# tf.contrib.layers.embedding_column

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


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, 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.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.