tf.feature_column.shared_embeddings

List of dense columns that convert from sparse, categorical input.

This is similar to embedding_column, except that it produces a list of embedding columns that share the same embedding weights.

Use this when your inputs are sparse and of the same type (e.g. watched and impression video IDs that share the same vocabulary), and you want to convert them to a dense representation (e.g., to feed to a DNN).

Inputs must be a list of categorical columns created by any of the categorical_column_* function. They must all be of the same type and have the same arguments except key. E.g. they can be categorical_column_with_vocabulary_file with the same vocabulary_file. Some or all columns could also be weighted_categorical_column.

Here is an example embedding of two features for a DNNClassifier model:

watched_video_id = categorical_column_with_vocabulary_file(
    'watched_video_id', video_vocabulary_file, video_vocabulary_size)
impression_video_id = categorical_column_with_vocabulary_file(
    'impression_video_id', video_vocabulary_file, video_vocabulary_size)
columns = shared_embedding_columns(
    [watched_video_id, impression_video_id], dimension=10)

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 shared_embedding_columns with model_fn:

def model_fn(features, ...):
  watched_video_id = categorical_column_with_vocabulary_file(
      'watched_video_id', video_vocabulary_file, video_vocabulary_size)
  impression_video_id = categorical_column_with_vocabulary_file(
      'impression_video_id', video_vocabulary_file, video_vocabulary_size)
  columns = shared_embedding_columns(
      [watched_video_id, impression_video_id], dimension=10)
  dense_tensor = input_layer(features, columns)
  # Form DNN layers, calculate loss, and return EstimatorSpec.
  ...

categorical_columns List of categorical columns created by a categorical_column_with_* function. These columns produce the sparse IDs that are inputs to the embedding lookup. All columns must be of the same type and have the same arguments except key. E.g. they can be categorical_column_with_vocabulary_file with the same vocabulary_file. Some or all columns could also be weighted_categorical_column.
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).
shared_embedding_collection_name Optional collective name of these columns. If not given, a reasonable name will be chosen based on the names of ca