optimizers.py
def clip(grads_and_vars, max_global_norm):
  """ Clip the gradients that are returned from a TensorFlow Optimizer.

  Note that the term "clipping" is often used in literature but here is actually
  the wrong term: if the norm of all gradients concatenated does not exceed
  `max_global_norm`, then don't modify them. If the norm does exceed
  `max_global_norm`, then rescale all gradients globally so that the new norm
  becomes `max_global_norm`.

  Args:
    grads_and_vars: A list of `(grad, var)` pairs.
    max_global_norm: A float.

  Returns:
    A list of `(grad, var)` pairs with clipped gradients.
  """

  grads, vars = zip(*grads_and_vars)
  grads, _ = tf.clip_by_global_norm(grads, clip_norm=max_global_norm)
  grads_and_vars = list(zip(grads, vars))
  return grads_and_vars
 Clip the gradients that are returned from a TensorFlow Optimizer.

  Note that the term "clipping" is often used in literature but here is actually
  the wrong term: if the norm of all gradients concatenated does not exceed
  `max_global_norm`, then don't modify them. If the norm does exceed
  `max_global_norm`, then rescale all gradients globally so that the new norm
  becomes `max_global_norm`.

  Args:
    grads_and_vars: A list of `(grad, var)` pairs.
    max_global_norm: A float.

  Returns:
    A list of `(grad, var)` pairs with clipped gradients.
  
Clip the gradients that are returned from a TensorFlow Optimizer.
Note that the term "clipping" is often used in literature but here is actually
the wrong term: if the norm of all gradients concatenated does not exceed
`max_global_norm`, then don't modify them. If the norm does exceed
`max_global_norm`, then rescale all gradients globally so that the new norm
becomes `max_global_norm`.
Clip the gradients that are returned from a TensorFlow Optimizer . Note that the term " clipping " is often used in literature but here is actually the wrong term : if the norm of all gradients concatenated does not exceed ` max_global_norm ` then don ' t modify them . If the norm does exceed ` max_global_norm ` then rescale all gradients globally so that the new norm becomes ` max_global_norm ` .
def clip(grads_and_vars, max_global_norm):
  grads, vars = zip(*grads_and_vars)
  grads, _ = tf.clip_by_global_norm(grads, clip_norm=max_global_norm)
  grads_and_vars = list(zip(grads, vars))
  return grads_and_vars
def clip ( grads_and_vars , max_global_norm ) : grads , vars = zip ( * grads_and_vars ) grads , _ = tf . clip_by_global_norm ( grads , clip_norm = max_global_norm ) grads_and_vars = list ( zip ( grads , vars ) ) return grads_and_vars
Clip the gradients that are returned from a TensorFlow Optimizer.
Clip the gradients that are returned from a TensorFlow Optimizer .
""" Clip the gradients that are returned from a TensorFlow Optimizer.

  Note that the term "clipping" is often used in literature but here is actually
  the wrong term: if the norm of all gradients concatenated does not exceed
  `max_global_norm`, then don't modify them. If the norm does exceed
  `max_global_norm`, then rescale all gradients globally so that the new norm
  becomes `max_global_norm`.

  Args:
    grads_and_vars: A list of `(grad, var)` pairs.
    max_global_norm: A float.

  Returns:
    A list of `(grad, var)` pairs with clipped gradients.
  """
A list of `(grad, var)` pairs with clipped gradients.
A list of ` ( grad var ) ` pairs with clipped gradients .
grads_and_vars
A list of `(grad, var)` pairs.
A list of ` ( grad var ) ` pairs .
max_global_norm
None