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model.py
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import torch
import torch.nn as nn
from torchvision import datasets,transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch.autograd import Function
import torch.optim as optim
from binary_modules import BinarizeLinear
BATCH_SIZE = 100
train_loader = DataLoader(
datasets.MNIST(root='./mnist_data',train=True,download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BATCH_SIZE,shuffle=True)
test_loader = DataLoader(
datasets.MNIST(root='./mnist_data',train=False,download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BATCH_SIZE,shuffle=True)
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.infl_ratio = 3
self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio)
self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio)
self.htanh1 = nn.Hardtanh()
self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio)
self.htanh2 = nn.Hardtanh()
self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
self.drop=nn.Dropout(0.5)
self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio)
self.htanh3 = nn.Hardtanh()
self.fc4 = nn.Linear(2048*self.infl_ratio, 10)
self.logsoftmax=nn.LogSoftmax()
def forward(self, x):
x = x.view(-1, 28*28)
x = self.fc1(x)
x = self.bn1(x)
x = self.htanh1(x)
x = self.fc2(x)
x = self.bn2(x)
x = self.htanh2(x)
x = self.fc3(x)
x = self.drop(x)
x = self.bn3(x)
x = self.htanh3(x)
x = self.fc4(x)
return self.logsoftmax(x)
def accuracy(output,target,topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_,pred = output.float().topk(maxk,1)
pred = pred.t()
correct = pred.eq(target.view(1,-1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
model = Net()
model.cuda()
criterion = nn.NLLLoss()
criterion.cuda()
optimizer = optim.Adam(model.parameters())
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data,target = data.cuda(),target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
if epoch%40==0:
optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
optimizer.zero_grad()
loss.backward()
for p in list(model.parameters()):
p.data.copy_(p.data.clamp_(-1,1))
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.data[0]))
torch.save(model.state_dict(),'model_params.pkl')
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
data,target = data.cuda(),target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += criterion(output, target).data[0] # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).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, 41):
train(epoch)
test()