Sign in

Data Analyst @Novartis | Researcher | Full-time Learner
Source: MNIST database(Wikipedia)

Quick Navigation

1. Brief about PyTorch

2. Working with images in PyToch(using MNIST Dataset)

3. Splitting a dataset into training, Validation and test sets

4. Creating PyTorch models with custom logic by extending the nn.Module Class

5. Interpreting model outputs as probabilities using softmax, and picking predicted labels

6. Picking a good evaluation metric(accuracy) and loss function(cross entropy) for Classification problems

7.Setting up a training loop that also evaluates the model using Validation set

8. Testing the model manually on randomly picked examples

9.Saving and loading the model checkpoints to avoid retraining from scratch

10. References

## Imports
import torch
import torchvision ## Contains some utilities for working with the image data
from torchvision.datasets import MNIST
import matplotlib.pyplot as plt
#%matplotlib inline
import torchvision.transforms as transforms
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch.nn.functional as F


Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store