# tf.contrib.estimator.clip_gradients_by_norm

<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/contrib/estimator/python/estimator/extenders.py) |

Returns an optimizer which clips gradients before applying them.

    tf.contrib.estimator.clip_gradients_by_norm(
        optimizer, clip_norm
    )

#### Example:

    optimizer = tf.train.ProximalAdagradOptimizer(
        learning_rate=0.1,
        l1_regularization_strength=0.001)
    optimizer = tf.contrib.estimator.clip_gradients_by_norm(
        optimizer, clip_norm)
    estimator = tf.estimator.DNNClassifier(
        feature_columns=[...],
        hidden_units=[1024, 512, 256],
        optimizer=optimizer)

<br />

<br />

<br />

| ## Args ||
|---|---|
| `optimizer` | An `tf.Optimizer` object to apply gradients. |
| `clip_norm` | A 0-D (scalar) `Tensor` \> 0. The clipping ratio. |

<br />

<br />

<br />

<br />

| ## Returns ||
|---|---|
| A `tf.Optimizer`. ||

<br />