How to use unsqueeze() method In Python
This is used to reshape a tensor by adding new dimensions at given positions.
Syntax: tensor.unsqueeze(position)
where, position is the dimension index which will start from 0.
Example 1: Python code to create 2 D tensors and add a dimension in 0 the dimension.
Python3
# importing torch moduleimport torch# create two dimensional tensor a = torch.Tensor([[2,3], [1,2]]) # display shapeprint(a.shape)# add dimension at 0 position added = a.unsqueeze(0)print(added.shape) |
Output:
torch.Size([2, 2]) torch.Size([1, 2, 2])
Example 2: Python code to create 1 D tensor and add dimensions
Python3
# importing torch moduleimport torch# create one dimensional tensor a = torch.Tensor([1, 2, 3, 4, 5]) # display shapeprint(a.shape)# add dimension at 0 position added = a.unsqueeze(0)print(added.shape)# add dimension at 1 position added = a.unsqueeze(1)print(added.shape) |
Output:
torch.Size([5]) torch.Size([1, 5]) torch.Size([5, 1])
Reshaping a Tensor in Pytorch
In this article, we will discuss how to reshape a Tensor in Pytorch. Reshaping allows us to change the shape with the same data and number of elements as self but with the specified shape, which means it returns the same data as the specified array, but with different specified dimension sizes.
Creating Tensor for demonstration:
Python code to create a 1D Tensor and display it.
Python3
# import torch moduleimport torch# create an 1 D etnsor with 8 elementsa = torch.tensor([1,2,3,4,5,6,7,8])# display tensor shapeprint(a.shape)# display tensora |
Output:
torch.Size([8]) tensor([1, 2, 3, 4, 5, 6, 7, 8])