TensorFlow 1 version | View source on GitHub |
DenseColumn
that converts from sparse, categorical input.
tf.feature_column.embedding_column(
categorical_column, dimension, combiner='mean', initializer=None,
ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True
)
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 CategoricalColumn
created by any of the
categorical_column_*
function. Here is an example of using
embedding_column
with DNNClassifier
:
video_id = categorical_column_with_identity(
key='video_id', num_buckets=1000000, default_value=0)
columns = [embedding_column(video_id, 9),...]
estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)
label_column = ...
def input_fn():
features = tf.io.parse_example(
..., features=make_parse_example_spec(columns + [label_column]))
labels = features.pop(label_column.name)
return features, labels
estimator.train(input_fn=input_fn, steps=100)
Here is an example using embedding_column
with model_fn:
def model_fn(features, ...):
video_id = categorical_column_with_identity(
key='video_id', num_buckets=1000000, default_value=0)
columns = [embedding_column(video_id, 9),...]
dense_tensor = input_layer(features, columns)
# Form DNN layers, calculate loss, and return EstimatorSpec.
...
Args | |
---|---|
categorical_column
|
A CategoricalColumn created by a
categorical_column_with_* function. This column produces the sparse IDs
that are inputs to the embedding lookup.
|
dimension
|
An integer specifying dimension of the embedding, must be > 0. |
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. For more information, see
tf.embedding_lookup_sparse .
|
initializer
|
A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
truncated_normal_initializer with mean 0.0 and
standard deviation 1/sqrt(dimension) .
|
ckpt_to_load_from
|
String representing checkpoint name/pattern from which to
restore column weights. Required if tensor_name_in_ckpt is not None .
|
tensor_name_in_ckpt
|
Name of the Tensor in ckpt_to_load_from from which
to restore the column weights. Required if ckpt_to_load_from is not
None .
|
max_norm
|
If not None , embedding values are l2-normalized to this value.
|
trainable
|
Whether or not the embedding is trainable. Default is True. |
Returns | |
---|---|
DenseColumn that converts from sparse input.
|
Raises | |
---|---|
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.
|
RuntimeError
|
If eager execution is enabled. |