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Replace lr with learning_rate in Keras optimizers, fixes #456

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Aurélien Geron
2021-08-31 20:54:35 +12:00
parent 1568ac3b94
commit 108fe1fa53
10 changed files with 94 additions and 94 deletions
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8
15_processing_sequences_using_rnns_and_cnns.ipynb
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@@ -305,7 +305,7 @@
" keras.layers.SimpleRNN(1, input_shape=[None, 1])\n",
"])\n",
"\n",
"optimizer = keras.optimizers.Adam(lr=0.005)\n",
"optimizer = keras.optimizers.Adam(learning_rate=0.005)\n",
"model.compile(loss=\"mse\", optimizer=optimizer)\n",
"history = model.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid))"
@@ -711,7 +711,7 @@
"def last_time_step_mse(Y_true, Y_pred):\n",
" return keras.metrics.mean_squared_error(Y_true[:, -1], Y_pred[:, -1])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(lr=0.01), metrics=[last_time_step_mse])\n",
"model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(learning_rate=0.01), metrics=[last_time_step_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
@@ -1478,7 +1478,7 @@
" keras.layers.LSTM(128),\n",
" keras.layers.Dense(len(class_names), activation=\"softmax\")\n",
"])\n",
"optimizer = keras.optimizers.SGD(lr=1e-2, clipnorm=1.)\n",
"optimizer = keras.optimizers.SGD(learning_rate=1e-2, clipnorm=1.)\n",
"model.compile(loss=\"sparse_categorical_crossentropy\",\n",
" optimizer=optimizer,\n",
" metrics=[\"accuracy\", \"sparse_top_k_categorical_accuracy\"])\n",
@@ -1818,7 +1818,7 @@
"metadata": {},
"outputs": [],
"source": [
"optimizer = keras.optimizers.Nadam(lr=1e-3)\n",
"optimizer = keras.optimizers.Nadam(learning_rate=1e-3)\n",
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
" metrics=[\"accuracy\"])\n",
"model.fit(train_set, epochs=20, validation_data=valid_set)"