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)
