restart = args.restart
config_path = args.config_path
# Read config file
config = read_config(config_path)
torch.manual_seed(config.seed); np.random.seed(config.seed)
# Initialize model
model = LanguageModel(config=config) #CTCModel(config=config)
print(model)
# Generate datasets
train_dataset, valid_dataset, test_dataset = get_text_datasets(config)
trainer = Trainer(model=model, config=config)
if restart: trainer.load_checkpoint()
# Train the final model
if train:
for epoch in range(config.num_epochs):
print("========= Epoch %d of %d =========" % (epoch+1, config.num_epochs))
train_loss = trainer.train(train_dataset)
model = model.cpu()
valid_loss = trainer.test(valid_dataset, set="valid")
if torch.cuda.is_available(): model = model.cuda()
print("========= Results: epoch %d of %d =========" % (epoch+1, config.num_epochs))
print("train loss: %.2f| valid loss: %.2f\n" % (train_loss, valid_loss) )
trainer.save_checkpoint()
import torch
from models import HMM
from data import get_datasets, read_config
from training import Trainer
# Generate datasets from text file
path = "data"
N = 128
config = read_config(N,path)
train_dataset, valid_dataset = get_datasets(config)
checkpoint_path = "."
# Initialize model
model = HMM(config=config)
# Train the model
num_epochs = 10
trainer = Trainer(model, config, lr=0.003)
trainer.load_checkpoint(checkpoint_path)
for epoch in range(num_epochs):
print("========= Epoch %d of %d =========" % (epoch+1, num_epochs))
train_loss = trainer.train(train_dataset)
valid_loss = trainer.test(valid_dataset)
trainer.save_checkpoint(epoch, checkpoint_path)
print("========= Results: epoch %d of %d =========" % (epoch+1, num_epochs))
print("train loss: %.2f| valid loss: %.2f\n" % (train_loss, valid_loss) )
