Method 2 : Using reshape() Method
This method is also used to resize the tensors. This method returns a new tensor with a modified size. the below syntax is used to resize the tensor using reshape() method.
Syntax: tensor.reshape( [row,column] )
- row represents the number of rows in the reshaped tensor.
- column represents the number of columns in the reshaped tensor.
Return: return a resized tensor.
Example 3:
The following program is to know how to resize a 1D tensor to a 2D tensor.
Python
# import torch moduleimport torch# Define an 1D tensortens = torch.tensor([10, 20, 30, 40, 50, 60, 70, 80])# display tensorprint("\n Original 1D Tensor: ", tens)# resize this tensor into 2x4tens_1 = tens.reshape([2, 4])print("\n After Resize this Tensor to 2x4 : \n", tens_1)# resize this tensor into 4x2tens_2 = tens.reshape([4, 2])print("\n After Resize this Tensor to 4x2 : \n", tens_2) |
Output:
Example 4:
The following program is to know how to resize a 2D tensor using reshape() method.
Python
# import torch moduleimport torch# Define an 2D tensortens = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])# display tensorprint(" Original 2D Tensor: \n", tens)# resize this tensor to 2x6tens_1 = tens.reshape([2, 6])print("\n After Resize this Tensor to 2x6 : \n", tens_1)# resize this tensor into 6x2tens_2 = tens.reshape([6, 2])print("\n After Resize this Tensor to 6x2 : \n", tens_2) |
Output:
How to resize a tensor in PyTorch?
In this article, we will discuss how to resize a Tensor in Pytorch. Resize allows us to change the size of the tensor. we have multiple methods to resize a tensor in PyTorch. let’s discuss the available methods.