Use this when your inputs are sparse, but you want to convert them to a dense
representation (e.g., to feed to a DNN). Inputs must be a
categorical column created by any of the column_categorical_*()
functions.
column_embedding(categorical_column, dimension, combiner = "mean",
initializer = NULL, ckpt_to_load_from = NULL,
tensor_name_in_ckpt = NULL, max_norm = NULL, trainable = TRUE)A categorical column created by a
column_categorical_*() function. This column produces the sparse IDs
that are inputs to the embedding lookup.
A positive integer, specifying dimension of the embedding.
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.
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(dimension).
String representing checkpoint name/pattern from
which to restore column weights. Required if tensor_name_in_ckpt is not
NULL.
Name of the Tensor in ckpt_to_load_from from
which to restore the column weights. Required if ckpt_to_load_from is not
NULL.
If not NULL, embedding values are l2-normalized to this
value.
Whether or not the embedding is trainable. Default is TRUE.
A dense column that converts from sparse input.
ValueError: if dimension not > 0.
ValueError: if exactly one of ckpt_to_load_from and tensor_name_in_ckpt
is specified.
ValueError: if initializer is specified and is not callable.
Other feature column constructors: column_bucketized,
column_categorical_weighted,
column_categorical_with_hash_bucket,
column_categorical_with_identity,
column_categorical_with_vocabulary_file,
column_categorical_with_vocabulary_list,
column_crossed,
column_numeric, input_layer