Python math_ops.add_n方法代码示例

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _MultiDeviceAddN(tensor_list):
  """Adds tensors from potentially multiple devices."""
  # Basic function structure comes from control_flow_ops.group().
  # Sort tensors according to their devices.
  tensors_on_device = collections.defaultdict(lambda: [])
  for tensor in tensor_list:
    tensors_on_device[tensor.device].append(tensor)

  # For each device, add the tensors on that device first.
  # Then gather the partial sums from multiple devices.
  # TODO(sjhwang): Create hierarchical aggregation tree as pbar's suggestion.
  # E.g., aggregate per GPU, then per task, and so on.
  summands = []

  def DeviceKey(dev):
    return "" if dev is None else dev

  for dev in sorted(six.iterkeys(tensors_on_device), key=DeviceKey):
    tensors = tensors_on_device[dev]
    with ops.colocate_with(tensors[0].op, ignore_existing=True):
      summands.append(math_ops.add_n(tensors))

  return math_ops.add_n(summands) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def get_total_loss(add_regularization_losses=True, name="total_loss"):
  """Returns a tensor whose value represents the total loss.

  In particular, this adds any losses you have added with `tf.add_loss()` to
  any regularization losses that have been added by regularization parameters
  on layers constructors e.g. `tf.layers`. Be very sure to use this if you
  are constructing a loss_op manually. Otherwise regularization arguments
  on `tf.layers` methods will not function.

  Args:
    add_regularization_losses: A boolean indicating whether or not to use the
      regularization losses in the sum.
    name: The name of the returned tensor.

  Returns:
    A `Tensor` whose value represents the total loss.

  Raises:
    ValueError: if `losses` is not iterable.
  """
  losses = get_losses()
  if add_regularization_losses:
    losses += get_regularization_losses()
  return math_ops.add_n(losses, name=name) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _init_clusters_random(data, num_clusters, random_seed):
  """Does random initialization of clusters.

  Args:
    data: a list of Tensors with a matrix of data, each row is an example.
    num_clusters: an integer with the number of clusters.
    random_seed: Seed for PRNG used to initialize seeds.

  Returns:
    A Tensor with num_clusters random rows of data.
  """
  assert isinstance(data, list)
  num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in data])
  with ops.control_dependencies(
      [check_ops.assert_less_equal(num_clusters, num_data)]):
    indices = random_ops.random_uniform(
        [num_clusters],
        minval=0,
        maxval=math_ops.cast(num_data, dtypes.int64),
        seed=random_seed,
        dtype=dtypes.int64)
  indices %= math_ops.cast(num_data, dtypes.int64)
  clusters_init = embedding_lookup(data, indices, partition_strategy='div')
  return clusters_init 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _prepare_gramian(self, factors, gramian):
    """Helper function to create ops to prepare/calculate gramian.

    Args:
      factors: Variable or list of Variable representing (sharded) factors.
        Used to compute the updated corresponding gramian value.
      gramian: Variable storing the gramian calculated from the factors.

    Returns:
      A op that updates the gramian with the calcuated value from the factors.
    """
    partial_gramians = []
    for f in factors:
      with ops.colocate_with(f):
        partial_gramians.append(math_ops.matmul(f, f, transpose_a=True))

    with ops.colocate_with(gramian):
      prep_gramian = state_ops.assign(gramian,
                                      math_ops.add_n(partial_gramians)).op

    return prep_gramian 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _mean(self):
    with ops.control_dependencies(self._assertions):
      distribution_means = [d.mean() for d in self.components]
      cat_probs = self._cat_probs(log_probs=False)
      # This was checked to not be None at construction time.
      static_event_rank = self.event_shape.ndims
      # Expand the rank of x up to static_event_rank times so that
      # broadcasting works correctly.
      def expand(x):
        expanded_x = x
        for _ in range(static_event_rank):
          expanded_x = array_ops.expand_dims(expanded_x, -1)
        return expanded_x
      cat_probs = [expand(c_p) for c_p in cat_probs]
      partial_means = [
          c_p * m for (c_p, m) in zip(cat_probs, distribution_means)
      ]
      # These should all be the same shape by virtue of matching
      # batch_shape and event_shape.
      return math_ops.add_n(partial_means) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def reduce_sum_n(tensors, name=None):
  """Reduce tensors to a scalar sum.

  This reduces each tensor in `tensors` to a scalar via `tf.reduce_sum`, then
  adds them via `tf.add_n`.

  Args:
    tensors: List of tensors, all of the same numeric type.
    name: Tensor name, and scope for all other ops.

  Returns:
    Total loss tensor, or None if no losses have been configured.

  Raises:
    ValueError: if `losses` is missing or empty.
  """
  if not tensors:
    raise ValueError('No tensors provided.')
  with ops.name_scope(name, 'reduce_sum_n', tensors) as name_scope:
    tensors = [
        math_ops.reduce_sum(t, name='%s/sum' % t.op.name) for t in tensors]
    if len(tensors) == 1:
      return tensors[0]
    return math_ops.add_n(tensors, name=name_scope) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def get_total_loss(add_regularization_losses=True, name="total_loss"):
  """Returns a tensor whose value represents the total loss.

  Notice that the function adds the given losses to the regularization losses.

  Args:
    add_regularization_losses: A boolean indicating whether or not to use the
      regularization losses in the sum.
    name: The name of the returned tensor.

  Returns:
    A `Tensor` whose value represents the total loss.

  Raises:
    ValueError: if `losses` is not iterable.
  """
  losses = get_losses()
  if add_regularization_losses:
    losses += get_regularization_losses()
  return math_ops.add_n(losses, name=name) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _init_clusters_random(self):
    """Does random initialization of clusters.

    Returns:
      Tensor of randomly initialized clusters.
    """
    num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
    # Note that for mini-batch k-means, we should ensure that the batch size of
    # data used during initialization is sufficiently large to avoid duplicated
    # clusters.
    with ops.control_dependencies(
        [check_ops.assert_less_equal(self._num_clusters, num_data)]):
      indices = random_ops.random_uniform(
          array_ops.reshape(self._num_clusters, [-1]),
          minval=0,
          maxval=math_ops.cast(num_data, dtypes.int64),
          seed=self._random_seed,
          dtype=dtypes.int64)
      clusters_init = embedding_lookup(
          self._inputs, indices, partition_strategy='div')
      return clusters_init 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _init_clusters_random(data, num_clusters, random_seed):
  """Does random initialization of clusters.

  Args:
    data: a list of Tensors with a matrix of data, each row is an example.
    num_clusters: an integer with the number of clusters.
    random_seed: Seed for PRNG used to initialize seeds.

  Returns:
    A Tensor with num_clusters random rows of data.
  """
  assert isinstance(data, list)
  num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in data])
  with ops.control_dependencies(
      [check_ops.assert_less_equal(num_clusters, num_data)]):
    indices = random_ops.random_uniform(
        [num_clusters],
        minval=0,
        maxval=math_ops.cast(num_data, dtypes.int64),
        seed=random_seed,
        dtype=dtypes.int64)
  indices = math_ops.cast(indices, dtypes.int32) % num_data
  clusters_init = embedding_lookup(data, indices, partition_strategy='div')
  return clusters_init 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def sum_regularizer(regularizer_list, scope=None):
  """Returns a function that applies the sum of multiple regularizers.

  Args:
    regularizer_list: A list of regularizers to apply.
    scope: An optional scope name

  Returns:
    A function with signature `sum_reg(weights)` that applies the
    sum of all the input regularizers.
  """
  regularizer_list = [reg for reg in regularizer_list if reg is not None]
  if not regularizer_list:
    return None

  def sum_reg(weights):
    """Applies the sum of all the input regularizers."""
    with ops.name_scope(scope, 'sum_regularizer', [weights]) as name:
      regularizer_tensors = [reg(weights) for reg in regularizer_list]
      return math_ops.add_n(regularizer_tensors, name=name)

  return sum_reg 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _mean(self):
    with ops.control_dependencies(self._assertions):
      distribution_means = [d.mean() for d in self.components]
      cat_probs = self._cat_probs(log_probs=False)
      # This was checked to not be None at construction time.
      static_event_rank = self.get_event_shape().ndims
      # Expand the rank of x up to static_event_rank times so that
      # broadcasting works correctly.
      def expand(x):
        expanded_x = x
        for _ in range(static_event_rank):
          expanded_x = array_ops.expand_dims(expanded_x, -1)
        return expanded_x
      cat_probs = [expand(c_p) for c_p in cat_probs]
      partial_means = [
          c_p * m for (c_p, m) in zip(cat_probs, distribution_means)
      ]
      # These should all be the same shape by virtue of matching
      # batch_shape and event_shape.
      return math_ops.add_n(partial_means) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def reduce_sum_n(tensors, name=None):
  """Reduce tensors to a scalar sum.

  This reduces each tensor in `tensors` to a scalar via `tf.reduce_sum`, then
  adds them via `tf.add_n`.

  Args:
    tensors: List of tensors, all of the same numeric type.
    name: Tensor name, and scope for all other ops.

  Returns:
    Total loss tensor, or None if no losses have been configured.

  Raises:
    ValueError: if `losses` is missing or empty.
  """
  if not tensors:
    raise ValueError('No tensors provided.')
  tensors = [math_ops.reduce_sum(t, name='%s/sum' % t.op.name) for t in tensors]
  if len(tensors) == 1:
    return tensors[0]
  with ops.name_scope(name, 'reduce_sum_n', tensors) as scope:
    return math_ops.add_n(tensors, name=scope) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _acc_grads(*lists_of_grads):
  """Accumulates lists of gradients."""
  acc_grads = []
  for grads in zip(*lists_of_grads):
    grads = [g for g in grads if g is not None]
    if grads:
      acc_grads.append(math_ops.add_n(grads))
    else:
      acc_grads.append(None)
  return acc_grads 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def _force_data_dependency(first_compute, then_compute):
  """Force all of `then_compute` to depend on all of `first_compute`.

  Uses a dummy data dependency, which is useful when running on TPUs because
  XLA ignores control dependencies. Only supports float arguments.

  Args:
    first_compute: `list<Tensor>`. These will be made to run before the
      `Tensor`s `then_compute`.
    then_compute: `list<Tensor>`. These will run after all the `Tensor`s in
      `first_compute`.

  Returns:
    `list<Tensor>`, same length as `then_compute`.

  Raises:
    ValueError: if ranks are unknown or types are not floating.
  """

  def _first_element(x):
    if x.get_shape().ndims is None:
      raise ValueError("Rank of Tensor %s must be known" % x)
    ndims = x.get_shape().ndims
    begin = framework_ops.convert_to_tensor([0] * ndims, dtype=dtypes.int32)
    size = framework_ops.convert_to_tensor([1] * ndims, dtype=dtypes.int32)
    return array_ops.reshape(array_ops.slice(x, begin, size), [])

  first_compute_sum = math_ops.add_n(
      [_first_element(x) for x in first_compute if x is not None])
  dtype = first_compute_sum.dtype
  if not dtype.is_floating:
    raise ValueError("_force_data_dependency only supports floating dtypes.")
  epsilon = np.finfo(dtype.as_numpy_dtype).tiny
  zero = array_ops.stop_gradient(epsilon * first_compute_sum)

  return [
      array_ops.identity(x) + zero if x is not None else None
      for x in then_compute
  ] 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import add_n [as 别名]
def sum_regularizer(regularizer_list, scope=None):
  """Returns a function that applies the sum of multiple regularizers.

  Args:
    regularizer_list: A list of regularizers to apply.
    scope: An optional scope name

  Returns:
    A function with signature `sum_reg(weights)` that applies the
    sum of all the input regularizers.
  """
  regularizer_list = [reg for reg in regularizer_list if reg is not None]
  if not regularizer_list:
    return None

  def sum_reg(weights):
    """Applies the sum of all the input regularizers."""
    with ops.name_scope(scope, 'sum_regularizer', [weights]) as name:
      regularizer_tensors = []
      for reg in regularizer_list:
        tensor = reg(weights)
        if tensor is not None:
          regularizer_tensors.append(tensor)
      return math_ops.add_n(
          regularizer_tensors, name=name) if regularizer_tensors else None

  return sum_reg