tf.contrib.linear_optimizer.SparseFeatureColumn

Class SparseFeatureColumn

Defined in tensorflow/contrib/linear_optimizer/python/ops/sparse_feature_column.py.

Represents a sparse feature column.

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>

Properties

example_indices

The example indices represented as a dense tensor.

Returns:

A 1-D Tensor of int64 with shape [N].

feature_indices

The feature indices represented as a dense tensor.

Returns:

A 1-D Tensor of int64 with shape [N].

feature_values

The feature values represented as a dense tensor.

Returns:

May return None, or a 1-D Tensor of float32 with shape [N].

Methods

__init__

__init__(
    example_indices,
    feature_indices,
    feature_values
)

Creates a SparseFeatureColumn representation.

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.

Returns:

A SparseFeatureColumn