SGD now defaults to lr=0.01 so use 1e-3 explicitly

13_loading_and_preprocessing_data.ipynb

@@ -894,7 +894,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(loss=\"mse\", optimizer=\"sgd\")"
+    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
    ]
   },
   {
@@ -2606,7 +2606,9 @@
     "    keras.layers.DenseFeatures(feature_columns=columns_without_target),\n",
     "    keras.layers.Dense(1)\n",
     "])\n",
-    "model.compile(loss=\"mse\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
+    "model.compile(loss=\"mse\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
+    "              metrics=[\"accuracy\"])\n",
     "model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
    ]
   },
@@ -2825,7 +2827,9 @@
     "    keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
     "    keras.layers.Lambda(lambda images: tf.cast(images, tf.float32)),\n",
     "    keras.layers.Dense(10, activation=\"softmax\")])\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
+    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
+    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
+    "              metrics=[\"accuracy\"])\n",
     "model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
    ]
   },