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

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

See the guide: Training > Gradient Clipping

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

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.

Any of the entries of t_list that are of type None are ignored.

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

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.

Defined in tensorflow/python/ops/clip_ops.py.