Pytorch reshape tensor dimension

Question

For example, I have 1D vector with dimension (5). I would like to reshape it into 2D matrix (1,5).

Here is how I do it with numpy

>>> import num         


        

Answer 1

For in-place modification of the shape of the tensor, you should use tensor.resize_():

In [23]: a = torch.Tensor([1, 2, 3, 4, 5])

In [24]: a.shape
Out[24]: torch.Size([5])


# tensor.resize_((`new_shape`))    
In [25]: a.resize_((1,5))
Out[25]: 

 1  2  3  4  5
[torch.FloatTensor of size 1x5]

In [26]: a.shape
Out[26]: torch.Size([1, 5])

In PyTorch, if there's an underscore at the end of an operation (like tensor.resize_()) then that operation does in-place modification to the original tensor.

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Also, you can simply use np.newaxis in a torch Tensor to increase the dimension. Here is an example:

In [34]: list_ = range(5)
In [35]: a = torch.Tensor(list_)
In [36]: a.shape
Out[36]: torch.Size([5])

In [37]: new_a = a[np.newaxis, :]
In [38]: new_a.shape
Out[38]: torch.Size([1, 5])