epoch_train_loss_I = []
epoch_train_acc_I = []
epoch_val_loss_I = []
epoch_val_acc_I = []
max_val_acc_I = 0
epoch_train_loss_C = []
epoch_val_loss_C = []
epoch_train_loss_tot = []
epoch_val_loss_tot = []
for epoch in range(n_epochs):
# TRAIN
model_B.train()
model_I.train()
correct_B = 0
train_loss_B = 0
correct_I = 0
train_loss_I = 0
train_loss_C = 0
train_loss_tot = 0
train_num = 0
for i, (XI, XB, y) in enumerate(train_loader):
XI, XB, y = XI.to(device), XB.to(device), y.long().to(device)
if XI.size()[0] != batch_size:
break
# In[8]:
# 1st stage training: with recon_loss
training_start=datetime.now()
#split fit
epoch_train_loss = []
epoch_train_acc = []
epoch_val_loss = []
epoch_val_acc = []
max_val_acc = 0
for epoch in range(n_epochs):
# TRAIN
model.train()
correct = 0
train_loss = 0
train_num = 0
for i, (XI, XB, y) in enumerate(train_loader):
if i >= len(train_loader)-removal:
break
if model.header == 'CNN':
x = XI
else:
x = XB
x, y = x.to(device), y.long().to(device)
if x.size()[0] != batch_size:
# print("batch {} size {} < {}, skip".format(i, x.size()[0], batch_size))
break