def exc_train(self): # only import train staff in training env from train_util.data_set import generate_data_set, SiameseNetworkTrainDataSet from train_util.common_train import train from torch.utils.data import dataloader as DataLoader print("verify model start training") print(str(self)) optimizer = torch.optim.Adam(self.parameters(), lr=LEARNING_RATE) lr_scheduler = torch.optim.lr_scheduler.ExponentialLR( optimizer=optimizer, gamma=0.1) batch_k = 48 data_set = generate_data_set(0.06, SiameseNetworkTrainDataSet, batch_k) loss_func = ContrastiveLoss() data_loader = { 'train': DataLoader.DataLoader( data_set['train'], shuffle=True, batch_size=BATCH_SIZE, ), 'test': DataLoader.DataLoader( data_set['test'], shuffle=True, ) } train(model=self, model_name='verify_68', EPOCH=EPOCH, optimizer=optimizer, exp_lr_scheduler=lr_scheduler, loss_func=loss_func, save_dir='./params', data_set=data_set, data_loader=data_loader, test_result_output_func=test_result_output, cuda_mode=1, print_inter=2, val_inter=25, scheduler_step_inter=50)
def exc_train(self): # only import train staff in training env from train_util.data_set import generate_data_set, MyDataset from train_util.common_train import train print("CNN classify model start training") print(str(self)) optimizer = torch.optim.Adam(self.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY) loss_func = nn.CrossEntropyLoss() lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1) data_set = generate_data_set(0.06, MyDataset) data_loader = { 'train': DataLoader.DataLoader(data_set['train'], shuffle=True, batch_size=BATCH_SIZE, num_workers=1), 'test': DataLoader.DataLoader(data_set['test'], shuffle=True, batch_size=1, num_workers=1) } train(model=self, model_name='cnn_68', EPOCH=EPOCH, optimizer=optimizer, exp_lr_scheduler=lr_scheduler, loss_func=loss_func, save_dir='./params', data_set=data_set, data_loader=data_loader, test_result_output_func=test_result_output, cuda_mode=1, print_inter=2, val_inter=30, scheduler_step_inter=50)