A tf.contrib.layers style linear prediction builder based on FeatureColumn.
tf.contrib.layers.weighted_sum_from_feature_columns(
columns_to_tensors, feature_columns, num_outputs, weight_collections=None,
trainable=True, scope=None
)
Generally a single example in training data is described with feature columns.
This function generates weighted sum for each num_outputs. Weighted sum refers
to logits in classification problems. It refers to prediction itself for
linear regression problems.
Example:
# Building model for training
feature_columns = (
real_valued_column("my_feature1"),
...
)
columns_to_tensor = tf.io.parse_example(...)
logits = weighted_sum_from_feature_columns(
columns_to_tensors=columns_to_tensor,
feature_columns=feature_columns,
num_outputs=1)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=logits)
Args |
columns_to_tensors
|
A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline. For example, inflow may have handled transformations.
|
feature_columns
|
A set containing all the feature columns. All items in the
set should be instances of classes derived from FeatureColumn.
|
num_outputs
|
An integer specifying number of outputs. Default value is 1.
|
weight_collections
|
List of graph collections to which weights are added.
|
trainable
|
If True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
|
scope
|
Optional scope for variable_scope.
|
Returns |
A tuple containing:
- A Tensor which represents predictions of a linear model.
- A dictionary which maps feature_column to corresponding Variable.
- A Variable which is used for bias.
|
Raises |
ValueError
|
if FeatureColumn cannot be used for linear predictions.
|