From 772daa72c591b7d661ca896c2a9824cd20566af0 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Fri, 16 Aug 2024 09:34:25 -0700
Subject: [PATCH] Keep column name, and add an axis.

---
 site/en/tutorials/keras/regression.ipynb | 79 ++++++++++++++----------
 1 file changed, 48 insertions(+), 31 deletions(-)

diff --git a/site/en/tutorials/keras/regression.ipynb b/site/en/tutorials/keras/regression.ipynb
index d5da28a782..5064d7ce17 100644
--- a/site/en/tutorials/keras/regression.ipynb
+++ b/site/en/tutorials/keras/regression.ipynb
@@ -368,10 +368,7 @@
         "test_features = test_dataset.copy()\n",
         "\n",
         "train_labels = train_features.pop('MPG')\n",
-        "test_labels = test_features.pop('MPG')",
-	"\n",
-	"train_features = tf.convert_to_tensor(train_features, dtype=tf.float32)\n",
-	"test_features = tf.convert_to_tensor(test_features, dtype=tf.float32)"
+        "test_labels = test_features.pop('MPG')"
       ]
     },
     {
@@ -492,7 +489,7 @@
       },
       "outputs": [],
       "source": [
-        "first = np.array(train_features[:1])\n",
+        "first = np.array(train_features[:1], dtype=float)\n",
         "\n",
         "with np.printoptions(precision=2, suppress=True):\n",
         "  print('First example:', first)\n",
@@ -548,9 +545,11 @@
       },
       "outputs": [],
       "source": [
-        "horsepower = np.array(train_features[:, 2])\n",
+        "horsepower = np.array(train_features['Horsepower'], dtype=float)\n",
+        "horsepower = horsepower[:, None]\n",
+        "print(horsepower.shape)\n",
         "\n",
-        "horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None)\n",
+        "horsepower_normalizer = layers.Normalization(axis=1, input_shape=[1])\n",
         "horsepower_normalizer.adapt(horsepower)"
       ]
     },
@@ -574,9 +573,7 @@
         "horsepower_model = tf.keras.Sequential([\n",
         "    horsepower_normalizer,\n",
         "    layers.Dense(units=1)\n",
-        "])\n",
-        "\n",
-        "horsepower_model.summary()"
+        "])"
       ]
     },
     {
@@ -601,6 +598,17 @@
         "horsepower_model.predict(horsepower[:10])"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eNDk5U4S-WVR"
+      },
+      "outputs": [],
+      "source": [
+        "horsepower_model.summary()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -642,7 +650,7 @@
       "source": [
         "%%time\n",
         "history = horsepower_model.fit(\n",
-        "    train_features[:, 2],\n",
+        "    train_features['Horsepower'],\n",
         "    train_labels,\n",
         "    epochs=100,\n",
         "    # Suppress logging.\n",
@@ -722,7 +730,7 @@
         "test_results = {}\n",
         "\n",
         "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
-        "    test_features[:, 2],\n",
+        "    test_features['Horsepower'],\n",
         "    test_labels, verbose=0)"
       ]
     },
@@ -756,7 +764,7 @@
       "outputs": [],
       "source": [
         "def plot_horsepower(x, y):\n",
-        "  plt.scatter(train_features[:, 2], train_labels, label='Data')\n",
+        "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
         "  plt.plot(x, y, color='k', label='Predictions')\n",
         "  plt.xlabel('Horsepower')\n",
         "  plt.ylabel('MPG')\n",
@@ -1026,17 +1034,6 @@
         "This model has quite a few more trainable parameters than the linear models:"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ReAD0n6MsFK-"
-      },
-      "outputs": [],
-      "source": [
-        "dnn_horsepower_model.summary()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1056,12 +1053,23 @@
       "source": [
         "%%time\n",
         "history = dnn_horsepower_model.fit(\n",
-        "    train_features[:, 2],\n",
+        "    train_features['Horsepower'],\n",
         "    train_labels,\n",
         "    validation_split=0.2,\n",
         "    verbose=0, epochs=100)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ReAD0n6MsFK-"
+      },
+      "outputs": [],
+      "source": [
+        "dnn_horsepower_model.summary()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1132,7 +1140,7 @@
       "outputs": [],
       "source": [
         "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n",
-        "    test_features[:, 2], test_labels,\n",
+        "    test_features['Horsepower'], test_labels,\n",
         "    verbose=0)"
       ]
     },
@@ -1162,8 +1170,7 @@
       },
       "outputs": [],
       "source": [
-        "dnn_model = build_and_compile_model(normalizer)\n",
-        "dnn_model.summary()"
+        "dnn_model = build_and_compile_model(normalizer)"
       ]
     },
     {
@@ -1182,6 +1189,17 @@
         "    verbose=0, epochs=100)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5R9yfjpC_qcT"
+      },
+      "outputs": [],
+      "source": [
+        "dnn_model.summary()"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1324,7 +1342,7 @@
       },
       "outputs": [],
       "source": [
-        "dnn_model.save('dnn_model.tf', save_format='tf')"
+        "dnn_model.save('dnn_model.keras')"
       ]
     },
     {
@@ -1344,7 +1362,7 @@
       },
       "outputs": [],
       "source": [
-        "reloaded = tf.keras.models.load_model('dnn_model.tf')\n",
+        "reloaded = tf.keras.models.load_model('dnn_model.keras')\n",
         "\n",
         "test_results['reloaded'] = reloaded.evaluate(\n",
         "    test_features, test_labels, verbose=0)"
@@ -1380,7 +1398,6 @@
   ],
   "metadata": {
     "colab": {
-      "collapsed_sections": [],
       "name": "regression.ipynb",
       "toc_visible": true
     },