tf.contrib.linear_optimizer.SparseFeatureColumn

class tf.contrib.linear_optimizer.SparseFeatureColumn

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>

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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__(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

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