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Keep column name, and add an axis, move some summaries. #2

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Keep column name, and add an axis, move some summaries. #2

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79 changes: 48 additions & 31 deletions site/en/tutorials/keras/regression.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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')"
]
},
{
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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)"
]
},
Expand All @@ -574,9 +573,7 @@
"horsepower_model = tf.keras.Sequential([\n",
" horsepower_normalizer,\n",
" layers.Dense(units=1)\n",
"])\n",
"\n",
"horsepower_model.summary()"
"])"
]
},
{
Expand All @@ -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": {
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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)"
]
},
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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": {
Expand All @@ -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": {
Expand Down Expand Up @@ -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)"
]
},
Expand Down Expand Up @@ -1162,8 +1170,7 @@
},
"outputs": [],
"source": [
"dnn_model = build_and_compile_model(normalizer)\n",
"dnn_model.summary()"
"dnn_model = build_and_compile_model(normalizer)"
]
},
{
Expand All @@ -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,
Expand Down Expand Up @@ -1324,7 +1342,7 @@
},
"outputs": [],
"source": [
"dnn_model.save('dnn_model.tf', save_format='tf')"
"dnn_model.save('dnn_model.keras')"
]
},
{
Expand All @@ -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)"
Expand Down Expand Up @@ -1380,7 +1398,6 @@
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "regression.ipynb",
"toc_visible": true
},
Expand Down