egQEEtVpnPi2n7bAJ+fQMAlyUQ++XvgBNwVu6z9peuf+EvC/8CwBvr3+DcJucSYPb74idMOxYSkJ/uObHn5zL7O/MzcVz+jo+jqjkOp+W624fScv3+vzEVHd//n6yz8Nxzz/Hll1+yePFigoODS+zz7LPP8vjjjxc7P3fuXEJD/T/1OW/ePH+HILWIPi9SGfq8SGXo81Jxy/KWMTtvtvt4UNAgOqV0Yvbs2Rx1HMVJ+WWnI02RhJhr+MamZSjv8xJScIxztz7qPt4TO4wdv24kL/BAkX7heYcxuRxl3svlcpFkbsEeZzL7Mvfx3P+eo2dQz6oH7yUhBccYFhBBkL0CRSmAgm0L2PzJI+V3LO8+ARGkRnbGZarZX+P37jVzurxBamoqs2bN8ms8OTkVm73y62+1YcOGWCwWjhw5UuT8kSNHSEhIKPPaF198keeee4758+fTtWvXUvs98sgj3Hfffe7jjIwMdxGHyMjIs3sDZ8FmszFv3jxGjhyJ1Vrx6WWpn/R5kcrQ50UqQ5+Xyll9ZDWzF3gSowkdJ3BX97vcVdTO+/Y80vLTyr3PUwOeYnTSaF+F6TMV/bxYvhqH2ZHtPm51dB6JcRE4RhedOQl4pROm7JKLcBUWcd5D3LInGYCFzoVc3PtiesT1qOK78CLH9diyj5b6csA7gzDlG8lTsD2dnsnvemfYIQ/iHPqgV+7lK2t/3MrPKcbfWVxcHKNH+zehPb16rDx+TY4CAwPp1asXCxYs4PLLLwfA6XSyYMECJk+eXOp1//znP3n66af56aef6N27d5ljBAUFERQUVOy81WqtEf9PoKbEIbWDPi9SGfq8SGXo81IxuzN2Fzm+v8/9VSovbQmw1Orfd7mflxPFq/aZTWbMVXzPfSMTGRrXm59T15CWn8btC2/nsQGPcXmby6t0P6+xWiG4Zemvx7aGwxu8Pqwl8yCWgop92a+Q4MhKPQtWEWazp3CGyWTy++e9ouP7fT7uvvvuY8KECfTu3Zu+ffvyyiuvkJ2d7a5eN378eJo2bcqzzz4LwPPPP8+UKVP4/PPPSUxMJCUlBYDw8HDCw8NLHUdERETkbI1tO5Y8Rx47Tu6gX+N+xRKjS1pdQo69/OU7zSOa+yrEmuHK92D9Z2DP85xr2qt4vy5XQ34FvuRnH+OZNf/j/ladWWk/id1p59Flj7I7bTd397wbi9nivdi9adzXsGMu2PPL71uedf+Fw78Z7Q2fGT/eEhwNN8+C+HO8d89ayu/J0bXXXsvRo0eZMmUKKSkpdO/enTlz5riLNCQnJxfJPP/zn/9QUFDAVVddVeQ+jz32GFOnTq3O0EVERKSeCbOGManLpFJff6jvQ8XOpWSnsOLQCsa2PYu9gmqbJj2Mn/KMeqb8PvlZ8NZgohwO/rPjN57vcTHT0n4H4MNNH7I7fTfPD32eMGvYWQbtA+Fx0ONG79xrx1xPcuRteWlG2XYlR/5PjgAmT55c6jK6xYsXFzneu3ev7wMSERER8YKDWQe55adbOJh1kAJHAdd20CahlTb3/+DkHgCszfvxf2M+oc32r3lu1XM4XA6WHFjCjbNu5OG+D9M3oW+VljnWCuc/BkERkOfF5XSpmyF9v9GOSfTefWuxGpEciYiIiNQ1yRnJ3DL3FlKyjUcAPtnyCZe1uYzggJIr7EoJdsyDtR8abWsYXP4fMFu4rsN1tIxsyf1L7iezIJOdaTuZNHcS3Rt158/d/szAJgPrXpIU38lYruhNn13jSY7iO3n33rVUDdpiWERERKRu2HlyJxPnTHQnRklRSXxw4QdKjCoj+zj8r9DKogufggat3YcDmgzg89Gf0ya6jfvchqMb+PP8P3PDjzeweP9iXC5XNQZcC6VuNv4MjICoOv4cXAUpORIRERHxovWp6xk/ZzxHc40Sz21j2vLhhR8SFxrn58hqEacTZvwZsozkkjYjodfNxbolRiXyzZhveH7I87SO8iROfxz/gzsX3sm1P1zL/H3zcbrK33uq3snL8MwaxXUEL8+0RQQHFGrXnsqMSo5EREREvGTx/sXcOvdWMguMvW06NejEBxd8QIOQBv4NrLZZ8bpRgAAgtCFc9u9Sv7xbzBZGtxrN9Mum89Kwl2gX08792pYTW7h38b1cOfNK5uyZg8NZ9oaz9UrqFk/bB0vq2sVHuNsdG0eU0bNmUXIkIiIi4gXf7fiOexbdQ77DKNs8oPEAPrjwA6KDo/0bWG2zfxUseOLUgQmueAciEsq9zGwyc0HiBXw95mteHf4qnRp4vvDvTNvJ337+G2NnjuX7Xd9jd9p9FHwtkrrJ045TlbrTlByJiIiInKWsgixeW/8aDpcxM3FR0kW8cf4bNbO8dE0X3RJaDjTaQ+6DNudX6nKzycx5Lc7jy4u/5M3z36Rro67u1/ak7+Hvv/ydy2Zcxnc7vsPmtHkz8trlyGZPW8UY3JQciYiIiJyl8MBw3jz/TcKsYYzrOI7nhjyH1VJ7nrOoUSLi4aYZcOnrcO7fq3wbk8nEkGZD+PSiT3ln5Dv0jOvpfi05M5kpy6dwyfRL+O+m/5JR4MXy2LVFaqHkKE7J0Wkq5S0iIiLiBR0bdGT6pdNpHNa47pWRrm5mC/Qc75VbmUwmBjQZwIAmA1idspq3N77NysMrATiUfYgX17zIGxve4NLWlzKu4ziSopK8Mm6N5nLBkT+MdkRjCI31+hBbDnsSzj8O1p7kUzNHIiIiIpV0PPc4L699udizK03CmygxqooDayEr1efD9Enow3sXvMcnF33C4KaD3edz7blM2zaNS2dcyp/n/5mlB5bW7Qp3J/dAXrrRbtzNJ0PkFDgKtWvPM16aORIRERGphJ0ndzJ54WQOZh0kz57H3/tVfemXACf3wedXQ0AwXPspNO1Z/jVnqXtcd/4z4j/sTtvN51s/Z+aumeTacwFYdnAZyw4uIzEykes7XM9lbS6re8+OHVrvaTfp4b84aiDNHImIiIhU0LKDy7hp9k0czDoIwIJ9CziRd8LPUdViBdnw5TjIOQ4ZB2HpS9U6fKvoVvxf//9j/tXzeaD3AzQNb+p+bW/GXp5d9Swjvh7B86ueZ3/m/mqNzacKJ0eNu/stjJpIyZGIiIhIBXyx9QvuWHAHWbYsADrGduTziz8nNtj7z2vUCy4XzLgDjvxuHMe2NvYz8oPIwEgmnDOBH8f+yCvDX6FvQl/3a1m2LD7d8ikXT7+YOxfeya+Hf8XlcvklTq85tMHTbtLdX1HUSFpWJyIiIlIGu9POP1f/ky+2fuE+d17z83h2yLOEWkP9GFktt/Ql2DzDaAdGwPVfQEiMX0OymC2c3+J8zm9xPttObOPzrZ/z4+4fyXfk48LF4v2LWbx/MW2i23BDxxu4pNUlhASE+DXmSnM64fBvRjuiSYX2kKpPlByJiIiIlCKrIItxs8axO323+9yfOv+Ju3vezZYTW9h4dGO594gJimFU0qgi5xYkLyA1p/wCBB1jO9I9rrv72OF08NX2ryoU+/Dmw0kI83zxPZB5gKUHl5Z7nRkz13a4tkJjVNm2ObDwqVMHJrjyXWjU3rdjVlL72PY8PvBx7ul5D9/u+JYvtn7h/jvbmbaTJ1Y8wStrX+HKdldyffvraRze2M8RV9CJ3ZB/qnqcZo2KUXIkIiIiUoq3fnurSGL0xMAnGNt2LAArDq3g1XWvlnuP9jHtiyVHn235jNUpq8u99k+d/1QkOXK6nDyz8pkKxd46qnWR5Ghn2s4KXRtgDiiWHKXmpBJoDiTM4oXCBIc3wreTgFNL0877P2h/0dnf10digmOY1GUSE86ZwILkBXy+5XPWpxrP7GQUZPDhHx/y8aaPOa/FeYzrOI6ecT1rdsXCwxs8bRVjKEbJkYiIiEgpCm/k+sKwFxiVOKqM3nXXuxvf5Zvt39A3oS/x+fEMLhhMA2uDyt8oLRk+uwoKMo3jTpfDkPu9GquvWM1WRiWOYlTiKDYd38Rnmz9j9t7Z2J12HC4H8/bNY96+eXSM7cgNHW/goqSLCLIE+Tvs4lSprkxKjkRERERKMbn7ZPrE9+F43nHObXZukdeGNRtGfGh8ufeICooqdm5Sl0mMbTO23GvbRLcpcmw2mXlmcMVmjlpFtypy3CG2Q4WuNZuK1utyOI0v/naXneWHlwPw/fTvGdhkIBcmXsjw5sOJCIyoUExsmw1ZR4x2s74w9i2oybMspTinwTk8M+QZ7ut9H19v+5pp26ZxPO84AFtObOHRZY/yr7X/4qp2V3Ft+2uJC43zc8SFHFznaatSXTFKjkRERERKYTFbGNh0YImvtY1pS9uYtlW678AmJd+zIvGMaT2mStcmhCVU6do8Rx5jWo/hp70/cTj7MGAUqfj5wM/8fOBnrGYrg5oOcidKZe4J1O92MAfAyrfh+i/BWsuKGZyhYUhD/tL9L0zqMok5e+fw2ZbP2HR8EwAn8k7wzsZ3+OD3D7gg8QLGdRxH10Zd/RuwvQAOnUqOoltCeCOfDdU02vN32yym9vw9q5S3iIiIiJQqzBrG/b3v56crf+KjCz5iYNDAIjNmNqeNxfsX88jSR9h0bFP5N+xzC/x5KYRVYVleDWW1WBnTegxfXPwFn1z0CRclXkSAyZiDsLvszNozi3GzxjHux3H8uPtHbA6bfwI9/BvY84x2i/4+HSohKtjdbhJde5IjzRyJiIiISLlMJhNdG3ZldMhoRl00ii1pW5izdw5z987laO5RYoNj6RXfq8g1Kw6tIP3ETs7tcDXBAZ4vywTUwGdxvMBkMtE9rjvd47pzJPsI07ZN4+vtX5OWnwbAxmMb2bh0Iy+teYlr2l/D1e2upkFINSaJ+3/1tJv3Lb1fPabkSEREREQqxWwyu5OAB/s8yPrU9RzNPYrFbCnS77Z5twHQZvN/+eaqn4q9XpfFh8VzV8+7uK3rbczeM5tPt3zK9pPbATiae5Q3NrzBuxvf5aKkixjXcRwdG3T0fVD7V3razX07c1RbKTkSERERkSozm8zFZowAXvt+gru9NyeFrMPriWrauzpDqxGCA4IZ23Ysl7e5nDVH1vD5ls9ZuH8hTpeTAmcB/9v1P/6363/0jOvJuI7jOK/FeQSYffAV3eWC5FPJUVAkxPk2GbM5nO52gd1ZRs+aRcmRiIiIiHjVtJ/u4t0TnqpodzboUy8To8JMJhN9EvrQJ6EPB7MOMm3rNL7Z8Q2Zp8qar0tdx7rUdSSEJXBd++u4qt1VJVY6rLKTeyD71MbDzfqAj2fxNh5Id7c37E/z6VjepIIMIiIiIuI1383/G0+lLHIf/yWyM38a86EfI6p5moY35b7e9zH/qvk82v9RWkV5yq6nZKfwyrpXGPH1CB5f8Tg7Tu7wzqD7VnjaPi7GUJspORIRERERr/hh8RQeOzDbffyn8Hb85bLP/BhRzRZqDeWa9tcw47IZvD3ybYY1G4YJY9+nPEce32z/hitmXsGkuZNYlLwIh9NR9cH2/OxpJw4+y8jrLi2rExEREZGz9tPSp/jH3um4Tm3qemNoEveM/RqTWf8WXx6TycTAJgMZ2GQgyRnJfLH1C77b+R3ZtmwAVh5eycrDK2kW3ozrO1zP2LZjK77xLhjPG+1ZYrStoVDPlziWRcmRiIiIiJyVrN2LeGrH5zgtxnMs1wY148ErZ2A6uQfeH1mxm9y2BKKbe47XfgQLnij/upgkuHVB0XPf/Al2Ly7/2p7jYcTUoudeaAuuCszQXPketD7Pc7z3F/hqfPnXAdy/HSyFvoYvehZWvwtAC+Ah4K8m+F9wAJ+HWkkOMBLMA1kHeGHNC7yx4Q0ua3MZN3S4gcTpd8DRrWWP53RAXprRbjEAAgIhKxXerODyuok/Fi3g8Ps3MPvBMi/5W4GDGwLDuKDghYqNUUMoORIRERGRsxK+dRZvphzl9oQ4zg9pzN+v/t6YMXI5Ied4xW7iOqOimS2vYteGxBQ/l59ZsWsLcoqfyzleseTozI1cHbaKv9cz2bKLXRsOjMuG64/DLyHBfBYZwfJQYzPVHHsOX2z9gi+2fsFgp4VxrmwG5uZV7HmZpKHGn5X5uzlzOZ89v9xrw4BoU8VuX5MoORIRERGRs9NzPF1S/mBaYl+aDP0H5tOzIuYAY2anIs4sXx0cWbFro5oVPxceX7FrQ0vYgDU2qXgyUBJrSPHjir7XM4XElnqtGRgKDC2AXQ278EXz9szcNZNcey4AvwQ4+CUhjkQH3JBv4rICE6GckZVkHQHbqUSw1TDjT5Ol4vFaAoseB4WXe+2JnAIO5IaU2acmUnIkIiIiIpXicrlYmLyQc5ufi9lkhoQu8KfZND+zY2wS3L2haoN0v8H4qYrL/l216wDuXFu161r0r/p7HXKf8VOO1sD/AXf2uJMZO2fwxdYvOJh1EIC9Fngm1MXrUeGMbTuW6zpcR/OI5uB0wgutjOQoOAoSuho3C29U9Xg7XWb8lOG1mZv4aPneqt3fj/SEnIiIiIhUmMvl4pX1r3D3ort5btVzuFwuf4dU70QFRTHhnAn8OPZHXjn3Ffok9HG/lmnL5OPNH3Px9Iu5a+FdrNz0Oa7ck8aLiUN8vr9RbaeZIxERERGpEIfTwYzcGazdasyufLH1C0YljqJbw27YbLZyrhZfGBQ/iEHxg9iTtocf9vzA4uTF2JzG38XWI1t59MhWWrTqyS3pGfRoczHk5VVLXA1J44LIZACaWgLI8/G4VqsVi+XsEz8lRyIiIiJSLpfLxT+W/4O1BZ5lZ62jWnPw8EFMqSaCA4KL9Lc5bJhMJkyYivwpvnNJ9CWMjhpNti2bHFsOjkKFJfKA3SFxmPbsqZZYBje1cn4jY3PbvIBI9lTDuNHR0SQkJJzV50zJkYiIiIiU67ejvzE3eW6Rc50COxHpiCQyLpImMU3cX0pdLhc703Ya7VP/B3AqRcJsMmMyGX82CmlESKHiBgX2AtIL0jGbzEX6mTG7rz193mq2KuEqhdPlJDs/g2M5qdhP/Y6aBTUg0FTJp2osVmNvpEr+nnMyThCaZ6Qa2daGhMXEVW7cSnC5XOTk5JCamgpA48aNq3wvJUciIiIiUq6kqCSahDXhUPYhAILNwQxtMJSYBjGER4UTEuJJcJwuJ2Zr6V/CnRhlux04CAwKJDjQM+tkK7CRkZdRoZg6NehUJDlKzUklLT/NnUCZMXvahX6CLEFEBUUVudfp6m+FrzmdmNVWoeTjyIfjFuM9mPNTCa7KM2IxiRBcQsn0MjjyrATbjb8be2AAwcHB5Vxxdk5//lJTU4mLi6vyEjslRyIiIiJSrqigKGaMmcGXP35JvyH9yC/Ix3TCROOYxkQERRTrHxsci9PlxInT+LOEH5fLVWzmx3nmfkelOJ28FGZ32rGduf9QCSICI4olR/sz95d4rclkcidMJpOJuNC4ItfanXaO5hwtMQkzm4rOdgVaAqs32TJZCCyUDBWYTFCV5Mhe4MWgfCc0NBQAm82m5EhEREREfCvAHECsJZa20W1xOBzsydhDRFBEseeNzCYzjcMrtrTpzGp3YdYwEqMSS02onDhLrZBnwoTFbHEnXqUpKUEpLSlzuVw4cLif3zmzn91p50TeiTLf42ntYtphtnjGPpF7gqO5JSRWFFpOaDJjNVtpEFJ0T6Zce647uTxzlsydNAZHEhjRGHKPAlAQHAkB4RWKlbw0KMgy2tbKz/o4C/3+Hc7qqWjojSWWSo5ERERExG/O/EIbYA4g4MwNYSuocXhjGmMkZS6Xq1hCdfrYUkI565igGBwuhzuxKm3G68zEqqIzXVD8vTpcDuxOe7nXBQcEF0uOUrJTyDm9sWsJ45xOmArPctnMAbjCGnI4+3CRGbESf/LTCQQsAAGVT45sjupPjrxByZGIiIiI1DkmkwmLyYKFii2vig+Lr9I4QZYgkqKS6Ni2I3+Z/Bf+cudfSpztKimxOl1U4vTrpc12VWamC07NdrkcOHC4KwW6cJHvzMfpcnIy72SZ76lzo868+t9XuWXkUI7uP0RS056sX7+eVh1bcTDrYOkJ1akZL4vJgrUCv7uaSMmRiIiIiNQb5S29euyxx5g6dWqF72cxWwg1h7Jm9RrCwsIIDQqt8LUNQxrSMKSh+/jcc89lyZIl7uO4uDgGDh7Ic88/B0UfkSI6KBqb1VZqIuaeJTNZsFqsFDgKsDlslZrpMgPNWyRy+PBhGjZsSJY9y33fsljMFuKoXAGHmkLJkYiIiIjUG4cPH3a3p02bxpQpU9i2bZv7XHi455kcl8uFw+EgIKD8r8yNGjXySny33norTzzxBC6Xi3379nHPPfcw6eZJLF26tEi/M5fZlSXLlkWBo8Cd1LSObl1mQgUQ7nQS4HJhsZhICAFyj2FyFhBosuDEhRMXLtfpIu1FmV0ugh2ZVf0V+FXtrU0oIiIiIlJJCQkJ7p+oqChMJpP7eOvWrURERDB79mx69epFUFAQv/zyC7t27eKyyy4jPj6e8PBw+vTpw/z584vcNzExkVdeecV9bDKZeO+99xg7diyhoaG0bduWmTNnlhtfaGgoCQkJNG7cmP79+zN58mTWrVvnft3hcHDLLbeQlJRESEgI7du359VXXy1yj8WLF9O3b1/CwsKIjo7mqguu4tB+owR7gbOAn378icH9BtMwsiHdO3bn1edeJTIgkoYhDYkLNfYjinU6sQJ79+zGFNmYDSsWE5V9nIOLltKhYScOzf+Z8edfTZ/mvZk06gZcm7fTxmajlc1Gs4J85s36np4X3kBwq/707DuQxx9/HLu9/Oer/E3JkYiIiIhIIQ8//DDPPfccW7ZsoWvXrmRlZTF69GgWLFjA+vXrGTVqFGPGjCE5ObnM+zz++ONcc801bNy4kdGjRzNu3DhOnKhYZTuAEydO8NVXX9GvXz/3OafTSbNmzfj666/ZvHkzU6ZM4e9//ztfffUVAHa7ncsvv5xhw4axceNGVqxYwcRbJrqXEy5ZsoTx48dz9913s3nzZt5++20++ugjnn766Ur9jv7x/Bu8NOU+1sz+FGuAhdvvf5wgl4sQl4u1v65l/N1TGHfbOOb9PIPnXnyqSmP4g5bViYiIiIjXjHn9F45m5lf7uI0igvj+zsFeudcTTzzByJEj3cexsbF069bNffzkk0/y3XffMXPmTCZPnlzqfSZOnMj1118PwDPPPMNrr73GqlWrGDVqVKnXvPnmm7z33nu4XC5ycnJo164dP/30k/t1q9XK448/7j5OSkpixYoVfPXVV1xzzTVkZGSQnp7OJZdcQuvWrQFo1roZyRlGIvfc08/x8MMPM2HCBABatWrFk08+yYMPPshjjz3mCSSyGcQkQcapghaRTY3jiIMAPP3UUwwbPgyAhx9ycPEV15IX0pjg4GAef/1eJt91O6OuHwtAYmK7kseogZQciYiIiIjXHM3MJyUjz99hnJXevXsXOc7KymLq1Kn8+OOPHD58GLvdTm5ubrkzR127dnW3w8LCiIyMJDU1tcxrxo0bxz/+8Q8Ajhw5wjPPPMMFF1zA2rVriYgwNtt94403+OCDD0hOTiY3N5eCggK6d+8OGIncxIkTufDCCxk5ciQjRozgsisugxDj/pt+38TqX1cXmcVxOBzk5eWRk5Pj3kiVgEAIiYbgNOM4OMI4DjKeyeraZ6BxDDRObAtAamYBLWIS+O33TSxb8Sv/+tebgLH/lNPpLD5GDaTkSERERES8plFEUK0fNywsrMjxAw88wLx583jxxRdp06YNISEhXHXVVRQUFJR5H6u1aEFrk8lIEsoSFRVFmzZtAGjTpg3vv/8+jRs3Ztq0aUyaNIkvv/ySBx54gJdeeokBAwYQERHBCy+8wMqVK933+PDDD7nrrruYM2cO06ZN4//+7/94++u36da7G9nZ2Tzx+BNcccUVxcYODq74fkaF39vpJXun31tWVhb3P3gfgy4eAkC4K5DGDVpWegx/UHIkIiIiIl7jraVtNcmyZcuYOHEiY8cay8SysrLYu3dvtYxtsRjL2nJzc92xDBw4kDvuuMPdZ9euXcWu69GjBz169OCRRx5hwIABzJk+h269u9Gpaye2bdvmTsB8oWfPnuzeuZPrW40DINoZSNM4343nTUqORERERETK0LZtW6ZPn86YMWMwmUw8+uij5c4AVVVOTg4pKSmAsazuySefJDg4mAsuuMAdy8cff8xPP/1EUlISn3zyCatXryYpKQmAPXv28M4773DppZfSpEkTtm3bxo4dO7j46osB+PP9f+av4/5KixYtuOqqqzCbzfz222/88ccfPPXUU155D1OmTOGSSy4hplkjLhhzAbEmK0sXrvXqGL6ianUiIiIiImV4+eWXiYmJYeDAgYwZM4YLL7yQnj17+mSsd999l8aNG9O4cWOGDx/OsWPHmDVrFu3btwfg9ttv54orruDaa6+lX79+HD9+vMgsUmhoKFu3buXKK6+kXbt23Hbbbfz1r39l4qSJAAw6bxDfzPiGuXPn0qdPH/r378+//vUvWrZs6bX3cOGFF/LZZ++wfNFyrrvgOi6+6Fqvj+ErJpfLVdLeTXVWRkYGUVFRpKenExkZ6bc4bDYbs2bNYvTo0cXWo4qcSZ8XqQx9XqQy9HmRyij8eXE4HOzZs4ekpKQa/xyJwLHcYxzJPgJAs4hmRAVF+Xa8Y7s4glGYo6ErgPhG7X06HkBeXl6pn8mK5gCaORIRERERqeMCzYHudr6j+kut1xZKjkRERERE6rhAiyc5sjlsPh/PWWhxWm1aqKaCDCIiIiIidZzV7Fk2W+AsuwS5NwS4zDRwOoyxXbUn5ag9kYqIiIiISJVYzBYCzAHYnXYKHL5PjgIxE+swzSKvJQAAPudJREFUkqPsU/sg1QZaViciIiIiUg+cXlpnd9pxnJrVkaKUHImIiIiI1AOFizLYnL5/7qg2UnIkIiIiIlIPFC7K4Oulda5CaYbLZPHpWN6kZ45EREREROqBwsmRr8t5Z5ohOdAYL9IJ4T4dzXs0cyQiIiIiUg9oWV35lByJiIiIiNQDVounnPfPi3/GZDKRlpYGwEcffUR0dLR/AqtBlByJiIiISL1hMpnK/Jk6depZ3XvGjBmViiEgIIAWLVpw3333kZ/v26VuAeYALGbj+Z8zZ46uvfZatm/f7tPxawM9cyQiIiIi9cbhw4fd7WnTpjFlyhS2bdvmPhceXj1Px3z44YeMGjUKm83Gb7/9xs0330xYWBhPPvmkT8cNNAeS68zF7rIXOR8SEkJISIjXxgnAXmK7ptPMkYiIiIjUGwkJCe6fqKgoTCZTkXNffvklHTt2JDg4mA4dOvDmm2+6ry0oKGDy5Mk0btyY4OBgWrZsybPPPgtAYmIiAGPHjsVkMrmPSxMdHU1CQgLNmzfnkksu4bLLLmPdunXu13ft2sVll11GfHw84eHh9OnTh/nz5xe5x5tvvknbtm0JDg4mPj6eq666yv2a0+nk2WefJSkpiZCQELp168Y333xTpChDYWcuq5s6dSrdu3fnk08+ITExkaioKK677joyMzPLHQPAjMvdz1x79oDVzJGIiIiICMBnn33GlClT+Pe//02PHj1Yv349t956K2FhYUyYMIHXXnuNmTNn8tVXX9GiRQv279/P/v37AVi9ejVxcXHuGSGLpeLlq7dv387ChQuZOHGi+1xWVhajR4/m6aefJigoiI8//pgxY8awbds2WrRowZo1a7jrrrv45JNPGDhwICdOnGDp0qXu65999lk+/fRT3nrrLdq2bcvPP//MjTfeyJf/+5K2vdpWKK5du3YxY8YMfvjhB06ePMk111zDc889x9NPP13mGI0aNeKcc5pV+P3XJEqORERERMSr3lu6m/eW7im3X+emkbw3oU+Rc5P+u5o/DmaUe+2kIUlMGtKqyjGW5LHHHuOll17iiiuuACApKYnNmzfz9ttvM2HCBJKTk2nbti2DBw/GZDLRsmVL97WNGjUCPDNC5bn++uuxWCzY7Xby8/O55JJLeOSRR9yvd+vWjW7durmPn3zySb777jtmzpzJ5MmTSU5OJiwsjEsuuYSIiAhatmxJjx49AMjPz+eZZ55h/vz5DBgwAIBWrVrxyy+/8OkHn/J4r8cr9PtwOp189NFHREREAHDTTTexYMECnn766TLHePvtt3ntNd8uD/QVJUciIiIi4lWZeXZSMvLK7dc4OrjYuePZBRW6NjPPu8+xZGdns2vXLm655RZuvfVW93m73U5UVBQAEydOZOTIkbRv355Ro0ZxySWXcMEFF1RpvH/961+MGDECh8PBzp07ue+++7jpppv48ssvAWPmaOrUqfz4448cPnwYu91Obm4uycnJAIwcOZKWLVvSqlUrRo0axahRoxg7diyhoaHs3LmTnJwcRo4cWWTMgoICunXvViyW0iQmJroTI4DGjRuTmpoKUOYYp5O02kjJkYiIiIh4VURwAAmRxROfMzUIK/78S4OwwApdGxHs3a+xWVlZALz77rv069evyGunl8j17NmTPXv2MHv2bObPn88111zDiBEj3M/ZVEZCQgJt2rQBoH379mRmZnL99dfz1FNP0aZNGx544AHmzZvHiy++SJs2bQgJCeGqq66ioKAAgIiICNatW8fixYuZO3cuU6ZMYerUqaxevdr9Xn788UeaNm1aZFyz1UwuuRWK0Wq1Fjk2mUw4nU6AMscICgrC4cqHU88aOV0uagslRyIiIiLiVZOGtKrykrczl9lVl/j4eJo0acLu3bsZN25cqf0iIyO59tprufbaa7nqqqsYNWoUJ06cIDY2FqvVisPhqNL4pxOw3FwjcVm2bBkTJ05k7NixgJGM7N27t8g1AQEBjBgxghEjRvDYY48RHR3NwoULGTlyJEFBQSQnJzNs2LAi17hcLrae2FqlGAvr1KlTqWMApKfuoaXdKBfuMFmLvV5TKTkSEREREQEef/xx7rrrLqKiohg1ahT5+fmsWbOGkydPct999/Hyyy/TuHFjevTogdls5uuvvyYhIcFd5S0xMZEFCxYwaNAggoKCiImJKXWstLQ0UlJScDqd7NixgyeeeIJ27drRsWNHANq2bcv06dMZM2YMJpOJRx991D1rA/DDDz+we/duhg4dSkxMDLNmzcLpdNK+fXsiIiJ44IEHuPfee3E6nQwePJj09HSWLVtGZGQkgy8b7L6P0+UsFltFlDfGlRcNI/zUjFF2lUbwDyVHIiIiIiLApEmTCA0N5YUXXuBvf/sbYWFhdOnShXvuuQcwEoJ//vOf7NixA4vFQp8+fZg1axZms7E7zksvvcR9993Hu+++S9OmTYvN9BR28803A7hLiQ8dOpRnnnmGgADj6/nLL7/Mn/70JwYOHEjDhg156KGHyMjwFKqIjo5m+vTpTJ06lby8PNq2bcsXX3zBOeecAxgFHBo1asSzzz7L7t27iY6OpmfPnvz9738vUs7b7qz6s1tljVFbmVyuWrQI0AsyMjKIiooiPT2dyMhIv8Vhs9mYNWsWo0ePLraeU+RM+rxIZejzIpWhz4tURuHPi8PhYM+ePSQlJREcXP4zQlJzHMk+wrHcYwC0jGxJeKD3N77NSt1LuP0kANmmMMIat/P6GGfKy8sr9TNZ0RxAm8CKiIiIiNQjhWeOsm2+WfRWYLKQZTKRZTKRb649i9WUHImIiIiI1CNh1jBMp0rJHc87ToGjwOtj5Jlc7LNa2We1km2qPQvVlByJiIiIiNQjgZZAYkNiAaN6XWpOqp8jqjmUHImIiIiI1DONQhphMRvlw9Pz08mx5fg5oppByZGIiIiISD1jMVuIC4lzHx/OPkw9q9NWIiVHIiIiIiL1UExwDEEBQQDk2fNIz0/32r2t2Eps13RKjkRERERE6iGTyURCaIL7+EjOERxOh3fufcY4tYWSIxERERGReio8MJyIwAjA2BD29P5H9ZWSIxERERGReiw+LN49u+Or0t61hZIjEREREZGzlJiYyCuvvOLvMIr56KOPiI6OLrNPkCWI2GBPae9x48dx+eWXey2G8go91KTfnZIjEREREak3TCZTmT9Tp06t0n1Xr17NbbfdVuW4Hn74YTp06FDk3NatWzGZTEycOLHI+Y8++oigoCByc3PLve+1117L9u3by+1XuLS3zWEr99mj8hIaR6GEqDYVwQvwdwAiIiIiItXl8OHD7va0adOYMmUK27Ztc58LDw93t10uFw6Hg4CA8r8yN2rU6KziGj58OM8//zwpKSkkJBhFEhYtWkTz5s1ZvHhxkb6LFi2if//+hISElHvfkJCQCvWzmC3EhcZxOMv4/eQ58nC5XFUuphDmCiDelglAtimsSvfwB80ciYiIiEi9kZCQ4P6JiooyKradOt66dSsRERHMnj2bXr16ERQUxC+//MKuXbu47LLLiI+PJzw8nD59+jB//vwi9z1zJsVkMvHee+8xduxYQkNDadu2LTNnziw1rsGDB2O1WoskQosXL+avf/0rJ06cYO/evUXODx8+HID8/HweeOABmjZtSlhYGP369Styj5KW1T311FPExcURERHBpEmTePjhh+nevTsxQTEEBwQD4HQ5efK5J2ncuDENGjTgr3/9KzabUZL73HPPZd++fdx7773uGbfTfvnlF4YMGUKjlh1o3vsi7nr0n2TneDaYTU1NZcyYMYSEhJCUlMRnn31W5t9XdVNyJCIiIiJSyMMPP8xzzz3Hli1b6Nq1K1lZWYwePZoFCxawfv16Ro0axZgxY0hOTi7zPo8//jjXXHMNGzduZPTo0YwbN44TJ06U2DcsLIw+ffqwaNEi97nFixdz/vnnM2jQIPf53bt3k5yc7E6OJk+ezIoVK/jyyy/ZuHEjV199NaNGjWLHjh0ljvPZZ5/x9NNP8/zzz7N27VpatGjBf/7zH+BUae8wY9Zq1S+r2LJ9C/MXzOe///0vH330ER999BEA06dPp1mzZjzxxBMcPnzYPRu3a9cuRo0axZVXXsmKRbOZ9p/n+GXVBu7/+5Pu8SdOnMj+/ftZtGgR33zzDW+++Sapqanl/ZVUGy2rExERERHveXsYZPnhy254HNy+xCu3euKJJxg5cqT7ODY2lm7durmPn3zySb777jtmzpzJ5MmTS73PxIkTuf766wF45plneO2111i1ahWjRo0qsf/w4cP5+uuvAdi8eTN5eXn06NGDoUOHsnjxYm6++WYWL15McHAw/fv3Jzk5mQ8//JDk5GSaNGkCwAMPPMCcOXP48MMPeeaZZ4qN8frrr3PLLbdw8803AzBlyhTmzp1LVlYWAGHWMKwWK5HRkTzy3CM0DG/IOZ3O4eKLL2bBggXceuutxMbGYrFYiIiIcC8BBHj22WcZN24c99xzD1mpewlvEc1rT/6NYVfeyrt5eSQnJzN79mxWrVpFnz59AHj//ffp2LFj+X8p1UTJkYiIiIh4T1YqZB7ydxRnpXfv3kWOs7KymDp1Kj/++COHDx/GbreTm5tb7sxR165d3e2wsDAiIyPLnCU599xzefrppzl8+DCLFy9m8ODBWCwWhg0bxltvvQUYs0kDBw4kKCiI33//HYfDQbt27YrcJz8/nwYNGpQ4xrZt27jjjjuKnOvbty8LFy50H4cEhNCmfRssFgvH844TExxD48aN+f3338t8v7/99hsbN27ks88+O1WhzoXL5cLpdLJnzx62b99OQEAAvXr1cl/ToUOHcqvpVSclRyIiIiLiPeFxtX7csLCiBQQeeOAB5s2bx4svvkibNm0ICQnhqquuoqCg7P2ArFZrkWOTyYTT6Sy1/6BBgwgMDGTRokUsWrSIYcOGAdCnTx+OHTvG7t27Wbx4MbfffjtgJG0Wi4W1a9disViK3KtwYYnKMpvMhAaHAkZRiiPZR8qN/XQ8t99+O3fddRdHThwig2wjFlcgrVu3rlDVPH9TciQiIiIi3uOlpW01ybJly5g4cSJjx44FjCSgcIEEbwkJCXEXVFiyZAl/+9vfACPJ6t+/P++//z779+93P2/Uo0cPHA4HqampDBkypEJjtG/fntWrVzN+/Hj3udWrVxfrF2gOJMAcgN1pJ6MgA5vTVvT1wEAcjqLlvnv27MnmzZtp06YNoccCOYlRrS7SGUxgYCAdOnTAbrezdu1a97K6bdu2kZaWVrFfUDVQQQYRERERkTK0bduW6dOns2HDBn777TduuOGGcmdRqmr48OF8+eWX5OXl0bNnT/f5YcOG8frrr7sLNwC0a9eOcePGMX78eKZPn86ePXtYtWoVzz77LD/++GOJ97/zzjt5//33+e9//8uOHTt46qmn2LhxY7GS3SaTibhQz2xcji2nyOuJiYn8/PPPHDx4kGPHjgHw0EMPsXz5ciZPnswfv29i3659LJy9kEcfmQoYidmoUaO4/fbbWblyJWvXrmXSpEkVKjVeXZQciYiIiIiU4eWXXyYmJoaBAwcyZswYLrzwwiKJizcNHz6czMxMBg0aVGR/pWHDhpGZmeku+X3ahx9+yPjx47n//vtp3749l19+OatXr6ZFixYl3n/cuHE88sgjPPDAA/Ts2ZM9e/YwceJEgoODi/WNDop2l/a2O+3YnXb3a0888QR79+6ldevW7j2eunbtypIlS9i+fTtXjLmaq867in8//2/i4z17QH344Yc0adKEYcOGccUVV3DbbbcRF+enpZglMLlctWnP2rOXkZFBVFQU6enpREZG+i0Om83GrFmzGD16dLH1qCJn0udFKkOfF6kMfV6kMgp/XhwOB3v27CEpKanEL9ZSe4wcOZKEhAQ++eSTYq9l27LZm74XMDaKbRvdFovZUqzfmY4d28UR8gBo6LIQ36iDV2MuSd7/t3fnUVXV+//Hn4d5BtGYCqdAJK8azmBlFgrX9KuZ6TV/Cl0cUsm8Zlerm5gTZmpmapkW2jdnr3q9aeYQpKmlOWVXhURQK4f6msogMu3fHy7PlQD1IHAcXo+1zlqcfT57f95781543n4++7Pz8srNyZutAXTPkYiIiIjIPSI3N5cPPviAqKgobG1tWbJkCZs3b2bTpk1ltne1d8XD0YOLly9SVFzEr5d+NT8L6eaZbtzkNqFpdSIiIiIi9wiTycT69et57LHHaN68Of/+97/55z//SWRkZLn7+Lr4mu9JOpd3jstFl6sr3GqnkSMRERERkXuEs7MzmzdvtmgfB1sHajrV5LdLv5mX9q7tUfY9TXc6jRyJiIiIiMh11XKuhZ3NlXGVrPwssvOzr9v+2mUN7qQlDlQciYiIiIjIddna2OLr4mt+fzr39HWLHpNhwq24GLfiYmwN3XMkIiIiIiJ3EU9HT5ztrjyT6HLhZX7P+73ctk7YUqewkDqFhbgUqzgSEREREZG7iMlkKrFS3dlLZykqLrJiRJVPxZGIiIiIiNwUF3sXPB09AcxLe99NVByJiIiIiMhNK7G096VzXC4sa2nva6bSmTStTkREREREqklsbCzdunWzaJ+6desyY8YMi/uyt7WnlnMtAAwMTueeLtUmy8aGo/b2HLW353cbWzIzMzGZTOzfv9/i/qqTiiMRERERuWeYTKbrvsaOHXtLx16zZs1127Rp04YXXnihxLYPPvgAk8nEggULSmyPjY3l0Ucfvam+33333VL736rrFTQ1nWqal/bOzs8utbS3gcFlk4nLJhMGWspbREREROS2c+rUKfNrxowZeHh4lNg2cuTIKu2/ffv2pKSklNiWnJxMYGBgqe0pKSk88cQTN3VcT09PvLy8KifIm2BrY4uv6zVLe+dcf2nvO4WKIxERERG5Z/j5+Zlfnp6eV1Zgu2bb0qVLCQ0NxcnJiYYNGzJnzhzzvvn5+cTHx+Pv74+TkxN16tQhMTERuDJFDeDpp5/GZDKZ3/9R+/btSU1N5fTp/05F++qrrxg9enSJ4igjI4Pjx4/Tvn17AE6ePEnPnj3x8vLC29ubrl27kpmZaW7/x2l1WVlZ9OnTB1dXV/z9/XnnnXd4/PHHGT58eIl4cnNz+etf/4q7uzu1a9fmww8/NH9Wr149AMLCwjCZTDz++OPmz+bPn0/4w+E0e6AZXcK7sHDeQs7lnTN/vm/vfnq070GzB5rxVMdu7Nu3r9zfye3EztoBiIiIiMhdZscs2Dn7xu38m8JzS0tuW/wXOHXgxvuGD4WI+IrFV45FixYxZswYZs2aRVhYGPv27WPAgAG4uroSExPDzJkzWbt2LcuXL6d27dqcPHmSkydPArB79258fHxISkoiOjoaW1vbMvto27Yt9vb2JCcn07t3bw4dOsSlS5eIi4tj1KhRZGRkUK9ePZKTk3FyciI8PJyCggKioqIIDw9n27Zt2NnZMWHCBKKjo/n+++9xcHAo1c+IESPYvn07a9euxdfXlzFjxrB3714efvjhEu2mTZvG+PHjee2111i5ciWDBw+mXbt2hISEsGvXLlq1asXmzZtp1KiRuZ9rr1PDPzVk446NjB0xFldXV0YOHklebh4xfZ6nTbtwJr8/mfOZp6t8RK6yqDgSERERkcp1OQuyfrlxO8/7S2/L/e3m9r2cZXlcN5CQkMC0adPo3r07cGXk5NChQ8ydO5eYmBhOnDhBcHAwjzzyCCaTiTp16pj3ve+++wDw8vLCz8+vzOMDuLq60qpVK1JSUujduzcpKSk88sgjODo6EhERQUpKCvXq1SMlJYXw8HAcHR359NNPKS4uZv78+eZV4pKSkvDy8iIlJYWOHTuW6CMrK4uFCxeyePFinnzySXP7gICAUvF06tSJIUOGADBq1CjeeecdkpOTCQkJMZ9TzZo1S5zTH6+Th78Hx1KPsXThUmJiYvj34n9jFBuMmzEORydHaoU05FK2weDBgy37hViBiiMRERERqVyO7uBe+ot4KS61yt52M/s6ulse13Xk5OSQnp5OXFwcAwYMMG8vLCzE0/PKc31iY2Pp0KEDISEhREdH07lz51KFyc14/PHHWbFiBXDlvqKr09XatWtHSkoKzz//PCkpKeY4Dhw4wNGjR3F3L3nOeXl5pKenlzr+sWPHKCgooFWrVuZtnp6ehISElGrbpEkT889XpxiePXu23NjLu04FhQW4ebhxLu8cP/znBx56KARHJ0fz5+Hh4de7JLcNFUciIiIiUrki4is+5e2P0+yqSXb2ldXW5s2bR+vWrUt8dnWKXLNmzcjIyODzzz9n8+bN9OzZk8jISFauXGlRX+3bt2fixIn8/PPPpKSkmKectWvXjrlz55Kens7JkyfNizFkZ2fTvHlzFi1aVOpYV0d3Ksre3r7Ee5PJRHFxcbnty7tO/3fp/ziffx6AnMKcW4rJmlQciYiIiMg9z9fXl4CAAI4dO0afPn3Kbefh4UGvXr3o1asXPXr0IDo6mnPnzuHt7Y29vT1FRUU37CsiIgIHBwfmzJlDXl4ezZs3B6Bly5b8+uuvfPzxx+bpd3ClKFu2bBk+Pj54eHjc8Pj169fH3t6e3bt3U7t2bQAuXLhAWloajz322M1cDgDzPUbXnlN516m+UZ+jvx+loLiAwAcDWb10FZfzLptHj7755pub7teatFqdiIiIiAjw5ptvkpiYyMyZM0lLS+PgwYMkJSUxffp0AKZPn86SJUs4cuQIaWlprFixAj8/P/MS2nXr1mXLli2cPn2a33//vdx+nJ2dadOmDe+99x5t27Y1j0w5ODiU2H51VKdPnz7UqlWLrl27sm3bNjIyMkhJSWHYsGH89NNPpY7v7u5OTEwMr7zyCsnJyfznP/8hLi4OGxsb8z1LN8PHxwdnZ2c2bNjAmTNnuHDhQrnXaeGChayYf2Wq4FPdnwKTiYQRCaSnprNpUwpTp0696X6tScWRiIiIiAjQv39/5s+fT1JSEo0bN6Zdu3YsWLDAvKS1u7s7U6ZMoUWLFrRs2ZLMzEzWr1+Pjc2Vr9TTpk1j06ZNBAYGEhYWdt2+2rdvT1ZWVonlseHK1LqsrCzzEt4ALi4ubN26ldq1a9O9e3dCQ0OJi4sjLy+v3JGk6dOnEx4eTufOnYmMjKRt27bmJcpvlp2dHTNnzmTu3LkEBATQtWvX616n0KBQXOxdcHFzYdanszh26EeefaIHUxLf5a233rrpfq3JZNwNT2uywMWLF/H09OTChQs3NSxZVQoKCli/fj2dOnUqNddT5I+UL2IJ5YtYQvkilrg2X4qKiszLTlvyhVusIycnh/vvv59p06YRFxdXZf1cKrjEsQvHALDBIDi/gMsmV1z9G1RZn1fl5eWVm5M3WwPoniMRERERkbvMvn37OHLkCK1ateLChQuMGzcOwDz6U1Wc7Z3xcvTi/OXzFGPirK0tnuWv73DbUXEkIiIiInIXmjp1KqmpqTg4ONC8eXO2bdtGrVplLJ9eyXxcfLhw+TwG8LutLS530EQ1FUciIiIiIneZsLAw9uzZY5W+7W3tcTccuGjKByDHBrysEonltCCDiIiIiIhUqiLTf8uMSze/QJ7VqTgSERERERFBxZGIiIiIiAig4khERERERARQcSQiIiIiIpXMlqJrfr5z1vJWcSQiIiIiIpXKVM7PtzsVRyIiIiIit6hu3brMmDHD2mHctlJSUjCZTJw/f/6WjlPV11nFkYiIiIjcM0wm03VfY8eOrdBxd+/ezcCBA28ptscff9wch5OTEw0aNCAxMRHjDnqI6p1OD4EVERERkXvGqVOnzD8vW7aMMWPGkJqaat7m5uZm/tkwDIqKirCzu/FX5vvuu69S4hswYADjxo3j8uXLfPnllwwcOBAvLy8GDx5cKceX69PIkYiIiIjcM/z8/MwvT09PTCaT+f2RI0dwd3fn888/p3nz5jg6OvL111+Tnp5O165d8fX1xc3NjZYtW7J58+YSx/3jdC+TycT8+fN5+umncXFxITg4mLVr194wPhcXF/z8/KhTpw7PP/88TZo0YdOmTebPL1++zMiRI7n//vtxdXWldevWpKSkmD8/fvw4Xbp0oUaNGri6utKoUSPWr18PQFFREXFxcdSrVw9nZ2dCQkJ49913S/QfGxtLt27dmDRpEr6+vnh5eTFu3DgKCwt55ZVX8Pb25oEHHiApKcm8T2ZmJiaTiaVLlxIREYGTkxNPPtqZ3dt3A2BQ9sjX119/zaOPPoqzszOBgYEMGzaMnJwc8+dnz56lS5cuODs7U69ePRYtWnTD63erNHIkIiIiIpWm12e9+O3Sb9Xeby3nWizrvKxSjjV69GimTp1K/fr1qVGjBidPnqRTp05MnDgRR0dHPvnkE7p06UJqaiq1a9cu9zhvvvkmU6ZM4e233+a9996jT58+HD9+HG9v7xvGYBgGX3/9NUeOHCE4ONi8PT4+nkOHDrF06VICAgJYvXo10dHRHDx4kODgYIYOHUp+fj5bt27F1dWVQ4cOmUfDiouLeeCBB1ixYgU1a9Zkx44dDBw4EH9/f3r27Gnu48svv+SBBx5g69atbN++nbi4OHbs2MFjjz3Gt99+y7Jlyxg0aBAdOnTggQceMO/3yiuvMGPGDB566CEmTEog/v/F88WeL6jhVfp809PTiY6OZsKECXz88cf8+uuvxMfHEx8fby68YmNj+eWXX0hOTsbe3p5hw4Zx9uzZG/8Cb4GKIxERERGpNL9d+o2zuVX7BbaqjRs3jg4dOpjfe3t707RpU/P78ePHs3r1atauXUt8fHy5x4mNjaV3794ATJo0iZkzZ7Jr1y6io6PL3WfOnDnMnz+f/Px8CgoKcHJyYtiwYQCcOHGCpKQkTpw4QUBAAAAjR45kw4YNJCUlMWnSJE6cOMEzzzxD48aNAahfv7752Pb29rz55pvm9/Xq1WPnzp0sX768RHHk7e3NzJkzsbGxISQkhClTppCbm8trr70GwKuvvsrkyZP5+uuv+ctf/mLeLz4+nmeeeQaAKVPGseXLraxatIqRQwaUOs/ExET69OnD8OHDAQgODmbmzJm0a9eO999/nxMnTvD555+za9cuWrZsCcBHH31EaGhoudeuMqg4EhEREZFKU8u51h3fb4sWLUq8z87OZuzYsaxbt45Tp05RWFjIpUuXOHHixHWP06RJE/PPrq6ueHh43HDko0+fPrz++uv8/vvvJCQkEBERQUREBAAHDx6kqKiIBg0alNjn8uXL1KxZE4Bhw4YxePBgNm7cSGRkJM8880yJOGbPns3HH3/MiRMnuHTpEvn5+Tz88MMljteoUSNsbP57942vry9/+tOfzO9tbW2pWbNmqXMJDw83/2xnZ0ejhxtxLO1Ymed54MABvv/++xJT5QzDoLi4mIyMDNLS0rCzs6N58+bmzxs2bIiXl9f1Lt8tuy2Ko9mzZ/P2229z+vRpmjZtynvvvUerVq3Kbb9ixQreeOMNMjMzCQ4O5q233qJTp07VGLGIiIiIlKWyprZZk6ura4n3I0eOZNOmTUydOpWgoCCcnZ3p0aMH+fn51z2Ovb19ifcmk4ni4us/ENXT05OgoCAAli9fTlBQEG3atCEyMpLs7GxsbW3Zs2cPtra2Jfa7OnWuf//+REVFsW7dOjZu3EhiYiLTpk3jxRdfZOnSpYwcOZJp06YRHh6Ou7s7b7/9Nt9+++0N467IuVxPdnY2gwYNMo+KXat27dqkpaVV+Ni3wuoLMixbtowRI0aQkJDA3r17adq0KVFRUeVW1Tt27KB3797ExcWxb98+unXrRrdu3fjhhx+qOXIRERERuRds376d2NhYnn76aRo3boyfnx+ZmZlV3q+bmxsvvfQSI0eOxDAMwsLCKCoq4uzZswQFBZV4+fn5mfcLDAzkhRdeYNWqVbz88svMmzfPfB4REREMGTKEsLAwgoKCSE9Pr7R4v/nmG/PPeYXFHDpwiPoN6pNvKl1yNGvWjEOHDpU6j6CgIBwcHGjYsCGFhYXs2bPHvE9qauotPyfpRqxeHE2fPp0BAwbw/PPP89BDD/HBBx/g4uLCxx9/XGb7d999l+joaF555RVCQ0MZP348zZo1Y9asWdUcuYiIiIjcC4KDg1m1ahX79+/nwIEDPPfcc7c0amKJQYMGkZaWxj//+U8aNGhAnz596NevH6tWrSIjI4Ndu3aRmJjIunXrABg+fDhffPEFGRkZ7N27l+TkZPN9OsHBwXz33Xd88cUXpKWl8cYbb7B79+5Ki3X27NmsXr2aI0eO8OroBC6ev8jTzz1Ngan0anWjRo1ix44dxMfHs3//fn788Uf+9a9/me/hCgkJITo6mkGDBvHtt9+yZ88e+vfvj7Ozc6XFWxarFkf5+fns2bOHyMhI8zYbGxsiIyPZuXNnmfvs3LmzRHuAqKioctuLiIiIiNyK6dOnU6NGDSIiIujSpQtRUVE0a9asWvr29vamX79+jB07luLiYpKSkujXrx8vv/wyISEhdOvWjd27d5tXzSsqKmLo0KGEhoYSHR1NgwYNmDNnDnCl0OrevTu9evWidevW/N///R9DhgyptFgnT57M5MmTadq0Kd99+x2zPp1FjZo1ymzbpEkTvvrqK9LS0nj00UcJCwtjzJgx5oUmAJKSkggICKBdu3Z0796dgQMH4uPjU2nxlsVkWPGRu7/88gv3338/O3bsKHED19///ne++uqrUvMfARwcHFi4cKF55Q+4sqrHm2++yZkzZ0q1v3z5MpcvXza/v3jxIoGBgfz22294eHhU8hndvIKCAjZt2kSHDh1KzeEU+SPli1hC+SKWUL6IJa7Nl6KiIk6ePEndunVxcnKydmhiRZmZmTz44IPs2bPHvLjDT+eOk0UuAC6GPXVqBlV5HHl5eWRmZhIYGFgqJy9evEitWrW4cOHCdWuA22JBhqqUmJhYYsnCqzZu3IiLi4sVIirp2od6idyI8kUsoXwRSyhfxBKbNm3Czs4OPz8/srOzb7gwgdzdsrOzAcjJyeHixYsA2BZjnqNmZ9iYt1el/Px8Ll26xNatWyksLCzxWW5u7k0dw6rFUa1atbC1tS014nPmzJkSN5Vdy8/Pz6L2r776KiNGjDC/vzpy1LFjR40cyR1D+SKWUL6IJZQvYomyRo7c3Nw0cnSPu7pS3tXlygFci1zwKrxMbm4uHl6e2Ns5VHkceXl5ODs789hjj5U5cnQzrFocOTg40Lx5c7Zs2UK3bt2AK0/u3bJlS7kP1AoPD2fLli3mB0bBlf+9uHZa3rUcHR1xdHQstd3e3v62+EfgdolD7gzKF7GE8kUsoXwRS9jb22NjY4PJZMLGxqbEM3Hk3lO/fn3+eKeOjY0DtrZ2FFwuwt7OoVpy5GpOlvX37Gb/vll9Wt2IESOIiYmhRYsWtGrVihkzZpCTk8Pzzz8PQL9+/bj//vtJTEwE4KWXXqJdu3ZMmzaNp556iqVLl/Ldd9/x4YcfWvM0RERERETkDmf14qhXr178+uuvjBkzhtOnT/Pwww+zYcMGfH19AThx4kSJSjMiIoLFixfzj3/8g9dee43g4GDWrFlT4qm9IiIiIiIilrJ6cQQQHx9f7jS6lJSUUtueffZZnn322SqOSkRERERE7iWaICoiIiIiIoKKIxEREREREUDFkYiIiIiICKDiSEREREREKsnYsWN5+OGHb+kYmZmZmEwm9u/fXykxWULFkYiIiIjcM0wm03VfY8eOvaVjr1mzxqIYPDw8aNmyJf/6178q3K9UHhVHIiIiInLPOHXqlPk1Y8YMPDw8SmwbOXJktcSRlJTEqVOn+O6772jbti09evTg4MGD1dK3lE/FkYiIiIjcM/z8/MwvT09PTCZTiW1Lly4lNDQUJycnGjZsyJw5c8z75ufnEx8fj7+/P05OTtSpU4fExEQA6tatC8DTTz+NyWQyvy+Pl5cXfn5+NGjQgPHjx1NYWEhycrL585MnT9KzZ0+8vLzw9vama9euZGZmmj9PSUmhVatWuLq64uXlRdu2bTl+/DgA6enpdO3aFV9fX9zc3GjZsiWbN28u0X/dunWZMGEC/fr1w83NjTp16rB27Vp+/fVXunbtipubG02aNOG7774z77NgwQK8vLxYs2YNwcHBODk5ERUVxcmTJ697rvPnzy/3mgLs2rWLsLAwnJycaNGiBfv27bvu8arSbfGcIxERERG5eyz8z0I+OfTJDds95P0Q7z35XoltL255kUPnDt1w334P9SOmUUyFYyzLokWLGDNmDLNmzSIsLIx9+/YxYMAAXF1diYmJYebMmaxdu5bly5dTu3ZtTp48aS4Mdu/ejY+PD0lJSURHR2Nra3tTfRYWFvLRRx8B4ODgAEBBQQFRUVGEh4ezbds27OzsmDBhAtHR0Xz//ffY2NjQrVs3BgwYwJIlS8jPz2fXrl2YTCYAsrOz6dSpExMnTsTR0ZFPPvmELl26kJqaSu3atc19v/POO0yaNIk33niDd955h759+xIREcFf//pX3n77bUaNGkW/fv34z3/+Yz52bm4uEydO5JNPPsHBwYEhQ4bwl7/8he3bt5d5fsuXL2fs2LHlXtPs7Gw6d+5Mhw4d+PTTT8nIyOCll16q2C+wEqg4EhEREZFKlVOQw9ncszds5+fqV2rbucvnbmrfnIKcCsV2PQkJCUybNo3u3bsDUK9ePQ4dOsTcuXOJiYnhxIkTBAcH88gjj2AymahTp4553/vuuw/474jQjfTu3RtbW1suXbpEcXExdevWpWfPngAsW7aM4uJi5s+fby5KkpKS8PLyIiUlhRYtWnDhwgU6d+7Mgw8+CEBoaKj52E2bNqVp06bm9+PHj2f16tWsXbuW+Ph48/ZOnToxaNAgAMaMGcP7779Py5YtefbZZwEYNWoU4eHhnDlzxnxOBQUFzJo1i9atWwOwcOFCQkND2bVrF61atSp1npMnT+btt98u95ouXryY4uJiPvroI5ycnGjUqBE//fQTgwcPvuE1rAoqjkRERESkUrnau+Lj4nPDdt6O3mVuu5l9Xe1dKxRbeXJyckhPTycuLo4BAwaYtxcWFuLp6QlAbGwsHTp0ICQkhOjoaDp37kzHjh0r1N8777xDZGQkx44d429/+xszZ87E2/vK9Thw4ABHjx7F3d29xD55eXmkp6fTsWNHYmNjiYqKokOHDkRGRtKzZ0/8/f2BKyNHY8eOZd26dZw6dYrCwkIuXbrEiRMnShyvSZMm5p99fX0BaNy4caltZ8+eNRdHdnZ2tGzZ0tymYcOGeHl5cfjw4VLFUU5ODhkZGQwYMMBchEHJa3r48GGaNGmCk5OT+fPw8HBLLmWlUnEkIiIiIpUqplFMhae8/XGaXXXJzs4GYN68eeZRkauuTpFr1qwZGRkZfP7552zevJmePXsSGRnJypUrLe7Pz8+PoKAggoKCSEpKolOnThw6dAgfHx+ys7Np3rw5ixYtKrXf1RGqpKQkhg0bxoYNG1i2bBn/+Mc/2LRpE23atGHkyJFs2rSJqVOnEhQUhLOzMz169CA/P7/Esezt7c0/Xx2hKmtbcXGxxecH/72mc+fOLVXw3Oy0w+qm4khERERE7nm+vr4EBARw7Ngx+vTpU247Dw8PevXqRa9evejRowfR0dGcO3cOb29v7O3tKSoqsrjvVq1a0bx5cyZOnMi7775Ls2bNWLZsGT4+Pnh4eJS7X1hYGGFhYbz66quEh4ezePFi2rRpw/bt24mNjeXpp58GrhQp1y7mcCsKCwv57rvvzKNEqampnD9/vsS0vqt8fX3x9/cnIyODvn37lnm80NBQ/vd//5e8vDzz6NE333xTKbFWhFarExEREREB3nzzTRITE5k5cyZpaWkcPHiQpKQkpk+fDsD06dNZsmQJR44cIS0tjRUrVuDn54eXlxdwZQW4LVu2cPr0aX7//XeL+h4+fDhz587l559/pk+fPtSqVYuuXbuybds2MjIySElJYdiwYfz0009kZGTw6quvsnPnTo4fP87GjRv58ccfzQVKcHAwq1atYv/+/Rw4cIDnnnuuwqM/f2Rvb8+LL77It99+y549e4iNjaVNmzZl3m8EMHr0aCZPnlzuNX3uuecwmUwMGDCAQ4cOsX79eqZOnVopsVaEiiMREREREaB///7Mnz+fpKQkGjduTLt27ViwYAH16tUDwN3dnSlTptCiRQtatmxJZmYm69evx8bmylfqadOmsWnTJgIDAwkLC7Oo7+joaOrVq8fEiRNxcXFh69at1K5dm+7duxMaGkpcXBx5eXl4eHjg4uLCkSNHeOaZZ2jQoAEDBw5k6NCh5vt6pk+fTo0aNYiIiKBLly5ERUXRrFmzSrlGLi4ujBo1iueee462bdvi5ubGsmXLym3fr18/Pvzww3KvqZubG//+9785ePAgYWFhvP7667z11luVEmtFmAzDMKzWuxVcvHgRT09PLly4cN1hyqpWUFDA+vXr6dSpU4m5nSJlUb6IJZQvYgnli1ji2nwpKioiIyODevXqlbiZXu5eCxYsYPjw4Zw/f/6m2hcXF3Px4kU8PDzMBWRVysvLKzcnb7YG0MiRiIiIiIgIKo5EREREREQAFUciIiIiInITYmNjb3pK3Z1KxZGIiIiIiAgqjkRERETkFtxja3vJbawyclHFkYiIiIhY7Orqhrm5uVaOROSKq7l4Kytv2lVWMCIiIiJy77C1tcXLy4uzZ88CV55/YzKZrByV3E6Ki4vJz88nLy+vSpfyNgyD3Nxczp49i5eXF7a2thU+loojEREREakQPz8/AHOBJHItwzC4dOkSzs7O1VI4e3l5mXOyolQciYiIiEiFmEwm/P398fHxoaCgwNrhyG2moKCArVu38thjj1X5Q6bt7e1vacToKhVHIiIiInJLbG1tK+WLqdxdbG1tKSwsxMnJqcqLo8qiBRlERERERERQcSQiIiIiIgKoOBIREREREQHuwXuOrj4c6uLFi1aNo6CggNzcXC5evHjHzMEU61G+iCWUL2IJ5YtYQvkilrid8uXqd/8bPSj2niuOsrKyAAgMDLRyJCIiIiIiUp2ysrLw9PQs93OTcaPy6S5TXFzML7/8gru7u1UfVHbx4kUCAwM5efIkHh4eVotD7gzKF7GE8kUsoXwRSyhfxBK3U74YhkFWVhYBAQHXfSDtPTdyZGNjwwMPPGDtMMw8PDysnixy51C+iCWUL2IJ5YtYQvkilrhd8uV6I0ZXaUEGERERERERVByJiIiIiIgAKo6sxtHRkYSEBBwdHa0ditwBlC9iCeWLWEL5IpZQvogl7sR8uecWZBARERERESmLRo5ERERERERQcSQiIiIiIgKoOBIREREREQFUHImIiIiIiAAqjqrU7NmzqVu3Lk5OTrRu3Zpdu3Zdt/2KFSto2LAhTk5ONG7cmPXr11dTpHI7sCRf5s2bx6OPPkqNGjWoUaMGkZGRN8wvubtY+vflqqVLl2IymejWrVvVBii3FUvz5fz58wwdOhR/f38cHR1p0KCB/k26h1iaLzNmzCAkJARnZ2cCAwP529/+Rl5eXjVFK9a0detWunTpQkBAACaTiTVr1txwn5SUFJo1a4ajoyNBQUEsWLCgyuO0hIqjKrJs2TJGjBhBQkICe/fupWnTpkRFRXH27Nky2+/YsYPevXsTFxfHvn376NatG926deOHH36o5sjFGizNl5SUFHr37k1ycjI7d+4kMDCQjh078vPPP1dz5GINlubLVZmZmYwcOZJHH320miKV24Gl+ZKfn0+HDh3IzMxk5cqVpKamMm/ePO6///5qjlyswdJ8Wbx4MaNHjyYhIYHDhw/z0UcfsWzZMl577bVqjlysIScnh6ZNmzJ79uybap+RkcFTTz1F+/bt2b9/P8OHD6d///588cUXVRypBQypEq1atTKGDh1qfl9UVGQEBAQYiYmJZbbv2bOn8dRTT5XY1rp1a2PQoEFVGqfcHizNlz8qLCw03N3djYULF1ZViHIbqUi+FBYWGhEREcb8+fONmJgYo2vXrtUQqdwOLM2X999/36hfv76Rn59fXSHKbcTSfBk6dKjxxBNPlNg2YsQIo23btlUap9x+AGP16tXXbfP3v//daNSoUYltvXr1MqKioqowMsto5KgK5Ofns2fPHiIjI83bbGxsiIyMZOfOnWXus3PnzhLtAaKiosptL3ePiuTLH+Xm5lJQUIC3t3dVhSm3iYrmy7hx4/Dx8SEuLq46wpTbREXyZe3atYSHhzN06FB8fX3505/+xKRJkygqKqqusMVKKpIvERER7Nmzxzz17tixY6xfv55OnTpVS8xyZ7kTvu/aWTuAu9Fvv/1GUVERvr6+Jbb7+vpy5MiRMvc5ffp0me1Pnz5dZXHK7aEi+fJHo0aNIiAgoNQfHLn7VCRfvv76az766CP2799fDRHK7aQi+XLs2DG+/PJL+vTpw/r16zl69ChDhgyhoKCAhISE6ghbrKQi+fLcc8/x22+/8cgjj2AYBoWFhbzwwguaVidlKu/77sWLF7l06RLOzs5Wiuy/NHIkcoebPHkyS5cuZfXq1Tg5OVk7HLnNZGVl0bdvX+bNm0etWrWsHY7cAYqLi/Hx8eHDDz+kefPm9OrVi9dff50PPvjA2qHJbSglJYVJkyYxZ84c9u7dy6pVq1i3bh3jx4+3dmgiFaKRoypQq1YtbG1tOXPmTIntZ86cwc/Pr8x9/Pz8LGovd4+K5MtVU6dOZfLkyWzevJkmTZpUZZhym7A0X9LT08nMzKRLly7mbcXFxQDY2dmRmprKgw8+WLVBi9VU5O+Lv78/9vb22NramreFhoZy+vRp8vPzcXBwqNKYxXoqki9vvPEGffv2pX///gA0btyYnJwcBg4cyOuvv46Njf4fXv6rvO+7Hh4et8WoEWjkqEo4ODjQvHlztmzZYt5WXFzMli1bCA8PL3Of8PDwEu0BNm3aVG57uXtUJF8ApkyZwvjx49mwYQMtWrSojlDlNmBpvjRs2JCDBw+yf/9+8+t//ud/zCsFBQYGVmf4Us0q8velbdu2HD161FxEA6SlpeHv76/C6C5XkXzJzc0tVQBdLawNw6i6YOWOdEd837X2ihB3q6VLlxqOjo7GggULjEOHDhkDBw40vLy8jNOnTxuGYRh9+/Y1Ro8ebW6/fft2w87Ozpg6dapx+PBhIyEhwbC3tzcOHjxorVOQamRpvkyePNlwcHAwVq5caZw6dcr8ysrKstYpSDWyNF/+SKvV3VsszZcTJ04Y7u7uRnx8vJGammp89tlnho+PjzFhwgRrnYJUI0vzJSEhwXB3dzeWLFliHDt2zNi4caPx4IMPGj179rTWKUg1ysrKMvbt22fs27fPAIzp06cb+/btM44fP24YhmGMHj3a6Nu3r7n9sWPHDBcXF+OVV14xDh8+bMyePduwtbU1NmzYYK1TKEXFURV67733jNq1axsODg5Gq1atjG+++cb8Wbt27YyYmJgS7ZcvX240aNDAcHBwMBo1amSsW7eumiMWa7IkX+rUqWMApV4JCQnVH7hYhaV/X66l4ujeY2m+7Nixw2jdurXh6Oho1K9f35g4caJRWFhYzVGLtViSLwUFBcbYsWONBx980HBycjICAwONIUOGGL///nv1By7VLjk5uczvI1dzJCYmxmjXrl2pfR5++GHDwcHBqF+/vpGUlFTtcV+PyTA05ikiIiIiIqJ7jkRERERERFBxJCIiIiIiAqg4EhERERERAVQciYiIiIiIACqOREREREREABVHIiIiIiIigIojERERERERQMWRiIjcQ0wmE2vWrKn0tiIicndQcSQiIlYRGxuLyWTCZDLh4OBAUFAQ48aNo7CwsMr6PHXqFH/+858rva2IiNwd7KwdgIiI3Luio6NJSkri8uXLrF+/nqFDh2Jvb8+rr75aol1+fj4ODg633J+fn1+VtBURkbuDRo5ERMRqHB0d8fPzo06dOgwePJjIyEjWrl1LbGws3bp1Y+LEiQQEBBASEgLAyZMn6dmzJ15eXnh7e9O1a1cyMzNLHPPjjz+mUaNGODo64u/vT3x8vPmza6fK5efnEx8fj7+/P05OTtSpU4fExMQy2wIcPHiQJ554AmdnZ2rWrMnAgQPJzs42f3415qlTp+Lv70/NmjUZOnQoBQUFlX/hRESkSqg4EhGR24azszP5+fkAbNmyhdTUVDZt2sRnn31GQUEBUVFRuLu7s23bNrZv346bmxvR0dHmfd5//32GDh3KwIEDOXjwIGvXriUoKKjMvmbOnMnatWtZvnw5qampLFq0iLp165bZNicnh6ioKGrUqMHu3btZsWIFmzdvLlF4ASQnJ5Oenk5ycjILFy5kwYIFLFiwoNKuj4iIVC1NqxMREaszDIMtW7bwxRdf8OKLL/Lrr7/i6urK/PnzzdPpPv30U4qLi5k/fz4mkwmApKQkvLy8SElJoWPHjkyYMIGXX36Zl156yXzsli1bltnniRMnCA4O5pFHHsFkMlGnTp1y41u8eDF5eXl88sknuLq6AjBr1iy6dOnCW2+9ha+vLwA1atRg1qxZ2Nra0rBhQ5566im2bNnCgAEDKuU6iYhI1dLIkYiIWM1nn32Gm5sbTk5O/PnPf6ZXr16MHTsWgMaNG5e4z+jAgQMcPXoUd3d33NzccHNzw9vbm7y8PNLT0zl79iy//PILTz755E31HRsby/79+wkJCWHYsGFs3Lix3LaHDx+madOm5sIIoG3bthQXF5Oammre1qhRI2xtbc3v/f39OXv27M1eDhERsTKNHImIiNW0b9+e999/HwcHBwICArCz++8/S9cWIgDZ2dk0b96cRYsWlTrOfffdh42NZf/f16xZMzIyMvj888/ZvHkzPXv2JDIykpUrV1bsZAB7e/sS700mE8XFxRU+noiIVC8VRyIiYjWurq7l3hP0R82aNWPZsmX4+Pjg4eFRZpu6deuyZcsW2rdvf1PH9PDwoFevXvTq1YsePXoQHR3NuXPn8Pb2LtEuNDSUBQsWkJOTYy7atm/fjo2NjXmxCBERufNpWp2IiNwR+vTpQ61atejatSvbtm0jIyODlJQUhg0bxk8//QTA2LFjmTZtGjNnzuTHH39k7969vPfee2Ueb/r06SxZsoQjR46QlpbGihUr8PPzw8vLq8y+nZyciImJ4YcffiA5OZkXX3yRvn37mu83EhGRO5+KIxERuSO4uLiwdetWateuTffu3QkNDSUuLo68vDzzSFJMTAwzZsxgzpw5NGrUiM6dO/Pjjz+WeTx3d3emTJlCixYtaNmyJZmZmaxfv77M6XkuLi588cUXnDt3jpYtW9KjRw+efPJJZs2aVaXnLCIi1ctkGIZh7SBERERERESsTSNHIiIiIiIiqDgSEREREREBVByJiIiIiIgAKo5EREREREQAFUciIiIiIiKAiiMRERERERFAxZGIiIiIiAig4khERERERARQcSQiIiIiIgKoOBIREREREQFUHImIiIiIiAAqjkRERERERAD4/xPRutoDdkiuAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ], + "id": "wgWXQ8aeOhCZ" + }, + "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", From 4b60eadc06e07d16af7c42e07d2c3f27ae257903 Mon Sep 17 00:00:00 2001 From: Prianka Liz Kariat Date: Thu, 30 May 2024 08:07:26 +0530 Subject: [PATCH 62/85] Applied formatting --- site/en/tutorials/structured_data/imbalanced_data.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/site/en/tutorials/structured_data/imbalanced_data.ipynb b/site/en/tutorials/structured_data/imbalanced_data.ipynb index b261d12f429..25b55071817 100644 --- a/site/en/tutorials/structured_data/imbalanced_data.ipynb +++ b/site/en/tutorials/structured_data/imbalanced_data.ipynb @@ -1730,7 +1730,8 @@ ], "metadata": { "colab": { - "provenance": [] + "name": "imbalanced_data.ipynb", + "toc_visible": true }, "kernelspec": { "display_name": "Python 3", @@ -1739,4 +1740,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 38c16cc0700caf348c253a8c3ea66bcfcc91a5d8 Mon Sep 17 00:00:00 2001 From: tilakrayal <81610181+tilakrayal@users.noreply.github.com> Date: Mon, 17 Jun 2024 12:34:24 +0530 Subject: [PATCH 63/85] Fixing the wrong predictions in transfer_learning.ipynb --- site/en/tutorials/images/transfer_learning.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index 57dbbfbcbbf..aa07807f5bd 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -1051,7 +1051,6 @@ "predictions = model.predict_on_batch(image_batch).flatten()\n", "\n", "# Apply a sigmoid since our model returns logits\n", - "predictions = tf.nn.sigmoid(predictions)\n", "predictions = tf.where(predictions < 0.5, 0, 1)\n", "\n", "print('Predictions:\\n', predictions.numpy())\n", From c68e3e9e040d8dd429c00d5f24b2d92092a4acaa Mon Sep 17 00:00:00 2001 From: tilakrayal <81610181+tilakrayal@users.noreply.github.com> Date: Tue, 25 Jun 2024 09:14:05 +0530 Subject: [PATCH 64/85] Update transfer_learning.ipynb --- site/en/tutorials/images/transfer_learning.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index aa07807f5bd..bb0d9f6ea33 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -1049,7 +1049,6 @@ "# Retrieve a batch of images from the test set\n", "image_batch, label_batch = test_dataset.as_numpy_iterator().next()\n", "predictions = model.predict_on_batch(image_batch).flatten()\n", - "\n", "# Apply a sigmoid since our model returns logits\n", "predictions = tf.where(predictions < 0.5, 0, 1)\n", "\n", From f04e30592d473fa3bad9732fe07a032cf81cb99d Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Wed, 26 Jun 2024 10:55:06 +0800 Subject: [PATCH 65/85] Remove comment that is no longer relevant --- site/en/tutorials/images/transfer_learning.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/site/en/tutorials/images/transfer_learning.ipynb b/site/en/tutorials/images/transfer_learning.ipynb index bb0d9f6ea33..172bb2700b4 100644 --- a/site/en/tutorials/images/transfer_learning.ipynb +++ b/site/en/tutorials/images/transfer_learning.ipynb @@ -1049,7 +1049,6 @@ "# Retrieve a batch of images from the test set\n", "image_batch, label_batch = test_dataset.as_numpy_iterator().next()\n", "predictions = model.predict_on_batch(image_batch).flatten()\n", - "# Apply a sigmoid since our model returns logits\n", "predictions = tf.where(predictions < 0.5, 0, 1)\n", "\n", "print('Predictions:\\n', predictions.numpy())\n", From 66f3dae5090400752395a9ce66e912e26557c98b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 2 Jul 2024 09:33:39 -0700 Subject: [PATCH 66/85] Update some old version requirements for the pip installation. PiperOrigin-RevId: 648745514 --- site/en/install/pip.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/site/en/install/pip.md b/site/en/install/pip.md index ed434bb9cdd..3faa6c58d39 100644 --- a/site/en/install/pip.md +++ b/site/en/install/pip.md @@ -140,7 +140,7 @@ Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards. ## Software requirements -* Python 3.9–3.11 +* Python 3.9–3.12 * pip version 19.0 or higher for Linux (requires `manylinux2014` support) and Windows. pip version 20.3 or higher for macOS. * Windows Native Requires @@ -150,9 +150,10 @@ Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards. The following NVIDIA® software are only required for GPU support. * [NVIDIA® GPU drivers](https://www.nvidia.com/drivers){:.external} - version 450.80.02 or higher. -* [CUDA® Toolkit 11.8](https://developer.nvidia.com/cuda-toolkit-archive){:.external}. -* [cuDNN SDK 8.6.0](https://developer.nvidia.com/cudnn){:.external}. + * >= 525.60.13 for Linux + * >= 528.33 for WSL on Windows +* [CUDA® Toolkit 12.3](https://developer.nvidia.com/cuda-toolkit-archive){:.external}. +* [cuDNN SDK 8.9.7](https://developer.nvidia.com/cudnn){:.external}. * *(Optional)* [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_7){:.external} to improve latency and throughput for inference. From 6680535155460f7eb0d2d615b9749a0cf721d4ec Mon Sep 17 00:00:00 2001 From: Fergus Henderson Date: Tue, 2 Jul 2024 11:33:43 -0700 Subject: [PATCH 67/85] Fix typo: delete extraneous ')'. PiperOrigin-RevId: 648788534 --- site/en/guide/versions.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/en/guide/versions.md b/site/en/guide/versions.md index 0b089885552..8443e549f42 100644 --- a/site/en/guide/versions.md +++ b/site/en/guide/versions.md @@ -59,7 +59,7 @@ patch versions. The public APIs consist of * The TensorFlow C API: - * [tensorflow/c/c_api.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h)) + * [tensorflow/c/c_api.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h) * The following protocol buffer files: From 773bcc865af5d5a45b405c80faf6fcc3cc510d7d Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 15 Jul 2024 07:17:13 -0700 Subject: [PATCH 68/85] Block the use of the builtin dict/tuple/list docstrings. PiperOrigin-RevId: 652471051 --- tools/tensorflow_docs/api_generator/parser.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/tools/tensorflow_docs/api_generator/parser.py b/tools/tensorflow_docs/api_generator/parser.py index b8f906bffd7..f3d087bc6fc 100644 --- a/tools/tensorflow_docs/api_generator/parser.py +++ b/tools/tensorflow_docs/api_generator/parser.py @@ -92,15 +92,20 @@ def _get_raw_docstring(py_object): obj_type = obj_type_lib.ObjType.get(py_object) if obj_type is obj_type_lib.ObjType.TYPE_ALIAS: - if inspect.getdoc(py_object) != inspect.getdoc(py_object.__origin__): - result = inspect.getdoc(py_object) - else: + result = inspect.getdoc(py_object) + if result == inspect.getdoc(py_object.__origin__): result = '' elif obj_type is obj_type_lib.ObjType.CLASS: if dataclasses.is_dataclass(py_object): result = _get_dataclass_docstring(py_object) else: result = inspect.getdoc(py_object) or '' + if ( + result == inspect.getdoc(dict) + or result == inspect.getdoc(list) + or result == inspect.getdoc(tuple) + ): + result = '' elif obj_type is obj_type_lib.ObjType.OTHER: result = '' else: From 84289c86548d7ca7890c83bf3c347e7d0070b538 Mon Sep 17 00:00:00 2001 From: Raviteja Gorijala Date: Tue, 23 Jul 2024 10:47:13 -0700 Subject: [PATCH 69/85] TF 2.17: Update documentation for wheel locations and toolchain changes PiperOrigin-RevId: 655222873 --- site/en/install/lang_c.ipynb | 20 +++++++----- site/en/install/pip.md | 51 ++++++++++++++++--------------- site/en/install/source.md | 4 ++- site/en/install/source_windows.md | 1 + 4 files changed, 42 insertions(+), 34 deletions(-) diff --git a/site/en/install/lang_c.ipynb b/site/en/install/lang_c.ipynb index 6d8d716fe92..184f626a208 100644 --- a/site/en/install/lang_c.ipynb +++ b/site/en/install/lang_c.ipynb @@ -130,19 +130,23 @@ " Linux\n", " \n", " Linux CPU only\n", - " https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-linux-x86_64.tar.gz\n", + " https://storage.googleapis.com/tensorflow/versions/2.17.0/libtensorflow-cpu-linux-x86_64.tar.gz\n", " \n", " \n", " Linux GPU support\n", - " https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-gpu-linux-x86_64.tar.gz\n", + " https://storage.googleapis.com/tensorflow/versions/2.17.0/libtensorflow-gpu-linux-x86_64.tar.gz\n", " \n", - " macOS\n", + " macOS\n", + " \n", + " \n", " \n", " macOS CPU only\n", - " https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-x86_64.tar.gz\n", + " https://storage.googleapis.com/tensorflow/versions/2.16.2/libtensorflow-cpu-darwin-x86_64.tar.gz\n", " \n", " macOS ARM64 CPU only\n", - " https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-arm64.tar.gz\n", + " https://storage.googleapis.com/tensorflow/versions/2.17.0/libtensorflow-cpu-darwin-arm64.tar.gz\n", " \n", " Windows\n", "