utils.py

import numpy as np
import torch


def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>5f}  [{current:>5d}/{size:>5d}]")


def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            pred = model(X)
            test_loss += loss_fn(pred, y).item()

            for i_y, i_pred in zip(list(y), list(pred)):
                i_y = list(i_y[0].numpy())[0]
                i_pred = np.round(list(i_pred[0].numpy())[0])
                correct += 1 if i_y == i_pred else 0

    test_loss /= num_batches

    print(f"Test Error: Avg loss: {test_loss:>8f}")
    print(f"Accuracy: {correct}/{size:>0.1f} = {correct/size * 100:<0.2f}% \n")