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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 .
|