How to resize a tensor in PyTorch?

To resize a PyTorch tensor, we use the .view() method. We can increase or decrease the dimension of the tensor, but we have to make sure that the total number of elements in a tensor must match before and after the resize.

Steps

• Import the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.

• Create a PyTorch tensor and print it.

• Resize the above-created tensor using .view() and assign the value to a variable. .view() does not resize the original tensor; it only gives a view with the new size, as its name suggests.

• Finally, print the tensor after the resize.

Example 1

# Python program to resize a tensor in PyTorch # Import the library import torch # Create a tensor T = torch.Tensor([1, 2, 3, 4, 5, 6]) print(T) # Resize T to 2x3 x = T.view(2,3) print("Tensor after resize:\n",x) # Other way to resize T to 2x3 x = T.view(-1,3) print("Tensor after resize:\n",x) # Other way resize T to 2x3 x = T.view(2,-1) print("Tensor after resize:\n",x)

Output

When you run the above Python 3 code, it will produce the following output

tensor([1., 2., 3., 4., 5., 6.]) Tensor after resize: tensor([[1., 2., 3.],          [4., 5., 6.]]) Tensor after resize: tensor([[1., 2., 3.],          [4., 5., 6.]]) Tensor after resize: tensor([[1., 2., 3.],          [4., 5., 6.]])

Example 2

# Import the library import torch # Create a tensor shape 4x3 T = torch.Tensor([[1,2,3],[2,1,3],[2,3,5],[5,6,4]]) print(T) # Resize T to 3x4 x = T.view(-1,4) print("Tensor after resize:\n",x) # Other way to esize T to 3x4 x = T.view(3,-1) print("Tensor after resize:\n",x) # Resize T to 2x6 x = T.view(2,-1) print("Tensor after resize:\n",x)

Output

When you run the above Python 3 code, it will produce the following output

tensor([[1., 2., 3.],          [2., 1., 3.],          [2., 3., 5.],          [5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2.],          [1., 3., 2., 3.],          [5., 5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2.],          [1., 3., 2., 3.],          [5., 5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2., 1., 3.],          [2., 3., 5., 5., 6., 4.]])