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_safe_embedding_lookup=True
)
Used in the notebooks
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.
|
use_safe_embedding_lookup
|
If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
expense of extra compute cost. This only applies to rank 2 (NxM) shaped
input tensors. Defaults to true, consider turning off if the above checks
are not needed. Note that having empty rows will not trigger any error
though the output result might be 0 or omitted.
|
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.
|
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TensorFlow 1 version
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