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

# Aliases:

• tf.compat.v1.clip_by_global_norm
• tf.compat.v2.clip_by_global_norm

 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. Pascanu et al., 2012This is the correct way to perform gradient clipping (for example, see (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.
• : A 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.