poxdetection9_alexnet.py

PATH = './alex_net.pth'

# setup
model_ft = models.alexnet(pretrained=True,)
model_ft.classifier[6] = nn.Linear(4096,len(classes))
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

# train
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=num_epochs)

torch.save(model_ft.state_dict(), PATH)

# load
model_ft3 = models.alexnet(pretrained=True)
model_ft3.classifier[6] = nn.Linear(4096,len(classes))
model_ft3.to(device)
model_ft3.load_state_dict(torch.load(PATH,map_location=device))
model_ft3.eval()

# test
print(accuracy(model_ft3, test_loader))