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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.ops.Concat

class mindspore.ops.Concat(*args, **kwargs)

Connect tensor in the specified axis.

Connect input tensors along with the given axis.

The input data is a tuple of tensors. These tensors have the same rank R. Set the given axis as m, and $0\le m<R$. Set the number of input tensors as N. For the $i$-th tensor $t_i$, it has the shape of $(x_1,x_2,...,x_{mi},...,x_R)$. $x_{mi}$ is the $m$-th dimension of the $i$-th tensor. Then, the shape of the output tensor is

$$(x_1,x_2,...,\sum _{i=1}^N{x}_{mi},...,x_R)$$

Parameters

axis (int) – The specified axis. Default: 0.

Inputs:

• input_x (tuple, list) - A tuple or a list of input tensors. input_x, input_y should has same data type.

• input_y (tuple, list) - A tuple or a list of input tensors. input_x, input_y should has same data type.

Outputs:

Tensor, the shape is $(x_1,x_2,...,\sum _{i=1}^N{x}_{mi},...,x_R)$.

The data type is the same with input_X and input_y.

Raises

TypeError – If axis is not an int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> input_y = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> op = ops.Concat()
>>> output = op((input_x, input_y))
>>> print(output)
[[0. 1.]
 [2. 1.]
 [0. 1.]
 [2. 1.]]
>>> op = ops.Concat(1)
>>> output = op((input_x, input_y))
>>> print(output)
[[0. 1. 0. 1.]
 [2. 1. 2. 1.]]