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import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
batch_size = 128
learning_rate = 1e-2
num_epoches = 20
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 = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
class Cnn(nn.Module):
def __init__(self, in_dim, n_class):
super(Cnn, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_dim, 6, 5, stride=1, padding=2),
nn.ReLU(True),
nn.MaxPool2d(2, 2),
nn.Conv2d(6, 16, 5, stride=1, padding=0),
nn.ReLU(True),
nn.MaxPool2d(2, 2))
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84),
nn.Linear(84, n_class))
def forward(self, x):
out = self.conv(x)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
model = Cnn(1, 10)
use_gpu = torch.cuda.is_available()
if use_gpu:
model = model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
for epoch in range(num_epoches):
print('epoch {}'.format(epoch + 1))
print('*' * 10)
running_loss = 0.0
running_acc = 0.0
for i, data in enumerate(train_loader, 1):
img, label = data
if use_gpu:
img = img.cuda()
label = label.cuda()
img = Variable(img)
label = Variable(label)
out = model(img)
loss = criterion(out, label)
running_loss += loss.item() * label.size(0)
_, pred = torch.max(out, 1)
num_correct = (pred == label).sum()
accuracy = (pred == label).float().mean()
running_acc += num_correct.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
"""
# ========================= Log ======================
step = epoch * len(train_loader) + i
# (1) Log the scalar values
info = {'loss': loss.data[0], 'accuracy': accuracy.data[0]}
for tag, value in info.items():
logger.scalar_summary(tag, value, step)
# (2) Log values and gradients of the parameters (histogram)
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, to_np(value), step)
logger.histo_summary(tag + '/grad', to_np(value.grad), step)
# (3) Log the images
info = {'images': to_np(img.view(-1, 28, 28)[:10])}
for tag, images in info.items():
logger.image_summary(tag, images, step)
if i % 300 == 0:
print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
epoch + 1, num_epoches, running_loss / (batch_size * i),
running_acc / (batch_size * i)))
"""
print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(train_dataset))))
model.eval()
eval_loss = 0
eval_acc = 0
for data in test_loader:
img, label = data
if use_gpu:
img = Variable(img, volatile=True).cuda()
label = Variable(label, volatile=True).cuda()
else:
img = Variable(img, volatile=True)
label = Variable(label, volatile=True)
out = model(img)
loss = criterion(out, label)
eval_loss += loss.item() * label.size(0)
_, pred = torch.max(out, 1)
num_correct = (pred == label).sum()
eval_acc += num_correct.item()
print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
test_dataset)), eval_acc / (len(test_dataset))))
print()
torch.save(model.state_dict(), './cnn.pth')
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