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A layer that produces a dense Tensor
based on given feature_columns
.
Inherits From: DenseFeatures
, Layer
, Module
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", 10000),
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)
Raises | |
---|---|
ValueError
|
if an item in feature_columns is not a DenseColumn .
|