Basic concepts of Deep Learning

The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities.The below are the five function we are going to see in this notebook.

Chunk
Bernouli
Rand
Add
Transpose

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 /usr/local/lib/python3.6/dist-packages (1.18.5)
Requirement already satisfied: torch==1.7.0+cpu in /usr/local/lib/python3.6/dist-packages (1.7.0+cpu)
Requirement already satisfied: torchvision==0.8.1+cpu in /usr/local/lib/python3.6/dist-packages (0.8.1+cpu)
Requirement already satisfied: torchaudio==0.7.0 in /usr/local/lib/python3.6/dist-packages (0.7.0)
Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (0.8)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (3.7.4.3)
Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cpu) (0.16.0)
Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.8.1+cpu) (7.0.0)

# Import torch and other required modules
import torch

Function 1 - torch.chunk

This function Splits a tensor into a specific number of chunks.
In this case, i created a 6 * 5 tensor as a example , that is 5 rows and 5 columns, it's a 2 dimensional tensor.

# Sample tenson
t1 = torch.tensor([[1,2,3,4,5],[1,2,3,4,5],[2,3,4,5,6],[3,4,5,6,7],[4,5,6,7,8],[2,3,4,5,6]])
t1

tensor([[1, 2, 3, 4, 5],
        [1, 2, 3, 4, 5],
        [2, 3, 4, 5, 6],
        [3, 4, 5, 6, 7],
        [4, 5, 6, 7, 8],
        [2, 3, 4, 5, 6]])