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' %