tf.feature_column.embedding_column

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DenseColumn that converts from sparse, categorical input.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.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_safe_embedding_lookup=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.
use_safe_embedding_lookup	If true, uses safe_embedding_lookup_sparse instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures there are no empty rows and all weights and ids are positive at the expense of extra compute cost. This only applies to rank 2 (NxM) shaped input tensors. Defaults to true, consider turning off if the above checks are not needed. Note that having empty rows will not trigger any error though the output result might be 0 or omitted.

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.