tf.contrib.linear_optimizer.SparseFeatureColumn

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Represents a sparse feature column.

tf.contrib.linear_optimizer.SparseFeatureColumn(
    example_indices, feature_indices, feature_values
)

Contains three tensors representing a sparse feature column, they are example indices (int64), feature indices (int64), and feature values (float). Feature weights are optional, and are treated as 1.0f if missing.

For example, consider a batch of 4 examples, which contains the following features in a particular SparseFeatureColumn:

  • Example 0: feature 5, value 1
  • Example 1: feature 6, value 1 and feature 10, value 0.5
  • Example 2: no features
  • Example 3: two copies of feature 2, value 1

This SparseFeatureColumn will be represented as follows:

 <0, 5,  1>
 <1, 6,  1>
 <1, 10, 0.5>
 <3, 2,  1>
 <3, 2,  1>

For a batch of 2 examples below:

  • Example 0: feature 5
  • Example 1: feature 6

is represented by SparseFeatureColumn as:

 <0, 5,  1>
 <1, 6,  1>

example_indices A 1-D int64 tensor of shape [N]. Also, accepts python lists, or numpy arrays.
feature_indices A 1-D int64 tensor of shape [N]. Also, accepts python lists, or numpy arrays.
feature_values An optional 1-D tensor float tensor of shape [N]. Also, accepts python lists, or numpy arrays.

example_indices The example indices represented as a dense tensor.
feature_indices The feature indices represented as a dense tensor.
feature_values The feature values represented as a dense tensor.