Python tensorflow.python.ops.nn.l2_loss() Examples

The following are 7 code examples of tensorflow.python.ops.nn.l2_loss(). 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.nn , or try the search function .
Example #1
Source File: regularizers.py    From tensornets with MIT License 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)

  return l2
Example #2
Source File: regularizers.py    From lambda-packs with MIT License 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)

  return l2
Example #3
Source File: regularizers.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)

  return l2
Example #4
Source File: regularizers.py    From tf-slim with Apache License 2.0 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)

  return l2
Example #5
Source File: regularizers.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.mul(my_scale, nn.l2_loss(weights), name=name)

  return l2
Example #6
Source File: model.py    From cite with MIT License 5 votes vote down vote up 
def weight_l2_regularizer(initial_weights, scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.
  Small values of L2 can help prevent overfitting the training data.
  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.
  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.
  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      weight_diff = initial_weights - weights
      return standard_ops.multiply(my_scale, nn.l2_loss(weight_diff), name=name)

  return l2
Example #7
Source File: regularizers.py    From keras-lambda with MIT License 5 votes vote down vote up 
def l2_regularizer(scale, scope=None):
  """Returns a function that can be used to apply L2 regularization to weights.

  Small values of L2 can help prevent overfitting the training data.

  Args:
    scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
    scope: An optional scope name.

  Returns:
    A function with signature `l2(weights)` that applies L2 regularization.

  Raises:
    ValueError: If scale is negative or if scale is not a float.
  """
  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % (scale,))
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g.' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _: None

  def l2(weights):
    """Applies l2 regularization to weights."""
    with ops.name_scope(scope, 'l2_regularizer', [weights]) as name:
      my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)

  return l2