tf.keras.layers.DenseFeatures

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A layer that produces a dense Tensor based on given feature_columns.

Inherits From: DenseFeatures

tf.keras.layers.DenseFeatures(
    feature_columns, trainable=True, name=None, **kwargs
)

Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor.

This layer can be called multiple times with different features.

This is the V2 version of this layer that uses name_scopes to create variables instead of variable_scopes. But this approach currently lacks support for partitioned variables. In that case, use the V1 version instead.

Example:

price = tf.feature_column.numeric_column('price')
keywords_embedded = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_hash_bucket("keywords", 10K),
    dimensions=16)
columns = [price, keywords_embedded, ...]
feature_layer = tf.keras.layers.DenseFeatures(columns)

features = tf.io.parse_example(
    ..., features=tf.feature_column.make_parse_example_spec(columns))
dense_tensor = feature_layer(features)
for units in [128, 64, 32]:
  dense_tensor = tf.keras.layers.Dense(units, activation='relu')(dense_tensor)
prediction = tf.keras.layers.Dense(1)(dense_tensor)

Args
feature_columns	An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from DenseColumn such as numeric_column, embedding_column, bucketized_column, indicator_column. If you have categorical features, you can wrap them with an embedding_column or indicator_column.
trainable	Boolean, whether the layer's variables will be updated via gradient descent during training.
name	Name to give to the DenseFeatures.
**kwargs	Keyword arguments to construct a layer.

Raises
ValueError	if an item in feature_columns is not a DenseColumn.