Python math_ops._as_indexed_slices_list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 _as_indexed_slices_list [as 别名]
def aggregate_indexed_slices_gradients(grads):
  """Aggregates gradients containing `IndexedSlices`s."""
  if len(grads) < 1:
    return None
  elif len(grads) == 1:
    return grads[0]
  else:
    grads = [g for g in grads if g is not None]
    # If any gradient is a `Tensor`, sum them up and return a dense tensor
    # object.
    if any(isinstance(g, ops.Tensor) for g in grads):
      return math_ops.add_n(grads)

    # The following `_as_indexed_slices_list` casts ids of IndexedSlices into
    # int64. It is to make sure the inputs of `concat` all have same the data
    # type.
    grads = math_ops._as_indexed_slices_list(grads)  # pylint: disable=protected-access

    grads = [flatten_nested_indexed_slices(x) for x in grads]
    # Form IndexedSlices out of the concatenated values and indices.
    concat_grad = ops.IndexedSlices(
        array_ops.concat([x.values for x in grads], axis=0),
        array_ops.concat([x.indices for x in grads], axis=0),
        grads[0].dense_shape)

    return concat_grad 

# 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 _as_indexed_slices_list [as 别名]
def merge(inputs, name=None):
  """Returns the value of an available element of `inputs`.

  This op tests each of the tensors in `inputs` in turn to determine if any of
  them is available. If it finds an available tensor, it returns it and its
  index in `inputs`.

  It is an error if more than one tensor in `inputs` is available. If no tensor
  in `inputs` is available, the returned tensor and index are not set.

  This op handles both `Tensor`s and `IndexedSlices`. If inputs has a mix of
  `Tensor`s and `IndexedSlices`, all inputs are converted to IndexedSlices
  before merging.

  Args:
    inputs: The input tensors, at most one of which is available.
    name: A name for this operation (optional).

  Returns:
    A tuple containing the chosen input tensor and its index in `inputs`.

  Raises:
    ValueError: If any of the inputs is None, or inputs are IndexedSlices and
      some but not all have a dense_shape property.
  """
  if any([inp is None for inp in inputs]):
    raise ValueError("At least one of the merge inputs is None: %s" % inputs)
  with ops.name_scope(name, "Merge", inputs) as name:
    inputs = [ops.internal_convert_to_tensor_or_indexed_slices(inp, as_ref=True)
              for inp in inputs]
    if all([isinstance(v, ops.Tensor) for v in inputs]):
      if all([v.dtype._is_ref_dtype for v in inputs]):  # pylint: disable=protected-access
        return gen_control_flow_ops._ref_merge(inputs, name)
      else:
        return gen_control_flow_ops._merge(inputs, name)
    elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]):
      # Only handle the case when all inputs are SparseTensor.
      values, _ = merge([inp.values for inp in inputs], name=name)
      indices, chosen_index = gen_control_flow_ops._merge(
          [inp.indices for inp in inputs], name="indices")
      dense_shape, _ = gen_control_flow_ops._merge(
          [inp.dense_shape for inp in inputs], name="dense_shape")
      return (sparse_tensor.SparseTensor(indices, values, dense_shape),
              chosen_index)
    else:
      # For now convert all the inputs as IndexedSlices.
      inputs = math_ops._as_indexed_slices_list(inputs, optimize=False)
      values, _ = merge([inp.values for inp in inputs], name=name)
      indices, chosen_index = gen_control_flow_ops._merge(
          [inp.indices for inp in inputs], name="indices")
      if any(inp.dense_shape is not None for inp in inputs):
        if any(inp.dense_shape is None for inp in inputs):
          raise ValueError("Either all merged IndexedSlices must have a "
                           "dense_shape, or none must have a dense_shape.")
        dense_shape, _ = gen_control_flow_ops._merge(
            [inp.dense_shape for inp in inputs], name="dense_shape")
      else:
        dense_shape = None
      return ops.IndexedSlices(values, indices, dense_shape), chosen_index
# pylint: enable=protected-access 

# 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 _as_indexed_slices_list [as 别名]
def merge(inputs, name=None):
  """Returns the value of an available element of `inputs`.

  This op tests each of the tensors in `inputs` in turn to determine if any of
  them is available. If it finds an available tensor, it returns it and its
  index in `inputs`.

  It is an error if more than one tensor in `inputs` is available. If no tensor
  in `inputs` is available, the returned tensor and index are not set.

  This op handles both `Tensor`s and `IndexedSlices`. If inputs has a mix of
  `Tensor`s and `IndexedSlices`, all inputs are converted to IndexedSlices
  before merging.

  Args:
    inputs: The input tensors, at most one of which is available.
    name: A name for this operation (optional).

  Returns:
    A tuple containing the chosen input tensor and its index in `inputs`.

  Raises:
    ValueError: If any of the inputs is None, or inputs are IndexedSlices and
      some but not all have a dense_shape property.
  """
  if any([inp is None for inp in inputs]):
    raise ValueError("At least one of the merge inputs is None: %s" % inputs)
  with ops.name_scope(name, "Merge", inputs) as name:
    inputs = [ops.convert_to_tensor_or_indexed_slices(inp, as_ref=True)
              for inp in inputs]
    if all([isinstance(v, ops.Tensor) for v in inputs]):
      if all([v.dtype.is_ref_dtype for v in inputs]):
        return gen_control_flow_ops._ref_merge(inputs, name)
      else:
        return gen_control_flow_ops._merge(inputs, name)
    elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]):
      # Only handle the case when all inputs are SparseTensor.
      values, _ = merge([inp.values for inp in inputs], name=name)
      indices, chosen_index = gen_control_flow_ops._merge(
          [inp.indices for inp in inputs], name="indices")
      dense_shape, _ = gen_control_flow_ops._merge(
          [inp.shape for inp in inputs], name="dense_shape")
      return (sparse_tensor.SparseTensor(indices, values, dense_shape),
              chosen_index)
    else:
      # For now convert all the inputs as IndexedSlices.
      inputs = math_ops._as_indexed_slices_list(inputs, optimize=False)
      values, _ = merge([inp.values for inp in inputs], name=name)
      indices, chosen_index = gen_control_flow_ops._merge(
          [inp.indices for inp in inputs], name="indices")
      if any(inp.dense_shape is not None for inp in inputs):
        if any(inp.dense_shape is None for inp in inputs):
          raise ValueError("Either all merged IndexedSlices must have a "
                           "dense_shape, or none must have a dense_shape.")
        dense_shape, _ = gen_control_flow_ops._merge(
            [inp.dense_shape for inp in inputs], name="dense_shape")
      else:
        dense_shape = None
      return ops.IndexedSlices(values, indices, dense_shape), chosen_index
# pylint: enable=protected-access