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Merge branch 'master' into changes-chap10

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Aurélien Geron
2021-03-02 12:21:32 +13:00
committed by GitHub
parent 30d160f8be d46938857c
commit 4e915ba6c2
33 changed files with 1724 additions and 1248 deletions
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29
10_neural_nets_with_keras.ipynb
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@@ -84,11 +84,7 @@
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)\n",
"\n",
"# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n",
"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
" plt.savefig(path, format=fig_extension, dpi=resolution)\n"
]
},
{
@@ -735,12 +731,20 @@
"y_proba.round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: `model.predict_classes(X_new)` is deprecated. It is replaced with `np.argmax(model.predict(X_new), axis=-1)`."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"#y_pred = model.predict_classes(X_new) # deprecated\n",
"y_pred = np.argmax(model.predict(X_new), axis=-1)\n",
"y_pred"
]
@@ -1505,7 +1509,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: the following cell crashes at the end of training. This seems to be caused by [Keras issue #13586](https://github.com/keras-team/keras/issues/13586), which was triggered by a recent change in Scikit-Learn. [Pull Request #13598](https://github.com/keras-team/keras/pull/13598) seems to fix the issue, so this problem should be resolved soon."
"**Warning**: the following cell crashes at the end of training. This seems to be caused by [Keras issue #13586](https://github.com/keras-team/keras/issues/13586), which was triggered by a recent change in Scikit-Learn. [Pull Request #13598](https://github.com/keras-team/keras/pull/13598) seems to fix the issue, so this problem should be resolved soon. In the meantime, I've added `.tolist()` and `.rvs(1000).tolist()` as workarounds."
]
},
{
@@ -1519,8 +1523,8 @@
"\n",
"param_distribs = {\n",
" \"n_hidden\": [0, 1, 2, 3],\n",
" \"n_neurons\": np.arange(1, 100),\n",
" \"learning_rate\": reciprocal(3e-4, 3e-2),\n",
" \"n_neurons\": np.arange(1, 100) .tolist(),\n",
" \"learning_rate\": reciprocal(3e-4, 3e-2) .rvs(1000).tolist(),\n",
"}\n",
"\n",
"rnd_search_cv = RandomizedSearchCV(keras_reg, param_distribs, n_iter=10, cv=3, verbose=2)\n",
@@ -1879,6 +1883,7 @@
"plt.gca().set_xscale('log')\n",
"plt.hlines(min(expon_lr.losses), min(expon_lr.rates), max(expon_lr.rates))\n",
"plt.axis([min(expon_lr.rates), max(expon_lr.rates), 0, expon_lr.losses[0]])\n",
"plt.grid()\n",
"plt.xlabel(\"Learning rate\")\n",
"plt.ylabel(\"Loss\")"
]
@@ -1887,7 +1892,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The loss starts shooting back up violently around 3e-1, so let's try using 2e-1 as our learning rate:"
"The loss starts shooting back up violently when the learning rate goes over 6e-1, so let's try using half of that, at 3e-1:"
]
},
{
@@ -1922,7 +1927,7 @@
"outputs": [],
"source": [
"model.compile(loss=\"sparse_categorical_crossentropy\",\n",
" optimizer=keras.optimizers.SGD(lr=2e-1),\n",
" optimizer=keras.optimizers.SGD(lr=3e-1),\n",
" metrics=[\"accuracy\"])"
]
},
@@ -1949,7 +1954,7 @@
"\n",
"history = model.fit(X_train, y_train, epochs=100,\n",
" validation_data=(X_valid, y_valid),\n",
" callbacks=[early_stopping_cb, checkpoint_cb, tensorboard_cb])"
" callbacks=[checkpoint_cb, early_stopping_cb, tensorboard_cb])"
]
},
{
@@ -2002,7 +2007,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.7.9"
},
"nav_menu": {
"height": "264px",