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fixed tensorflow issue https://github.com/tensorflow/tensorflow/issues/75194 #2325

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merged 3 commits into from
Sep 16, 2024
Merged

fixed tensorflow issue https://github.com/tensorflow/tensorflow/issues/75194 #2325

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2305816
edited the compile_and_fit method in to correctly passs the train and…
sanskarmodi8 Sep 8, 2024
58ee6f8
removed commit from another pr, ensured proper linting and formatting…
sanskarmodi8 Sep 12, 2024
f8341de
format
MarkDaoust Sep 13, 2024
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9 changes: 4 additions & 5 deletions site/en/tutorials/keras/overfit_and_underfit.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -543,10 +543,10 @@
" model.summary()\n",
"\n",
" history = model.fit(\n",
" train_ds,\n",
" train_ds.map(lambda x, y: (x, tf.expand_dims(y, axis=-1))),\n",
" steps_per_epoch = STEPS_PER_EPOCH,\n",
" epochs=max_epochs,\n",
" validation_data=validate_ds,\n",
" validation_data=validate_ds.map(lambda x, y: (x, tf.expand_dims(y, axis=-1))),\n",
" callbacks=get_callbacks(name),\n",
" verbose=0)\n",
" return history"
Expand Down Expand Up @@ -977,7 +977,7 @@
"source": [
"`l2(0.001)` means that every coefficient in the weight matrix of the layer will add `0.001 * weight_coefficient_value**2` to the total **loss** of the network.\n",
"\n",
"That is why we're monitoring the `binary_crossentropy` directly. Because it doesn't have this regularization component mixed in.\n",
"That is why you need to monitor the `binary_crossentropy` directly. Because it doesn't have this regularization component mixed in.\n",
"\n",
"So, that same `\"Large\"` model with an `L2` regularization penalty performs much better:\n"
]
Expand Down Expand Up @@ -1228,10 +1228,9 @@
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "overfit_and_underfit.ipynb",
"toc_visible": true
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
Expand Down
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