tf.compat.v1.feature_column.shared_embedding_columns

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

tf.compat.v1.feature_column.shared_embedding_columns(
    categorical_columns,
    dimension,
    combiner='mean',
    initializer=None,
    shared_embedding_collection_name=None,
    ckpt_to_load_from=None,
    tensor_name_in_ckpt=None,
    max_norm=None,
    trainable=True,
    use_safe_embedding_lookup=True
)

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.
  ...

Args
`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 name of the collection where shared embedding weights are added. If not given, a reasonable name will be chosen based on the names of `categorical_columns`. This is also used in `variable_scope` when creating shared embedding weights.
`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`, each embedding is clipped if its l2-norm is larger than this value, before combining.
`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
A list of dense columns that converts from sparse input. The order of results follows the ordering of `categorical_columns`.

Raises
`ValueError`	if `dimension` not > 0.
`ValueError`	if any of the given `categorical_columns` is of different type or has different arguments than the others.
`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.

tf.compat.v1.feature_column.shared_embedding_columns

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Returns

Raises