def train_model():
print('#### Start Training ####')
data = np.load(data_dirc+'raw_data.npy')
train_data, train_label, val_data, val_label = create_data(data, RAW_LABELS, PERMUTATION, RATIO, PREPROCESS, MAX_SENTENCE_LENGTH, AUGMENTED, PADDING)
train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
val_dataset = torch.utils.data.TensorDataset(val_data, val_label)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=BATCH_SIZE,
shuffle=False)
file_name = 'best_model'
model = CNN(num_classes=4)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model = model.to(device)
# Criterion and Optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
best_acc = 0.0
for epoch in range(NUM_EPOCHS):
train_loss = 0.0
for i, (data, labels) in enumerate(train_loader):
model.train()
data_batch, label_batch = data.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(data_batch)
loss = criterion(outputs, label_batch)
loss.backward()
optimizer.step()
train_loss += loss.item()
# validate
val_acc, val_F1 = cal_F1(val_loader, model)
if val_acc > best_acc:
best_acc = val_acc
best_F1 = val_F1
torch.save(model.state_dict(),'saved_model/'+file_name+'.pth')
train_acc = test_model(train_loader, model)
train_loss /= len(train_loader.sampler)
print('Epoch: [{}/{}], Step: [{}/{}], Val Acc: {}, Val F1: {}, Train Acc: {}, Train Loss: {}'.format(
epoch + 1, NUM_EPOCHS, i + 1, len(train_loader), val_acc, val_F1, train_acc, train_loss))
sys.stdout.flush()
print('#### End Training ####')
print('best val acc:', best_acc)
print('best F1:', best_F1)
print('args.gpu_ids', args.gpu_ids)
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids)
model = model.to(device)
# criterion = torch.nn.BCELoss()
criterion = torch.nn.CrossEntropyLoss(
weight=train_dataset.weight.to(device))
#
# test_loss = []
#
for epoch in range(args.num_epochs):
#
start_time = time.time()
each_train_loss = []
model.train()
origin_inputs = []
origin_outputs = []
out_prob = []
with torch.set_grad_enabled(True):
for inputs, targets in train_dataloader:
inputs = inputs.to(device)
targets = targets.to(device)
if (len(inputs) != args.batch_size):
break
optimizer.zero_grad()
