tf.contrib.layers.apply_regularization

Returns the summed penalty by applying regularizer to the weights_list.

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

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.