Introduction to Deep Learning with PyTorch > Let's play with tensors! | Python Programming | Department of Computing

Introduction to Deep Learning with PyTorch

Chapter 3: PyTorch Tensors

Let's play with tensors!

face Luca Grillotti

Mathematical operations with a scalar

As with numpy arrays, it is easy to perform mathematical operations (addition/subtraction/multiplication/division) on all the components of a tensor with a scalar:

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4]])
tensor_add_item = tensor + 5
tensor_multiply_item = tensor / 2
...
print(tensor_add_item, tensor_multiply_item)

Mathematical operations with another tensor

And you can add, multiply, subtract, divide tensors together:

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4]])
tensor_add = tensor + tensor
tensor_multiply = tensor * tensor
...
print(tensor_add, tensor_multiply)

Even better, you can easily apply an operation to all the sub-tensors of a tensor. For example:

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4]])
row_tensor = torch.Tensor([1, 2])

tensor_subtract = tensor - row_tensor
print(tensor_subtract)

Mathematical functions

The torch library provides several mathematical functions that can be applied to all elements of a tensor. For example:

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4]])

tensor_exp = torch.exp(tensor)
tensor_log = torch.log(tensor)
...  # Every single function you could imagine.
print(tensor_exp, tensor_log)

Statistical operations

There are also plenty of statistical operations available: min, max, mean, … For example:

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4]])

tensor_min = torch.min(tensor)
tensor_max = torch.max(tensor)
tensor_mean = torch.mean(tensor)
...  # Every single mathematical function you could imagine.

print(tensor_min, tensor_max, tensor_mean)

In practice, you will mostly apply those operations on a single axis. For instance, with the tensors above, we could find the minimal element for each row, or for each column.

import torch

tensor = torch.Tensor([[1, 2],
                       [3, 4],
                       [5, 6]])  # shape = (3, 2)

tensor_mean_row = torch.mean(tensor, dim=0)  # shape = (2,) Averaging over 1st dimension (along columns)
tensor_mean_col = torch.mean(tensor, dim=1)  # shape = (3,) Averaging over 2nd dimension (along rows)

print(tensor_mean_row, tensor_mean_col)