Python tensorflow.python.ops.clip_ops.global_norm() Examples

The following are 16 code examples of tensorflow.python.ops.clip_ops.global_norm(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.clip_ops , or try the search function .
Example #1
Source File: learning.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #2
Source File: learning.py    From lambda-packs with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #3
Source File: training.py    From lambda-packs with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '_gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #4
Source File: learning.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.histogram(var.op.name + '/gradient_norm',
                            clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #5
Source File: training.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.histogram(var.op.name + '_gradient_norm',
                            clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #6
Source File: learning.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.
  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).
  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #7
Source File: learning.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.
  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).
  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #8
Source File: learning.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.
  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).
  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #9
Source File: learning.py    From tf-slim with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #10
Source File: training.py    From tf-slim with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '_gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #11
Source File: learning.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + ':gradient', grad_values))
      summaries.append(
          summary.histogram(var.op.name + ':gradient_norm',
                            clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #12
Source File: training.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(summary.histogram_summary(
          var.op.name + ':gradient', grad_values))
      summaries.append(summary.histogram_summary(
          var.op.name + ':gradient_norm', clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #13
Source File: learning.py    From mtl-ssl with Apache License 2.0 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '/gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      log.warn('Var %s has no gradient', var.op.name)

  return summaries
Example #14
Source File: learning.py    From keras-lambda with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '/gradient', grad_values))
      summaries.append(
          summary.histogram(var.op.name + '/gradient_norm',
                            clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #15
Source File: training.py    From keras-lambda with MIT License 6 votes vote down vote up 
def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.histogram(var.op.name + '_gradient_norm',
                            clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries
Example #16
Source File: graph_single.py    From SketchySceneColorization with MIT License 5 votes vote down vote up 
def optimize(gradients, optim, global_step, summaries, global_norm=None, global_norm_clipped=None, appendix=''):
    """Modified from sugartensor"""

    # Add Summary
    if summaries is None:
        summaries = ["loss", "learning_rate"]
    # if "gradient_norm" in summaries:
    #     if global_norm is None:
    #         tf.summary.scalar("global_norm/gradient_norm" + appendix,
    #                           clip_ops.global_norm(list(zip(*gradients))[0]))
    #     else:
    #         tf.summary.scalar("global_norm/gradient_norm" + appendix,
    #                           global_norm)
    #     if global_norm_clipped is not None:
    #         tf.summary.scalar("global_norm/gradient_norm_clipped" + appendix,
    #                           global_norm_clipped)

    # Add histograms for variables, gradients and gradient norms.
    for gradient, variable in gradients:
        if isinstance(gradient, ops.IndexedSlices):
            grad_values = gradient.values
        else:
            grad_values = gradient

        if grad_values is not None:
            var_name = variable.name.replace(":", "_")
            # if "gradients" in summaries:
            #     tf.summary.histogram("gradients/%s" % var_name, grad_values)
            # if "gradient_norm" in summaries:
            #     tf.summary.scalar("gradient_norm/%s" % var_name,
            #                       clip_ops.global_norm([grad_values]))

    # Gradient Update OP
    return optim.apply_gradients(gradients, global_step=global_step)