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Class DenseFeatures

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

Inherits From: DenseFeatures


  • Class tf.compat.v2.keras.layers.DenseFeatures

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.

大牛时代配资This layer can be called multiple times with different features.

大牛时代配资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.


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 =, features=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)


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大牛时代配资Creates a DenseFeatures object.


  • 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.


  • ValueError: if an item in feature_columns is not a DenseColumn.

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