elif args.optimizer =='Adam':
model = CoxPH(net, optimizer=tt.optim.Adam(lr=args.lr, weight_decay=args.weight_decay),device=device)
wandb.init(project='icml_new_'+args.dataset,
group=f'fold{fold}_'+args.loss+args.optimizer,
name=f'L{args.num_layers}N{args.num_nodes}D{args.dropout}W{args.weight_decay}B{args.batch_size}',
config=args)
wandb.watch(net)
# Loss configuration ============================================================
patience=5
if args.loss =='rank':
model.loss = DSAFTRankLoss(alpha=args.alpha, beta=args.beta)
elif args.loss == 'mae':
model.loss = DSAFTMAELoss()
elif args.loss == 'rmse':
model.loss = DSAFTRMSELoss()
elif args.loss =='kspl':
model.loss = DSAFTNKSPLLoss(args.an, args.sigma)
elif args.loss =='kspl_new':
model.loss = DSAFTNKSPLLossNew(args.an, args.sigma)
# Training ======================================================================
batch_size = args.batch_size
lrfinder = model.lr_finder(x_train, y_train, batch_size, tolerance=10)
best = lrfinder.get_best_lr()
model.optimizer.set_lr(best)
device=device)
wandb.init(
project=args.dataset,
group=args.loss + '_' + args.optimizer,
name=
f'L{args.num_layers}N{args.num_nodes}D{args.dropout}W{args.weight_decay}B{args.batch_size}',
config=args)
wandb.watch(net)
# Loss configuration ============================================================
patience = 10
if args.loss == 'rank':
model.loss = DSAFTRankLoss()
elif args.loss == 'mae':
model.loss = DSAFTMAELoss()
elif args.loss == 'rmse':
model.loss = DSAFTRMSELoss()
elif args.loss == 'kspl':
model.loss = DSAFTNKSPLLoss(args.an, args.sigma)
elif args.loss == 'kspl_new':
model.loss = DSAFTNKSPLLossNew(args.an, args.sigma)
# Training ======================================================================
batch_size = args.batch_size
lrfinder = model.lr_finder(x_train, y_train, batch_size, tolerance=10)
best = lrfinder.get_best_lr()
# model.optimizer.set_lr(args.lr)
