Are view() in Pytorch and reshape() in Numpy similar?
Yes, for most intents and purposes, they can do the same job. From this link, an example:

&gt;&gt;&gt; import torch
&gt;&gt;&gt; t = torch.ones((2, 3, 4))
&gt;&gt;&gt; t.size()
torch.Size([2, 3, 4])
&gt;&gt;&gt; t.view(-1, 12).size()
torch.Size([2, 12])




If you are concerned with memory allocation, here is another answer on StackOverflow with a little more information. PyTorch's view function actually does what the name suggests - returns a view to the data. The data is not altered in memory as far as I can see.

In numpy, the reshape function does not guarantee that a copy of the data is made or not. It will depend on the original shape of the array and the target shape. Have a look here for further information.