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

Introduction to Deep Learning with PyTorch

Chapter 3: PyTorch Tensors

Let's create tensors!

face Luca Grillotti

There are plenty of ways to initialise a torch tensor. Let’s have a look at some of them.

Initialising from list

The most straightforward way to initialise a Tensor is to directly do it from a list:

import torch

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

Of course, if the list does not represent a valid multi-dimensional matrix (where all dimensions have the same length), the initialisation will return an error. For example,

import torch

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

will return an error:

ValueError: expected sequence of length 2 at dim 1 (got 1)

Initialising from numpy array

If you are used to the numpy library, you’ll be happy to know that torch has already a function for automatically converting a numpy array to a torch tensor.

import numpy as np

numpy_array = np.array([[1, 2],
                        [3, 4]])
tensor = torch.from_numpy(numpy_array)

Some other functions

There are plenty of functions in torch for initialising tensors. Those functions are sometimes very similar to the ones available in numpy.

Creating a tensor full of 0s

import torch

shape = (3, 4)
tensor_zeros_1 = torch.zeros(size=shape) # creating 3x4 tensor of 0s

tensor = torch.Tensor([[1, 2],
                       [3, 4]])
tensor_zeros_2 = torch.zeros_like(tensor) # creating 2x2 tensor of 0s

Creating a tensor full of 1s

import torch

shape = (3, 4)
tensor_ones_1 = torch.ones(size=shape) # creating 3x4 tensor of 1s

tensor = torch.Tensor([[1, 2],
                       [3, 4]])
tensor_ones_2 = torch.ones_like(tensor) # creating 2x2 tensor of 1s

Creating random tensors

import torch

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

# creating 3x4 tensor where each component follows normal distribution with mean 0 and standard deviation 1
tensor_random_normal_1 = torch.randn(size=shape) 
tensor_random_normal_2 = torch.randn_like(tensor) 

# creating 3x4 tensor following uniform distribution between 0 and 1
tensor_random_uniform_1 = torch.rand(size=shape)
tensor_random_uniform_2 = torch.rand_like(tensor)