TensorFlow 1 version View source on GitHub

DenseColumn that converts from sparse, categorical input.


Used in the notebooks

Used in the tutorials

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 =
      ..., features=make_parse_example_spec(columns + [label_column]))
  labels = features.pop(
  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.


  • 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.


DenseColumn 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.
  • RuntimeError: If eager execution is enabled.