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import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms
batch_size=200 learning_rate=0.01 epochs=10
train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=batch_size, shuffle=True)
class MLP(nn.Module):
def (self): super(MLP, self).__init__()
self.model = nn.Sequential( nn.Linear(784, 200), nn.LeakyReLU(inplace=True), nn.Linear(200, 200), nn.LeakyReLU(inplace=True), nn.Linear(200, 10), nn.LeakyReLU(inplace=True), )
def forward(self, x): x = self.model(x)
return x
device = torch.device('cuda:0') net = MLP().to(device) optimizer = optim.SGD(net.parameters(), lr=learning_rate) criteon = nn.CrossEntropyLoss().to(device)
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader): data = data.view(-1, 28*28) data, target = data.to(device), target.cuda()
logits = net(data) loss = criteon(logits, target)
optimizer.zero_grad() loss.backward() optimizer.step()
if batch_idx % 100 == 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()))
test_loss = 0 correct = 0 for data, target in test_loader: data = data.view(-1, 28 * 28) data, target = data.to(device), target.cuda() logits = net(data) test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1] correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset) print('nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
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