View source on GitHub |
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. |