fp = Fingerprints()
fp.dxs = fp_dx
fp.dys = fp_target
from model import CW2_Net as Net
#from res_model import ResNet as Net
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
fp = Fingerprints()
fp.dxs = fp_dx
fp.dys = fp_target
from model import CW2_Net as Net
#from res_model import ResNet as Net
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,