Returns a dense Tensor
as input layer based on given feature_columns
.
tf.compat.v1.feature_column.input_layer(
features,
feature_columns,
weight_collections=None,
trainable=True,
cols_to_vars=None,
cols_to_output_tensors=None
)
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. _FeatureColumn s 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 Variable s. 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 Tensor s.
|
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 .
|