tf.compat.v1.keras.layers.DenseFeatures

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

tf.compat.v1.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 V1 version of this layer that uses variable_scope's to create variables which works well with PartitionedVariables. Variable scopes are deprecated in V2, so the V2 version uses name_scopes instead. But currently that lacks support for partitioned variables. Use this if you need partitioned variables.

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.compat.v1.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.compat.v1.keras.layers.Dense(
                     units, activation='relu')(dense_tensor)
prediction = tf.compat.v1.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.