tf.contrib.estimator.clip_gradients_by_norm

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)

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

Returns
A `tf.Optimizer`.

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Last updated 2020-10-01 UTC.

tf.contrib.estimator.clip_gradients_by_norm

Example:

Args

Returns