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
)
Defined in tensorflow/contrib/layers/python/layers/feature_column.py
.
Creates an _EmbeddingColumn
for feeding sparse data into a DNN.
Args:
sparse_id_column
: A_SparseColumn
which is created by for examplesparse_column_with_*
or crossed_column functions. Note thatcombiner
defined insparse_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 totf.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 iftensor_name_in_ckpt
is not None.tensor_name_in_ckpt
: (Optional). Name of theTensor
in the provided checkpoint from which to restore the column weights. Required ifckpt_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
.