src/resnet-selection-improved-labels.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nfrom tensorflow.keras.utils import image_dataset_from_directory\nfrom tensorflow.keras.optimizers.legacy import SGD\nfrom tensorflow.keras.regularizers import L2\nfrom tensorflow.keras.losses import SparseCategoricalCrossentropy\nfrom tensorflow.keras.metrics import SparseCategoricalAccuracy\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport wandb\nfrom wandb.keras import WandbMetricsLogger\nimport resnets as rn","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:29.910038Z","iopub.execute_input":"2023-04-06T10:24:29.910745Z","iopub.status.idle":"2023-04-06T10:24:38.277566Z","shell.execute_reply.started":"2023-04-06T10:24:29.910704Z","shell.execute_reply":"2023-04-06T10:24:38.276378Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"wandb.login()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:38.280287Z","iopub.execute_input":"2023-04-06T10:24:38.280692Z","iopub.status.idle":"2023-04-06T10:24:50.379722Z","shell.execute_reply.started":"2023-04-06T10:24:38.280649Z","shell.execute_reply":"2023-04-06T10:24:50.378666Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"  ········································\n"},{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"},{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}]},{"cell_type":"markdown","source":"## Set hyperparameters","metadata":{}},{"cell_type":"markdown","source":"Hyperparameters are inspired by *Deep Residual Learning for Image Recognition* by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (2015).","metadata":{}},{"cell_type":"code","source":"train_path = \"facial-affect-dataset-relabeled-unbalanced/train\"\ntest_path = \"facial-affect-dataset-relabeled-unbalanced/test\"\n\nimage_size = 96\nchannels = 3\ncolor_mode = \"rgb\"\nvalidation_split = 0.2\nseed = 27\n\nconfig = {\n    \"batch_size\": 32,\n    \"epochs\": 100,\n    \"learning_rate\": 0.1,\n    \"momentum\": 0.9,\n    \"l2\": 0.0001,\n    \"early_stop_patience\": 10,\n    \"lr_reduce_patience\": 5\n}","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:50.381242Z","iopub.execute_input":"2023-04-06T10:24:50.382818Z","iopub.status.idle":"2023-04-06T10:24:50.391114Z","shell.execute_reply.started":"2023-04-06T10:24:50.382770Z","shell.execute_reply":"2023-04-06T10:24:50.390083Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## Run the following block on Kaggle only!","metadata":{}},{"cell_type":"code","source":"train_path = \"/kaggle/input/\" + train_path\ntest_path = \"/kaggle/input/\" + test_path","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:50.393256Z","iopub.execute_input":"2023-04-06T10:24:50.393520Z","iopub.status.idle":"2023-04-06T10:24:50.401958Z","shell.execute_reply.started":"2023-04-06T10:24:50.393495Z","shell.execute_reply":"2023-04-06T10:24:50.400979Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Get class names","metadata":{}},{"cell_type":"code","source":"classes = next(os.walk(train_path))[1]\nprint(classes)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:50.403136Z","iopub.execute_input":"2023-04-06T10:24:50.403918Z","iopub.status.idle":"2023-04-06T10:24:50.417250Z","shell.execute_reply.started":"2023-04-06T10:24:50.403889Z","shell.execute_reply":"2023-04-06T10:24:50.416429Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"['surprise', 'fear', 'neutral', 'sad', 'disgust', 'contempt', 'happy', 'anger']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Create training, validation and test datasets","metadata":{}},{"cell_type":"code","source":"train, validation = image_dataset_from_directory(\n    directory=train_path,\n    validation_split=validation_split,\n    subset=\"both\",\n    labels=\"inferred\",\n    class_names=classes,\n    label_mode=\"int\",\n    color_mode=color_mode,\n    batch_size=config[\"batch_size\"],\n    image_size=(image_size, image_size),\n    seed=seed,\n    shuffle=True\n)\n\ntest = image_dataset_from_directory(\n    directory=test_path,\n    labels=\"inferred\",\n    class_names=classes,\n    label_mode=\"int\",\n    color_mode=color_mode,\n    batch_size=config[\"batch_size\"],\n    image_size=(image_size, image_size),\n    shuffle=False\n)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:24:50.418352Z","iopub.execute_input":"2023-04-06T10:24:50.418739Z","iopub.status.idle":"2023-04-06T10:25:02.822940Z","shell.execute_reply.started":"2023-04-06T10:24:50.418701Z","shell.execute_reply":"2023-04-06T10:25:02.821755Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Found 22930 files belonging to 8 classes.\nUsing 18344 files for training.\nUsing 4586 files for validation.\nFound 5734 files belonging to 8 classes.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Training on different models with SOTA hyperparameters","metadata":{}},{"cell_type":"markdown","source":"### ResNet-18 - training and evaluation","metadata":{}},{"cell_type":"code","source":"wandb.init(config=config, project='resnet-improved-labels', name='resnet18')\n\nregularizer = L2(l2=config[\"l2\"])\noptimizer = SGD(learning_rate=config[\"learning_rate\"], momentum=config[\"momentum\"])\nloss = SparseCategoricalCrossentropy()\nmetrics = [SparseCategoricalAccuracy()]\n\nmodel = rn.ResNet18(\n    len(classes),\n    (image_size, image_size, channels),\n    normalize=True,\n    kernel_regularizer=regularizer\n)\n\nearly_stopping = EarlyStopping(\n    monitor=\"val_loss\",\n    mode=\"min\",\n    verbose=1,\n    patience=config[\"early_stop_patience\"],\n    restore_best_weights=True\n)\n\nlr_schedule_sota = ReduceLROnPlateau(\n    monitor=\"val_loss\",\n    factor=0.1,\n    patience=config[\"lr_reduce_patience\"],\n    verbose=1,\n    mode=\"min\"\n)\n\ncallbacks = [\n    early_stopping,\n    lr_schedule_sota,\n    WandbMetricsLogger()\n]\n\nmodel.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nhistory = model.fit(train, validation_data=validation, epochs=config[\"epochs\"], callbacks=callbacks)\nwandb.finish()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:25:02.824666Z","iopub.execute_input":"2023-04-06T10:25:02.825014Z","iopub.status.idle":"2023-04-06T10:33:27.564086Z","shell.execute_reply.started":"2023-04-06T10:25:02.824977Z","shell.execute_reply":"2023-04-06T10:33:27.562584Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mv-modroczky\u001b[0m (\u001b[33mnsiete23\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.14.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.13.10"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20230406_102502-4xtl9kti</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/4xtl9kti' target=\"_blank\">resnet18</a></strong> to <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/4xtl9kti' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/4xtl9kti</a>"},"metadata":{}},{"name":"stdout","text":"Epoch 1/100\n574/574 [==============================] - 46s 63ms/step - loss: 3.6383 - sparse_categorical_accuracy: 0.1521 - val_loss: 3.0496 - val_sparse_categorical_accuracy: 0.1546 - lr: 0.1000\nEpoch 2/100\n574/574 [==============================] - 19s 32ms/step - loss: 2.9447 - sparse_categorical_accuracy: 0.1545 - val_loss: 2.8469 - val_sparse_categorical_accuracy: 0.1524 - lr: 0.1000\nEpoch 3/100\n574/574 [==============================] - 18s 31ms/step - loss: 2.7666 - sparse_categorical_accuracy: 0.1564 - val_loss: 2.6864 - val_sparse_categorical_accuracy: 0.1531 - lr: 0.1000\nEpoch 4/100\n574/574 [==============================] - 19s 32ms/step - loss: 2.6237 - sparse_categorical_accuracy: 0.1565 - val_loss: 2.5580 - val_sparse_categorical_accuracy: 0.1535 - lr: 0.1000\nEpoch 5/100\n574/574 [==============================] - 21s 36ms/step - loss: 2.5095 - sparse_categorical_accuracy: 0.1563 - val_loss: 2.4566 - val_sparse_categorical_accuracy: 0.1520 - lr: 0.1000\nEpoch 6/100\n574/574 [==============================] - 18s 31ms/step - loss: 2.4210 - sparse_categorical_accuracy: 0.1550 - val_loss: 2.3797 - val_sparse_categorical_accuracy: 0.1542 - lr: 0.1000\nEpoch 7/100\n574/574 [==============================] - 18s 32ms/step - loss: 2.2814 - sparse_categorical_accuracy: 0.2137 - val_loss: 2.1438 - val_sparse_categorical_accuracy: 0.2850 - lr: 0.1000\nEpoch 8/100\n574/574 [==============================] - 18s 31ms/step - loss: 2.0584 - sparse_categorical_accuracy: 0.3071 - val_loss: 1.9567 - val_sparse_categorical_accuracy: 0.3312 - lr: 0.1000\nEpoch 9/100\n574/574 [==============================] - 19s 32ms/step - loss: 1.8533 - sparse_categorical_accuracy: 0.3712 - val_loss: 1.8990 - val_sparse_categorical_accuracy: 0.3718 - lr: 0.1000\nEpoch 10/100\n574/574 [==============================] - 19s 33ms/step - loss: 1.6898 - sparse_categorical_accuracy: 0.4362 - val_loss: 1.6819 - val_sparse_categorical_accuracy: 0.4307 - lr: 0.1000\nEpoch 11/100\n574/574 [==============================] - 18s 31ms/step - loss: 1.5401 - sparse_categorical_accuracy: 0.4982 - val_loss: 1.9811 - val_sparse_categorical_accuracy: 0.3596 - lr: 0.1000\nEpoch 12/100\n574/574 [==============================] - 19s 33ms/step - loss: 1.4294 - sparse_categorical_accuracy: 0.5468 - val_loss: 1.6315 - val_sparse_categorical_accuracy: 0.4688 - lr: 0.1000\nEpoch 13/100\n574/574 [==============================] - 18s 31ms/step - loss: 1.3325 - sparse_categorical_accuracy: 0.5896 - val_loss: 2.2271 - val_sparse_categorical_accuracy: 0.3149 - lr: 0.1000\nEpoch 14/100\n574/574 [==============================] - 19s 33ms/step - loss: 1.2816 - sparse_categorical_accuracy: 0.6193 - val_loss: 1.8346 - val_sparse_categorical_accuracy: 0.4339 - lr: 0.1000\nEpoch 15/100\n574/574 [==============================] - 19s 32ms/step - loss: 1.2222 - sparse_categorical_accuracy: 0.6531 - val_loss: 1.9433 - val_sparse_categorical_accuracy: 0.4472 - lr: 0.1000\nEpoch 16/100\n574/574 [==============================] - 20s 34ms/step - loss: 1.1904 - sparse_categorical_accuracy: 0.6861 - val_loss: 2.0558 - val_sparse_categorical_accuracy: 0.4091 - lr: 0.1000\nEpoch 17/100\n574/574 [==============================] - ETA: 0s - loss: 1.1530 - sparse_categorical_accuracy: 0.7243\nEpoch 17: ReduceLROnPlateau reducing learning rate to 0.010000000149011612.\n574/574 [==============================] - 19s 32ms/step - loss: 1.1530 - sparse_categorical_accuracy: 0.7243 - val_loss: 2.1145 - val_sparse_categorical_accuracy: 0.4649 - lr: 0.1000\nEpoch 18/100\n574/574 [==============================] - 18s 31ms/step - loss: 0.9056 - sparse_categorical_accuracy: 0.8331 - val_loss: 1.7046 - val_sparse_categorical_accuracy: 0.6010 - lr: 0.0100\nEpoch 19/100\n574/574 [==============================] - 19s 32ms/step - loss: 0.6842 - sparse_categorical_accuracy: 0.9158 - val_loss: 1.8475 - val_sparse_categorical_accuracy: 0.5990 - lr: 0.0100\nEpoch 20/100\n574/574 [==============================] - 19s 33ms/step - loss: 0.5545 - sparse_categorical_accuracy: 0.9614 - val_loss: 2.0416 - val_sparse_categorical_accuracy: 0.6044 - lr: 0.0100\nEpoch 21/100\n574/574 [==============================] - 21s 36ms/step - loss: 0.4734 - sparse_categorical_accuracy: 0.9850 - val_loss: 2.2798 - val_sparse_categorical_accuracy: 0.6053 - lr: 0.0100\nEpoch 22/100\n572/574 [============================>.] - ETA: 0s - loss: 0.4323 - sparse_categorical_accuracy: 0.9938Restoring model weights from the end of the best epoch: 12.\n\nEpoch 22: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n574/574 [==============================] - 19s 33ms/step - loss: 0.4323 - sparse_categorical_accuracy: 0.9938 - val_loss: 2.5151 - val_sparse_categorical_accuracy: 0.6053 - lr: 0.0100\nEpoch 22: early stopping\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"052804368eec43749aba428593409cd3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▂▂▂▃▃▃▄▄▄▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>epoch/learning_rate</td><td>████████████████▂▂▂▂▂▁</td></tr><tr><td>epoch/loss</td><td>█▆▆▆▆▅▅▅▄▄▃▃▃▃▃▃▃▂▂▁▁▁</td></tr><tr><td>epoch/lr</td><td>█████████████████▁▁▁▁▁</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▂▂▃▃▄▄▅▅▅▅▆▇▇███</td></tr><tr><td>epoch/val_loss</td><td>█▇▆▆▅▅▄▃▂▁▃▁▄▂▃▃▃▁▂▃▄▅</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▃▄▄▅▄▆▄▅▆▅▆█████</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>21</td></tr><tr><td>epoch/learning_rate</td><td>0.001</td></tr><tr><td>epoch/loss</td><td>0.4323</td></tr><tr><td>epoch/lr</td><td>0.01</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>0.99379</td></tr><tr><td>epoch/val_loss</td><td>2.51509</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>0.60532</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">resnet18</strong> at: <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/4xtl9kti' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/4xtl9kti</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20230406_102502-4xtl9kti/logs</code>"},"metadata":{}}]},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n\nhistory_df[[\"sparse_categorical_accuracy\", \"val_sparse_categorical_accuracy\"]].plot()\nhistory_df[[\"loss\", \"val_loss\"]].plot()\n\nmodel.evaluate(test)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:33:27.565692Z","iopub.execute_input":"2023-04-06T10:33:27.566815Z","iopub.status.idle":"2023-04-06T10:33:38.264060Z","shell.execute_reply.started":"2023-04-06T10:33:27.566768Z","shell.execute_reply":"2023-04-06T10:33:38.262915Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"180/180 [==============================] - 8s 47ms/step - loss: 1.6199 - sparse_categorical_accuracy: 0.4637\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"[1.6198748350143433, 0.46372514963150024]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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qcP333/PrVu3sm0dKVu2LMePH880LywsLMe+XAcOHECv1zN9+nTTIcOVK1dmee4tW7YwZcqUR+5n6NChzJw5kytXrtC2bVs5hV+Iu9L0BtYdiWbB7khOXL0/ztZT1coyuHlFnqpatsSGjkeRCzBo5N9//+WTTz7hwIEDREVF8euvv3Ljxg3TF016ejpDhgwhPDyc//3vf0yaNIk333wTCwsLypQpg4eHB9999x1nz55l69atpsNlORk1ahQbN24kMjKSQ4cOsXXrVtNzvvHGG9y6dYu+ffuyb98+zp8/z6ZNmxg8eHCWkPAwOzs7xo0bx9ixY1myZAnnzp1j7969/PDDD4DaxyMqKoqff/6Zc+fOMXv2bFPwuudeZ8iwsDBiY2NJS0ujbdu2NG3alG7durFx40YuXLjAnj17eP/9901f9G+99RY//PADixcvJiIigqlTp3L06NFMrR5jxozhs88+Y8WKFZw+fZp3332XsLCwTP1DcmPSpEksX76cSZMmcfLkSY4dO8bnn38OgL+/PzY2Nnz11VecP3+etWvX8tFHH+Vp/w+qUqWKqfXs5MmTvPbaa5nOTMvpPe/bty/lypWjW7du7N69m/Pnz7N69Wr++ecfAJ555hkOHDjAkiVLiIiIYNKkSVnCSXYqV66MXq83vc6lS5dmud7U+PHj2b9/PyNGjODo0aOcOnWKb7/9NtOZWS+//DJXrlxh/vz5DB48ON/vkxClRWxiGl/+FUHzT7fxn1VHOHE1HjtrC15q7M/md55iyeBGPF3dq9QFEQCUEiAuLk4BlLi4uCzLUlJSlPDwcCUlJUWDyvIvPDxc6dChg1K2bFnF1tZWqVatmvLVV18piqIoAwYMULp27apMnDhR8fDwUJycnJShQ4cqqamppu03b96s1KxZU7G1tVXq1KmjbN++XQGUNWvWKIqiKJGRkQqgHD58ONPzvvnmm0rlypUVW1tbpWzZskq/fv2U2NhY0/IzZ84o3bt3V9zc3BR7e3ulRo0ayqhRoxSj0ZjjazIYDMrUqVOVgIAAxdraWvH391c++eQT0/IxY8aYXk+fPn2UmTNnKq6urqblqampSs+ePRU3NzcFUBYuXKgoiqLEx8crb731luLr66tYW1srfn5+yssvv6xERUWZtv3www8VT09PxcnJSRk8eLAycuRIpUmTJplqmzJlilK+fHnF2tpaqVu3rvK///3PtPxR79fChQsz1agoirJ69WqlXr16io2NjeLp6an06NHDtOynn35SKlasqNja2ipNmzZV1q5dm2m/27ZtUwDl9u3bOb6fN2/eVLp27ao4OTkpXl5eyvvvv6/0799f6dq1a67f8wsXLig9e/ZUXFxcFAcHByU0NFT5999/TcsnTpyoeHt7K66urso777yjvPnmm0qrVq1My1u1aqW8/fbbWWqbMWOG4uPjo9jb2ysdOnRQlixZkuV1bd++XWnWrJlia2uruLm5KR06dMjyuvv166e4u7tn+t0uDkrq3xVRMoVfjVP+b2WYUnXCBiVg3DolYNw6pfHHfynfbItQbiWmaV3eE3nc9/eD8nw2jRbycjZNaZDdWQ4ib9q1a0e5cuVYunSp1qWIx2jXrh01a9Zk9uzZWpeSSWn8uyKKF6NRYeupGBbsjmTPuZum+XX93BjSIpCOQeUK/UyXolBoZ9MIUdwkJyczd+5cOnTogKWlJcuXL+evv/5i8+bNWpcmHuHWrVts2rSJrVu35voK4EKUBolpen45cIlFey5w4WYyAJYWOp4NKsfg5oGEBJTRuEJtSBgRuZLTqanh4eH4+/sXYUX36XQ6NmzYwNSpU0lLS6N69eqsXr2atm3balJPbnXs2JGdO3dmu+y9997LNCZKadOgQQNu377NZ599RvXq1bUuR4hCd+lWMov3XGDFA6fmuthZ0bexP/2bVqS82+PPQCvt5DCNyBW9Xs+FCxceuTw/Z+CYuytXrpCSkpLtMnd39yceH0Tkn/xdEQVBURQOXLzNgl2RbDxxzXRqbqWyjgxqHkjPBuVLxFAUT0IO04gCZWVlRZUqVbQuo1QpX7681iUIIQrJ3vM3+Xj9SY5diTPNa1nVk8EtAmlVCk/NfVISRoQQQogCtHxfFB/8dhy9UcHWyoIeDcozqHkg1bydtS6t2JIwIoQQQhQAg1Hh0/+dZP7OSAC61PVlcpfauDvaaFxZ8SdhRAghhHhCSWl63v45jL9OXgfgnbbVGNmmilxvKZckjAghhBBPIDouhSGLDhAeHY+NlQVf9KpLl7q+WpdVokgYEUIIIfLp2OU4hizeT0xCGp5ONszrF2q2Y4U8iZI/vJsZq1ixIrNmzdK6DFGABg4cSLdu3Qpsf5MnT6ZevXoFtj8hxH1/Ho+m17w9xCSkUc3biTUjmksQySdpGRFm6+mnn6ZevXrFKtB9+eWXlIChf4Qwa4qiMHfHeT778xQAraqV5euX6uNs9/grXotHkzAiCoXBYECn05kuMS8e79775erqqnUpxUpGRgbW1vIHXhQf6Xoj7/92jJUHLgMwoGkAHzxfC6tScB0ZLZW+d09RID1Jm1se/qOdN28e5cuXx2g0ZprfpUsXBgwYwLlz5+jatSve3t44OTnRsGFD/vrrr3y/LZMnT8bf3x9bW1t8fX0ZOXKkaVnFihX56KOPeOmll3BycsLX15evvvoq0/YzZswgODgYR0dH/Pz8GDFiBImJiablixYtws3NjXXr1lGrVi1sbW25ePEi27dvp1GjRjg6OuLm5kbz5s25ePGiabs//viDkJAQ7OzsqFSpElOmTEGv1+fqNd25c4dXX30Vb29v7OzsCAoKYt26dQDcvHmTvn37UqFCBRwcHAgODmb58uWmbQcOHMiOHTv48ssv0el06HQ60wiz4eHhdOrUCScnJ7y9venXrx+xsbGmbRMSEnj55ZdxdHTEx8eHmTNn8vTTTzNq1CjTOrdv36Z///6UKVMGBwcHOnbsSERERI7v18OHaYxGI5999hlVqlTB1tYWf39/Pv74Y9PycePGUa1aNRwcHKhUqRIffPABGRkZuXr/HrZ//37atWuHp6cnrq6utGrVikOHDuX6PQfYvXs3rVq1wsHBgTJlytChQwdu374NZH9YsV69ekyePNn0WKfTMXfuXLp27YqjoyNTp07FYDAwZMgQAgMDsbe3p3r16nz55ZdZ6l+wYAG1a9fG1tYWHx8f3nzzTQAGDx7M888/n2ldvV5PuXLlWLBgQb7eK2Ge7iSn03/Bv6w8cBkLHUzuXIspXYMkiBSA0tcykpEMn2jUi/m9q2DjmKtVe/XqxciRI9m2bRtt2rQB1C+wjRs38scff5CYmEinTp2YOnUqdnZ2LF68mM6dO3P69Ok8XwPml19+YebMmfz888/Url2ba9euceTIkUzr/Pe//+W9995j8uTJbNy4kXfeeYcaNWrQrl07ACwsLJg9ezYVK1YkMjKSESNGMHbsWObMmWPaR3JyMtOmTeP777/Hw8MDd3d36tevz7Bhw1i+fDnp6ens27fPdKrbxo0beeWVV5g9ezYtW7bk3LlzvPrqqwBMmjTpsa/JaDTSsWNHEhIS+PHHH6lcuTLh4eFYWloC6nDeISEhjBs3DhcXF9avX0+/fv2oVKkSjRs35ssvv+TMmTMEBQXx4YcfAlC2bFmio6Np1aoVw4YNY8aMGaSkpDBu3Dh69+7N1q1bARg9ejS7d+9m7dq1eHt7M3HiRA4dOpSpb8bAgQOJiIhg7dq1uLi4MG7cODp16kR4eLjpP/2H3y8vL68sr3P8+PHMnz+fmTNn0qJFC6Kjozl16pRpubOzM4sWLcLX15djx44xbNgwnJ2dGTt2bM6/GA9JSEhgwIABpivoTp8+nU6dOhEREYGzs3OO73lYWBht2rRh8ODBzJ49GysrK7Zt24bBYMhTHZMmTWLatGnMnDkTS0tLjEYjFSpUYOXKlXh6erJnzx5effVVfHx86N27NwDffvsto0eP5tNPP6Vjx47ExcWxe/duAIYOHcpTTz1FdHQ0Pj4+AGzYsIHExETT9kLk5PyNRIYsPkBkbBJOtlZ81bc+rWtk/cyKfFJKgLi4OAVQ4uLisixLSUlRwsPDlZSUFHVGWqKiTHLR5paWmKfX1aVLF2Xw4MGmx/PmzVPKlSun6PX6bNevVauW8tVXX5keBwQEKDNnzszxeaZPn65Uq1ZNSU9Pz3Z5QECA8uyzz2aa16dPH6Vjx46P3OfKlSsVDw8P0+OFCxcqgBIWFmaad/PmTQVQtm/fnu0+WrZsqXzyySeZ5i1dulTx8fHJ8TVt3LhRsbCwUE6fPp3juvd06tRJ+c9//mN63KpVK+Xtt9/OtM4HH3ygtG/fPtO8S5cuKYBy+vRpJT4+XrG2tlZWrVplWn7nzh3FwcHBtK8zZ84ogLJ7927TOrGxsYq9vb2ycuVKRVGyf78URVEGDBigdO3aVVEURYmPj1dsbW2V+fPn5/o1fv7550pISIjp8aRJk5S6devmevsH6fV6xdnZWfnjjz8URcn5Pe/bt6/SvHnzR+4vu9/XunXrKpMmTTI9BpRRo0blWNuIESOUnj17mh77+voqEyZMeOT6tWrVUj777DPT427duikDBw585PpZ/q4Is7bnbKxSZ/JGJWDcOqXZtC3Kyeis30Uie4/7/n5Q6WsZsXZQWyi0eu48ePnll3n11VeZM2cOtra2LFu2jBdffBFLS0uSkpKYMmUK69at4+rVq+j1elJSUoiKispzWb169WLWrFlUqlSJZ599lk6dOtG5c+dMF7Zr2rRppm2aNm2aqUl927ZtfPLJJ4SHhxMfH49eryc1NZWkpCQcHdXWIBsbG+rUqWPaxt3dnYEDB9KhQwfatWtH27Zt6d27t+m/04MHD7J///5Mhx0MBgOpqakkJyfj4PDo9zMsLIwKFSpQrVq1bJcbDAY+/fRTVqxYwZUrV0hLSyMtLc1U66McPHiQbdu24eTklGXZuXPnSElJISMjg0aNGpnmu7q6Zrry7MmTJ7GysqJx48ameR4eHlSvXp2TJ0+a5j38fj3s5MmTpKWlmVrOsvPLL78wa9Yszp49S2JiInq9/rEXo3qcmJgYJk6cyNatW7l+/ToGg4Hk5GTT71xO73lYWBi9evXK13M/KDQ0NMu8uXPn8v3333Px4kVSUlJIT083tUTFxMRw9erVx75PQ4cO5bvvvmPs2LHExMSwfv16tmzZ8sS1itJv5f5LvLfmGHqjQj0/N+b3D6Wss63WZZU6pe9Al06nHirR4pbHkfY6d+6M0Whk/fr1XLp0iZ07d/LKK68AMGbMGFavXs3HH3/Mzp07CQsLIzg4mPT09Dy/JX5+fpw+fZpvvvkGe3t7RowYwVNPPZVj34J7h1MuXrxIp06dCAoKYvXq1Rw8eJBvvvkGINM+7O3ts4w2uHDhQv755x+aNWvGihUrqFatGnv37gXUQy1TpkwhLCzMdDt27BgRERE5XinV3v7xl9uePn06M2fOZOzYsWzdupWwsDA6dOiQ4/tnNBrp3LlzpprCwsKIiIjgqaeeMp3p8vDrVB7oL6Q8ou+QoiiZtsvu/crLa9y7dy8vvvgiHTt2ZN26dRw+fJgJEybk63cE1ENLBw8eZNasWezZs4ewsDA8PDxM+8upnpyWW1hYZHlvsvsdfDgwrly5knfeeYfBgwezadMmwsLCGDRoUK7rAujfvz/nz5/nn3/+4ccff6RixYq0bNkyx+2E+TIaFab97yRjVx9Fb1R4vo4PP7/aRIJIISl9LSMliL29PT169GDZsmWcPXuWatWqERISAsDOnTsZOHAg3bt3ByAxMdHUwTK/z9WlSxe6dOnCG2+8QY0aNTh27BgNGjQAMAWEe/bu3UuNGjUAOHDgAHq9nunTp5vOjlm5cmWun7t+/frUr1+f8ePH07RpU3766SeaNGlCgwYNOH36dL6uBlynTh0uX77MmTNnsv1PfefOnXTt2tUU7oxGIxEREdSsWdO0jo2NTZb+DA0aNGD16tVUrFgxU8vRPZUrV8ba2pp9+/bh5+cHqJfIjoiIoFWrVgDUqlULvV7Pv//+S7NmzQC1Q+2ZM2cyPX9Oqlatir29PVu2bGHo0KFZlu/evZuAgAAmTJhgmvdg5+C82rlzJ3PmzKFTp04AXLp0KVPH3Zze8zp16rBlyxamTJmS7f7v9cm5Jz4+nsjIyFzV1axZM0aMGGGad+7cOdO0s7MzFStWZMuWLbRu3TrbfXh4eNCtWzdTOB40aFCOzyvMV3K6nndWhLHxhDq0+8hnqjCqbTW50m4hkjCisZdffpnOnTtz4sQJ0xcnQJUqVfj111/p3LkzOp2ODz74IMuZN7m1aNEiDAYDjRs3xsHBgaVLl2Jvb09AQIBpnd27d/P555/TrVs3Nm/ezKpVq1i/fj2gfgHr9Xq++uorOnfuzO7du5k7d26OzxsZGcl3331Hly5d8PX15fTp05w5c4b+/fsDMHHiRJ5//nn8/Pzo1asXFhYWHD16lGPHjjF16tTH7rtVq1Y89dRT9OzZkxkzZlClShVOnTqFTqfj2WefpUqVKqxevZo9e/ZQpkwZZsyYwbVr1zKFgYoVK/Lvv/9y4cIFnJyccHd354033mD+/Pn07duXMWPG4OnpydmzZ/n555+ZP38+zs7ODBgwgDFjxuDu7o6XlxeTJk3CwsLC1MpRtWpVunbtyrBhw5g3bx7Ozs68++67lC9fnq5du+b652ZnZ8e4ceMYO3YsNjY2NG/enBs3bnDixAmGDBlClSpViIqK4ueff6Zhw4asX7+eNWvW5Hr/D6tSpQpLly4lNDSU+Ph4xowZk6nVIaf3fPz48QQHBzNixAiGDx+OjY0N27Zto1evXnh6evLMM8+waNEiOnfuTJkyZfjggw9MnV9zqmvJkiVs3LiRwMBAli5dyv79+wkMDDStM3nyZIYPH46Xl5epk+3u3bt56623TOsMHTqU559/HoPBwIABA/L9PonS7Xp8KkMW7+f4lXhsLC347IVgutevoHVZpV/hd195cnnqwFrC6PV6xcfHRwGUc+fOmeZHRkYqrVu3Vuzt7RU/Pz/l66+/ztLhMrcdWNesWaM0btxYcXFxURwdHZUmTZoof/31V6b9TJkyRendu7fi4OCgeHt7K7Nmzcq0jxkzZig+Pj6Kvb290qFDB2XJkiUKoNy+fVtRFLVDpqura6Ztrl27pnTr1k3x8fFRbGxslICAAGXixImKwWAwrfPnn38qzZo1U+zt7RUXFxelUaNGynfffZer9+7mzZvKoEGDFA8PD8XOzk4JCgpS1q1bZ1rWtWtXxcnJSfHy8lLef/99pX///qbOoYqiKKdPn1aaNGmi2NvbK4ASGRmpKIraAbV79+6Km5ubYm9vr9SoUUMZNWqUYjQaFUVRO5a+9NJLioODg1KuXDllxowZSqNGjZR3333XtO9bt24p/fr1U1xdXU3v2ZkzZ0zLs3u/FCVzB1ZFURSDwaBMnTpVCQgIUKytrRV/f/9MnX7HjBmjeHh4KE5OTkqfPn2UmTNnZtpvXjqwHjp0SAkNDVVsbW2VqlWrKqtWrcryO/a491xRFGX79u1Ks2bNFFtbW8XNzU3p0KGD6XckLi5O6d27t+Li4qL4+fkpixYtyrYD65o1azLVlZqaqgwcOFBxdXVV3NzclNdff1159913s7yuuXPnKtWrV1esra0VHx8f5a233sq03Gg0KgEBAUqnTp1yfC9K+t8VkT/HLt9RGn/8lxIwbp1S/8NNyv7Im1qXVOLltgOrTlGK/3CP8fHxuLq6EhcXl6VzXmpqKpGRkQQGBubYz0Bkr2LFiowaNSrTOBki95KSkihfvjzTp09nyJAhWpcjHiE5ORlfX18WLFhAjx49Hruu/F0xP5tOXOPtn8NIyTBQxcuJBQMa4u+Rt5MSRFaP+/5+kBymESKPDh8+zKlTp2jUqBFxcXGmcUrycghGFB2j0ci1a9eYPn06rq6udOnSReuSRDHz/c7zfLzhJIoCLat68vVLDXC1l5F/i1LpO5vGDC1btgwnJ6dsb7Vr19a6vHwp7q/piy++oG7durRt25akpCR27tyJp6en1mU91qPeTycnJ3bu3Kl1eYUmKiqK8uXLs3LlShYsWJBtx2Rhvv4Kv87U9WoQebmxPwsGNpQgogE5TFMKJCQkcP369WyXWVtbZ+qoWlKUxtektbNnzz5yWfny5XN1iqy5kL8r5iE+NYN2M3ZwPT6NQc0rMvH5Wo893V7knRymMSPOzs44OztrXUaBKo2vSWv5OYVaiNJs2oaTXI9Po6KHA+OerSFBREOl5jBNCWjgEUKUEPL3pPTbczaW5fsuAfBZzzrYWed8mrkoPCU+jDx40TEhhCgI9/6e3Pv7IkqX5HQ97/56DIBXmvjTuJKHxhWJEn+YxtLSEjc3N2JiYgBwcHCQpjYhRL4oikJycjIxMTG4ubnlalA2UfLM2HSGqFvJ+LjaMe7ZGlqXIygFYQSgXLlyAKZAIoQQT8LNzc30d0WULoejbrNgt3oZgk+6B+NsJ61fxUGpCCM6nQ4fHx+8vLxyvPibEEI8jrW1tbSIlFJpegNjfzmKUYHu9cvTuoaX1iWJu0pFGLnH0tJS/ogIIYTI1pxt54iIScTD0YYPnq+ldTniASW+A6sQQgiRk1PX4vlmmzrWzpSutXF3tNG4IvEgCSNCCCFKNb3ByNhfjqI3KrSr5c1zwT5alyQeImFECCFEqbZgdyRHL8fhbGfF1G5BcsZlMSRhRAghRKl1ITaJ6ZvOAPD+czXxdpHh/YsjCSNCCCFKJaNRYdzqo6TpjTSv4kHvUD+tSxKPIGFECCFEqbR8fxT/Rt7C3tqSad3ryOGZYkzCiBBCiFInOi6FaRtOAfB/Harj7+GgcUXicSSMCCGEKFUURWHCmuMkpump7+/GwGYVtS5J5EDCiBBCiFJl7ZGrbD0Vg42lBZ/3rIOlhRyeKe4kjAghhCg1YhPTmLz2BABvPlOFqt7OGlckckPCiBBCiFJjyh/h3E7OoEY5Z4a3qqx1OSKXJIwIIYQoFTaHX+ePI1ex0MHnL9TBxkq+4koK+UkJIYQo8eJSMnj/t2MADHuqEnUquGlbkMgTCSNCCCFKvE//d5Lr8WkEejryTttqWpcj8kjCiBBCiBJtz9lYlu+7BMCnPYKxs7bUuCKRVxJGhBBClFjJ6Xre/VU9PPNKE38aV/LQuCKRHxJGhBBClFgzNp0h6lYyvq52jHu2htbliHySMCKEEKJEOhx1mwW7IwH4uHswznbWGlck8kvCiBBCiBInTW9g7C9HMSrQvX55Wtfw0rok8QQkjAghhChxvtl2joiYRDwcbZj4fC2tyxFPSMKIEEKIEuVkdDxztp0FYErX2pRxtNG4IvGkJIwIIYQoMfQGI+NWH0VvVGhXy5vngn20LkkUAAkjQgghSowFuyM5ejkOZzsrpnYLQqeTK/KWBhJGhBBClAgXYpOYvukMAO8/VxNvFzuNKxIFRcKIEEKIEmHq+pOk6Y00r+JB71A/rcsRBUjCiBBCiGLv+JU4/jp5HZ0OpnSRwzOljYQRIYQQxd5XWyMA6FzHlypeThpXIwqahBEhhBDF2snoeDaeUFtF3nqmitbliEIgYUQIIUSxdq9VpFOwD1W9nTWuRhQGCSNCCCGKrdPXEthw7BoAI5+pqnE1orBIGBFCCFFs3W8VKUf1ctIqUlpJGBFCCFEsRVxPYP2xaADeklaRUk3CiBBCiGLp621nURToUNubmj4uWpcjCpGEESGEEMXOuRuJ/HHkKiCtIuYgX2Fkzpw5BAYGYmdnR0hICDt37nzs+suWLaNu3bo4ODjg4+PDoEGDuHnzZr4KFkIIUfp9s/UsRgXa1vQmqLyr1uWIQpbnMLJixQpGjRrFhAkTOHz4MC1btqRjx45ERUVlu/6uXbvo378/Q4YM4cSJE6xatYr9+/czdOjQJy5eCCFE6RMZm8RvYVcAeLuNtIqYgzyHkRkzZjBkyBCGDh1KzZo1mTVrFn5+fnz77bfZrr93714qVqzIyJEjCQwMpEWLFrz22mscOHDgiYsXQghR+nx9t1XkmRpeBFeQVhFzkKcwkp6ezsGDB2nfvn2m+e3bt2fPnj3ZbtOsWTMuX77Mhg0bUBSF69ev88svv/Dcc8898nnS0tKIj4/PdBNCCFH6Xbx5v1VkpLSKmI08hZHY2FgMBgPe3t6Z5nt7e3Pt2rVst2nWrBnLli2jT58+2NjYUK5cOdzc3Pjqq68e+TzTpk3D1dXVdPPzk6szCiGEOfhm21kMRoVW1cpSz89N63JEEclXB9aHr5aoKMojr6AYHh7OyJEjmThxIgcPHuTPP/8kMjKS4cOHP3L/48ePJy4uznS7dOlSfsoUQghRgly6lcyvh6RVxBxZ5WVlT09PLC0ts7SCxMTEZGktuWfatGk0b96cMWPGAFCnTh0cHR1p2bIlU6dOxcfHJ8s2tra22Nra5qU0IYQQJdyc7WfRGxVaVvUkJKCM1uWIIpSnlhEbGxtCQkLYvHlzpvmbN2+mWbNm2W6TnJyMhUXmp7G0tATUFhUhhBDi8u1kfjl4GZAzaMxRng/TjB49mu+//54FCxZw8uRJ3nnnHaKiokyHXcaPH0///v1N63fu3Jlff/2Vb7/9lvPnz7N7925GjhxJo0aN8PX1LbhXIoQQosT6dvs5MgwKzSp7EFrRXetyRBHL02EagD59+nDz5k0+/PBDoqOjCQoKYsOGDQQEBAAQHR2dacyRgQMHkpCQwNdff81//vMf3NzceOaZZ/jss88K7lUIIYQosa7eSWHlAbVvoLSKmCedUgKOlcTHx+Pq6kpcXBwuLnJ9AiGEKE0m/n6cJf9cpEkld35+tanW5YgClNvvb7k2jRBCCM1ci0vl531qq4icQWO+JIwIIYTQzNwd50g3GGlU0Z2mlTy0LkdoRMKIEEIITcTEp7J8n9rHcGSbqo8cr0qUfhJGhBBCaGLe3+dJ0xsJCShD8yrSKmLOJIwIIYQocjcS0lj270VAWkWEhBEhhBAamL/zPKkZRur5ufFUVU+tyxEakzAihBCiSMUmprH0H7VV5O220ioiJIwIIYQoYvN3niclw0CdCq48Xa2s1uWIYkDCiBBCiCJzKyn9fquI9BURd0kYEUIIUWS+33me5HQDQeVdeKaGl9bliGJCwogQQogicSc5ncV7LgAw8hlpFRH3SRgRQghRJH7YFUlSuoGaPi60q+WtdTmiGJEwIoQQotDFJWewaPcFAN5uU0VaRUQmEkaEEEIUugW7I0lI01OjnDPta5XTuhxRzEgYEUIIUajiUjJYsDsSgLeeqYqFhbSKiMwkjAghhChUi/dcICFVT1UvJzoGSauIyErCiBBCiEKTkJrBD7vutoq0kVYRkT0JI0IIIQrNkn8uEpeSQeWyjjwX7KN1OaKYkjAihBCiUCSm6Zm/8zyg9hWxlFYR8QgSRoQQQhSKpf9c5E5yBpU8Helc11frckQxJmFECCFEgUt6oFXkzWeqSKuIeCwJI0IIIQrcj3svcispnYoeDnSRVhGRAwkjQgghCtTBi7eZu+McAG+0roKVpXzViMez0roAIYQQpYPeYOSbbeeYvTUCg1Ghurcz3eqX17osUQJIGBFCCPHELt1KZtSKMA5evA1A13q+fNQtCGtpFRG5IGFECCFEvimKwprDV5j4+wkS0/Q421rxUbcgaREReSJhRAghRL7EpWTw/m/H+ePIVQBCA8ows089/NwdNK5MlDQSRoQQQuTZv+dvMnrlEa7cScHSQseoNlV5/enK0llV5IuEESGEELmWYTAy668zzNl+DkWBAA8HZvWpR33/MlqXJkowCSNCCCFyJTI2ibd/PszRy3EA9A6twMTOtXGyla8S8WTkN0gIIcRjKYrCiv2XmPJHOCkZBlztrZnWI5hOcuE7UUAkjAghhHik20npvPvrUTaeuA5A00oezOhTFx9Xe40rE6WJhBEhhBDZ2hURy39WhXE9Pg1rSx3/1746w1pWwkKuMyMKmIQRIYQQmaTpDXyx8TTzd0YCUKmsI7NfrE9QeVeNKxOllYQRIYQQJhHXExj5cxgno+MBeLmxP+8/Vwt7G0uNKxOlmYQRIYQQKIrCj3svMnX9SdL0Rtwdbfi8Zx3a1vLWujRhBiSMCCGEmbuRkMa41UfZeioGgKeqleWLXnXwcrbTuDJhLiSMCCGEmVIUhT+PX+OD348Tm5iOjZUF4zvWYEDTitJJVRQpCSNCCGGGom4mM3HtcbafvgFAdW9nvuxbjxrlXDSuTJgjCSNCCGFG0vQGvttxnq+3nSVNb8TG0oLhrSoxonUV7Kylk6rQhoQRIYQwE7vPxvLB78c5fyMJgOZVPPiwaxCVyzppXJkwdxJGhBCilItJSGXqupOsPXIVgLLOtrz/XE261PVFp5O+IUJ7EkaEEKKUMhjV03W/2HiahDQ9Fjro1ySA/3SojoudtdblCWEiYUQIIUqho5fvMGHNcY5dUa+wW6eCKx93Cya4goyiKoofCSNCCFGKxKVk8MXG0/z470UUBZztrBjboTovNQ7AUk7XFcWUhBEhhCgFFEXh97CrTF1/ktjENAC61fPlvedqyuBlotiTMCKEECXc2ZhEJv5+nD3nbgLqhe2mdg2iWRVPjSsTInckjAghRAmVmmHg661nmff3OTIMCrZWFoxsU5WhLQOxtZIxQ0TJIWFECCFKoG2nYpi49jiXbqUA0Lp6WT7sGoSfu4PGlQmRdxJGhBCiBLl6J4UP/wjnzxPXAPBxtWNS59p0qO0tY4aIEkvCiBBClAB6g5FFey4wY/MZktMNWFroGNIikLfbVMXRVv6Ui5JNfoOFEKKYO3LpDuN/PUZ4dDwAoQFlmNo9SC5qJ0oNCSNCCFFMJabp+WLjaZb8cwGjAq721rzXqQa9QvywkDFDRCkiYUQIIYqhTSeuMWntCaLjUgF1zJD3n6+Fp5OtxpUJUfAkjAghRDESHZfCpN9PsCn8OgD+7g583D2IllXLalyZEIVHwogQQhQDBqPC0n8u8MWmMySm6bGy0DHsqUqMfKYq9jYyZogo3SSMCCGExsKvxjN+zTGOXLoDQAN/Nz7pESwdVIXZkDAihBAaSU7X8+VfEXy/KxKDUcHZ1oqxHWvwciN/6aAqzIqEESGE0MC20zF88NtxLt9WR1B9LtiHiZ1r4e0iF7UT5kfCiBBCFKGYhFQ+/COcdUejASjvZs+HXWvTpqa3xpUJoR0JI0IIUQSMRoWf91/i0/+dJD5Vj4UOBjcP5J121WQEVWH25BMghBCF7Mz1BN779RgHLt4GILi8K9N6BBNU3lXjyoQoHiSMCCFEIUnNMPD11rPM+/scGQYFBxtL/tO+OgOaBmBlaaF1eUIUGxJGhBCiEOw+G8uENce4cDMZgLY1vZjSNYjybvYaVyZE8SNhRAghCtCF2CQ+33iKDceuAeDtYsuULrXpULscOp2critEdiSMCCFEAbiVlM7sLREs+/ciGQYFnQ76NQng/zpUx8XOWuvyhCjWJIwIIcQTSM0wsGjPBb7ZdpaEVD0AraqVZXynGjKCqhC5JGFECCHywWhU+P3IFb7YeIYrd9SBy2r6uPBepxpyUTsh8ihf3bnnzJlDYGAgdnZ2hISEsHPnzseun5aWxoQJEwgICMDW1pbKlSuzYMGCfBUshBBa23M2li7f7OKdFUe4cicFH1c7vuhVl3VvtZAgIkQ+5LllZMWKFYwaNYo5c+bQvHlz5s2bR8eOHQkPD8ff3z/bbXr37s3169f54YcfqFKlCjExMej1+icuXgghitKZ6wlM23CSbadvAOBka8XrT1dmSItA7KzlyrriLqMRUm5BUiwYM8CoB6Ph7r0eDBmZHz/yZri7rj7zPlAKp+5a3aBcUOHsOwc6RVHy9KoaN25MgwYN+Pbbb03zatasSbdu3Zg2bVqW9f/8809efPFFzp8/j7u7e76KjI+Px9XVlbi4OFxc5BisEKJoxcSnMmPzGVYeuIRRASsLHS839mdkm6p4ONlqXZ4oKooCKbch4RokXlPvE6Ifur97M2ZoXW3e9fwBgl8o0F3m9vs7Ty0j6enpHDx4kHfffTfT/Pbt27Nnz55st1m7di2hoaF8/vnnLF26FEdHR7p06cJHH32Evb2cby+EKL6S0vR89/d5vvv7PCkZBgCerV2Osc9Wp1JZJ42rM0MXdsOOTwEdWNurNyt7sLYDawewsnto+lHrOKiPre4tt4P0xEeEi4fuDWm5r9fODaxswcIKLCzv3ls/9Njq/mNL68yPTdPWDzy2BF0hDZjnUblw9psLeQojsbGxGAwGvL0zX9DJ29uba9euZbvN+fPn2bVrF3Z2dqxZs4bY2FhGjBjBrVu3HtlvJC0tjbS0+z/w+Pj4vJQphBBPRG8wsvLAZWb+dYYbCerfovr+bkzoVJPQivlr4RUFYON4iD6idRVg7w7O5e7efLK/d/QCKxutKy0x8nU2zcMD9yiK8sjBfIxGIzqdjmXLluHqql6HYcaMGbzwwgt888032baOTJs2jSlTpuSnNCGEyDdFUdh2OoZpG04REZMIgL+7A+OerUGnYBm0TFNXw9QgYmkDnb9U+1LoUyEjGTJSQZ8CGXdv+tSHph9e5+68hw+l2LmCUw4hw8lbbVURBSpPYcTT0xNLS8ssrSAxMTFZWkvu8fHxoXz58qYgAmofE0VRuHz5MlWrVs2yzfjx4xk9erTpcXx8PH5+fnkpVQgh8uTY5Tg+2XCSf87fBMDNwZqRz1TllSYB2FjJdWQ0d2ixel+zM9R7qWD2aTTcDy02jmDjUDD7FXmWpzBiY2NDSEgImzdvpnv37qb5mzdvpmvXrtlu07x5c1atWkViYiJOTuox1jNnzmBhYUGFChWy3cbW1hZbW+kUJoQofFfupPDfP0/xW9hVAGysLBjUvCIjnq6Cq72MnFospCfB0VXqdIMBBbdfC0uwdVJvQlN5jvujR4/m+++/Z8GCBZw8eZJ33nmHqKgohg8fDqitGv379zet/9JLL+Hh4cGgQYMIDw/n77//ZsyYMQwePFg6sAohNLU5/DodZv5tCiLd6vmy9T+tGN+xpgSR4uTEGkhPgDKBULGl1tWIQpDnPiN9+vTh5s2bfPjhh0RHRxMUFMSGDRsICAgAIDo6mqioKNP6Tk5ObN68mbfeeovQ0FA8PDzo3bs3U6dOLbhXIYQQeWA0Kny5JYIvt0QA0MDfjSldggiu4JrDlkITBxep9w36g4UcMiuN8jzOiBZknBEhREGJT83gnZ/D2HIqBoCBzSoy4bmaWFvKl1yxdD0cvm2qntb6Tjg4Z98/URRPhTLOiBBClGQR1xN4delBImOTsLGy4JPuwbwQkn3fNVFM3Ou4Wr2jBJFSTMKIEMIs/Hk8mv+sPEJSugFfVzvm9QuVwzLFXUYqHPlZnW4wUNNSROGSMCKEKNUMRoUZm0/zzbZzADSp5M43LzWQYdxLgpNrIfUOuPpB5dZaVyMKkYQRIUSpFZecwcifD7PjjHphuyEtAhnfsQZW0j+kZLjXcbV+P/U0XFFqSRgRQpRKp67F89rSg1y8mYydtQWf9axD13rltS5L5FZsBFzcrV6Hpf4rWlcjCpmEESFEqbPu6FXGrDpKSoaBCmXsmdcvhNq+0j+kRLnXcbVqe3CVEFnaSRgRQpQaeoOR/246zbwd5wFoUcWTr/rWp4yjXLCsRNGnQ9hydbogR1wVxZaEESFEqXA7KZ23lh9m19lYAF5rVYkx7atL/5CS6PR6SI5VL05Xtb3W1YgiIGFECFHinbgax2tLD3L5dgr21pb8t1cdnq/jq3VZIr/udVyt9zJYyteUOZCfshCiRPs97ArjVh8lNcOIv7sD3/UPoUY5Gam5xLoVCee3Azpo0E/rakQRkTAihCiR9AYj0/53ih92RQLQqlpZZr9YH1cHucBdiXZ4qXpfuTWUqahpKaLoSBgRQpQ4NxPTePOnw/xz/iYAb7SuzOh21bG00GlcmXgiBj0cXqZOS8dVsyJhRAhRohy7HMdrSw9wNS4VRxtLpveuy7NBPlqXJQpCxEZIvAYOnlC9k9bViCIkYUQIUWL8cvAy7605RrreSKCnI9/1C6Gqt7PWZYmCYhpx9WWwktOxzYmEESFEsacoCp9vPM2329Xry7Sp4cXMF+vhYif9Q0qNuMtw9i91Wg7RmB0JI0KIYi3DYOTd1cdYfegyACOfqcKottWwkP4hpcvhH0ExQsWW4FFZ62pEEZMwIoQotpLT9byx7BDbTt/A0kLHtO7B9G7op3VZoqAZDXDo7lk00ipiliSMCCGKpVtJ6QxetJ+wS3ews7bgm5ca0Kamt9ZlicJwdgvEXwb7MlCzs9bVCA1IGBFCFDuXbyfTf8E+zt9IwtXemgUDGxISUEbrskRhuXdRvLp9wdpO21qEJiSMCCGKlVPX4hmwYB/X49PwdbVjyZBGVPGSM2ZKrYRrcPp/6rQcojFbEkaEEMXGv+dvMnTJARJS9VTzdmLx4Eb4uNprXZYoTGHLQDGAX2PwqqF1NUIjEkaEEMXCn8evMfLnw6TrjTSsWIbv+zeUod1LO6MRDi1Rp6VVxKxJGBFCaO7HvReZ+PtxjAq0q+XNV33rY2dtqXVZorBF7oDbF8DWFWp317oaoSEJI0IIzSiKwqy/IvhySwQAfRv581HX2lhZWmhcmSgS9zqu1ukFNg7a1iI0JWFECKEJg1Hh/d+Os3xfFAAj21TlnbZV0elkMDOzkBQLJ9ep03KIxuxJGBFCFLnUDAMjlx9mU/h1dDr4qGsQrzQJ0LosUZSOLAdjBvjWB586WlcjNCZhRAhRpOJSMhi2+AD7LtzCxsqC2S/Wk6vumhtFgYN3D9FIq4hAwogQoghdi0tlwIJ9nL6egLOtFfMHhNKkkofWZYmidnEP3IwAa0cIfkHrakQxIGFECFEkzsYkMmDBPq7cScHL2ZbFgxtR08dF67KEFu51XA3uCbYyoJ2QMCKEKAKHom4zeNF+7iRnUMnTkcWDG+HnLmdPmKWU2xD+uzrdYKCmpYjiQ8KIEKJQbT11nRHLDpGaYaSunxsLBzbE3dFG67KEVo6uBH0qeAdB+QZaVyOKCQkjQohCs/LAJcb/egyDUeHp6mWZ83IDHGzkz47ZerDjashAkNO4xV3yV0EIUeAUReHbHef4/M/TAPRsUIFPewZjLYOZmbfLByDmBFjZQ3AvrasRxYiEESFEgVIUhU82nGT+zkgAhreqzLhnq8tgZgIOLVLva3cDezcNCxHFjYQRIUSBmr/zvCmIfPB8LYa0CNS4IlEspMbD8V/VaRlbRDxE2kyFEAVm/dFoPtlwCoD3n6spQUTcd/wXyEgGz+rg30TrakQxI2FECFEgDl68xTsrwwAY2KyiBBGRmanj6gDpuCqykDAihHhiF2KTGLbkIOl6I21revHB87Wkj4i472oYRIeBpQ3UeVHrakQxJGFECPFEbielM2jRfm4lpRNc3pXZfetjaSFBRDzg3oirNTuDowz/L7KSMCKEyLfUDAOvLj1AZGwS5d3s+WFgqIwjIjJLT4Kjq9Rp6bgqHkHCiBAiX4xGhTG/HGX/hds421mxcFBDvJzttC5LFDcn1kB6ArhXgootta5GFFMSRoQQ+fLFptP8ceQqVhY65r0SQjVvueCZyMa9jqsN+oOFfOWI7MlvhhAiz5bvi2LO9nMAfNqzDs2qeGpckSiWrofD5X1gYQX1Xta6GlGMSRgRQuTJjjM3eP+34wC83aYqL4RU0LgiUWzd67havSM4eWlbiyjWJIwIIXIt/Go8byw7hMGo0KN+eUa1rap1SaK4ykiFIz+r0w0GalqKKP4kjAghcuVaXCqDF+0nMU1P00oefNqzTtGOJXJ+O3waAIeWFN1zivw7uRZS74CrP1RurXU1opiTMCKEyFFimp5Bi/ZzLT6VKl5OzH0lBBurIvzzYTTCxgnql9uumeql6EXxdnCRet+gH1hYalqKKP4kjAghHktvMPLGskOcjI7H08mGhQMb4upgXbRFnN4A19V+Ktw6D1cPF+3zi7zZOxcu7gadhXRcFbkiYUQI8UiKovDB7yfYceYGdtYW/DCgIX7uDkVdBOz4TJ22ujuOybFfirYGkXsHFsKf49Tpp8eDa3lt6xElgoQRIcQjzfv7PMv3RaHTwewX61PXz63oizizEa4dBWtH6PSFOu/Er2A0FH0t4vGO/Azr3lGnm42Ep8ZoW48oMSSMCCGyte7oVT793ykAJj5fi/a1yxV9EQ+2ijQaCnV6g50rJESrhwFE8XFiDfz2OqBAo1eh3YdydV6RaxJGhBBZHLhwi9ErjwAwqHlFBjUP1KaQs1vg6iGwdoCmb4GVLdTsoi6TQzXFx6kNsHooKEZ1pNVnP5MgIvJEwogQIpPI2CSGLTlAut5Iu1revP9cLW0KURTY8ak6HToYnMqq08G91Pvw30Gfrk1t4r6zf8GqAWDUQ3BveH6WDPsu8kx+Y4QQJreS0hm0cB+3kzOoW8GVL1+sh6WFRv/hnt8Ol/ernVabjbw/v2ILcCqnnuZ7bos2tWkh6abaAlGcziSK3Ak/vwyGdKjVFbp9K6fxinyRa30LIQBIzTAwbMkBLtxMpkIZe74f0BAHG43+RDzYVyRkEDh7319mYQm1u8O/38KxVepQ46VR4g24uAsu7IYLu+DGyfvLGg6Fdh+BTRGf2fSgS/vgpz6gT4Vqz0KP78FSvlJE/shvjhACo1HhP6uOcPDibVzsrFg0qCFlnW21K+jCLoj6ByxtofnbWZcH91LDyOn/QXoS2DgWfY0FLeGa+rov7lYDSOzprOu4V4Zb52D/92qrRM/vwadO0dd69TD82BMykqBSa+i1GKxsir4OUWpIGBFC8PnG06w/Go21pY65/UKo4uWsbUH3WkUa9AcXn6zLyzeAMoFwO1INJMEvFG19BSHuyt3gcTeA3DybdR3vIAhorh6aCmgGjp5qp97fRqhhZf4z0GYiNH2z6PppXD8BS7tDWrxa24s/gbVd0Ty3KLUkjAhh5pb9e5G5O84B8FnPOjSr7KltQRf3wIWdYGENLUZlv45OpwaQv/+rHqopCWHkzqW7wePuoZfbkQ+toINywXeDR3M1fDi4Z91PlTbw+h74YyScWgebP4Czm6Hb3MIfYOzGGVjSFVJuQ/lQeGmFtoeKRKkhYUQIM7bjzA0m/n4CgHfaVqNHgwoaVwTs+Fy9r/8KuD6mnqC7YeTsX5B8K/svbi3diVIPpdwLIHeiMi/XWUC5Omr4qNgC/JuAfZnc7dvRA/r8qF408M93IfJv+LYZdP4Sancr8JcCqMPwL+kCSTfUul9ZDbYat6CJUkPCiBBm6mxMIm/+dAiDUaFHg/KMbFNF65LUTpHnt4GFFbR45/HretUA72C4fkw9zTd0UNHUmBvHf4XVQ9RxN+7RWYJvvbuHXVqCf2N1ALf80ukgZIC6v1+Hqv04Vg2AiFeg46cFGxTuXILFXdTB5rxqQb/fwN6t4PYvzJ6EESHM0J3kdIYu3o9X2kW+c/2N0OC30OnqaV3W/b4idftCmYCc1w/uqYaR46uLTxgxGmDLFDWIlKujHlYJaKGGj8JoSfCsAkM2w/ZpsHMGhP2o9kHp+T1UCH3y/cdHw+LOEHcJPKpA/9/VlhkhCpCMMyKEmckwGHnjp0Ok34xiue2nNE3bjfXqgWrHRC1dPqgectFZQsvRudsmqKd6f2EXxF8tvNry4tQ6uH1BPeQy+E9oOxmqti3cQxqW1mpH1oHrwaWC2h/lh/bqIS+DPv/7Tbyh9hG5HQluAdB/LTh5FVzdQtwlYUQIMzN1XTjhZyP50fZTvLip9l3Qp8LKAZCWoF1hf9/tK1KnD7hXyt02bv7g1wRQ1EMjxcGer9X70CFFf8pxxebw+m41pCkG2PYxLHpODUd5lXwLlnZTz9pxKQ8D/pAr8IpCI2FECDOy7N+LrPrnNAttPqeS7qr6X/SrO8DZF25GwB9vqwOOFbWrYXDmTzUYtfxP3ra9dybNsVUFXlaeRf0Ll/eBpY16sTgt2LtBzx+g+3dg4wyX9sK3LeDIitz/bFPj4McecP04OHmrQSQ3h82EyCcJI0KYiX/O3WTq70eYaz2Tehbnwd4d+q1RB83qtVA9PHJ8NRxYUPTF/f1f9T7oBbUPRF7U7q7WHh0GsdmM1VGU9sxW7+v0yTxqbFHT6aBuH3h9l9pylJ4Aa15VO9Wm3Hn8tmmJsKy32iHWwUM9NONRuUjKFuZLwogQZiDqZjIjftzP55bf8JTlMRRrR3j5FyhbTV3Bv4natwHUU0WvhhVdcdeOqf0s0MFT/5f37R09oXJrdfq4hlfyvXkOTq1Xp5u+qV0dDypTUe1H0nrC/bA5t4U6zkl2MlJg+Ytqa4qdq3rWjFeNoqxYmCkJI0KUcgmpGQxZtI+3M36gs+VeFAtrdH2WQoWQzCs2ewuqdVQverZqgNpUXxTutYrU7g5lq+dvH0H3DtX8os1hJoC9cwAFqrYvXl/gllbQaiwM2aSOWht3Se1H8teUzFc91qfBilfUAedsnOGVNdoMNS/MkoQRIUoxg1Hh7Z/DePbWUgZabUJBh677XPV004fpdND9W3D1Vzs8/v5G4X+xXw9XxwgBeGpM/vdT4zn16r43I+Da0YKpLS+Sb8HhZep0s7eK/vlzo0IoDN+pDiaHArtmwIL26qEtQwb8Mlg9m8naAV5elTWsClGIJIwIUYp9/ucpfCJ+4j/W6uELXcfPHj90un0Z6LVIHYr95B/w79zCLXDnF+p9zS7gXSv/+7FzgWod1GktOrLu/wH0KXdHVG1Z9M+fW7bO0PUb6L0E7NzUfiHzWqqn755ap16YsO9yCGiqdaXCzOQrjMyZM4fAwEDs7OwICQlh586dudpu9+7dWFlZUa9evfw8rRAiD345eJmoXcv5yGqhOuOpsdD4tZw3rBACHT5Wpze9D5cPFE6BN87cPx33SVpF7rl3qOb4r2A0Pn7dgpSRCvvmqdPNRqotTMVdra7q9W0Cn4KMZHWQNAtrdYj5Sk9rXZ0wQ3kOIytWrGDUqFFMmDCBw4cP07JlSzp27EhUVNRjt4uLi6N///60aZNN87AQokAdvHiLtWuWM8v6Gyx0CoQMgtbv5X4HjV5Vv7CMelg1UD0MUdB2fgEoUP25gumbULU92LpA/BWI+ufJ95dbR1eo12txqVB414UpDK7lod/v0H6qOqx+7yVQrb3WVQkzlecwMmPGDIYMGcLQoUOpWbMms2bNws/Pj2+//fax27322mu89NJLNG0qzX9CFKYrd1KYuWQlcyy/wFanR6nZBZ6bnrf/2HU66PKVOvhY3CX47fWCbW24ee7+4ZRWBdAqAupl7Gt2VqeL6qwaoxH++UadbjJcHQm1JLGwUPu4vL4LanTSuhphxvIURtLT0zl48CDt22dOz+3bt2fPnj2P3G7hwoWcO3eOSZMm5ep50tLSiI+Pz3QTQuQsKU3PxAW/M0v/MU66VAwBLdH1/B4sLPO+MztX6LVY7Udw5s/7Y2gUhJ3T1Wu3VO0AvvULbr/3+sOc+E3tlFnYzm5WRyi1dYEGAwr/+YQopfIURmJjYzEYDHh7Zx7Mx9vbm2vXrmW7TUREBO+++y7Lli3Dyip31+WbNm0arq6uppufn19eyhTCLBmNCh/+tIXJd97HUxdPulcwln1/Aivb/O/Upw50vHvxui0fwsUCOPxxKxKO/KxOtxr75Pt7UMWnwLEspNyCc9sKdt/Z2fOVeh8yQO1EK4TIl3x1YNU91NyrKEqWeQAGg4GXXnqJKVOmUK1atVzvf/z48cTFxZluly5dyk+ZQpiVb/88wKDI/+BncYNU54rY9F9TMF+QIQMhuLd6rZNfBkFS7JPtb9cMdV+V2xTMVWUfZGkFtXuo04V9Vs3Vw+qYHBZW0Hh44T6XEKVcnsKIp6cnlpaWWVpBYmJisrSWACQkJHDgwAHefPNNrKyssLKy4sMPP+TIkSNYWVmxdevWbJ/H1tYWFxeXTDchxKNtOHiORntHUMPiEim2ntgN/h2cyhbMznU6eH4meFaDhGj4dRgYDfnb1+2LEPaTOv30uwVT38PuHao5tR7SkwvnOeD+BfFq9wDXCoX3PEKYgTyFERsbG0JCQti8eXOm+Zs3b6ZZs2ZZ1ndxceHYsWOEhYWZbsOHD6d69eqEhYXRuHHjJ6teCMHRqBvY/T6UhhZnSLV0wn7Q7+ow4AXJ1kntP2JlD+e2qn0+8mPXTPUMnUpPg1+jAi3RpEJD9Wq+GUlw5n+F8xx3LsGJNep0s2Iy9LsQJVieD9OMHj2a77//ngULFnDy5EneeecdoqKiGD5cbaYcP348/fv3V3duYUFQUFCmm5eXF3Z2dgQFBeHoWMSX1xailLkel0LUoqE8Y3GIdJ0N1q+shHJBhfNk3rXg+Rnq9PZpcH5H3raPuwyHf1SnW40r2NoepNM9MDz86sJ5jn/nqoeaAluBT93CeQ4hzEiew0ifPn2YNWsWH374IfXq1ePvv/9mw4YNBASol5eOjo7OccwRIcSTS80wsHvuGzxv3I4BC/Q9FmAZ2Lxwn7TeS+pw4ooRVg+FhOw7rmdr1ywwZqgjlAZkbUktUMG91PuITZByu2D3nXIHDi5Sp4vr0O9ClDA6RdHqqlK5Fx8fj6urK3FxcdJ/RAjUTuO/z3mXbjfU4dpj287Es8Xgonny9GT4vi3EnICAFtD/d7Xj6OPEX4Uv66oX4RvwhzryZ2Gb0xRiwtXxUhr0L7j97v4SNk+EsjVgxN6SMeKqEBrJ7fe3XJtGiBJoy8+zTEHkQoNxRRdEAGwcoPdisHGCi7vUQzY52T1bDSL+TYvu2i1BPdX7YwU4AJo+HfbevV5Ps7ckiAhRQCSMCFHCHNq8nKdPfQhAeMUBVOySh2HeC4pnVej8pTq98wuI+OvR6yZch4N3r4/TamzRfYHfO6sm8u+8HU56nBNrIOEqOHnfPxQkhHhiEkaEKEEuHPqLWrvewkpn5LB7R2r1n6VdMcEvQOgQdfrXYWoH1ezsmQ36VPUsl0qti66+MhXV50S5f+bLk1CU+4OcNXr1yQaTE0JkImFEiBLiduRh3Nf2x06XwSG7xgQNX6xeW0RLHT5RzyZJuQW/DM46BHviDTiwQJ1uNa7oD2vca70oiAHQInfA9WNg7QChRXhYTAgzIGFEiBIg48QfWC95HheSOGZRk0qvr8Taphj8Z25tB70WqddmufSvOmT8g/75Wr1EvW99qNK26Our3R10FnDlINw6/2T7utcqUv8VcHB/8tqEECYSRoQozgwZKH++h/WqV3BSEjmiVMVx4C+4ubppXdl97pWg690r1+6ZDac2qNNJN2HffHVai1YRACcvdSwQeLIxR66Hw9m/1GDT5PWCqU0IYSJhRIji6s4lWNgR3V71i/57QyfiX/ydSv7FcOjxWl2gyQh1+rfh6rDve+eoo6CWC4Zqz2pX272OrMdWqf0+8uOfu2GrZmc1fAkhCpSEESGKozMbYV5LuLyfOMWBYemjsek0jZY1y2td2aO1nQLlQyE1Dlb2g3/nqfO1ahW5p2ZnsLSF2NNw/Xjet0+4BkdXqNNNZZAzIQqDhBEhihNDhjqg1k+9IeU2R5XKPJf+CT6Ne9K/aUWtq3s8KxvotRDs3CD6CKQngFdtqP6ctnXZuULVdup0fsYc+XeeOnKsXxPwa1iwtQkhAAkjQhQfcVdg0fPqCJ/ACovneCFtIoFVajHx+VoaF5dLbv7Qfd79x63GaH/GD9w/q+b4ajAac79dWuL9s4Fk6HchCk0OYzgLIYpExF+w5lVIvoli68ynNm8x70YQlcs68vVLDbCyLAZf6LlV/VnoNlcdd6RmV62rUVXrADbOEHcJLu8D/ya52y5sGaTeUfuJVO9YqCUKYc4kjAihJYMetn8CO6cDoPjUZbLNGBaftqCMgzULBjbE1d5a4yLzoV5frSvIzNoeaj4PR5arh2pyE0aMhvsdV5u+ARaWhVujEGasBP27JUQpEx8NS7qYgggNhzI74GsWn7bA2lLH3FdCCPBw1LbG0iTo7lk1J9aoITAnJ/+AOxfB3h3qvlS4tQlh5iSMCKGFc1thbgu4uFs9fPDCAn4vP5qZ2y8B8HH3YBpX8tC4yFKmUitw8ITkWIjc/vh1FUUdMwWg0TD14oBCiEIjYUSIomQ0wLZPYGkP9UvROxhe28FB52cY88tRAF57qhK9Q/00LrQUsrSG2t3U6ZzOqonaq47aamkLDYcVemlCmDsJI0IUlYTrsLQb7PgMUCBkIAzdzGULH15beoB0vZG2Nb0Z+2wNjQstxe4dqjm5DjJSHr3eP1+r93VfBKeyhV+XEGZOwogQReH8DvWwTOTfYO0IPeZD5y9JNFozdPEBYhPTqenjwpcv1sPSQsMBwko7v8bg6qeOgXJmY/brxJ6FU+vV6aZvFl1tQpgxCSNCFCajAXZ8rraIJMWAVy14dTvU6Y3BqPD28sOcupaAp5Mt3w8IxdFWTnArVBYWENRDnT7+iEM1e78BFHUI+7LViqw0IcyZhBEhCkviDfixJ2z7GBSjerXXoVtMX3Cf/u8kW07FYGtlwfz+IZR3s9e4YDNxbwC0M5vUoesflBQLYT+p0zLImRBFRsKIEIXhwm71sMz5bWDtoA4C1vUb01kZK/ZHMX9nJABf9KpLff8yWlZrXryDwLM6GNLUviMP2v8D6FPBtz4ENNemPiHMkIQRIQravvmwuDMkXlO/9IZtyzQI2D/nbjJhjXrBtlFtq9K5rq9WlZonne5+68ixVffnZ6TCvu/U6aZvantxPyHMjIQRIQqK0QB/vgcb/g8UA9TpA69uA6/7Z8dciE3i9WUH0RsVOtf15e02VTUs2Izd6zcSuQMSY9Tpoz+rp1u7+kGtbpqVJoQ5kjAiREFIT4IV/e52fgTaTFQvGGdzfwTVuOQMBi/ez53kDOr5ufHfF+qgk/++teFRGcqHqH15TqxRL5635+7pvE1GgKV0JBaiKMknTognlXANfuoD0WHqIFndv4WgnplWyTAYeeOnQ5y/kYSvqx3f9Q/BzlqudaKpoBfUgc2O/QJuAXAzAmxdoUE/rSsTwuxIy4gQT+L6CZjfRg0iDh4w4I8sQURRFKb8cYJdZ2NxsLHk+wEN8XK206ZecV9QD0CnXsX3r0nqvNCBYOusZVVCmCUJI0Lk19m/4IcOEH8ZPKrC0L/Av3GW1RbvucCPe6PQ6eDLF+tTy9dFg2JFFs7lILClOn3jFFhYQePh2tYkhJmSMCJEfhxYCMt6qyN5VmwJQzeDe6Usq20/HcOH68IBePfZGrSr5V3UlYrHuXdWDaiHbVzkzCYhtCBhRIi8MBph0wewbpR6xkzdvvDKr2CfdZyQiOsJvPXTYYwK9AqpwKtPZQ0rQmM1O4PV3UNmzWTodyG0Ih1Yhcit9GRY8xqcXKs+bj0BnhqT7XgUNxPTGLx4PwlpehoFuvNx92A5c6Y4si8D/daoZ0OVC9a6GiHMloQRIXIjMQaWv3j3svI26miqdXpnu2q63sjwHw9y6VYK/u4OzH0lBBsraYQstgKaaV2BEGZPwogQOYk5Bct6QVyU+p/0iz899gvsu7/Psf/CbZztrFgwMBR3R5siLFYIIUoeCSNCPM757bCiP6TFqR1UX/5FHTDrEa7FpTJn+zkAPuoaRBUvOU1UCCFyImFEiEc5tFTtqGrUg39TtUXEwf2xm3z+5ymS0w2EBJShaz05M0MIIXJDwogQDzMaYetHsGuG+ji4N3T9GqxsH7vZ4ajb/Hr4CgATn68lHVaFECKXJIwI8aCMFPjtdfV6JQCt3oWn383xCq5Go8KUP9TxRF4IqUBdP7dCLlQIIUoPCSNC3JMUC8v7qsODW1hDl6+gXt9cbfr7kSuEXbqDo40lYztUL+RChRCidJEwIgTAjTPwUy+4fQHsXKHPsvtDhecgKU3Pp/87BcAbz1TBy0WuOyOEEHkhYUSIyJ2w4mVIjYMyFeGlVVC2Wq43n7vjHNfj0/Bzt2dw88DCq1MIIUopCSPCvJ1YA6uHgTEDKjSCvsvB0TPXm1+6lcy8v88DMKFTLeysLQurUiGEKLUkjAjzlZEK60arQaR2D+j2LVjn7RDLp/87RbreSNNKHnSoLRfBE0KI/JAwIsxX+G+Qcgtc/aDn92CRt1aNvedvsv5YNBY6mNhZTuUVQoj8kgtmCPO1/wf1PmRAnoOIwajw4d1Tefs28qemj0tBVyeEEGZDwogwT9FH757CawX1++d581UHLhEeHY+LnRWj2+W+s6sQQoisJIwI83TgbqtIzc7gnLe+HvGpGfx342kA3m5bDQ+nx4/MKoQQ4vEkjAjzkxoPR1ep0w2H5nnzr7ee5WZSOpXLOtK/aUABFyeEEOZHwogwP0dXQEYSlK0BAc3ztGlkbBILd0cC8P7ztbC2lI+QEEI8KflLKsyLosD+79Xp0ME5XnPmYR+vDyfDoNC6ellaV/cqhAKFEML8SBgR5uXiHrhxCqwdoO6Ledr07zM3+OtkDFYWOt5/vlYhFSiEEOZHwogwL/c6rgb3Uq9Bk0sZBiMfrVNP5R3QrCKVyzoVRnVCCGGWJIwI85EYA+Fr1emGQ/K06bK9F4mIScTd0YaRbaoWQnFCCGG+JIwI83FoiTr0e/lQ8Kmb681uJ6Uz868IAP7Tvhqu9taFVaEQQpglCSPCPBgNcHCROp3HVpGZf50hLiWDGuWcebGhf8HXJoQQZk7CiDAPEZsh7hLYl4Ha3XO92elrCSz7NwpQrz9jaSHXnxFCiIImYUSYh3sdV+u9DNb2udpEURQ+WheOwajwbO1yNKvsWYgFCiGE+ZIwIkq/2xfUlhFQxxbJpb9OxrDrbCw2Vha816lm4dQmhBBCwogwAwcWAgpUfgY8KudqkzS9gY/Xq6fyDm0RiL+HQyEWKIQQ5k3CiCjd9GlweKk6HZr7jquLdl/gws1kvJxtGdG6SiEVJ4QQAiSMiNIufC0k3wSX8lDt2VxtciMhja+2ngVg7LM1cLK1KswKhRDC7EkYEaXbvevQNBgAlrkLFV9sPE1imp66FVzpUb98IRYnhBACJIyI0uz6Cbi0FyysoEH/XG1y/EocKw9eAmBi59pYyKm8QghR6CSMiNJr/93TeWs8By4+Oa6uKApT/jiBokDXer6EBJQp5AKFEEKAhBFRWqUlwNEV6nQuO66uPxbN/gu3sbe25N2ONQqxOCGEEA+SMCJKp6MrID0RPKpC4FM5rp6aYWDahlMADG9VGR/X3A2MJoQQ4slJGBGlj6LA/gXqdMMhoMu538d3f5/nyp0UyrvZ8+pTlQq5QCGEEA+SMCJKn0v/QswJsLKHun1zXD06LoVvt58D4N2ONbC3sSzsCoUQQjxAwogofe51XA3uCfZuOa7+2f9OkZJhoGHFMjxfJ+eOrkIIIQqWhBFRuiTFQvhv6nQuOq4evHib38KuotPBpM610eXikI4QQoiCJWFElC6Hl4IhHXwbQPkGj131TnI6764+CkDvED+CyrsWRYVCCCEeImFElB5Gw92L4qF2XH2MlHQDgxftJyImEW8XW/6vQ/UiKFAIIUR28hVG5syZQ2BgIHZ2doSEhLBz585Hrvvrr7/Srl07ypYti4uLC02bNmXjxo35LliIRzq7Be5cBDtXqN3jkatlGIy8vuwgh6Lu4GpvzZLBjSnrbFuEhQohhHhQnsPIihUrGDVqFBMmTODw4cO0bNmSjh07EhUVle36f//9N+3atWPDhg0cPHiQ1q1b07lzZw4fPvzExQuRyYG7HVfrvQI2DtmuYjQqjP3lKNtP38DO2oIFA0OpXs65CIsUQgjxMJ2iKEpeNmjcuDENGjTg22+/Nc2rWbMm3bp1Y9q0abnaR+3atenTpw8TJ07M1frx8fG4uroSFxeHi4tLXsoV5uJOFMyqAyjw5kHwrJJlFUVR+GjdSRbsjsTKQsf8/qG0ruFV9LUKIYSZyO33d55aRtLT0zl48CDt27fPNL99+/bs2bMnV/swGo0kJCTg7u7+yHXS0tKIj4/PdBPisQ4uAhQIbJVtEAGYs/0cC3ZHAvBFr7oSRIQQopjIUxiJjY3FYDDg7e2dab63tzfXrl3L1T6mT59OUlISvXv3fuQ606ZNw9XV1XTz8/PLS5nC3OjT4dASdfoRHVeX74vivxtPAzDx+Vp0q1++qKoTQgiRg3x1YH14LAZFUXI1PsPy5cuZPHkyK1aswMvr0f+Vjh8/nri4ONPt0qVL+SlTmIuTayHpBjj7QPVOWRb/eTyaCWuOAfBG68oMbhFY1BUKIYR4DKu8rOzp6YmlpWWWVpCYmJgsrSUPW7FiBUOGDGHVqlW0bdv2seva2tpiaytnN4hcOnD3OjQNBoCldaZFe87FMnJ5GEYF+jby4//ayym8QghR3OSpZcTGxoaQkBA2b96caf7mzZtp1qzZI7dbvnw5AwcO5KeffuK5557LX6VCZCfmJFzcDTpLCBmQadHxK3G8uuQg6QYjz9Yux9RuwTLCqhBCFEN5ahkBGD16NP369SM0NJSmTZvy3XffERUVxfDhwwH1EMuVK1dYskQ9hr98+XL69+/Pl19+SZMmTUytKvb29ri6yoiX4gnduw5N9Y7g4muaHRmbxIAF+0hM09O0kgezXqyHpYUEESGEKI7yHEb69OnDzZs3+fDDD4mOjiYoKIgNGzYQEBAAQHR0dKYxR+bNm4der+eNN97gjTfeMM0fMGAAixYtevJXIMxXWiIc+VmdbjjUNPt6fCr9fviXm0npBJV34bv+IdhZy5V4hRCiuMrzOCNakHFGRLYOLIR1o8C9Mrx5ACwsiEvOoPe8fzh9PYGKHg788nozPJ2k/5EQQmihUMYZEaLYUJT7I66GDgYLC1LSDQxZvJ/T1xPwcrZl6ZDGEkSEEKIEkDAiSqbL++HaMbCyg3ovkWEw8sZPhzhw8TYudlYsGdIIP/fsh4QXQghRvEgYESXTvY6rQT0x2pVh3C9H2Xoq5u71ZhpSo5wczhNCiJJCwogoeZJvwYk1ACihg/l4w0l+PXwFSwsdc15uQGjFR19qQAghRPEjYUSUPId/BEMa+NTl2whXftilXm/mvy/U4Zkajx98TwghRPEjYUSULEajacTVfZ7d+XzjGQDef64mPRpU0LIyIYQQ+SRhRJQs57fC7UgyrJ0ZdMAfgBFPV2Zoy0oaFyaEECK/JIyIkmW/2iryU1pzkhQ7Xmzox5gOcr0ZIYQoySSMiJIj7jLKmf8BsCSjDR1qezO1W5Bcb0YIIUq4PA8HLwpHhsFIaoaB1Az1Pk1/f/r+PCMACuqguffGzr03hO7Dg+kqSjbrPrTNPTpAp7t7Q8e973edTnd/GTos7q7D3XV0D69zbz0LHdaWOmwsLbA23XTqvZVFtstyChW3d86njGJkj6EWZQOD+fLF+lhZSp4WQoiSzrzDyMFFcOUgWDs8cLMHmwemrR3RW9qSii0p2JGi2JCELUlGGxIMlqRkKCSnG0hO16v3aep9yr0QoTeQlvHA4wyDKWA8GDj0xtyMyq9ggYIFRixQ0D0wrT6+P/3gY0uMWOgeXm68u72CAQvSsCZdsVbvsSIdawwUxfVcFKwxYE8aLpbpOFtm4GSRgbNlOk4W6ThaZOBkkYaDLoN+yYsB2OHahfn9Q+V6M0IIUUqYdRi5eOB/BET/meN6VoDT3dvDkhVbUrAhBVtSHphW0GGBEcu7twenH35sYf3gMgOWKFjqMq93L3QUJSMWpOus0WNNhs6aDKzR66xJ19mQ8cC89Lv3psfYYESHlTEVWyUVa2MatkoatkoqdkoatqRhTxp2pOFAGlY6Y9YnN9y9PeSmrgxDh76Fs511ob9+IYQQRcOsw8gmXQvuZLhgr1O/FO1Iw16XjsO9L8vspnVp2JFh2ofD3W0hQT1mUWzoQGeR/c3igWl0YNSDIR30qaDcDwYWGLFT0oC0rMd1nqCsR1F0lijWDhit7DFa2WGwdMBgZYfB0h69pfrYpfkg7Nyyi4VCCCFKKrMOI/7Ne5FRrSMO1pY42FjhYGuJg83daZvM0/Y2lthYWqj9GoxGyEiGjJS798n3H6cnQ0aS2jnDwhJ0lg/cW9y9t3pomUU26z5u/qOCxt1tdKaOHXln0KsDiunv3gxpoE/Pfp4+9W6ISXtoeToYDXcPczk8dNjL4ZHzdJbW6HQ66VUthBBmxqzDSIfa5fK3oYUF2Dqpt9LG0kq92ThqXYkQQggzIf+ECiGEEEJTEkaEEEIIoSkJI0IIIYTQlIQRIYQQQmhKwogQQgghNCVhRAghhBCakjAihBBCCE1JGBFCCCGEpiSMCCGEEEJTEkaEEEIIoSkJI0IIIYTQlIQRIYQQQmhKwogQQgghNFUirtqrKAoA8fHxGlcihBBCiNy6971973v8UUpEGElISADAz89P40qEEEIIkVcJCQm4uro+crlOySmuFANGo5GrV6/i7OyMTqcrsP3Gx8fj5+fHpUuXcHFxKbD9isIhP6+SQ35WJYf8rEqWkvbzUhSFhIQEfH19sbB4dM+QEtEyYmFhQYUKFQpt/y4uLiXihypU8vMqOeRnVXLIz6pkKUk/r8e1iNwjHViFEEIIoSkJI0IIIYTQlFmHEVtbWyZNmoStra3WpYhckJ9XySE/q5JDflYlS2n9eZWIDqxCCCGEKL3MumVECCGEENqTMCKEEEIITUkYEUIIIYSmJIwIIYQQQlNmHUbmzJlDYGAgdnZ2hISEsHPnTq1LEg+ZPHkyOp0u061cuXJalyXu+vvvv+ncuTO+vr7odDp+++23TMsVRWHy5Mn4+vpib2/P008/zYkTJ7Qp1szl9LMaOHBgls9akyZNtCnWzE2bNo2GDRvi7OyMl5cX3bp14/Tp05nWKW2fLbMNIytWrGDUqFFMmDCBw4cP07JlSzp27EhUVJTWpYmH1K5dm+joaNPt2LFjWpck7kpKSqJu3bp8/fXX2S7//PPPmTFjBl9//TX79++nXLlytGvXznS9KVF0cvpZATz77LOZPmsbNmwowgrFPTt27OCNN95g7969bN68Gb1eT/v27UlKSjKtU+o+W4qZatSokTJ8+PBM82rUqKG8++67GlUksjNp0iSlbt26WpchcgFQ1qxZY3psNBqVcuXKKZ9++qlpXmpqquLq6qrMnTtXgwrFPQ//rBRFUQYMGKB07dpVk3rE48XExCiAsmPHDkVRSudnyyxbRtLT0zl48CDt27fPNL99+/bs2bNHo6rEo0RERODr60tgYCAvvvgi58+f17okkQuRkZFcu3Yt0+fM1taWVq1ayeesmNq+fTteXl5Uq1aNYcOGERMTo3VJAoiLiwPA3d0dKJ2fLbMMI7GxsRgMBry9vTPN9/b25tq1axpVJbLTuHFjlixZwsaNG5k/fz7Xrl2jWbNm3Lx5U+vSRA7ufZbkc1YydOzYkWXLlrF161amT5/O/v37eeaZZ0hLS9O6NLOmKAqjR4+mRYsWBAUFAaXzs1UirtpbWHQ6XabHiqJkmSe01bFjR9N0cHAwTZs2pXLlyixevJjRo0drWJnILfmclQx9+vQxTQcFBREaGkpAQADr16+nR48eGlZm3t58802OHj3Krl27siwrTZ8ts2wZ8fT0xNLSMkuCjImJyZI0RfHi6OhIcHAwERERWpcicnDvrCf5nJVMPj4+BAQEyGdNQ2+99RZr165l27ZtVKhQwTS/NH62zDKM2NjYEBISwubNmzPN37x5M82aNdOoKpEbaWlpnDx5Eh8fH61LETkIDAykXLlymT5n6enp7NixQz5nJcDNmze5dOmSfNY0oCgKb775Jr/++itbt24lMDAw0/LS+Nky28M0o0ePpl+/foSGhtK0aVO+++47oqKiGD58uNaliQf83//9H507d8bf35+YmBimTp1KfHw8AwYM0Lo0ASQmJnL27FnT48jISMLCwnB3d8ff359Ro0bxySefULVqVapWrconn3yCg4MDL730koZVm6fH/azc3d2ZPHkyPXv2xMfHhwsXLvDee+/h6elJ9+7dNazaPL3xxhv89NNP/P777zg7O5taQFxdXbG3t0en05W+z5am5/Jo7JtvvlECAgIUGxsbpUGDBqbTpkTx0adPH8XHx0extrZWfH19lR49eignTpzQuixx17Zt2xQgy23AgAGKoqinIE6aNEkpV66cYmtrqzz11FPKsWPHtC3aTD3uZ5WcnKy0b99eKVu2rGJtba34+/srAwYMUKKiorQu2yxl93MClIULF5rWKW2fLZ2iKErRRyAhhBBCCJVZ9hkRQgghRPEhYUQIIYQQmpIwIoQQQghNSRgRQgghhKYkjAghhBBCUxJGhBBCCKEpCSNCCCGE0JSEESGEEEJoSsKIEEIIITQlYUQIIYQQmpIwIoQQQghNSRgRQgghhKb+H1+fcDYuYb7tAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### ResNet-34 - training and evaluation","metadata":{}},{"cell_type":"code","source":"wandb.init(config=config, project='resnet-improved-labels', name='resnet34')\n\nregularizer = L2(l2=config[\"l2\"])\noptimizer = SGD(learning_rate=config[\"learning_rate\"], momentum=config[\"momentum\"])\nloss = SparseCategoricalCrossentropy()\nmetrics = [SparseCategoricalAccuracy()]\n\nmodel = rn.ResNet34(\n    len(classes),\n    (image_size, image_size, channels),\n    normalize=True,\n    kernel_regularizer=regularizer\n)\n\nearly_stopping = EarlyStopping(\n    monitor=\"val_loss\",\n    mode=\"min\",\n    verbose=1,\n    patience=config[\"early_stop_patience\"],\n    restore_best_weights=True\n)\n\nlr_schedule_sota = ReduceLROnPlateau(\n    monitor=\"val_loss\",\n    factor=0.1,\n    patience=config[\"lr_reduce_patience\"],\n    verbose=1,\n    mode=\"min\"\n)\n\ncallbacks = [\n    early_stopping,\n    lr_schedule_sota,\n    WandbMetricsLogger()\n]\n\nmodel.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nhistory = model.fit(train, validation_data=validation, epochs=config[\"epochs\"], callbacks=callbacks)\nwandb.finish()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:33:38.265666Z","iopub.execute_input":"2023-04-06T10:33:38.266120Z","iopub.status.idle":"2023-04-06T10:45:19.651701Z","shell.execute_reply.started":"2023-04-06T10:33:38.266083Z","shell.execute_reply":"2023-04-06T10:45:19.650552Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.14.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.13.10"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20230406_103338-vvdjc9kc</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/vvdjc9kc' target=\"_blank\">resnet34</a></strong> to <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/vvdjc9kc' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/vvdjc9kc</a>"},"metadata":{}},{"name":"stdout","text":"Epoch 1/100\n574/574 [==============================] - 33s 49ms/step - loss: 5.8578 - sparse_categorical_accuracy: 0.1549 - val_loss: 4.6942 - val_sparse_categorical_accuracy: 0.1563 - lr: 0.1000\nEpoch 2/100\n574/574 [==============================] - 28s 48ms/step - loss: 4.4186 - sparse_categorical_accuracy: 0.1650 - val_loss: 4.1755 - val_sparse_categorical_accuracy: 0.1651 - lr: 0.1000\nEpoch 3/100\n574/574 [==============================] - 27s 48ms/step - loss: 3.9026 - sparse_categorical_accuracy: 0.1931 - val_loss: 3.6875 - val_sparse_categorical_accuracy: 0.2178 - lr: 0.1000\nEpoch 4/100\n574/574 [==============================] - 28s 48ms/step - loss: 3.4110 - sparse_categorical_accuracy: 0.2562 - val_loss: 3.2200 - val_sparse_categorical_accuracy: 0.2924 - lr: 0.1000\nEpoch 5/100\n574/574 [==============================] - 27s 48ms/step - loss: 2.9296 - sparse_categorical_accuracy: 0.3426 - val_loss: 2.7405 - val_sparse_categorical_accuracy: 0.3705 - lr: 0.1000\nEpoch 6/100\n574/574 [==============================] - 28s 48ms/step - loss: 2.5749 - sparse_categorical_accuracy: 0.3878 - val_loss: 2.7248 - val_sparse_categorical_accuracy: 0.2747 - lr: 0.1000\nEpoch 7/100\n574/574 [==============================] - 27s 47ms/step - loss: 2.2889 - sparse_categorical_accuracy: 0.4381 - val_loss: 2.2381 - val_sparse_categorical_accuracy: 0.4272 - lr: 0.1000\nEpoch 8/100\n574/574 [==============================] - 28s 48ms/step - loss: 2.0667 - sparse_categorical_accuracy: 0.4773 - val_loss: 2.1881 - val_sparse_categorical_accuracy: 0.4043 - lr: 0.1000\nEpoch 9/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.8614 - sparse_categorical_accuracy: 0.5196 - val_loss: 2.2177 - val_sparse_categorical_accuracy: 0.3831 - lr: 0.1000\nEpoch 10/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.7137 - sparse_categorical_accuracy: 0.5499 - val_loss: 2.2022 - val_sparse_categorical_accuracy: 0.3940 - lr: 0.1000\nEpoch 11/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.5840 - sparse_categorical_accuracy: 0.5865 - val_loss: 2.1266 - val_sparse_categorical_accuracy: 0.4014 - lr: 0.1000\nEpoch 12/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.4837 - sparse_categorical_accuracy: 0.6157 - val_loss: 2.2361 - val_sparse_categorical_accuracy: 0.3929 - lr: 0.1000\nEpoch 13/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.4103 - sparse_categorical_accuracy: 0.6460 - val_loss: 1.9185 - val_sparse_categorical_accuracy: 0.4634 - lr: 0.1000\nEpoch 14/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.3445 - sparse_categorical_accuracy: 0.6753 - val_loss: 2.1438 - val_sparse_categorical_accuracy: 0.4714 - lr: 0.1000\nEpoch 15/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.2934 - sparse_categorical_accuracy: 0.7103 - val_loss: 2.3218 - val_sparse_categorical_accuracy: 0.4420 - lr: 0.1000\nEpoch 16/100\n574/574 [==============================] - 28s 48ms/step - loss: 1.2781 - sparse_categorical_accuracy: 0.7399 - val_loss: 2.5942 - val_sparse_categorical_accuracy: 0.4010 - lr: 0.1000\nEpoch 17/100\n574/574 [==============================] - 28s 49ms/step - loss: 1.2578 - sparse_categorical_accuracy: 0.7713 - val_loss: 2.6145 - val_sparse_categorical_accuracy: 0.4501 - lr: 0.1000\nEpoch 18/100\n574/574 [==============================] - ETA: 0s - loss: 1.2569 - sparse_categorical_accuracy: 0.7954\nEpoch 18: ReduceLROnPlateau reducing learning rate to 0.010000000149011612.\n574/574 [==============================] - 27s 47ms/step - loss: 1.2569 - sparse_categorical_accuracy: 0.7954 - val_loss: 2.5538 - val_sparse_categorical_accuracy: 0.4758 - lr: 0.1000\nEpoch 19/100\n574/574 [==============================] - 27s 47ms/step - loss: 1.0951 - sparse_categorical_accuracy: 0.8736 - val_loss: 2.1961 - val_sparse_categorical_accuracy: 0.5798 - lr: 0.0100\nEpoch 20/100\n574/574 [==============================] - 27s 47ms/step - loss: 0.8518 - sparse_categorical_accuracy: 0.9603 - val_loss: 2.4053 - val_sparse_categorical_accuracy: 0.5861 - lr: 0.0100\nEpoch 21/100\n574/574 [==============================] - 27s 47ms/step - loss: 0.7579 - sparse_categorical_accuracy: 0.9871 - val_loss: 2.6412 - val_sparse_categorical_accuracy: 0.5792 - lr: 0.0100\nEpoch 22/100\n574/574 [==============================] - 27s 47ms/step - loss: 0.7098 - sparse_categorical_accuracy: 0.9954 - val_loss: 2.9366 - val_sparse_categorical_accuracy: 0.5746 - lr: 0.0100\nEpoch 23/100\n574/574 [==============================] - ETA: 0s - loss: 0.6843 - sparse_categorical_accuracy: 0.9968Restoring model weights from the end of the best epoch: 13.\n\nEpoch 23: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n574/574 [==============================] - 27s 47ms/step - loss: 0.6843 - sparse_categorical_accuracy: 0.9968 - val_loss: 3.0763 - val_sparse_categorical_accuracy: 0.5783 - lr: 0.0100\nEpoch 23: early stopping\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06ad342c6f4944f394568d34743ca4bb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▂▂▂▃▃▃▄▄▄▅▅▅▅▆▆▆▇▇▇██</td></tr><tr><td>epoch/learning_rate</td><td>█████████████████▂▂▂▂▂▁</td></tr><tr><td>epoch/loss</td><td>█▆▅▅▄▄▃▃▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁</td></tr><tr><td>epoch/lr</td><td>██████████████████▁▁▁▁▁</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>▁▁▁▂▃▃▃▄▄▄▅▅▅▅▆▆▆▆▇████</td></tr><tr><td>epoch/val_loss</td><td>█▇▅▄▃▃▂▂▂▂▂▂▁▂▂▃▃▃▂▂▃▄▄</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>▁▁▂▃▄▃▅▅▅▅▅▅▆▆▆▅▆▆█████</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>22</td></tr><tr><td>epoch/learning_rate</td><td>0.001</td></tr><tr><td>epoch/loss</td><td>0.68427</td></tr><tr><td>epoch/lr</td><td>0.01</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>0.99684</td></tr><tr><td>epoch/val_loss</td><td>3.07625</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>0.57828</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">resnet34</strong> at: <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/vvdjc9kc' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/vvdjc9kc</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20230406_103338-vvdjc9kc/logs</code>"},"metadata":{}}]},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n\nhistory_df[[\"sparse_categorical_accuracy\", \"val_sparse_categorical_accuracy\"]].plot()\nhistory_df[[\"loss\", \"val_loss\"]].plot()\n\nmodel.evaluate(test)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:45:19.655382Z","iopub.execute_input":"2023-04-06T10:45:19.656236Z","iopub.status.idle":"2023-04-06T10:45:23.788011Z","shell.execute_reply.started":"2023-04-06T10:45:19.656193Z","shell.execute_reply":"2023-04-06T10:45:23.786924Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"180/180 [==============================] - 4s 21ms/step - loss: 1.9055 - sparse_categorical_accuracy: 0.4636\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"[1.9055142402648926, 0.46355074644088745]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### ResNet-50 - training and evaluation","metadata":{}},{"cell_type":"code","source":"wandb.init(config=config, project='resnet-improved-labels', name='resnet50')\n\nregularizer = L2(l2=config[\"l2\"])\noptimizer = SGD(learning_rate=config[\"learning_rate\"], momentum=config[\"momentum\"])\nloss = SparseCategoricalCrossentropy()\nmetrics = [SparseCategoricalAccuracy()]\n\nmodel = rn.ResNet50(\n    len(classes),\n    (image_size, image_size, channels),\n    normalize=True,\n    kernel_regularizer=regularizer\n)\n\nearly_stopping = EarlyStopping(\n    monitor=\"val_loss\",\n    mode=\"min\",\n    verbose=1,\n    patience=config[\"early_stop_patience\"],\n    restore_best_weights=True\n)\n\nlr_schedule_sota = ReduceLROnPlateau(\n    monitor=\"val_loss\",\n    factor=0.1,\n    patience=config[\"lr_reduce_patience\"],\n    verbose=1,\n    mode=\"min\"\n)\n\ncallbacks = [\n    early_stopping,\n    lr_schedule_sota,\n    WandbMetricsLogger()\n]\n\nmodel.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nhistory = model.fit(train, validation_data=validation, epochs=config[\"epochs\"], callbacks=callbacks)\nwandb.finish()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T10:45:23.789729Z","iopub.execute_input":"2023-04-06T10:45:23.790156Z","iopub.status.idle":"2023-04-06T11:08:00.754853Z","shell.execute_reply.started":"2023-04-06T10:45:23.790117Z","shell.execute_reply":"2023-04-06T11:08:00.753826Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.14.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.13.10"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20230406_104523-ig57xopd</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/ig57xopd' target=\"_blank\">resnet50</a></strong> to <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/ig57xopd' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/ig57xopd</a>"},"metadata":{}},{"name":"stdout","text":"Epoch 1/100\n574/574 [==============================] - 39s 57ms/step - loss: 19.9657 - sparse_categorical_accuracy: 0.1512 - val_loss: 17.3672 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 2/100\n574/574 [==============================] - 31s 55ms/step - loss: 15.7415 - sparse_categorical_accuracy: 0.1590 - val_loss: 14.2227 - val_sparse_categorical_accuracy: 0.1544 - lr: 0.1000\nEpoch 3/100\n574/574 [==============================] - 32s 55ms/step - loss: 12.9345 - sparse_categorical_accuracy: 0.1574 - val_loss: 11.7233 - val_sparse_categorical_accuracy: 0.1570 - lr: 0.1000\nEpoch 4/100\n574/574 [==============================] - 32s 55ms/step - loss: 10.7017 - sparse_categorical_accuracy: 0.1534 - val_loss: 9.7422 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 5/100\n574/574 [==============================] - 31s 54ms/step - loss: 8.9288 - sparse_categorical_accuracy: 0.1566 - val_loss: 8.1635 - val_sparse_categorical_accuracy: 0.1542 - lr: 0.1000\nEpoch 6/100\n574/574 [==============================] - 31s 54ms/step - loss: 7.5198 - sparse_categorical_accuracy: 0.1561 - val_loss: 6.9121 - val_sparse_categorical_accuracy: 0.1526 - lr: 0.1000\nEpoch 7/100\n574/574 [==============================] - 33s 58ms/step - loss: 6.4009 - sparse_categorical_accuracy: 0.1550 - val_loss: 5.9194 - val_sparse_categorical_accuracy: 0.1568 - lr: 0.1000\nEpoch 8/100\n574/574 [==============================] - 31s 53ms/step - loss: 5.5112 - sparse_categorical_accuracy: 0.1586 - val_loss: 5.1274 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 9/100\n574/574 [==============================] - 31s 55ms/step - loss: 4.8050 - sparse_categorical_accuracy: 0.1568 - val_loss: 4.4981 - val_sparse_categorical_accuracy: 0.1546 - lr: 0.1000\nEpoch 10/100\n574/574 [==============================] - 32s 55ms/step - loss: 4.2431 - sparse_categorical_accuracy: 0.1584 - val_loss: 4.0015 - val_sparse_categorical_accuracy: 0.1520 - lr: 0.1000\nEpoch 11/100\n574/574 [==============================] - 31s 54ms/step - loss: 3.7988 - sparse_categorical_accuracy: 0.1553 - val_loss: 3.6027 - val_sparse_categorical_accuracy: 0.1526 - lr: 0.1000\nEpoch 12/100\n574/574 [==============================] - 31s 54ms/step - loss: 3.4426 - sparse_categorical_accuracy: 0.1608 - val_loss: 3.2858 - val_sparse_categorical_accuracy: 0.1526 - lr: 0.1000\nEpoch 13/100\n574/574 [==============================] - 31s 54ms/step - loss: 3.0843 - sparse_categorical_accuracy: 0.2157 - val_loss: 2.8700 - val_sparse_categorical_accuracy: 0.2612 - lr: 0.1000\nEpoch 14/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.7412 - sparse_categorical_accuracy: 0.2612 - val_loss: 2.6040 - val_sparse_categorical_accuracy: 0.2728 - lr: 0.1000\nEpoch 15/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.5124 - sparse_categorical_accuracy: 0.2781 - val_loss: 2.4316 - val_sparse_categorical_accuracy: 0.2802 - lr: 0.1000\nEpoch 16/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.3170 - sparse_categorical_accuracy: 0.3344 - val_loss: 2.4500 - val_sparse_categorical_accuracy: 0.2793 - lr: 0.1000\nEpoch 17/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.6966 - sparse_categorical_accuracy: 0.2608 - val_loss: 2.5923 - val_sparse_categorical_accuracy: 0.2881 - lr: 0.1000\nEpoch 18/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.3893 - sparse_categorical_accuracy: 0.3388 - val_loss: 2.3001 - val_sparse_categorical_accuracy: 0.3487 - lr: 0.1000\nEpoch 19/100\n574/574 [==============================] - 31s 55ms/step - loss: 2.1819 - sparse_categorical_accuracy: 0.3742 - val_loss: 2.2212 - val_sparse_categorical_accuracy: 0.3293 - lr: 0.1000\nEpoch 20/100\n574/574 [==============================] - 31s 54ms/step - loss: 2.0344 - sparse_categorical_accuracy: 0.3984 - val_loss: 2.4109 - val_sparse_categorical_accuracy: 0.3064 - lr: 0.1000\nEpoch 21/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.9117 - sparse_categorical_accuracy: 0.4166 - val_loss: 2.0811 - val_sparse_categorical_accuracy: 0.3607 - lr: 0.1000\nEpoch 22/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.8102 - sparse_categorical_accuracy: 0.4447 - val_loss: 1.8600 - val_sparse_categorical_accuracy: 0.4169 - lr: 0.1000\nEpoch 23/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.7246 - sparse_categorical_accuracy: 0.4747 - val_loss: 1.9853 - val_sparse_categorical_accuracy: 0.4021 - lr: 0.1000\nEpoch 24/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.6508 - sparse_categorical_accuracy: 0.4966 - val_loss: 1.8696 - val_sparse_categorical_accuracy: 0.4182 - lr: 0.1000\nEpoch 25/100\n574/574 [==============================] - 31s 55ms/step - loss: 1.5863 - sparse_categorical_accuracy: 0.5214 - val_loss: 1.7838 - val_sparse_categorical_accuracy: 0.4291 - lr: 0.1000\nEpoch 26/100\n574/574 [==============================] - 33s 58ms/step - loss: 1.5449 - sparse_categorical_accuracy: 0.5383 - val_loss: 1.8653 - val_sparse_categorical_accuracy: 0.4274 - lr: 0.1000\nEpoch 27/100\n574/574 [==============================] - 34s 58ms/step - loss: 1.5067 - sparse_categorical_accuracy: 0.5570 - val_loss: 1.7900 - val_sparse_categorical_accuracy: 0.4546 - lr: 0.1000\nEpoch 28/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.4743 - sparse_categorical_accuracy: 0.5734 - val_loss: 1.8375 - val_sparse_categorical_accuracy: 0.4407 - lr: 0.1000\nEpoch 29/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.4573 - sparse_categorical_accuracy: 0.5896 - val_loss: 2.0183 - val_sparse_categorical_accuracy: 0.3818 - lr: 0.1000\nEpoch 30/100\n573/574 [============================>.] - ETA: 0s - loss: 1.4388 - sparse_categorical_accuracy: 0.5997\nEpoch 30: ReduceLROnPlateau reducing learning rate to 0.010000000149011612.\n574/574 [==============================] - 31s 54ms/step - loss: 1.4389 - sparse_categorical_accuracy: 0.5997 - val_loss: 1.8869 - val_sparse_categorical_accuracy: 0.4581 - lr: 0.1000\nEpoch 31/100\n574/574 [==============================] - 31s 54ms/step - loss: 1.2199 - sparse_categorical_accuracy: 0.6886 - val_loss: 1.7024 - val_sparse_categorical_accuracy: 0.5506 - lr: 0.0100\nEpoch 32/100\n574/574 [==============================] - 32s 55ms/step - loss: 1.0339 - sparse_categorical_accuracy: 0.7572 - val_loss: 1.7638 - val_sparse_categorical_accuracy: 0.5539 - lr: 0.0100\nEpoch 33/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.9148 - sparse_categorical_accuracy: 0.7994 - val_loss: 1.9100 - val_sparse_categorical_accuracy: 0.5397 - lr: 0.0100\nEpoch 34/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.8000 - sparse_categorical_accuracy: 0.8404 - val_loss: 2.0832 - val_sparse_categorical_accuracy: 0.5388 - lr: 0.0100\nEpoch 35/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.7009 - sparse_categorical_accuracy: 0.8766 - val_loss: 2.2741 - val_sparse_categorical_accuracy: 0.5273 - lr: 0.0100\nEpoch 36/100\n573/574 [============================>.] - ETA: 0s - loss: 0.6363 - sparse_categorical_accuracy: 0.8974\nEpoch 36: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n574/574 [==============================] - 31s 55ms/step - loss: 0.6364 - sparse_categorical_accuracy: 0.8974 - val_loss: 2.4601 - val_sparse_categorical_accuracy: 0.5218 - lr: 0.0100\nEpoch 37/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.5610 - sparse_categorical_accuracy: 0.9240 - val_loss: 2.3656 - val_sparse_categorical_accuracy: 0.5480 - lr: 1.0000e-03\nEpoch 38/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.4824 - sparse_categorical_accuracy: 0.9559 - val_loss: 2.4509 - val_sparse_categorical_accuracy: 0.5449 - lr: 1.0000e-03\nEpoch 39/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.4503 - sparse_categorical_accuracy: 0.9665 - val_loss: 2.5564 - val_sparse_categorical_accuracy: 0.5410 - lr: 1.0000e-03\nEpoch 40/100\n574/574 [==============================] - 31s 54ms/step - loss: 0.4305 - sparse_categorical_accuracy: 0.9732 - val_loss: 2.6562 - val_sparse_categorical_accuracy: 0.5403 - lr: 1.0000e-03\nEpoch 41/100\n574/574 [==============================] - ETA: 0s - loss: 0.4141 - sparse_categorical_accuracy: 0.9784Restoring model weights from the end of the best epoch: 31.\n\nEpoch 41: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n574/574 [==============================] - 32s 55ms/step - loss: 0.4141 - sparse_categorical_accuracy: 0.9784 - val_loss: 2.7575 - val_sparse_categorical_accuracy: 0.5414 - lr: 1.0000e-03\nEpoch 41: early stopping\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4b74ec6fac2747c3a96f2486e08cc657"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇██</td></tr><tr><td>epoch/learning_rate</td><td>█████████████████████████████▂▂▂▂▂▂▁▁▁▁▁</td></tr><tr><td>epoch/loss</td><td>█▆▅▅▄▄▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/lr</td><td>██████████████████████████████▂▂▂▂▂▂▁▁▁▁</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▃▂▃▃▃▃▃▄▄▄▄▄▅▅▅▆▆▆▇▇▇████</td></tr><tr><td>epoch/val_loss</td><td>█▇▅▅▄▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▃▃▃▃▃▄▄▄▅▆▅▆▆▆▆▆▅▆█████▇████</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>40</td></tr><tr><td>epoch/learning_rate</td><td>0.0001</td></tr><tr><td>epoch/loss</td><td>0.41411</td></tr><tr><td>epoch/lr</td><td>0.001</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>0.97841</td></tr><tr><td>epoch/val_loss</td><td>2.75752</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>0.54143</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">resnet50</strong> at: <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/ig57xopd' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/ig57xopd</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20230406_104523-ig57xopd/logs</code>"},"metadata":{}}]},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n\nhistory_df[[\"sparse_categorical_accuracy\", \"val_sparse_categorical_accuracy\"]].plot()\nhistory_df[[\"loss\", \"val_loss\"]].plot()\n\nmodel.evaluate(test)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T11:08:00.762399Z","iopub.execute_input":"2023-04-06T11:08:00.762699Z","iopub.status.idle":"2023-04-06T11:08:11.218049Z","shell.execute_reply.started":"2023-04-06T11:08:00.762669Z","shell.execute_reply":"2023-04-06T11:08:11.216874Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"180/180 [==============================] - 7s 36ms/step - loss: 1.6825 - sparse_categorical_accuracy: 0.5593\n","output_type":"stream"},{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"[1.6825206279754639, 0.5592954158782959]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### ResNet-101 - training and evaluation","metadata":{}},{"cell_type":"code","source":"wandb.init(config=config, project='resnet-improved-labels', name='resnet101')\n\nregularizer = L2(l2=config[\"l2\"])\noptimizer = SGD(learning_rate=config[\"learning_rate\"], momentum=config[\"momentum\"])\nloss = SparseCategoricalCrossentropy()\nmetrics = [SparseCategoricalAccuracy()]\n\nmodel = rn.ResNet101(\n    len(classes),\n    (image_size, image_size, channels),\n    normalize=True,\n    kernel_regularizer=regularizer\n)\n\nearly_stopping = EarlyStopping(\n    monitor=\"val_loss\",\n    mode=\"min\",\n    verbose=1,\n    patience=config[\"early_stop_patience\"],\n    restore_best_weights=True\n)\n\nlr_schedule_sota = ReduceLROnPlateau(\n    monitor=\"val_loss\",\n    factor=0.1,\n    patience=config[\"lr_reduce_patience\"],\n    verbose=1,\n    mode=\"min\"\n)\n\ncallbacks = [\n    early_stopping,\n    lr_schedule_sota,\n    WandbMetricsLogger()\n]\n\nmodel.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nhistory = model.fit(train, validation_data=validation, epochs=config[\"epochs\"], callbacks=callbacks)\nwandb.finish()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T11:08:11.219977Z","iopub.execute_input":"2023-04-06T11:08:11.220361Z","iopub.status.idle":"2023-04-06T11:49:35.360172Z","shell.execute_reply.started":"2023-04-06T11:08:11.220322Z","shell.execute_reply":"2023-04-06T11:49:35.359158Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.14.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.13.10"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20230406_110811-piwhxhhq</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/piwhxhhq' target=\"_blank\">resnet101</a></strong> to <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/piwhxhhq' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/piwhxhhq</a>"},"metadata":{}},{"name":"stdout","text":"Epoch 1/100\n574/574 [==============================] - 66s 95ms/step - loss: 117.9629 - sparse_categorical_accuracy: 0.1548 - val_loss: 107.2219 - val_sparse_categorical_accuracy: 0.1570 - lr: 0.1000\nEpoch 2/100\n574/574 [==============================] - 52s 90ms/step - loss: 96.0432 - sparse_categorical_accuracy: 0.1562 - val_loss: 85.6177 - val_sparse_categorical_accuracy: 0.1561 - lr: 0.1000\nEpoch 3/100\n574/574 [==============================] - 52s 90ms/step - loss: 76.7309 - sparse_categorical_accuracy: 0.1584 - val_loss: 68.4499 - val_sparse_categorical_accuracy: 0.1559 - lr: 0.1000\nEpoch 4/100\n574/574 [==============================] - 52s 90ms/step - loss: 61.3898 - sparse_categorical_accuracy: 0.1595 - val_loss: 54.8081 - val_sparse_categorical_accuracy: 0.1568 - lr: 0.1000\nEpoch 5/100\n574/574 [==============================] - 52s 90ms/step - loss: 49.2001 - sparse_categorical_accuracy: 0.1615 - val_loss: 43.9699 - val_sparse_categorical_accuracy: 0.1574 - lr: 0.1000\nEpoch 6/100\n574/574 [==============================] - 52s 90ms/step - loss: 39.5175 - sparse_categorical_accuracy: 0.1567 - val_loss: 35.3643 - val_sparse_categorical_accuracy: 0.1559 - lr: 0.1000\nEpoch 7/100\n574/574 [==============================] - 52s 90ms/step - loss: 31.8217 - sparse_categorical_accuracy: 0.1599 - val_loss: 28.5201 - val_sparse_categorical_accuracy: 0.1555 - lr: 0.1000\nEpoch 8/100\n574/574 [==============================] - 51s 89ms/step - loss: 25.7078 - sparse_categorical_accuracy: 0.1621 - val_loss: 23.0830 - val_sparse_categorical_accuracy: 0.1568 - lr: 0.1000\nEpoch 9/100\n574/574 [==============================] - 52s 90ms/step - loss: 20.8508 - sparse_categorical_accuracy: 0.1624 - val_loss: 18.7631 - val_sparse_categorical_accuracy: 0.1596 - lr: 0.1000\nEpoch 10/100\n574/574 [==============================] - 52s 90ms/step - loss: 16.9920 - sparse_categorical_accuracy: 0.1641 - val_loss: 15.3488 - val_sparse_categorical_accuracy: 0.1747 - lr: 0.1000\nEpoch 11/100\n574/574 [==============================] - 52s 90ms/step - loss: 13.8419 - sparse_categorical_accuracy: 0.2188 - val_loss: 12.3982 - val_sparse_categorical_accuracy: 0.2665 - lr: 0.1000\nEpoch 12/100\n574/574 [==============================] - 52s 91ms/step - loss: 11.2935 - sparse_categorical_accuracy: 0.2673 - val_loss: 10.3531 - val_sparse_categorical_accuracy: 0.2305 - lr: 0.1000\nEpoch 13/100\n574/574 [==============================] - 55s 95ms/step - loss: 9.3170 - sparse_categorical_accuracy: 0.2885 - val_loss: 8.4414 - val_sparse_categorical_accuracy: 0.3064 - lr: 0.1000\nEpoch 14/100\n574/574 [==============================] - 55s 95ms/step - loss: 7.7211 - sparse_categorical_accuracy: 0.3307 - val_loss: 15.5435 - val_sparse_categorical_accuracy: 0.1845 - lr: 0.1000\nEpoch 15/100\n574/574 [==============================] - 55s 95ms/step - loss: 6.4870 - sparse_categorical_accuracy: 0.3494 - val_loss: 5.9024 - val_sparse_categorical_accuracy: 0.3796 - lr: 0.1000\nEpoch 16/100\n574/574 [==============================] - 54s 93ms/step - loss: 5.4207 - sparse_categorical_accuracy: 0.3920 - val_loss: 5.0290 - val_sparse_categorical_accuracy: 0.3864 - lr: 0.1000\nEpoch 17/100\n574/574 [==============================] - 54s 94ms/step - loss: 4.6128 - sparse_categorical_accuracy: 0.4071 - val_loss: 4.3686 - val_sparse_categorical_accuracy: 0.3932 - lr: 0.1000\nEpoch 18/100\n574/574 [==============================] - 53s 92ms/step - loss: 3.9649 - sparse_categorical_accuracy: 0.4291 - val_loss: 3.8089 - val_sparse_categorical_accuracy: 0.4067 - lr: 0.1000\nEpoch 19/100\n574/574 [==============================] - 53s 92ms/step - loss: 3.4548 - sparse_categorical_accuracy: 0.4512 - val_loss: 3.2605 - val_sparse_categorical_accuracy: 0.4599 - lr: 0.1000\nEpoch 20/100\n574/574 [==============================] - 52s 91ms/step - loss: 3.0769 - sparse_categorical_accuracy: 0.4627 - val_loss: 3.0093 - val_sparse_categorical_accuracy: 0.4191 - lr: 0.1000\nEpoch 21/100\n574/574 [==============================] - 52s 91ms/step - loss: 2.7242 - sparse_categorical_accuracy: 0.4886 - val_loss: 2.6467 - val_sparse_categorical_accuracy: 0.4756 - lr: 0.1000\nEpoch 22/100\n574/574 [==============================] - 53s 92ms/step - loss: 2.4510 - sparse_categorical_accuracy: 0.5099 - val_loss: 2.4276 - val_sparse_categorical_accuracy: 0.4847 - lr: 0.1000\nEpoch 23/100\n574/574 [==============================] - 53s 91ms/step - loss: 2.2086 - sparse_categorical_accuracy: 0.5371 - val_loss: 2.3299 - val_sparse_categorical_accuracy: 0.4636 - lr: 0.1000\nEpoch 24/100\n574/574 [==============================] - 53s 91ms/step - loss: 2.0496 - sparse_categorical_accuracy: 0.5484 - val_loss: 2.2876 - val_sparse_categorical_accuracy: 0.4481 - lr: 0.1000\nEpoch 25/100\n574/574 [==============================] - 52s 91ms/step - loss: 1.9062 - sparse_categorical_accuracy: 0.5654 - val_loss: 2.5224 - val_sparse_categorical_accuracy: 0.4361 - lr: 0.1000\nEpoch 26/100\n574/574 [==============================] - 53s 92ms/step - loss: 1.8107 - sparse_categorical_accuracy: 0.5761 - val_loss: 2.2092 - val_sparse_categorical_accuracy: 0.4427 - lr: 0.1000\nEpoch 27/100\n574/574 [==============================] - 53s 91ms/step - loss: 1.7179 - sparse_categorical_accuracy: 0.5989 - val_loss: 2.0088 - val_sparse_categorical_accuracy: 0.4874 - lr: 0.1000\nEpoch 28/100\n574/574 [==============================] - 52s 91ms/step - loss: 1.6573 - sparse_categorical_accuracy: 0.6088 - val_loss: 2.0918 - val_sparse_categorical_accuracy: 0.4843 - lr: 0.1000\nEpoch 29/100\n574/574 [==============================] - 53s 93ms/step - loss: 1.6055 - sparse_categorical_accuracy: 0.6218 - val_loss: 1.9822 - val_sparse_categorical_accuracy: 0.4935 - lr: 0.1000\nEpoch 30/100\n574/574 [==============================] - 54s 94ms/step - loss: 1.5589 - sparse_categorical_accuracy: 0.6336 - val_loss: 2.1772 - val_sparse_categorical_accuracy: 0.4311 - lr: 0.1000\nEpoch 31/100\n574/574 [==============================] - 53s 91ms/step - loss: 1.5328 - sparse_categorical_accuracy: 0.6441 - val_loss: 2.0402 - val_sparse_categorical_accuracy: 0.4823 - lr: 0.1000\nEpoch 32/100\n574/574 [==============================] - 52s 91ms/step - loss: 1.5134 - sparse_categorical_accuracy: 0.6531 - val_loss: 2.1590 - val_sparse_categorical_accuracy: 0.4817 - lr: 0.1000\nEpoch 33/100\n574/574 [==============================] - 52s 90ms/step - loss: 1.5082 - sparse_categorical_accuracy: 0.6688 - val_loss: 2.0708 - val_sparse_categorical_accuracy: 0.4967 - lr: 0.1000\nEpoch 34/100\n574/574 [==============================] - ETA: 0s - loss: 1.5230 - sparse_categorical_accuracy: 0.6704\nEpoch 34: ReduceLROnPlateau reducing learning rate to 0.010000000149011612.\n574/574 [==============================] - 52s 90ms/step - loss: 1.5230 - sparse_categorical_accuracy: 0.6704 - val_loss: 2.3146 - val_sparse_categorical_accuracy: 0.4546 - lr: 0.1000\nEpoch 35/100\n574/574 [==============================] - 52s 91ms/step - loss: 1.2701 - sparse_categorical_accuracy: 0.7684 - val_loss: 2.0364 - val_sparse_categorical_accuracy: 0.5530 - lr: 0.0100\nEpoch 36/100\n574/574 [==============================] - 52s 91ms/step - loss: 1.0512 - sparse_categorical_accuracy: 0.8440 - val_loss: 2.2021 - val_sparse_categorical_accuracy: 0.5480 - lr: 0.0100\nEpoch 37/100\n574/574 [==============================] - 52s 90ms/step - loss: 0.9039 - sparse_categorical_accuracy: 0.8936 - val_loss: 2.4383 - val_sparse_categorical_accuracy: 0.5449 - lr: 0.0100\nEpoch 38/100\n574/574 [==============================] - 52s 91ms/step - loss: 0.7893 - sparse_categorical_accuracy: 0.9321 - val_loss: 2.6635 - val_sparse_categorical_accuracy: 0.5484 - lr: 0.0100\nEpoch 39/100\n574/574 [==============================] - ETA: 0s - loss: 0.7127 - sparse_categorical_accuracy: 0.9549Restoring model weights from the end of the best epoch: 29.\n\nEpoch 39: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n574/574 [==============================] - 52s 91ms/step - loss: 0.7127 - sparse_categorical_accuracy: 0.9549 - val_loss: 2.9264 - val_sparse_categorical_accuracy: 0.5440 - lr: 0.0100\nEpoch 39: early stopping\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d14e9c511f34b4bbed18518ff7a3f24"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>epoch/learning_rate</td><td>█████████████████████████████████▂▂▂▂▂▁</td></tr><tr><td>epoch/loss</td><td>█▇▆▅▄▃▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/lr</td><td>██████████████████████████████████▁▁▁▁▁</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▁▁▁▁▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▅▅▅▆▆▇▇██</td></tr><tr><td>epoch/val_loss</td><td>█▇▅▅▄▃▃▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>▁▁▁▁▁▁▁▁▁▁▃▂▄▂▅▅▅▅▆▆▇▇▆▆▆▆▇▇▇▆▇▇▇▆█████</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>38</td></tr><tr><td>epoch/learning_rate</td><td>0.001</td></tr><tr><td>epoch/loss</td><td>0.71271</td></tr><tr><td>epoch/lr</td><td>0.01</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>0.95492</td></tr><tr><td>epoch/val_loss</td><td>2.92641</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>0.54405</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">resnet101</strong> at: <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/piwhxhhq' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/piwhxhhq</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20230406_110811-piwhxhhq/logs</code>"},"metadata":{}}]},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n\nhistory_df[[\"sparse_categorical_accuracy\", \"val_sparse_categorical_accuracy\"]].plot()\nhistory_df[[\"loss\", \"val_loss\"]].plot()\n\nmodel.evaluate(test)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T11:49:35.361833Z","iopub.execute_input":"2023-04-06T11:49:35.362116Z","iopub.status.idle":"2023-04-06T11:49:41.851659Z","shell.execute_reply.started":"2023-04-06T11:49:35.362088Z","shell.execute_reply":"2023-04-06T11:49:41.850661Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"180/180 [==============================] - 6s 33ms/step - loss: 1.9541 - sparse_categorical_accuracy: 0.4990\n","output_type":"stream"},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"[1.9540808200836182, 0.49895361065864563]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### ResNet-152 - training and evaluation","metadata":{}},{"cell_type":"code","source":"wandb.init(config=config, project='resnet-improved-labels', name='resnet152')\n\nregularizer = L2(l2=config[\"l2\"])\noptimizer = SGD(learning_rate=config[\"learning_rate\"], momentum=config[\"momentum\"])\nloss = SparseCategoricalCrossentropy()\nmetrics = [SparseCategoricalAccuracy()]\n\nmodel = rn.ResNet152(\n    len(classes),\n    (image_size, image_size, channels),\n    normalize=True,\n    kernel_regularizer=regularizer\n)\n\nearly_stopping = EarlyStopping(\n    monitor=\"val_loss\",\n    mode=\"min\",\n    verbose=1,\n    patience=config[\"early_stop_patience\"],\n    restore_best_weights=True\n)\n\nlr_schedule_sota = ReduceLROnPlateau(\n    monitor=\"val_loss\",\n    factor=0.1,\n    patience=config[\"lr_reduce_patience\"],\n    verbose=1,\n    mode=\"min\"\n)\n\ncallbacks = [\n    early_stopping,\n    lr_schedule_sota,\n    WandbMetricsLogger()\n]\n\nmodel.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nhistory = model.fit(train, validation_data=validation, epochs=config[\"epochs\"], callbacks=callbacks)\nwandb.finish()","metadata":{"execution":{"iopub.status.busy":"2023-04-06T11:49:41.853311Z","iopub.execute_input":"2023-04-06T11:49:41.853675Z","iopub.status.idle":"2023-04-06T13:05:11.949655Z","shell.execute_reply.started":"2023-04-06T11:49:41.853617Z","shell.execute_reply":"2023-04-06T13:05:11.948689Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.14.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.13.10"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20230406_114941-lgfvz1dt</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/lgfvz1dt' target=\"_blank\">resnet152</a></strong> to <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/nsiete23/resnet-improved-labels' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/lgfvz1dt' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/lgfvz1dt</a>"},"metadata":{}},{"name":"stdout","text":"Epoch 1/100\n574/574 [==============================] - 93s 131ms/step - loss: 1406.2384 - sparse_categorical_accuracy: 0.1492 - val_loss: 1286.2908 - val_sparse_categorical_accuracy: 0.1542 - lr: 0.1000\nEpoch 2/100\n574/574 [==============================] - 72s 125ms/step - loss: 1149.6893 - sparse_categorical_accuracy: 0.1554 - val_loss: 1022.3950 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 3/100\n574/574 [==============================] - 72s 125ms/step - loss: 913.9898 - sparse_categorical_accuracy: 0.1541 - val_loss: 812.8367 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 4/100\n574/574 [==============================] - 71s 124ms/step - loss: 726.5955 - sparse_categorical_accuracy: 0.1579 - val_loss: 646.2288 - val_sparse_categorical_accuracy: 0.1542 - lr: 0.1000\nEpoch 5/100\n574/574 [==============================] - 72s 125ms/step - loss: 577.7070 - sparse_categorical_accuracy: 0.1595 - val_loss: 513.8594 - val_sparse_categorical_accuracy: 0.1539 - lr: 0.1000\nEpoch 6/100\n574/574 [==============================] - 71s 124ms/step - loss: 459.4145 - sparse_categorical_accuracy: 0.1597 - val_loss: 408.6884 - val_sparse_categorical_accuracy: 0.1638 - lr: 0.1000\nEpoch 7/100\n574/574 [==============================] - 72s 124ms/step - loss: 365.4239 - sparse_categorical_accuracy: 0.1757 - val_loss: 325.1476 - val_sparse_categorical_accuracy: 0.1605 - lr: 0.1000\nEpoch 8/100\n574/574 [==============================] - 72s 124ms/step - loss: 290.7297 - sparse_categorical_accuracy: 0.2005 - val_loss: 258.7303 - val_sparse_categorical_accuracy: 0.1799 - lr: 0.1000\nEpoch 9/100\n574/574 [==============================] - 72s 125ms/step - loss: 231.3450 - sparse_categorical_accuracy: 0.2304 - val_loss: 205.9483 - val_sparse_categorical_accuracy: 0.2172 - lr: 0.1000\nEpoch 10/100\n574/574 [==============================] - 72s 124ms/step - loss: 184.1822 - sparse_categorical_accuracy: 0.2429 - val_loss: 164.0237 - val_sparse_categorical_accuracy: 0.2303 - lr: 0.1000\nEpoch 11/100\n574/574 [==============================] - 71s 124ms/step - loss: 146.6995 - sparse_categorical_accuracy: 0.2594 - val_loss: 130.6968 - val_sparse_categorical_accuracy: 0.2499 - lr: 0.1000\nEpoch 12/100\n574/574 [==============================] - 72s 124ms/step - loss: 116.9387 - sparse_categorical_accuracy: 0.2701 - val_loss: 104.1599 - val_sparse_categorical_accuracy: 0.2728 - lr: 0.1000\nEpoch 13/100\n574/574 [==============================] - 71s 124ms/step - loss: 93.2748 - sparse_categorical_accuracy: 0.2822 - val_loss: 83.1953 - val_sparse_categorical_accuracy: 0.2630 - lr: 0.1000\nEpoch 14/100\n574/574 [==============================] - 72s 125ms/step - loss: 74.5564 - sparse_categorical_accuracy: 0.2788 - val_loss: 66.5026 - val_sparse_categorical_accuracy: 0.2778 - lr: 0.1000\nEpoch 15/100\n574/574 [==============================] - 71s 124ms/step - loss: 59.5893 - sparse_categorical_accuracy: 0.3049 - val_loss: 152.6463 - val_sparse_categorical_accuracy: 0.1132 - lr: 0.1000\nEpoch 16/100\n574/574 [==============================] - 71s 124ms/step - loss: 47.7058 - sparse_categorical_accuracy: 0.3106 - val_loss: 42.6183 - val_sparse_categorical_accuracy: 0.3251 - lr: 0.1000\nEpoch 17/100\n574/574 [==============================] - 72s 125ms/step - loss: 38.2094 - sparse_categorical_accuracy: 0.3518 - val_loss: 34.1508 - val_sparse_categorical_accuracy: 0.3559 - lr: 0.1000\nEpoch 18/100\n574/574 [==============================] - 72s 125ms/step - loss: 30.6759 - sparse_categorical_accuracy: 0.3804 - val_loss: 27.5322 - val_sparse_categorical_accuracy: 0.3397 - lr: 0.1000\nEpoch 19/100\n574/574 [==============================] - 71s 124ms/step - loss: 24.7000 - sparse_categorical_accuracy: 0.3914 - val_loss: 22.1624 - val_sparse_categorical_accuracy: 0.3855 - lr: 0.1000\nEpoch 20/100\n574/574 [==============================] - 72s 126ms/step - loss: 19.9460 - sparse_categorical_accuracy: 0.4047 - val_loss: 17.9344 - val_sparse_categorical_accuracy: 0.4012 - lr: 0.1000\nEpoch 21/100\n574/574 [==============================] - 72s 124ms/step - loss: 16.1758 - sparse_categorical_accuracy: 0.4222 - val_loss: 14.6055 - val_sparse_categorical_accuracy: 0.4095 - lr: 0.1000\nEpoch 22/100\n574/574 [==============================] - 72s 125ms/step - loss: 13.1713 - sparse_categorical_accuracy: 0.4298 - val_loss: 11.9646 - val_sparse_categorical_accuracy: 0.3997 - lr: 0.1000\nEpoch 23/100\n574/574 [==============================] - 72s 125ms/step - loss: 10.7831 - sparse_categorical_accuracy: 0.4446 - val_loss: 9.9153 - val_sparse_categorical_accuracy: 0.3884 - lr: 0.1000\nEpoch 24/100\n574/574 [==============================] - 72s 125ms/step - loss: 8.8805 - sparse_categorical_accuracy: 0.4588 - val_loss: 8.1866 - val_sparse_categorical_accuracy: 0.4355 - lr: 0.1000\nEpoch 25/100\n574/574 [==============================] - 72s 125ms/step - loss: 7.3841 - sparse_categorical_accuracy: 0.4673 - val_loss: 6.8484 - val_sparse_categorical_accuracy: 0.4433 - lr: 0.1000\nEpoch 26/100\n574/574 [==============================] - 72s 125ms/step - loss: 6.2006 - sparse_categorical_accuracy: 0.4754 - val_loss: 5.8545 - val_sparse_categorical_accuracy: 0.4058 - lr: 0.1000\nEpoch 27/100\n574/574 [==============================] - 71s 124ms/step - loss: 5.2336 - sparse_categorical_accuracy: 0.4925 - val_loss: 5.0460 - val_sparse_categorical_accuracy: 0.4276 - lr: 0.1000\nEpoch 28/100\n574/574 [==============================] - 71s 124ms/step - loss: 4.4870 - sparse_categorical_accuracy: 0.4986 - val_loss: 4.5235 - val_sparse_categorical_accuracy: 0.3801 - lr: 0.1000\nEpoch 29/100\n574/574 [==============================] - 71s 124ms/step - loss: 3.9118 - sparse_categorical_accuracy: 0.5063 - val_loss: 6.8199 - val_sparse_categorical_accuracy: 0.3393 - lr: 0.1000\nEpoch 30/100\n574/574 [==============================] - 72s 125ms/step - loss: 3.4325 - sparse_categorical_accuracy: 0.5153 - val_loss: 3.4386 - val_sparse_categorical_accuracy: 0.4363 - lr: 0.1000\nEpoch 31/100\n574/574 [==============================] - 72s 124ms/step - loss: 3.0359 - sparse_categorical_accuracy: 0.5302 - val_loss: 3.1391 - val_sparse_categorical_accuracy: 0.4357 - lr: 0.1000\nEpoch 32/100\n574/574 [==============================] - 72s 125ms/step - loss: 2.7481 - sparse_categorical_accuracy: 0.5357 - val_loss: 2.7846 - val_sparse_categorical_accuracy: 0.4802 - lr: 0.1000\nEpoch 33/100\n574/574 [==============================] - 71s 124ms/step - loss: 3.2453 - sparse_categorical_accuracy: 0.3493 - val_loss: 3.2077 - val_sparse_categorical_accuracy: 0.3432 - lr: 0.1000\nEpoch 34/100\n574/574 [==============================] - 71s 124ms/step - loss: 2.9673 - sparse_categorical_accuracy: 0.3767 - val_loss: 2.8173 - val_sparse_categorical_accuracy: 0.3783 - lr: 0.1000\nEpoch 35/100\n574/574 [==============================] - 72s 125ms/step - loss: 2.5687 - sparse_categorical_accuracy: 0.4374 - val_loss: 2.5317 - val_sparse_categorical_accuracy: 0.4126 - lr: 0.1000\nEpoch 36/100\n574/574 [==============================] - 72s 124ms/step - loss: 2.2951 - sparse_categorical_accuracy: 0.4724 - val_loss: 2.2401 - val_sparse_categorical_accuracy: 0.4540 - lr: 0.1000\nEpoch 37/100\n574/574 [==============================] - 72s 125ms/step - loss: 2.0822 - sparse_categorical_accuracy: 0.5040 - val_loss: 2.1365 - val_sparse_categorical_accuracy: 0.4525 - lr: 0.1000\nEpoch 38/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.9411 - sparse_categorical_accuracy: 0.5207 - val_loss: 2.0351 - val_sparse_categorical_accuracy: 0.4546 - lr: 0.1000\nEpoch 39/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.8641 - sparse_categorical_accuracy: 0.5232 - val_loss: 2.2030 - val_sparse_categorical_accuracy: 0.4180 - lr: 0.1000\nEpoch 40/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.9220 - sparse_categorical_accuracy: 0.5067 - val_loss: 2.6101 - val_sparse_categorical_accuracy: 0.3164 - lr: 0.1000\nEpoch 41/100\n574/574 [==============================] - 71s 123ms/step - loss: 2.1159 - sparse_categorical_accuracy: 0.4347 - val_loss: 2.0510 - val_sparse_categorical_accuracy: 0.4575 - lr: 0.1000\nEpoch 42/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.8837 - sparse_categorical_accuracy: 0.5021 - val_loss: 1.9446 - val_sparse_categorical_accuracy: 0.4562 - lr: 0.1000\nEpoch 43/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.9279 - sparse_categorical_accuracy: 0.4775 - val_loss: 1.9123 - val_sparse_categorical_accuracy: 0.4708 - lr: 0.1000\nEpoch 44/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.7312 - sparse_categorical_accuracy: 0.5408 - val_loss: 2.0205 - val_sparse_categorical_accuracy: 0.4117 - lr: 0.1000\nEpoch 45/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.7553 - sparse_categorical_accuracy: 0.5212 - val_loss: 1.8994 - val_sparse_categorical_accuracy: 0.4640 - lr: 0.1000\nEpoch 46/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.6782 - sparse_categorical_accuracy: 0.5421 - val_loss: 2.1710 - val_sparse_categorical_accuracy: 0.3251 - lr: 0.1000\nEpoch 47/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.7467 - sparse_categorical_accuracy: 0.5194 - val_loss: 1.9190 - val_sparse_categorical_accuracy: 0.4584 - lr: 0.1000\nEpoch 48/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.8302 - sparse_categorical_accuracy: 0.4901 - val_loss: 1.8610 - val_sparse_categorical_accuracy: 0.4642 - lr: 0.1000\nEpoch 49/100\n574/574 [==============================] - 71s 124ms/step - loss: 1.6028 - sparse_categorical_accuracy: 0.5619 - val_loss: 1.7924 - val_sparse_categorical_accuracy: 0.4828 - lr: 0.1000\nEpoch 50/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.5138 - sparse_categorical_accuracy: 0.5877 - val_loss: 1.7365 - val_sparse_categorical_accuracy: 0.5111 - lr: 0.1000\nEpoch 51/100\n574/574 [==============================] - 70s 122ms/step - loss: 1.4726 - sparse_categorical_accuracy: 0.6034 - val_loss: 2.0867 - val_sparse_categorical_accuracy: 0.3990 - lr: 0.1000\nEpoch 52/100\n574/574 [==============================] - 70s 122ms/step - loss: 1.5538 - sparse_categorical_accuracy: 0.5796 - val_loss: 1.7525 - val_sparse_categorical_accuracy: 0.5111 - lr: 0.1000\nEpoch 53/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.8451 - sparse_categorical_accuracy: 0.5064 - val_loss: 3.0248 - val_sparse_categorical_accuracy: 0.2826 - lr: 0.1000\nEpoch 54/100\n574/574 [==============================] - 71s 124ms/step - loss: 2.5909 - sparse_categorical_accuracy: 0.2938 - val_loss: 2.4629 - val_sparse_categorical_accuracy: 0.3221 - lr: 0.1000\nEpoch 55/100\n574/574 [==============================] - ETA: 0s - loss: 2.1581 - sparse_categorical_accuracy: 0.4046\nEpoch 55: ReduceLROnPlateau reducing learning rate to 0.010000000149011612.\n574/574 [==============================] - 71s 124ms/step - loss: 2.1581 - sparse_categorical_accuracy: 0.4046 - val_loss: 2.4341 - val_sparse_categorical_accuracy: 0.3088 - lr: 0.1000\nEpoch 56/100\n574/574 [==============================] - 70s 122ms/step - loss: 1.8739 - sparse_categorical_accuracy: 0.5023 - val_loss: 1.8839 - val_sparse_categorical_accuracy: 0.4965 - lr: 0.0100\nEpoch 57/100\n574/574 [==============================] - 70s 122ms/step - loss: 1.7883 - sparse_categorical_accuracy: 0.5272 - val_loss: 1.8535 - val_sparse_categorical_accuracy: 0.5028 - lr: 0.0100\nEpoch 58/100\n574/574 [==============================] - 70s 122ms/step - loss: 1.7293 - sparse_categorical_accuracy: 0.5430 - val_loss: 1.8488 - val_sparse_categorical_accuracy: 0.5039 - lr: 0.0100\nEpoch 59/100\n574/574 [==============================] - 71s 123ms/step - loss: 1.6814 - sparse_categorical_accuracy: 0.5601 - val_loss: 1.8782 - val_sparse_categorical_accuracy: 0.4922 - lr: 0.0100\nEpoch 60/100\n574/574 [==============================] - ETA: 0s - loss: 1.6458 - sparse_categorical_accuracy: 0.5721Restoring model weights from the end of the best epoch: 50.\n\nEpoch 60: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n574/574 [==============================] - 71s 124ms/step - loss: 1.6458 - sparse_categorical_accuracy: 0.5721 - val_loss: 1.8134 - val_sparse_categorical_accuracy: 0.5100 - lr: 0.0100\nEpoch 60: early stopping\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce21d5f27aa5487494bb5d9d69251966"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>epoch/learning_rate</td><td>████████████████████████████████████▂▂▂▁</td></tr><tr><td>epoch/loss</td><td>█▇▅▄▃▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/lr</td><td>█████████████████████████████████████▁▁▁</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>▁▁▁▁▁▂▂▃▃▃▄▄▅▅▅▆▆▆▇▇▇▇▅▆▇▇▇▆▆▇▇▇███▇▅▇▇█</td></tr><tr><td>epoch/val_loss</td><td>█▇▅▄▃▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>▁▁▁▁▁▂▂▃▃▃▄▅▆▆▆▆▇▆▅▅▇▇▅▆▇▇▄▇▇▆▄▇▇██▄▄███</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>59</td></tr><tr><td>epoch/learning_rate</td><td>0.001</td></tr><tr><td>epoch/loss</td><td>1.6458</td></tr><tr><td>epoch/lr</td><td>0.01</td></tr><tr><td>epoch/sparse_categorical_accuracy</td><td>0.57207</td></tr><tr><td>epoch/val_loss</td><td>1.81342</td></tr><tr><td>epoch/val_sparse_categorical_accuracy</td><td>0.51003</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">resnet152</strong> at: <a href='https://wandb.ai/nsiete23/resnet-improved-labels/runs/lgfvz1dt' target=\"_blank\">https://wandb.ai/nsiete23/resnet-improved-labels/runs/lgfvz1dt</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20230406_114941-lgfvz1dt/logs</code>"},"metadata":{}}]},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n\nhistory_df[[\"sparse_categorical_accuracy\", \"val_sparse_categorical_accuracy\"]].plot()\nhistory_df[[\"loss\", \"val_loss\"]].plot()\n\nmodel.evaluate(test)","metadata":{"execution":{"iopub.status.busy":"2023-04-06T13:05:11.951442Z","iopub.execute_input":"2023-04-06T13:05:11.951842Z","iopub.status.idle":"2023-04-06T13:05:25.938372Z","shell.execute_reply.started":"2023-04-06T13:05:11.951794Z","shell.execute_reply":"2023-04-06T13:05:25.937357Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"180/180 [==============================] - 8s 42ms/step - loss: 1.6998 - sparse_categorical_accuracy: 0.5218\n","output_type":"stream"},{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"[1.6998131275177002, 0.5217998027801514]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]}]}