train.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from model import ANN

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,),(0.5,))
])

trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)

trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testloader = DataLoader(testset, batch_size=64, shuffle=False)

model = ANN()
los_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# training loop
episodes = 10


for epoch in range(episodes):
    running_loss = 0.0
    for images, labels in trainloader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = los_fn(outputs,labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader)}")

# evaluation
correct = 0
total = 0
model.eval()
with torch.no_grad():
    for images, labels in testloader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data,1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(f"Test Accuracy: {100 * correct / total}%")

torch.save(model.state_dict(), "weights.pth")
print("weights saved")