from models import Model
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as opt
from utils import Image_Reader
trainloader = Image_Reader.get_trainloader()
net = Model.Net()
net.cuda()
params = list(net.parameters())
criterion = nn.CrossEntropyLoss()
optimizer = opt.SGD(iter(params), lr=1e-3, momentum=0.9)
for epoch in range(2):
running_loss = 0.
for i, data in enumerate(trainloader):
inputs, labels = data
inputs, labels = Variable(inputs.cuda()), Variable(labels.cudda())
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(loss.data)
running_loss += loss.data[0]
if i % 1000 == 999:
print('[%d, %5d] loss: %.3f' %
