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- from __future__ import print_function
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torch.optim as optim
- from torch.autograd import Variable
-
- from torchvision import datasets, transforms
-
- # Training settings
- batch_size = 64
-
- # MNIST Dataset
- dataset_path = "../data/mnist"
- train_dataset = datasets.MNIST(root=dataset_path,
- train=True,
- transform=transforms.ToTensor(),
- download=True)
-
- test_dataset = datasets.MNIST(root=dataset_path,
- train=False,
- transform=transforms.ToTensor())
-
- # Data Loader (Input Pipeline)
- train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
- batch_size=batch_size,
- shuffle=True)
-
- test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
- batch_size=batch_size,
- shuffle=False)
-
-
- # define Network
- class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.l1 = nn.Linear(784, 520)
- self.l2 = nn.Linear(520, 320)
- self.l3 = nn.Linear(320, 240)
- self.l4 = nn.Linear(240, 120)
- self.l5 = nn.Linear(120, 10)
-
- def forward(self, x):
- x = x.view(-1, 784) # Flatten the data (n, 1, 28, 28)-> (n, 784)
- x = F.relu(self.l1(x))
- x = F.relu(self.l2(x))
- x = F.relu(self.l3(x))
- x = F.relu(self.l4(x))
- return self.l5(x)
-
-
- model = Net()
-
- criterion = nn.CrossEntropyLoss()
- optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
-
-
- def train(epoch):
- #model.train()
- for batch_idx, (data, target) in enumerate(train_loader):
- data, target = Variable(data), Variable(target)
- optimizer.zero_grad()
- output = model(data)
- loss = criterion(output, target)
- loss.backward()
- optimizer.step()
- if batch_idx % 10 == 0:
- print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
- epoch, batch_idx * len(data), len(train_loader.dataset),
- 100. * batch_idx / len(train_loader), loss.data[0]))
-
-
- def test():
- model.eval()
- test_loss = 0
- correct = 0
- for data, target in test_loader:
- data, target = Variable(data, volatile=True), Variable(target)
- output = model(data)
- # sum up batch loss
- test_loss += criterion(output, target).data[0]
- # get the index of the max
- pred = output.data.max(1, keepdim=True)[1]
- correct += pred.eq(target.data.view_as(pred)).cpu().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)))
-
-
- for epoch in range(1, 10):
- train(epoch)
- test()
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