Python gen_array_ops._zeros_like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 _zeros_like [as 别名]
def zeros_like(tensor, dtype=None, name=None, optimize=True):
  """Creates a tensor with all elements set to zero.

  Given a single tensor (`tensor`), this operation returns a tensor of the
  same type and shape as `tensor` with all elements set to zero. Optionally,
  you can use `dtype` to specify a new type for the returned tensor.

  For example:

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

  Args:
    tensor: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
    name: A name for the operation (optional).
    optimize: if true, attempt to statically determine the shape of 'tensor'
    and encode it as a constant.

  Returns:
    A `Tensor` with all elements set to zero.
  """
  with ops.name_scope(name, "zeros_like", [tensor]) as name:
    tensor = ops.convert_to_tensor(tensor, name="tensor")

    if tensor.shape.is_fully_defined():
      # We can produce a zeros tensor independent of the value of 'tensor',
      # since the shape is known statically.
      return zeros(tensor.shape, dtype=dtype or tensor.dtype, name=name)

    if dtype is not None and dtype != tensor.dtype:
      return zeros(shape_internal(tensor, optimize=optimize), dtype=dtype,
                   name=name)
    else:
      return gen_array_ops._zeros_like(tensor, 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 _zeros_like [as 别名]
def zeros_like(tensor, dtype=None, name=None, optimize=True):
  """Creates a tensor with all elements set to zero.

  Given a single tensor (`tensor`), this operation returns a tensor of the
  same type and shape as `tensor` with all elements set to zero. Optionally,
  you can use `dtype` to specify a new type for the returned tensor.

  For example:

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

  Args:
    tensor: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
    name: A name for the operation (optional).
    optimize: if true, attempt to statically determine the shape of 'tensor'
    and encode it as a constant.

  Returns:
    A `Tensor` with all elements set to zero.
  """
  with ops.name_scope(name, "zeros_like", [tensor]) as name:
    tensor = ops.convert_to_tensor(tensor, name="tensor")
    if dtype is not None and tensor.dtype != dtype:
      ret = zeros(shape_internal(tensor, optimize=optimize), dtype, name=name)
      ret.set_shape(tensor.get_shape())
      return ret
    else:
      return gen_array_ops._zeros_like(tensor, 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 _zeros_like [as 别名]
def zeros_like(tensor, dtype=None, name=None, optimize=True):
  """Creates a tensor with all elements set to zero.

  Given a single tensor (`tensor`), this operation returns a tensor of the
  same type and shape as `tensor` with all elements set to zero. Optionally,
  you can use `dtype` to specify a new type for the returned tensor.

  For example:

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

  Args:
    tensor: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
    name: A name for the operation (optional).
    optimize: if true, attempt to statically determine the shape of 'tensor'
    and encode it as a constant.

  Returns:
    A `Tensor` with all elements set to zero.
  """
  with ops.name_scope(name, "zeros_like", [tensor]) as name:
    tensor = ops.convert_to_tensor(tensor, name="tensor")

    if context.in_eager_mode():
      if dtype is not None and dtype != tensor.dtype:
        return zeros(
            shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
      with ops.device(tensor.device):
        return gen_array_ops._zeros_like(tensor, name=name)

    # For now, variant types must be created via zeros_like; as we need to
    # pass the input variant object to the proper zeros callback.

    if optimize and tensor.shape.is_fully_defined() and \
        tensor.dtype != dtypes.variant:
      # We can produce a zeros tensor independent of the value of 'tensor',
      # since the shape is known statically.
      return zeros(tensor.shape, dtype=dtype or tensor.dtype, name=name)

    if dtype is not None and dtype != tensor.dtype and dtype != dtypes.variant:
      return zeros(
          shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
    else:
      return gen_array_ops._zeros_like(tensor, name=name)