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.
|