train_conv.py

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from models import NetConv


# Training settings
batch_size = 64
test_batch_size = 1000
epochs = 10
lr = 0.01
momentum = 0.5
seed = 1
log_interval = 10

torch.manual_seed(seed)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

kwargs = {'num_workers': 1, 'pin_memory': True} if torch.cuda.is_available() else {}

# MNIST Dataset
train_dataset = datasets.MNIST(root='./data/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data/', train=False, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=test_batch_size, shuffle=False, **kwargs)

model = NetConv().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# Training loop
for epoch in range(1, epochs + 1):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

torch.save(model,'mnist_conv.pth')