tf.contrib.layers.embedding_column

Creates an _EmbeddingColumn for feeding sparse data into a DNN.

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

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, 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: "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.compat.v1.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.
trainable	(Optional). Should the embedding be trainable. Default is True

Returns
An _EmbeddingColumn.