Python ops.convert_n_to_tensor_or_indexed_slices方法代码示例

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def add_n(inputs, name=None):
  """Adds all input tensors element-wise.

  Args:
    inputs: A list of `Tensor` objects, each with same shape and type.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of same shape and type as the elements of `inputs`.

  Raises:
    ValueError: If `inputs` don't all have same shape and dtype or the shape
    cannot be inferred.
  """
  if not inputs or not isinstance(inputs, (list, tuple)):
    raise ValueError("inputs must be a list of at least one Tensor with the "
                     "same dtype and shape")
  inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
  if not all(isinstance(x, ops.Tensor) for x in inputs):
    raise ValueError("inputs must be a list of at least one Tensor with the "
                     "same dtype and shape")

  if len(inputs) == 1:
    if name:
      return array_ops.identity(inputs[0], name=name)
    return inputs[0]
  return gen_math_ops._add_n(inputs, name=name) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def BuildLoop(self, pred, body, loop_vars, shape_invariants):
    """Add the loop termination condition and body to the graph."""

    # Keep original_loop_vars to identify which are TensorArrays
    original_loop_vars = loop_vars
    # Convert TensorArrays to their flow variables
    loop_vars = nest.map_structure(_convert_tensorarray_to_flow,
                                   nest.flatten(loop_vars))
    loop_vars = ops.convert_n_to_tensor_or_indexed_slices(loop_vars)
    try:
      self.Enter()
      original_body_result, exit_vars = self._BuildLoop(
          pred, body, original_loop_vars, loop_vars, shape_invariants)
    finally:
      self.Exit()

    flat_result = nest.flatten(original_body_result)
    # Convert TensorArray flow variables outside the context back into
    # their associated TensorArrays for returning to caller.
    exit_vars_with_tensor_arrays = (
        _convert_flows_to_tensorarrays(flat_result, exit_vars))
    packed_exit_vars = nest.pack_sequence_as(
        structure=original_body_result,
        flat_sequence=exit_vars_with_tensor_arrays)
    return (packed_exit_vars[0] if len(exit_vars) == 1
            else packed_exit_vars) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def _validate(tensor_list):
  tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)
  if not tensor_list:
    raise ValueError("Expected at least one tensor in batch().")
  return tensor_list 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def _validate_bucket(tensor_list):
  tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)
  if not tensor_list:
    raise ValueError("Expected at least one tensor in bucket().")
  return tensor_list 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def _DefaultGradYs(grad_ys, ys, colocate_gradients_with_ops):
  """Fill in default values for grad_ys.

  Args:
    grad_ys: List of gradients, can contain None.
    ys: List of tensors.
    colocate_gradients_with_ops: If True, try colocating gradients with
      the corresponding op.

  Returns:
    A list of gradients to use, without None.

  Raises:
    ValueError: If one of the grad_ys is invalid.
  """
  if len(grad_ys) != len(ys):
    raise ValueError("Passed %d grad_ys for %d ys" % (len(grad_ys), len(ys)))
  grad_ys = ops.convert_n_to_tensor_or_indexed_slices(grad_ys, name="grad_y")
  for i in xrange(len(grad_ys)):
    grad_y = grad_ys[i]
    y = ys[i]
    if grad_y is None:
      with _maybe_colocate_with(y.op, colocate_gradients_with_ops):
        grad_ys[i] = array_ops.fill(
            array_ops.shape(y), constant_op.constant(
                1, dtype=y.dtype))
    else:
      if grad_y.dtype != y.dtype:
        raise ValueError("Y and ys_grad must be of the same type, "
                         "not y: %s, ys_grad: %s " %
                         (dtypes.as_dtype(y.dtype).name,
                          dtypes.as_dtype(grad_y.dtype).name))
  return grad_ys 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def BuildLoop(self, pred, body, loop_vars, shape_invariants):
    """Add the loop termination condition and body to the graph."""

    # Keep original_loop_vars to identify which are TensorArrays
    original_loop_vars = loop_vars
    flat_loop_vars = nest.flatten(loop_vars)
    # Convert TensorArrays to their flow variables
    loop_vars = _convert_tensorarrays_to_flows(flat_loop_vars)
    loop_vars = ops.convert_n_to_tensor_or_indexed_slices(loop_vars)
    try:
      self.Enter()
      original_body_result, exit_vars = self._BuildLoop(
          pred, body, original_loop_vars, loop_vars, shape_invariants)
    finally:
      self.Exit()

    flat_result = nest.flatten(original_body_result)
    # Convert TensorArray flow variables outside the context back into
    # their associated TensorArrays for returning to caller.
    exit_vars_with_tensor_arrays = (
        _convert_flows_to_tensorarrays(flat_result, exit_vars))
    packed_exit_vars = nest.pack_sequence_as(
        structure=original_body_result,
        flat_sequence=exit_vars_with_tensor_arrays)
    return (packed_exit_vars[0] if len(exit_vars) == 1
            else packed_exit_vars) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def _DefaultGradYs(grad_ys, ys, colocate_gradients_with_ops):
  """Fill in default values for grad_ys.

  Args:
    grad_ys: List of gradients, can contain None.
    ys: List of tensors.
    colocate_gradients_with_ops: If True, try colocating gradients with
      the corresponding op.

  Returns:
    A list of gradients to use, without None.

  Raises:
    ValueError: If sizes of gradients and inputs don't match
    TypeError: If type of any gradient is not valid for its input.
  """
  if len(grad_ys) != len(ys):
    raise ValueError("Passed %d grad_ys for %d ys" % (len(grad_ys), len(ys)))
  grad_ys = ops.convert_n_to_tensor_or_indexed_slices(grad_ys, name="grad_y")
  for i in xrange(len(grad_ys)):
    grad_y = grad_ys[i]
    y = ys[i]
    if grad_y is None:
      if y.dtype.is_complex:
        raise TypeError(
            "Gradients of complex tensors must set grad_ys (y.dtype = %r)" %
            y.dtype)
      with _maybe_colocate_with(y.op, colocate_gradients_with_ops):
        grad_ys[i] = array_ops.fill(
            array_ops.shape(y), constant_op.constant(
                1, dtype=y.dtype))
      continue
    if y.dtype.is_floating or y.dtype.is_integer:
      if not grad_y.dtype.is_floating and not grad_y.dtype.is_integer:
        raise TypeError("Gradient type %s generated for real or "
                         "integer-valued tensor %s with type %s must be "
                         "real or integer" %
                         (dtypes.as_dtype(grad_y.dtype).name, y,
                          dtypes.as_dtype(y.dtype).name))
    elif y.dtype.is_complex:
      if not grad_y.dtype.is_complex:
        raise TypeError("Gradient type %s generated for complex-valued "
                         "tensor %s with type %s must be real" %
                         (dtypes.as_dtype(grad_y.dtype).name, y,
                          dtypes.as_dtype(y.dtype).name))
    else:
      raise TypeError("Tensor %s with type %s must be numeric "
                      "to obtain a default gradient" %
                      (y, dtypes.as_dtype(y.dtype).name))
  return grad_ys 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import convert_n_to_tensor_or_indexed_slices [as 别名]
def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None,
                         capacity=32, shared_name=None, name=None):
  """Produces a slice of each `Tensor` in `tensor_list`.

  Implemented using a Queue -- a `QueueRunner` for the Queue
  is added to the current `Graph`'s `QUEUE_RUNNER` collection.

  Args:
    tensor_list: A list of `Tensor` objects. Every `Tensor` in
      `tensor_list` must have the same size in the first dimension.
    num_epochs: An integer (optional). If specified, `slice_input_producer`
      produces each slice `num_epochs` times before generating
      an `OutOfRange` error. If not specified, `slice_input_producer` can cycle
      through the slices an unlimited number of times.
    shuffle: Boolean. If true, the integers are randomly shuffled within each
      epoch.
    seed: An integer (optional). Seed used if shuffle == True.
    capacity: An integer. Sets the queue capacity.
    shared_name: (optional). If set, this queue will be shared under the given
      name across multiple sessions.
    name: A name for the operations (optional).

  Returns:
    A list of tensors, one for each element of `tensor_list`.  If the tensor
    in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output
    tensor will have shape `[a, b, ..., z]`.

  Raises:
    ValueError: if `slice_input_producer` produces nothing from `tensor_list`.
  """
  with ops.name_scope(name, "input_producer", tensor_list):
    tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)
    if not tensor_list:
      raise ValueError(
          "Expected at least one tensor in slice_input_producer().")
    range_size = array_ops.shape(tensor_list[0])[0]
    # TODO(josh11b): Add an assertion that the first dimension of
    # everything in TensorList matches. Maybe just check the inferred shapes?
    queue = range_input_producer(range_size, num_epochs=num_epochs,
                                 shuffle=shuffle, seed=seed, capacity=capacity,
                                 shared_name=shared_name)
    index = queue.dequeue()
    output = [array_ops.gather(t, index) for t in tensor_list]
    return output


# Helpers for the batching functions ------------------------------------------