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)
outputs = cnn(images)
mtr.add(outputs.data, labels)
trainacc = mtr.value().diagonal().sum() * 1.0 / len(train_dataset)
mtr.reset()
# testing data test
for images, labels in test_loader:
if not args.no_cuda:
images = images.cuda()
images = Variable(images)
# forward
outputs = cnn(images)
mtr.add(outputs.data, labels)
testacc = mtr.value().diagonal().sum() * 1.0 / len(test_dataset)
mtr.reset()
# logging
print(
'Accuracy on training data is: %f . Accuracy on testing data is: %f. '
% (trainacc, testacc))
################## Main ##################
for epoch in range(args.epochs):
train(epoch)
test()
torch.save(cnn.state_dict(), 'cnn.pkl')