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
.
See the guide: Layers (contrib) > Feature columns
Creates an _EmbeddingColumn
for feeding sparse data into a DNN.
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: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 TrueAn _EmbeddingColumn
.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embedding_column