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tf.compat.v1.keras.layers.DenseFeatures

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Class DenseFeatures

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

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

features = tf.io.parse_example(..., features=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)

__init__

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__init__(
    feature_columns,
    trainable=True,
    name=None,
    **kwargs
)

Constructs a DenseFeatures layer.

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.