SGD now defaults to lr=0.01 so use 1e-3 explicitly
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3db31444cd
commit
400920f0aa
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@ -530,7 +530,7 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"model.compile(loss=\"mse\", optimizer=\"sgd\")"
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"model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
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]
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]
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},
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},
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{
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{
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@ -1511,7 +1511,9 @@
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" keras.layers.DenseFeatures(feature_columns=columns_without_target),\n",
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" keras.layers.DenseFeatures(feature_columns=columns_without_target),\n",
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" keras.layers.Dense(1)\n",
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" keras.layers.Dense(1)\n",
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"])\n",
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"])\n",
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"model.compile(loss=\"mse\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
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"model.compile(loss=\"mse\",\n",
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" optimizer=keras.optimizers.SGD(lr=1e-3),\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
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"model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
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]
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]
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},
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},
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@ -1635,7 +1637,9 @@
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" keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
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" keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
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" keras.layers.Lambda(lambda images: tf.cast(images, tf.float32)),\n",
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" keras.layers.Lambda(lambda images: tf.cast(images, tf.float32)),\n",
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" keras.layers.Dense(10, activation=\"softmax\")])\n",
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" keras.layers.Dense(10, activation=\"softmax\")])\n",
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"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
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"model.compile(loss=\"sparse_categorical_crossentropy\",\n",
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" optimizer=keras.optimizers.SGD(lr=1e-3),\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
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"model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
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]
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]
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},
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},
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