Python functional_ops.foldl方法代码示例

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import foldl [as 别名]
def foldl(fn, elems, initializer=None, name=None):
  """Reduce elems using fn to combine them from left to right.

  Arguments:
      fn: Callable that will be called upon each element in elems and an
          accumulator, for instance `lambda acc, x: acc + x`
      elems: tensor
      initializer: The first value used (`elems[0]` in case of None)
      name: A string name for the foldl node in the graph

  Returns:
      Tensor with same type and shape as `initializer`.
  """
  return functional_ops.foldl(fn, elems, initializer=initializer, name=name) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import foldl [as 别名]
def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import foldl [as 别名]
def foldl(fn, labeled_tensor, initial_value, name=None):
  """Left fold on the list of tensors unpacked from labeled_tensor.

  See tf.foldl.

  Args:
    fn: The function to apply to each unpacked LabeledTensor.
      It should have type (LabeledTensor, LabeledTensor) -> LabeledTensor.
      Its arguments are (accumulated_value, next_value).
    labeled_tensor: The input tensor.
    initial_value: The initial value of the accumulator.
    name: Optional op name.

  Returns:
    The accumulated value.
  """
  with ops.name_scope(name, 'lt_foldl',
                      [labeled_tensor, initial_value]) as scope:
    labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
    initial_value = core.convert_to_labeled_tensor(initial_value)

    @tc.returns(ops.Tensor)
    @tc.accepts(ops.Tensor, ops.Tensor)
    def tf_fn(accumulator, next_element):
      accumulator_lt = core.LabeledTensor(accumulator, initial_value.axes)
      next_element_lt = core.LabeledTensor(
          next_element, list(labeled_tensor.axes.values())[1:])
      return fn(accumulator_lt, next_element_lt).tensor

    foldl_op = functional_ops.foldl(
        tf_fn, labeled_tensor.tensor, initializer=initial_value.tensor)
    foldl_lt = core.LabeledTensor(foldl_op, initial_value.axes)

    return core.identity(foldl_lt, name=scope)