blob: 8216a4fa251bd4a14c8cf94fe66290f09691f638 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
|
### `tf.contrib.layers.apply_regularization(regularizer, weights_list=None)` {#apply_regularization}
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:
* <b>`regularizer`</b>: A function that takes a single `Tensor` argument and returns
a scalar `Tensor` output.
* <b>`weights_list`</b>: 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:
* <b>`ValueError`</b>: If `regularizer` does not return a scalar output.
|
