hstack¶

paddle. hstack ( x, name=None ) [source]

Stacks all the input tensors x along horizontal axis. All tensors must be of the same dtype.

Parameters

x (list[Tensor]|tuple[Tensor]) – Input x can be a list or tuple of tensors, the Tensors in x must be of the same shape and dtype. Supported data types: float16, float32, float64, int8, int32, int64 or bfloat16.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, The stacked tensor with same data type as input.

Examples

           >>> import paddle

>>> # hstack with 0-D tensors
>>> x1 = paddle.to_tensor(1.0)
>>> x2 = paddle.to_tensor(2.0)
>>> out = paddle.hstack((x1, x2))
>>> print(out)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[1., 2.])

>>> # hstack with 1-D tensors
>>> x1 = paddle.to_tensor([1.0, 2.0])
>>> x2 = paddle.to_tensor([3.0, 4.0, 5.0])
>>> out = paddle.hstack((x1, x2))
>>> print(out)
Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True,
[1., 2., 3., 4., 5.])

>>> # hstack mix with 0-D & 1-D tensors
>>> x1 = paddle.to_tensor(1.0)
>>> x2 = paddle.to_tensor([3.0, 4.0, 5.0])
>>> out = paddle.hstack((x1, x2))
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[1., 3., 4., 5.])

>>> # hstack with 2-D tensors
>>> x1 = paddle.to_tensor([[1.0, 2.0]])
>>> x2 = paddle.to_tensor([[3.0, 4.0, 5.0]])
>>> out = paddle.hstack((x1, x2))
>>> print(out)
Tensor(shape=[1, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1., 2., 3., 4., 5.]])

          