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:
Pick 5 interesting functions related to PyTorch tensors by reading the documentation,
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.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 .
(Optional) Write a blog post on Medium to accompany and showcase your Jupyter notebook. Embed cells from your notebook wherever necessary.
(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.
TITLE
An short introduction about PyTorch and about the chosen functions.
- arange
- chunk
- reshape
- std_mean()
- unfold(dimension, size, step) → Tensor
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
Function 1 - torch.arrange
Returns a one dimension tensor with the parameters chosen: START, END (only one mandatory) and STEP.
# Example 1 - working
torch.arange(8)
tensor([0, 1, 2, 3, 4, 5, 6, 7])