tf.contrib.layers.apply_regularization(regularizer, weights_list=None)

tf.contrib.layers.apply_regularization(regularizer, weights_list=None)

See the guide: Layers (contrib) > Regularizers

Returns the summed penalty by applying regularizer to the weights_list.

Adding a regularization penalty over the layer weights and embedding weights can help prevent overfitting the training data. Regularization over layer biases is less common/useful, but assuming proper data preprocessing/mean subtraction, it usually shouldn't hurt much either.

Args:

• regularizer: A function that takes a single Tensor argument and returns a scalar Tensor output.
• weights_list: List of weights Tensors or Variables to apply regularizer over. Defaults to the GraphKeys.WEIGHTS collection if None.

Returns:

A scalar representing the overall regularization penalty.

Raises:

• ValueError: If regularizer does not return a scalar output, or if we find no weights.

Defined in tensorflow/contrib/layers/python/layers/regularizers.py.