tf.compat.v1.feature_column.input_layer

Returns a dense Tensor as input layer based on given feature_columns. (deprecated)

Warning: tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either Keras preprocessing layers or through the one-stop utility tf.keras.utils.FeatureSpace built on top of them. See the migration guide for details.

tf.compat.v1.feature_column.input_layer(
    features,
    feature_columns,
    weight_collections=None,
    trainable=True,
    cols_to_vars=None,
    cols_to_output_tensors=None
)

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the tf.keras.utils.FeatureSpace utility. Each of tf.feature_column.* has a functional equivalent in tf.keras.layers for feature preprocessing when training a Keras model.

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.

Example:

price = numeric_column('price')
keywords_embedded = embedding_column(
    categorical_column_with_hash_bucket("keywords", 10K), dimensions=16)
columns = [price, keywords_embedded, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
for units in [128, 64, 32]:
  dense_tensor = tf.compat.v1.layers.dense(dense_tensor, units, tf.nn.relu)
prediction = tf.compat.v1.layers.dense(dense_tensor, 1)

Args
features	A mapping from key to tensors. _FeatureColumns look up via these keys. For example numeric_column('price') will look at 'price' key in this dict. Values can be a SparseTensor or a Tensor depends on corresponding _FeatureColumn.
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.
weight_collections	A list of collection names to which the Variable will be added. Note that variables will also be added to collections tf.GraphKeys.GLOBAL_VARIABLES and ops.GraphKeys.MODEL_VARIABLES.
trainable	If True also add the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
cols_to_vars	If not None, must be a dictionary that will be filled with a mapping from _FeatureColumn to list of Variables. For example, after the call, we might have cols_to_vars = {_EmbeddingColumn( categorical_column=_HashedCategoricalColumn( key='sparse_feature', hash_bucket_size=5, dtype=tf.string), dimension=10): [
cols_to_output_tensors	If not None, must be a dictionary that will be filled with a mapping from '_FeatureColumn' to the associated output Tensors.

Returns
A Tensor which represents input layer of a model. Its shape is (batch_size, first_layer_dimension) and its dtype is float32. first_layer_dimension is determined based on given feature_columns.

Raises
ValueError	if an item in feature_columns is not a _DenseColumn.