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Updated 3 years ago
A beginner's guide to Autograd in PyToch
An short introduction about PyTorch and about the chosen functions.
function 1 torch.full
function 2 torch.narrow
function 3 torch.rand
function 4 torch.abs
function 5 torch.max
Before we begin, let's install and import PyTorch
# Uncomment and run the appropriate command for your operating system, if required
# Linux / Binder
!pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# Windows
# !pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# MacOS
# !pip install numpy torch torchvision torchaudio
Looking in links: https://download.pytorch.org/whl/torch_stable.html
Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (1.19.2)
Requirement already satisfied: torch==1.7.0+cpu in /opt/conda/lib/python3.8/site-packages (1.7.0+cpu)
Requirement already satisfied: torchvision==0.8.1+cpu in /opt/conda/lib/python3.8/site-packages (0.8.1+cpu)
Requirement already satisfied: torchaudio==0.7.0 in /opt/conda/lib/python3.8/site-packages (0.7.0)
Requirement already satisfied: dataclasses in /opt/conda/lib/python3.8/site-packages (from torch==1.7.0+cpu) (0.6)
Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.8/site-packages (from torch==1.7.0+cpu) (3.7.4.3)
Requirement already satisfied: future in /opt/conda/lib/python3.8/site-packages (from torch==1.7.0+cpu) (0.18.2)
Requirement already satisfied: pillow>=4.1.1 in /opt/conda/lib/python3.8/site-packages (from torchvision==0.8.1+cpu) (8.0.0)
# Import torch and other required modules
import torch
Function 1 - torch.full
torch.full(size, fill_value) creates a tensor of size "size" filled with "fill_value". the tensor dtype is inferred form "fill_value"
torch.full((3,4),2020)
tensor([[2020, 2020, 2020, 2020],
[2020, 2020, 2020, 2020],
[2020, 2020, 2020, 2020]])