From 2a789562d0fe67328fa4498e60d6f29d5602a1c7 Mon Sep 17 00:00:00 2001
From: Shiva Subramanian <7301747+gitshiva@users.noreply.github.com>
Date: Tue, 13 Feb 2024 17:56:50 -0500
Subject: [PATCH 1/2] fixed errors - converted train_features and test_features
 to tensor, then updated Horsepower references to specific columns in array,
 otherwise this nb was failing due to data type mismatches

---
 site/en/tutorials/keras/regression.ipynb | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/site/en/tutorials/keras/regression.ipynb b/site/en/tutorials/keras/regression.ipynb
index e3af85a7b90..007f761a07d 100644
--- a/site/en/tutorials/keras/regression.ipynb
+++ b/site/en/tutorials/keras/regression.ipynb
@@ -368,7 +368,10 @@
         "test_features = test_dataset.copy()\n",
         "\n",
         "train_labels = train_features.pop('MPG')\n",
-        "test_labels = test_features.pop('MPG')"
+        "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)"
       ]
     },
     {
@@ -545,7 +548,7 @@
       },
       "outputs": [],
       "source": [
-        "horsepower = np.array(train_features['Horsepower'])\n",
+        "horsepower = np.array(train_features[:, 2])\n",
         "\n",
         "horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None)\n",
         "horsepower_normalizer.adapt(horsepower)"
@@ -639,7 +642,7 @@
       "source": [
         "%%time\n",
         "history = horsepower_model.fit(\n",
-        "    train_features['Horsepower'],\n",
+        "    train_features[:, 2],\n",
         "    train_labels,\n",
         "    epochs=100,\n",
         "    # Suppress logging.\n",
@@ -719,7 +722,7 @@
         "test_results = {}\n",
         "\n",
         "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
-        "    test_features['Horsepower'],\n",
+        "    test_features[:, 2],\n",
         "    test_labels, verbose=0)"
       ]
     },
@@ -753,7 +756,7 @@
       "outputs": [],
       "source": [
         "def plot_horsepower(x, y):\n",
-        "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
+        "  plt.scatter(train_features[:, 2], train_labels, label='Data')\n",
         "  plt.plot(x, y, color='k', label='Predictions')\n",
         "  plt.xlabel('Horsepower')\n",
         "  plt.ylabel('MPG')\n",
@@ -1053,7 +1056,7 @@
       "source": [
         "%%time\n",
         "history = dnn_horsepower_model.fit(\n",
-        "    train_features['Horsepower'],\n",
+        "    train_features[:, 2],\n",
         "    train_labels,\n",
         "    validation_split=0.2,\n",
         "    verbose=0, epochs=100)"
@@ -1129,7 +1132,7 @@
       "outputs": [],
       "source": [
         "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n",
-        "    test_features['Horsepower'], test_labels,\n",
+        "    test_features[:, 2], test_labels,\n",
         "    verbose=0)"
       ]
     },

From 995dbaebb91b0588d20cf3732289802564f22b4c Mon Sep 17 00:00:00 2001
From: Shiva Subramanian <7301747+gitshiva@users.noreply.github.com>
Date: Tue, 13 Feb 2024 18:21:53 -0500
Subject: [PATCH 2/2] the save model was giving errors in keras format, per
 https://github.com/keras-team/keras/issues/15348, changed it to TF format,
 works fine now!

---
 site/en/tutorials/keras/regression.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/site/en/tutorials/keras/regression.ipynb b/site/en/tutorials/keras/regression.ipynb
index 007f761a07d..d5da28a782b 100644
--- a/site/en/tutorials/keras/regression.ipynb
+++ b/site/en/tutorials/keras/regression.ipynb
@@ -1324,7 +1324,7 @@
       },
       "outputs": [],
       "source": [
-        "dnn_model.save('dnn_model.keras')"
+        "dnn_model.save('dnn_model.tf', save_format='tf')"
       ]
     },
     {
@@ -1344,7 +1344,7 @@
       },
       "outputs": [],
       "source": [
-        "reloaded = tf.keras.models.load_model('dnn_model.keras')\n",
+        "reloaded = tf.keras.models.load_model('dnn_model.tf')\n",
         "\n",
         "test_results['reloaded'] = reloaded.evaluate(\n",
         "    test_features, test_labels, verbose=0)"