Big fixes

ml/cc/exercises/representation_with_a_feature_cross.ipynb

@@ -213,7 +213,7 @@
         "  model = tf.keras.Model(inputs=my_inputs, outputs=my_outputs)\n",
         "\n",
         "  # Construct the layers into a model that TensorFlow can execute.\n",
-        "  model.compile(optimizer=tf.keras.optimizers.experimental.RMSprop(\n",
+        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(\n",
         "      learning_rate=my_learning_rate),\n",
         "      loss=\"mean_squared_error\",\n",
         "      metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
@@ -282,16 +282,12 @@
         "\n",
         "# The two Input layers are concatenated so they can be passed as a single\n",
         "# tensor to a Dense layer.\n",
-        "preprocessing_layer = tf.keras.layers.Concatenate()(inputs.values())\n",
+        "preprocessing_layer = tf.keras.layers.Concatenate()(list(inputs.values()))\n",
         "\n",
         "dense_output = layers.Dense(units=1, name='dense_layer')(preprocessing_layer)\n",
         "\n",
-        "outputs = {\n",
-        "  'dense_output': dense_output\n",
-        "}\n",
-        "\n",
         "# Create and compile the model's topography.\n",
-        "my_model = create_model(inputs, outputs, learning_rate)\n",
+        "my_model = create_model(inputs, dense_output, learning_rate)\n",
         "\n",
         "# To view a PNG of this model's layers, uncomment the call to\n",
         "# `tf.keras.utils.plot_model` below. After running this code cell, click\n",
@@ -413,12 +409,7 @@
         "# Concatenate latitude and longitude into a single tensor as input for the Dense layer.\n",
         "concatenate_layer = tf.keras.layers.Concatenate()([latitude, longitude])\n",
         "\n",
-        "dense_output = layers.Dense(units=1, name='dense_layer')(concatenate_layer)\n",
-        "\n",
-        "# Define an output dictionary we'll send to the model constructor.\n",
-        "outputs = {\n",
-        "  'dense_output': dense_output\n",
-        "}"
+        "dense_output = layers.Dense(units=1, name='dense_layer')(concatenate_layer)"
       ]
     },
     {
@@ -445,7 +436,7 @@
         "epochs = 35\n",
         "\n",
         "# Build the model.\n",
-        "my_model = create_model(inputs, outputs, learning_rate)\n",
+        "my_model = create_model(inputs, dense_output, learning_rate)\n",
         "\n",
         "# Train the model on the training set.\n",
         "epochs, rmse = train_model(my_model, train_df, epochs, batch_size, label_name)\n",
@@ -574,12 +565,7 @@
         "    output_mode='one_hot',\n",
         "    name='cross_latitude_longitude')([latitude, longitude])\n",
         "\n",
-        "dense_output = layers.Dense(units=1, name='dense_layer')(feature_cross)\n",
-        "\n",
-        "# Define an output dictionary we'll send to the model constructor.\n",
-        "outputs = {\n",
-        "  'dense_output': dense_output\n",
-        "}"
+        "dense_output = layers.Dense(units=1, name='dense_layer')(feature_cross)"
       ]
     },
     {
@@ -604,7 +590,7 @@
         "epochs = 35\n",
         "\n",
         "# Build the model, this time passing in the feature_cross_feature_layer:\n",
-        "my_model = create_model(inputs, outputs, learning_rate)\n",
+        "my_model = create_model(inputs, dense_output, learning_rate)\n",
         "\n",
         "# Train the model on the training set.\n",
         "epochs, rmse = train_model(my_model, train_df, epochs, batch_size, label_name)\n",
@@ -695,10 +681,6 @@
   ],
   "metadata": {
     "colab": {
-      "last_runtime": {
-        "build_target": "//learning/grp/tools/ml_python:ml_notebook",
-        "kind": "private"
-      },
       "name": "Representation with a Feature Cross.ipynb",
       "private_outputs": true,
       "provenance": []
@@ -710,4 +692,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}