# tf.contrib.layers.apply_regularization

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/layers/python/layers/regularizers.py#L170-L209) |

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.

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| ## Args ||
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| `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`. |

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| ## Returns ||
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| A scalar representing the overall regularization penalty. ||

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| ## Raises ||
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| `ValueError` | If `regularizer` does not return a scalar output, or if we find no weights. |

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