ML_Sample/main.py

import torch
from PIL.Image import Image
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt

class Net(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1: torch.nn.Linear = torch.nn.Linear(28 * 28, 64)
        self.fc2: torch.nn.Linear = torch.nn.Linear(64, 64)
        self.fc3: torch.nn.Linear = torch.nn.Linear(64, 64)
        self.fc4: torch.nn.Linear = torch.nn.Linear(64, 10)
    def forward(self, x: torch.Tensor):
        x = torch.nn.functional.rrelu(self.fc1(x))
        x = torch.nn.functional.rrelu(self.fc2(x))
        x = torch.nn.functional.rrelu(self.fc3(x))
        x = torch.nn.functional.log_softmax(self.fc4(x), dim=1)
        return x

def get_data_loader(is_train: bool) -> DataLoader[Image]:
    to_tensor = transforms.Compose([transforms.ToTensor()])
    data_set = MNIST("", is_train, transform=to_tensor, download=True)
    return DataLoader(data_set, batch_size=15, shuffle=True)

def evaluate(test_data: DataLoader[Image], net: Net) -> float:
    n_correct: int = 0
    n_total: int = 0
    with torch.no_grad():
        for (x, y) in test_data:
            outputs = net.forward(x.view(-1, 28 * 28))
            for i, output in enumerate(outputs):
                if torch.argmax(output) == y[i]:
                    n_correct += 1
                n_total += 1
    return n_correct / n_total

def main():
    print("=== START ===")
    torch.set_default_device(torch.device(type='cpu', index=0))
    torch.set_default_dtype(torch.float16)
    train_data = get_data_loader(is_train=True)
    print('train data size:',len(train_data))
    test_data = get_data_loader(is_train=False)
    print('test data size: ', len(test_data))
    net = Net()

    print('initial accuracy:', evaluate(test_data, net))
    optimizer = torch.optim.Adam(net.parameters(), lr=0.001) # type: ignore
    for epoch in range(1, 3, 1):
        for (x, y) in train_data:
            net.zero_grad()
            output = net.forward(x.view(-1, 28 * 28))
            loss = torch.nn.functional.nll_loss(output, y)
            loss.backward()
            optimizer.step()
        print('epoch', epoch, 'accuracy', evaluate(test_data, net))

    model_name = 'model.pth'
    print('saving model to: ', model_name)
    torch.save(net.state_dict(), model_name)
    print('=== DONE ===')
if __name__ == '__main__':
    main()