SWD_loss = torch.tensor(0.0).to(device)
- if SWD_param is not None:
+ if (SWD_param is not None) and (abs(SWD_param["beta"]) > 1E-15):
# Total maximum is limited, pick random subsample
if SWD_param['max_N'] is not None and targets.shape[0] > SWD_param['max_N']:
@@ -1097,7 +1097,7 @@ Source code for icenet.deep.iceboost
if 'opt_param' in param:
- if 'noise_reg' in param['opt_param'] and param['opt_param']['noise_reg'] > 0.0:
+ if ('noise_reg' in param['opt_param']) and (abs(param['opt_param']['noise_reg']) > 1E-15):
X_trn_orig = copy.deepcopy(X_trn)
noise_reg = param['opt_param']['noise_reg']
# -------------------------------------------
@@ -1117,7 +1117,7 @@ Source code for icenet.deep.iceboost
# ---------------------------------------
## What to evaluate
- if epoch == 0 or ((epoch+1) % param['evalmode']) == 0 or args['__raytune_running__']:
+ if epoch == 0 or ((epoch+1) % param['savemode']) == 0 or args['__raytune_running__']:
evallist = [(dtrain, 'train'), (deval, 'eval')]
else:
evallist = [(dtrain, 'train')]
@@ -1190,7 +1190,7 @@ Source code for icenet.deep.iceboost
# ==============================================
## Validate
- if epoch == 0 or ((epoch+1) % param['evalmode']) == 0 or args['__raytune_running__']:
+ if epoch == 0 or ((epoch+1) % param['savemode']) == 0 or args['__raytune_running__']:
# ------- AUC values ------
if len(args['primary_classes']) >= 2:
diff --git a/_modules/icenet/deep/train.html b/_modules/icenet/deep/train.html
index d92ae3d3..db796f75 100644
--- a/_modules/icenet/deep/train.html
+++ b/_modules/icenet/deep/train.html
@@ -867,13 +867,13 @@ Source code for icenet.deep.train
# Set current epoch (for special scheduling reasons)
opt_param['current_epoch'] = epoch
- # Train
+ # Train
loss = optimize.train(model=model, loader=train_loader, optimizer=optimizer, device=device, opt_param=opt_param, MI=MI)
- if epoch == 0 or ((epoch+1) % param['evalmode']) == 0 or args['__raytune_running__']:
+ if epoch == 0 or ((epoch+1) % param['savemode']) == 0 or args['__raytune_running__']:
_, train_acc, train_auc = optimize.test(model=model, loader=train_loader, device=device, opt_param=opt_param, MI=MI, compute_loss=False)
validate_loss, validate_acc, validate_auc = optimize.test(model=model, loader=test_loader, device=device, opt_param=opt_param, MI=MI, compute_loss=True)
-
+
# Temperature calibration
try:
from icenet.deep import tempscale
diff --git a/objects.inv b/objects.inv
index 7e901086..002405c3 100644
Binary files a/objects.inv and b/objects.inv differ