Python gen_array_ops._pack方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def stack(values, axis=0, name="stack"):
  """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

  Packs the list of tensors in `values` into a tensor with rank one higher than
  each tensor in `values`, by packing them along the `axis` dimension.
  Given a list of length `N` of tensors of shape `(A, B, C)`;

  if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
  if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
  Etc.

  For example:

  ```prettyprint
  # 'x' is [1, 4]
  # 'y' is [2, 5]
  # 'z' is [3, 6]
  stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
  stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
  ```

  This is the opposite of unstack.  The numpy equivalent is

  ```python
  tf.stack([x, y, z]) = np.asarray([x, y, z])
  ```

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    axis: An `int`. The axis to stack along. Defaults to the first dimension.
      Supports negative indexes.
    name: A name for this operation (optional).

  Returns:
    output: A stacked `Tensor` with the same type as `values`.

  Raises:
    ValueError: If `axis` is out of the range [-(R+1), R+1).
  """
  if axis == 0:
    try:
      # If the input is a constant list, it can be converted to a constant op
      return ops.convert_to_tensor(values, name=name)
    except (TypeError, ValueError):
      pass  # Input list contains non-constant tensors

  value_shape = ops.convert_to_tensor(values[0], name=name).get_shape()
  if value_shape.ndims is not None:
    expanded_num_dims = value_shape.ndims + 1
    if axis < -expanded_num_dims or axis >= expanded_num_dims:
      raise ValueError("axis = %d not in [%d, %d)" %
                       (axis, -expanded_num_dims, expanded_num_dims))

  return gen_array_ops._pack(values, axis=axis, name=name)


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def _autopacking_helper(list_or_tuple, dtype, name):
  """Converts the given list or tuple to a tensor by packing.

  Args:
    list_or_tuple: A (possibly nested) list or tuple containing a tensor.
    dtype: The element type of the returned tensor.
    name: A name for the returned tensor.

  Returns:
    A `tf.Tensor` with value equivalent to `list_or_tuple`.
  """
  must_pack = False
  converted_elems = []
  with ops.name_scope(name) as scope:
    for i, elem in enumerate(list_or_tuple):
      if ops.is_dense_tensor_like(elem):
        if dtype is not None and elem.dtype.base_dtype != dtype:
          raise TypeError(
              "Cannot convert a list containing a tensor of dtype "
              "%s to %s (Tensor is: %r)" % (elem.dtype, dtype, elem))
        converted_elems.append(elem)
        must_pack = True
      elif isinstance(elem, (list, tuple)):
        converted_elem = _autopacking_helper(elem, dtype, str(i))
        if ops.is_dense_tensor_like(converted_elem):
          must_pack = True
        converted_elems.append(converted_elem)
      else:
        converted_elems.append(elem)
    if must_pack:
      elems_as_tensors = []
      for i, elem in enumerate(converted_elems):
        if ops.is_dense_tensor_like(elem):
          elems_as_tensors.append(elem)
        else:
          # NOTE(mrry): This is inefficient, but it enables us to
          # handle the case where the list arguments are other
          # convertible-to-tensor types, such as numpy arrays.
          elems_as_tensors.append(
              constant_op.constant(elem, dtype=dtype, name=str(i)))
      return gen_array_ops._pack(elems_as_tensors, name=scope)
    else:
      return converted_elems 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def stack(values, axis=0, name="stack"):
  """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

  Packs the list of tensors in `values` into a tensor with rank one higher than
  each tensor in `values`, by packing them along the `axis` dimension.
  Given a list of length `N` of tensors of shape `(A, B, C)`;

  if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
  if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
  Etc.

  For example:

  ```prettyprint
  # 'x' is [1, 4]
  # 'y' is [2, 5]
  # 'z' is [3, 6]
  stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
  stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
  ```

  This is the opposite of unstack.  The numpy equivalent is

      tf.stack([x, y, z]) = np.asarray([x, y, z])

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    axis: An `int`. The axis to stack along. Defaults to the first dimension.
      Supports negative indexes.
    name: A name for this operation (optional).

  Returns:
    output: A stacked `Tensor` with the same type as `values`.

  Raises:
    ValueError: If `axis` is out of the range [-(R+1), R+1).
  """
  if axis == 0:
    try:
      # If the input is a constant list, it can be converted to a constant op
      return ops.convert_to_tensor(values, name=name)
    except (TypeError, ValueError):
      pass  # Input list contains non-constant tensors

  value_shape = ops.convert_to_tensor(values[0], name=name).get_shape()
  if value_shape.ndims is not None:
    expanded_num_dims = value_shape.ndims + 1
    if axis < -expanded_num_dims or axis >= expanded_num_dims:
      raise ValueError("axis = %d not in [%d, %d)" %
                       (axis, -expanded_num_dims, expanded_num_dims))

  return gen_array_ops._pack(values, axis=axis, name=name)


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def stack(values, axis=0, name="stack"):
  """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

  Packs the list of tensors in `values` into a tensor with rank one higher than
  each tensor in `values`, by packing them along the `axis` dimension.
  Given a list of length `N` of tensors of shape `(A, B, C)`;

  if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
  if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
  Etc.

  For example:

  ```prettyprint
  # 'x' is [1, 4]
  # 'y' is [2, 5]
  # 'z' is [3, 6]
  stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
  stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
  ```

  This is the opposite of unstack.  The numpy equivalent is

      tf.stack([x, y, z]) = np.asarray([x, y, z])

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    axis: An `int`. The axis to stack along. Defaults to the first dimension.
      Supports negative indexes.
    name: A name for this operation (optional).

  Returns:
    output: A stacked `Tensor` with the same type as `values`.

  Raises:
    ValueError: If `axis` is out of the range [-(R+1), R+1).
  """
  if axis == 0:
    try:
      # If the input is a constant list, it can be converted to a constant op
      return ops.convert_to_tensor(values, name=name)
    except (TypeError, ValueError):
      pass  # Input list contains non-constant tensors

  value_shape = ops.convert_to_tensor(values[0], name=name).get_shape()
  if value_shape.ndims is not None:
    expanded_num_dims = value_shape.ndims + 1
    if axis < -expanded_num_dims or axis >= expanded_num_dims:
      raise ValueError("axis = %d not in [%d, %d)" %
                       (axis, -expanded_num_dims, expanded_num_dims))

  return gen_array_ops._pack(values, axis=axis, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def stack(values, axis=0, name="stack"):
  """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

  Packs the list of tensors in `values` into a tensor with rank one higher than
  each tensor in `values`, by packing them along the `axis` dimension.
  Given a list of length `N` of tensors of shape `(A, B, C)`;

  if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
  if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
  Etc.

  For example:

  ```python
  x = tf.constant([1, 4])
  y = tf.constant([2, 5])
  z = tf.constant([3, 6])
  tf.stack([x, y, z])  # [[1, 4], [2, 5], [3, 6]] (Pack along first dim.)
  tf.stack([x, y, z], axis=1)  # [[1, 2, 3], [4, 5, 6]]
  ```

  This is the opposite of unstack.  The numpy equivalent is

  ```python
  tf.stack([x, y, z]) = np.stack([x, y, z])
  ```

  Args:
    values: A list of `Tensor` objects with the same shape and type.
    axis: An `int`. The axis to stack along. Defaults to the first dimension.
      Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
    name: A name for this operation (optional).

  Returns:
    output: A stacked `Tensor` with the same type as `values`.

  Raises:
    ValueError: If `axis` is out of the range [-(R+1), R+1).
  """
  if axis == 0:
    try:
      # If the input is a constant list, it can be converted to a constant op
      return ops.convert_to_tensor(values, name=name)
    except (TypeError, ValueError):
      pass  # Input list contains non-constant tensors

  value_shape = ops.convert_to_tensor(values[0], name=name).get_shape()
  if value_shape.ndims is not None:
    expanded_num_dims = value_shape.ndims + 1
    if axis < -expanded_num_dims or axis >= expanded_num_dims:
      raise ValueError("axis = %d not in [%d, %d)" % (axis, -expanded_num_dims,
                                                      expanded_num_dims))

  return gen_array_ops._pack(values, axis=axis, name=name)


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _pack [as 别名]
def _autopacking_helper(list_or_tuple, dtype, name):
  """Converts the given list or tuple to a tensor by packing.

  Args:
    list_or_tuple: A (possibly nested) list or tuple containing a tensor.
    dtype: The element type of the returned tensor.
    name: A name for the returned tensor.

  Returns:
    A `tf.Tensor` with value equivalent to `list_or_tuple`.
  """
  must_pack = False
  converted_elems = []
  with ops.name_scope(name) as scope:
    for i, elem in enumerate(list_or_tuple):
      if ops.is_dense_tensor_like(elem):
        if dtype is not None and elem.dtype.base_dtype != dtype:
          raise TypeError("Cannot convert a list containing a tensor of dtype "
                          "%s to %s (Tensor is: %r)" % (elem.dtype, dtype,
                                                        elem))
        converted_elems.append(elem)
        must_pack = True
      elif isinstance(elem, (list, tuple)):
        converted_elem = _autopacking_helper(elem, dtype, str(i))
        if ops.is_dense_tensor_like(converted_elem):
          must_pack = True
        converted_elems.append(converted_elem)
      else:
        converted_elems.append(elem)
    if must_pack:
      elems_as_tensors = []
      for i, elem in enumerate(converted_elems):
        if ops.is_dense_tensor_like(elem):
          elems_as_tensors.append(elem)
        else:
          # NOTE(mrry): This is inefficient, but it enables us to
          # handle the case where the list arguments are other
          # convertible-to-tensor types, such as numpy arrays.
          elems_as_tensors.append(
              constant_op.constant(elem, dtype=dtype, name=str(i)))
      return gen_array_ops._pack(elems_as_tensors, name=scope)
    else:
      return converted_elems