tf.feature_column.embedding_column

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DenseColumn that converts from sparse, categorical input.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.feature_column.embedding_column, `tf.compat.v2.feature_column.embedding_column`

tf.feature_column.embedding_column(
    categorical_column, dimension, combiner='mean', initializer=None,
    ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True
)

Use this when your inputs are sparse, but you want to convert them to a dense representation (e.g., to feed to a DNN).

Inputs must be a CategoricalColumn created by any of the categorical_column_* function. Here is an example of using embedding_column with DNNClassifier:

video_id = categorical_column_with_identity(
    key='video_id', num_buckets=1000000, default_value=0)
columns = [embedding_column(video_id, 9),...]

estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)

label_column = ...
def input_fn():
  features = tf.io.parse_example(
      ..., features=make_parse_example_spec(columns + [label_column]))
  labels = features.pop(label_column.name)
  return features, labels

estimator.train(input_fn=input_fn, steps=100)

Here is an example using embedding_column with model_fn:

def model_fn(features, ...):
  video_id = categorical_column_with_identity(
      key='video_id', num_buckets=1000000, default_value=0)
  columns = [embedding_column(video_id, 9),...]
  dense_tensor = input_layer(features, columns)
  # Form DNN layers, calculate loss, and return EstimatorSpec.
  ...

Args
categorical_column	A CategoricalColumn created by a categorical_column_with_* function. This column produces the sparse IDs that are inputs to the embedding lookup.
dimension	An integer specifying dimension of the embedding, must be > 0.
combiner	A string specifying how to reduce if there are multiple entries in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with 'mean' the default. 'sqrtn' often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column. For more information, see tf.embedding_lookup_sparse.
initializer	A variable initializer function to be used in embedding variable initialization. If not specified, defaults to truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(dimension).
ckpt_to_load_from	String representing checkpoint name/pattern from which to restore column weights. Required if tensor_name_in_ckpt is not None.
tensor_name_in_ckpt	Name of the Tensor in ckpt_to_load_from from which to restore the column weights. Required if ckpt_to_load_from is not None.
max_norm	If not None, embedding values are l2-normalized to this value.
trainable	Whether or not the embedding is trainable. Default is True.

Returns
DenseColumn that converts from sparse input.

Raises
ValueError	if dimension not > 0.
ValueError	if exactly one of ckpt_to_load_from and tensor_name_in_ckpt is specified.
ValueError	if initializer is specified and is not callable.
RuntimeError	If eager execution is enabled.