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 numpy as np
>>> a = np.array([1,2,3,4,5])
>>> a.shape
(5,)
>>> a = np.reshape(a, (1,5))
>>> a.shape
(1, 5)
>>> a
array([[1, 2, 3, 4, 5]])
>>> 

But how can I do that with Pytorch Tensor (and Variable). I don't want to switch back to numpy and switch to Torch variable again, because it will loss backpropagation information.

Here is what I have in Pytorch

>>> import torch
>>> from torch.autograd import Variable
>>> a = torch.Tensor([1,2,3,4,5])
>>> a

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

>>> a.size()
(5L,)
>>> a_var = variable(a)
>>> a_var = Variable(a)
>>> a_var.size()
(5L,)
.....do some calculation in forward function
>>> a_var.size()
(5L,)

Now I want it size to be (1, 5). How can I resize or reshape the dimension of pytorch tensor in Variable without loss grad information. (because I will feed into another model before backward)

Answer 1

Use torch.unsqueeze(input, dim, out=None)

>>> import torch
>>> a = torch.Tensor([1,2,3,4,5])
>>> a

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

>>> a = a.unsqueeze(0)
>>> a

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