Python gen_array_ops._slice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 _slice [as 别名]
def slice(input_, begin, size, name=None):
  # pylint: disable=redefined-builtin
  """Extracts a slice from a tensor.

  This operation extracts a slice of size `size` from a tensor `input` starting
  at the location specified by `begin`. The slice `size` is represented as a
  tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
  of `input` that you want to slice. The starting location (`begin`) for the
  slice is represented as an offset in each dimension of `input`. In other
  words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
  want to slice from.

  `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
  all remaining elements in dimension i are included in the
  slice. In other words, this is equivalent to setting:

  `size[i] = input.dim_size(i) - begin[i]`

  This operation requires that:

  `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`

  For example:

  ```python
  # 'input' is [[[1, 1, 1], [2, 2, 2]],
  #             [[3, 3, 3], [4, 4, 4]],
  #             [[5, 5, 5], [6, 6, 6]]]
  tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
  tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
                                              [4, 4, 4]]]
  tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
                                             [[5, 5, 5]]]
  ```

  Args:
    input_: A `Tensor`.
    begin: An `int32` or `int64` `Tensor`.
    size: An `int32` or `int64` `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` the same type as `input`.
  """
  return gen_array_ops._slice(input_, begin, size, 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 _slice [as 别名]
def slice(input_, begin, size, name=None):
  # pylint: disable=redefined-builtin
  """Extracts a slice from a tensor.

  This operation extracts a slice of size `size` from a tensor `input` starting
  at the location specified by `begin`. The slice `size` is represented as a
  tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
  of `input` that you want to slice. The starting location (`begin`) for the
  slice is represented as an offset in each dimension of `input`. In other
  words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
  want to slice from.

  Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to
  perform slices, as it allows you to write `foo[3:7, :-2]` instead of
  `tf.slice([3, 0], [4, foo.get_shape()[1]-2])`.

  `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
  all remaining elements in dimension i are included in the
  slice. In other words, this is equivalent to setting:

  `size[i] = input.dim_size(i) - begin[i]`

  This operation requires that:

  `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`

  For example:

  ```python
  t = tf.constant([[[1, 1, 1], [2, 2, 2]],
                   [[3, 3, 3], [4, 4, 4]],
                   [[5, 5, 5], [6, 6, 6]]])
  tf.slice(t, [1, 0, 0], [1, 1, 3])  # [[[3, 3, 3]]]
  tf.slice(t, [1, 0, 0], [1, 2, 3])  # [[[3, 3, 3],
                                     #   [4, 4, 4]]]
  tf.slice(t, [1, 0, 0], [2, 1, 3])  # [[[3, 3, 3]],
                                     #  [[5, 5, 5]]]
  ```

  Args:
    input_: A `Tensor`.
    begin: An `int32` or `int64` `Tensor`.
    size: An `int32` or `int64` `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` the same type as `input`.
  """
  return gen_array_ops._slice(input_, begin, size, name=name)


# pylint: disable=invalid-name