from models import *
print("Train using model", Net)
model = Net()
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=1e-6, momentum=args.momentum)
print("Args:", args)
val_losses=[]
for epoch in range(1, args.epochs + 1):
if(epoch==1):
test_loss = fp_train.test(epoch, args, model, test_loader, fp.dxs, fp.dys, test_length=0.1*len(valid_dataset))
fp_train.train(epoch, args, model, optimizer, train_loader, fp.dxs, fp.dys)
test_loss = fp_train.test(epoch, args, model, test_loader, fp.dxs, fp.dys, test_length=0.1*len(valid_dataset))
val_losses.append(test_loss)
loss_flag = 1
#for i in range(2,5):
# if(epoch<=15 or val_losses[-1]<val_losses[-i]):
# loss_flag = 1
path = os.path.join(args.log_dir, "ckpt", "state_dict-ep_{}.pth".format(epoch))
print("Saving model in", path)
torch.save(model.state_dict(), path)
if(not loss_flag == 1):
with open(os.path.join(args.log_dir, "termination_epoch"), "wb") as f:
f.write(str(epoch)+"\n")
"wb"))
fp_target = util.np2var(fp_target, args.cuda)
fp = Fingerprints()
fp.dxs = fp_dx
fp.dys = fp_target
#from model import Net
from model import CW_Net as Net
#from small_model import Very_Small_Net as Net
model = Net()
if args.cuda:
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
print("Args:", args)
for epoch in range(1, args.epochs + 1):
if (epoch == 1):
fp_train.test(epoch, args, model, test_loader, fp.dxs, fp.dys)
fp_train.train(epoch, args, model, optimizer, train_loader, fp.dxs, fp.dys)
fp_train.test(epoch, args, model, test_loader, fp.dxs, fp.dys)
path = os.path.join(args.log_dir, "ckpt",
"state_dict-ep_{}.pth".format(epoch))
print("Saving model in", path)
torch.save(model.state_dict(), path)