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Clips values of multiple tensors by the ratio of the sum of their norms.

tf.clip_by_global_norm( t_list, clip_norm, use_norm=None, name=None )

Given a tuple or list of tensors `t_list`

, and a clipping ratio `clip_norm`

, this operation returns a list of clipped tensors `list_clipped`

and the global norm (`global_norm`

) of all tensors in `t_list`

. Optionally, if you've already computed the global norm for `t_list`

, you can specify the global norm with `use_norm`

.

To perform the clipping, the values `t_list[i]`

are set to:

t_list[i] * clip_norm / max(global_norm, clip_norm)

where:

global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))

If `clip_norm > global_norm`

then the entries in `t_list`

remain as they are, otherwise they're all shrunk by the global ratio.

If `global_norm == infinity`

then the entries in `t_list`

are all set to `NaN`

to signal that an error occurred.

Any of the entries of `t_list`

that are of type `None`

are ignored.

This is the correct way to perform gradient clipping (Pascanu et al., 2012).

However, it is slower than `clip_by_norm()`

because all the parameters must be ready before the clipping operation can be performed.

Args | |
---|---|

`t_list` | A tuple or list of mixed `Tensors` , `IndexedSlices` , or None. |

`clip_norm` | A 0-D (scalar) `Tensor` > 0. The clipping ratio. |

`use_norm` | A 0-D (scalar) `Tensor` of type `float` (optional). The global norm to use. If not provided, `global_norm()` is used to compute the norm. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

`list_clipped` | A list of `Tensors` of the same type as `list_t` . |

`global_norm` | A 0-D (scalar) `Tensor` representing the global norm. |

Raises | |
---|---|

`TypeError` | If `t_list` is not a sequence. |

On the difficulty of training Recurrent Neural Networks: Pascanu et al., 2012 (pdf)

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/clip_by_global_norm