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3 years ago

Assignment Instructions (delete this cell before submission)

The objective of this assignment is to develop a solid understanding of PyTorch tensors. In this assignment you will:

  1. Pick 5 interesting functions related to PyTorch tensors by reading the documentation,

  2. Edit this starter template notebook to illustrate their usage and publish your notebook to Jovian using jovian.commit. Make sure to add proper explanations too, not just code.

  3. Submit the link to your published notebook on Jovian here: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/assignment-1-all-about-torch-tensor .

  4. (Optional) Write a blog post on Medium to accompany and showcase your Jupyter notebook. Embed cells from your notebook wherever necessary.

  5. (Optional) Share your work with the community and exchange feedback with other participants

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Colab". Run jovian.commit regularly to save your progress.

Try to give your notebook an interesting title e.g. "All about PyTorch tensor operations", "5 PyTorch functions you didn't know you needed", "A beginner's guide to Autograd in PyToch", "Interesting ways to create PyTorch tensors", "Trigonometic functions in PyTorch", "How to use PyTorch tensors for Linear Algebra" etc.

IMPORTANT NOTE: Make sure to submit a Jovian notebook link e.g. https://jovian.ai/aakashns/01-tensor-operations . Colab links will not be accepted.

Remove this cell containing instructions before making a submission or sharing your notebook, to make it more presentable.

Pytorch introduction

It’s a Python-based scientific computing package targeted at two sets of audiences:

  • A replacement for NumPy to use the power of GPUs
  • deep learning research platform that provides maximum flexibility and speed

below are the some common fuctions that every DS should know:

  • torch.tesnor(): Constructs a tensor with data.
  • torch.zeros_like(): Returns a tensor filled with the scalar value 0, with the same size as input.
  • torch.linspace(): Creates a one-dimensional tensor of size steps whose values are evenly spaced from start to end, inclusive.
  • torch.reshape(): Returns a tensor with the same data and number of elements as input, but with the specified shape.
  • torch.normal() :Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.
# 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
# Import torch and other required modules
import torch

Function 1 - torch.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

Constructs a tensor with data.