tf.contrib.linear_optimizer.SparseFeatureColumn

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>

Args
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.

Attributes
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.