shashank411 / era_s5_assignment

ERA - Session 5 Assignment

We are building a simple classifier model for the MNIST dataset. Our project comprises mainly of three files, which are -

1. S5.ipynb

This is the main notebook file of our project in which we train our classifier model and test its accuracy

2. model.py

This file contains the model definition - the layers involved along with the forward function

usage

paste the below code in the S5 notebook to use the model

from model import Net
import torch
cuda = torch.cuda.is_available()
device = torch.device("cuda" if cuda else "cpu")
model = Net().to(device)

3. utils.py

This file contains the functions we use for getting the data loaders. We can also define the train and test functions (for 1 epoch) used for generic model training and evaluation

usage

paste the below code in the S5 notebook to import and use the functions required for training, testing and getting data loaders

from utils import loaders, train, test
batch_size = 512
kwargs = {'batch_size': batch_size, 'shuffle': True, 'num_workers': 2, 'pin_memory': True}
train_loader, test_loader = loaders(batch_size, kwargs)

after initialising model, device, optimizer and criterion in the S5 notebook, run the following -

train_accuracy, train_loss = train(model, device, train_loader, optimizer, criterion)
test_accuracy, test_loss = test(model, device, test_loader, criterion)