stack

paddle. stack ( x, axis=0, name=None ) [源代码]

沿 axis 轴对输入 x 进行堆叠操作。要求所有输入 Tensor 有相同的 Shape 和数据类型。 例如,输入 x 为 N 个 Shape 为 [A, B]的 Tensor,如果 axis==0,则输出 Tensor 的 Shape 为 [N, A, B];如果 axis==1,则输出 Tensor 的 Shape 为 [A, N, B];以此类推。

Case 1:

    Input:
    x[0].shape = [1, 2]
    x[0].data = [ [1.0 , 2.0 ] ]
    x[1].shape = [1, 2]
    x[1].data = [ [3.0 , 4.0 ] ]
    x[2].shape = [1, 2]
    x[2].data = [ [5.0 , 6.0 ] ]

    Attrs:
    axis = 0

    Output:
    Out.dims = [3, 1, 2]
    Out.data =[ [ [1.0, 2.0] ],
                [ [3.0, 4.0] ],
                [ [5.0, 6.0] ] ]


Case 2:

    Input:
    x[0].shape = [1, 2]
    x[0].data = [ [1.0 , 2.0 ] ]
    x[1].shape = [1, 2]
    x[1].data = [ [3.0 , 4.0 ] ]
    x[2].shape = [1, 2]
    x[2].data = [ [5.0 , 6.0 ] ]


    Attrs:
    axis = 1 or axis = -2  # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1.

    Output:
    Out.shape = [1, 3, 2]
    Out.data =[ [ [1.0, 2.0]
                    [3.0, 4.0]
                    [5.0, 6.0] ] ]

参数

  • x (list[Tensor]|tuple[Tensor]) – 输入 x 是多个 Tensor,且这些 Tensor 的维度和数据类型必须相同。支持的数据类型:float32、float64、int32、int64。

  • axis (int,可选) – 指定对输入 Tensor 进行堆叠运算的轴,有效 axis 的范围是:[−(R+1),R+1],R 是输入中第一个 Tensor 的维数。如果 axis < 0,则 axis=axis+R+1。默认值为 0。

  • name (str,可选) - 具体用法请参见 Name,一般无需设置,默认值为 None。

返回

堆叠运算后的 Tensor,数据类型与输入 Tensor 相同。

代码示例

>>> import paddle

>>> x1 = paddle.to_tensor([[1.0, 2.0]])
>>> x2 = paddle.to_tensor([[3.0, 4.0]])
>>> x3 = paddle.to_tensor([[5.0, 6.0]])

>>> out = paddle.stack([x1, x2, x3], axis=0)
>>> print(out.shape)
[3, 1, 2]
>>> print(out)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.]],
 [[3., 4.]],
 [[5., 6.]]])

>>> out = paddle.stack([x1, x2, x3], axis=-2)
>>> print(out.shape)
[1, 3, 2]
>>> print(out)
Tensor(shape=[1, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 2.],
  [3., 4.],
  [5., 6.]]])