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Merge pull request #2318 from tensorflow:MarkDaoust-patch-1
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PiperOrigin-RevId: 662885206
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@copybara-github
copybara-github committed Aug 14, 2024
2 parents 460419a + 117c1d3 commit 5b47d57
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117 changes: 68 additions & 49 deletions site/en/tutorials/load_data/pandas_dataframe.ipynb
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},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"\n",
Expand Down Expand Up @@ -292,7 +293,7 @@
"outputs": [],
"source": [
"normalizer = tf.keras.layers.Normalization(axis=-1)\n",
"normalizer.adapt(numeric_features)"
"normalizer.adapt(np.array(numeric_features))"
]
},
{
Expand Down Expand Up @@ -446,79 +447,77 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "U3QDo-jwHYXc"
"id": "voDoA447GBC3"
},
"outputs": [],
"source": [
"numeric_dict_ds = tf.data.Dataset.from_tensor_slices((dict(numeric_features), target))"
"numeric_features_dict = {key: value.to_numpy()[:, tf.newaxis] for key, value in dict(numeric_features).items()}\n",
"target_array = target.to_numpy()[:, tf.newaxis]"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yyEERK9ldIi_"
"id": "U3QDo-jwHYXc"
},
"outputs": [],
"source": [
"Here are the first three examples from that dataset:"
"numeric_dict_ds = tf.data.Dataset.from_tensor_slices((numeric_features_dict , target_array))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "q0tDwk0VdH6D"
"id": "HL4Bf1b7M7DT"
},
"outputs": [],
"source": [
"for row in numeric_dict_ds.take(3):\n",
" print(row)"
"len(numeric_features_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DEAM6HAFxlMy"
"id": "yyEERK9ldIi_"
},
"source": [
"### Dictionaries with Keras"
"Here are the first three examples from that dataset:"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dnoyoWLWx07i"
"id": "q0tDwk0VdH6D"
},
"outputs": [],
"source": [
"Typically, Keras models and layers expect a single input tensor, but these classes can accept and return nested structures of dictionaries, tuples and tensors. These structures are known as \"nests\" (refer to the `tf.nest` module for details).\n",
"\n",
"There are two equivalent ways you can write a Keras model that accepts a dictionary as input."
"for row in numeric_dict_ds.take(3):\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5xUTrm0apDTr"
"id": "dnoyoWLWx07i"
},
"source": [
"#### 1. The Model-subclass style\n",
"Typically, Keras models and layers expect a single input tensor, but these classes can accept and return nested structures of dictionaries, tuples and tensors. These structures are known as \"nests\" (refer to the `tf.nest` module for details).\n",
"\n",
"You write a subclass of `tf.keras.Model` (or `tf.keras.Layer`). You directly handle the inputs, and create the outputs:"
"There are two equivalent ways you can write a Keras model that accepts a dictionary as input."
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {
"id": "Zc3HV99CFRWL"
"id": "5xUTrm0apDTr"
},
"outputs": [],
"source": [
" def stack_dict(inputs, fun=tf.stack):\n",
" values = []\n",
" for key in sorted(inputs.keys()):\n",
" values.append(tf.cast(inputs[key], tf.float32))\n",
"### 1. The Model-subclass style\n",
"\n",
" return fun(values, axis=-1)"
"You write a subclass of `tf.keras.Model` (or `tf.keras.Layer`). You directly handle the inputs, and create the outputs:"
]
},
{
Expand All @@ -544,22 +543,31 @@
" tf.keras.layers.Dense(1)\n",
" ])\n",
"\n",
" self.concat = tf.keras.layers.Concatenate(axis=1)\n",
"\n",
" def _stack(self, input_dict):\n",
" values = []\n",
" for key, value in sorted(input_dict.items()):\n",
" values.append(value)\n",
"\n",
" return self.concat(values)\n",
"\n",
" def adapt(self, inputs):\n",
" # Stack the inputs and `adapt` the normalization layer.\n",
" inputs = stack_dict(inputs)\n",
" inputs = self._stack(inputs)\n",
" self.normalizer.adapt(inputs)\n",
"\n",
" def call(self, inputs):\n",
" # Stack the inputs\n",
" inputs = stack_dict(inputs)\n",
" inputs = self._stack(inputs)\n",
" # Run them through all the layers.\n",
" result = self.seq(inputs)\n",
"\n",
" return result\n",
"\n",
"model = MyModel()\n",
"\n",
"model.adapt(dict(numeric_features))\n",
"model.adapt(numeric_features_dict)\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
Expand All @@ -584,7 +592,7 @@
},
"outputs": [],
"source": [
"model.fit(dict(numeric_features), target, epochs=5, batch_size=BATCH_SIZE)"
"model.fit(numeric_features_dict, target_array, epochs=5, batch_size=BATCH_SIZE)"
]
},
{
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"id": "QIIdxIYm13Ik"
},
"source": [
"#### 2. The Keras functional style"
"### 2. The Keras functional style"
]
},
{
Expand All @@ -652,10 +660,13 @@
},
"outputs": [],
"source": [
"x = stack_dict(inputs, fun=tf.concat)\n",
"xs = [value for key, value in sorted(inputs.items())]\n",
"\n",
"concat = tf.keras.layers.Concatenate(axis=1)\n",
"x = concat(xs)\n",
"\n",
"normalizer = tf.keras.layers.Normalization(axis=-1)\n",
"normalizer.adapt(stack_dict(dict(numeric_features)))\n",
"normalizer.adapt(np.concatenate([value for key, value in sorted(numeric_features_dict.items())], axis=1))\n",
"\n",
"x = normalizer(x)\n",
"x = tf.keras.layers.Dense(10, activation='relu')(x)\n",
Expand All @@ -678,7 +689,7 @@
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(model, rankdir=\"LR\", show_shapes=True)"
"tf.keras.utils.plot_model(model, rankdir=\"LR\", show_shapes=True, show_layer_names=True)"
]
},
{
Expand All @@ -698,7 +709,7 @@
},
"outputs": [],
"source": [
"model.fit(dict(numeric_features), target, epochs=5, batch_size=BATCH_SIZE)"
"model.fit(numeric_features_dict, target, epochs=5, batch_size=BATCH_SIZE)"
]
},
{
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" else:\n",
" dtype = tf.float32\n",
"\n",
" inputs[name] = tf.keras.Input(shape=(), name=name, dtype=dtype)"
" inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)"
]
},
{
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"\n",
"for name in binary_feature_names:\n",
" inp = inputs[name]\n",
" inp = inp[:, tf.newaxis]\n",
" float_value = tf.cast(inp, tf.float32)\n",
" preprocessed.append(float_value)\n",
" preprocessed.append(inp)\n",
"\n",
"preprocessed"
]
Expand All @@ -879,7 +888,7 @@
"outputs": [],
"source": [
"normalizer = tf.keras.layers.Normalization(axis=-1)\n",
"normalizer.adapt(stack_dict(dict(numeric_features)))"
"normalizer.adapt(np.concatenate([value for key, value in sorted(numeric_features_dict.items())], axis=1))"
]
},
{
Expand All @@ -899,11 +908,11 @@
},
"outputs": [],
"source": [
"numeric_inputs = {}\n",
"numeric_inputs = []\n",
"for name in numeric_feature_names:\n",
" numeric_inputs[name]=inputs[name]\n",
" numeric_inputs.append(inputs[name])\n",
"\n",
"numeric_inputs = stack_dict(numeric_inputs)\n",
"numeric_inputs = tf.keras.layers.Concatenate(axis=-1)(numeric_inputs)\n",
"numeric_normalized = normalizer(numeric_inputs)\n",
"\n",
"preprocessed.append(numeric_normalized)\n",
Expand Down Expand Up @@ -985,7 +994,7 @@
" else:\n",
" lookup = tf.keras.layers.IntegerLookup(vocabulary=vocab, output_mode='one_hot')\n",
"\n",
" x = inputs[name][:, tf.newaxis]\n",
" x = inputs[name]\n",
" x = lookup(x)\n",
" preprocessed.append(x)"
]
Expand Down Expand Up @@ -1036,7 +1045,7 @@
},
"outputs": [],
"source": [
"preprocessed_result = tf.concat(preprocessed, axis=-1)\n",
"preprocessed_result = tf.keras.layers.Concatenate(axis=1)(preprocessed)\n",
"preprocessed_result"
]
},
Expand Down Expand Up @@ -1068,7 +1077,7 @@
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(preprocessor, rankdir=\"LR\", show_shapes=True)"
"tf.keras.utils.plot_model(preprocessor, rankdir=\"LR\", show_shapes=True, show_layer_names=True)"
]
},
{
Expand Down Expand Up @@ -1183,6 +1192,17 @@
" metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "i_Z2C2ZcZ3oC"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
Expand Down Expand Up @@ -1258,7 +1278,6 @@
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "pandas_dataframe.ipynb",
"toc_visible": true
},
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