tf.contrib.layers.embedding_column(sparse_id_column, dimension, combiner=None, initializer=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None)

tf.contrib.layers.embedding_column(sparse_id_column, dimension, combiner=None, initializer=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None)

See the guide: Layers (contrib) > Feature columns

Creates an _EmbeddingColumn for feeding sparse data into a DNN.

Args:

  • sparse_id_column: A _SparseColumn which is created by for example sparse_column_with_* or crossed_column functions. Note that combiner defined in sparse_id_column is ignored.
  • dimension: An integer specifying dimension of the embedding.
  • combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each of this can be considered an example level normalization on the column:
    • "sum": do not normalize
    • "mean": do l1 normalization
    • "sqrtn": do l2 normalization For more information: tf.embedding_lookup_sparse.
  • initializer: A variable initializer function to be used in embedding variable initialization. If not specified, defaults to tf.truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(sparse_id_column.length).
  • ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if tensor_name_in_ckpt is not None.
  • tensor_name_in_ckpt: (Optional). Name of the Tensor in the provided checkpoint from which to restore the column weights. Required if ckpt_to_load_from is not None.
  • max_norm: (Optional). If not None, embedding values are l2-normalized to the value of max_norm.

Returns:

An _EmbeddingColumn.

Defined in tensorflow/contrib/layers/python/layers/feature_column.py.