import os
import sys
sys.path.append(".")
import logging
import logging.config
from text_classification import utils
# Directories
BASE_DIR = os.getcwd() # project root
APP_DIR = os.path.dirname(__file__) # app root
LOGS_DIR = os.path.join(BASE_DIR, 'logs')
EMBEDDINGS_DIR = os.path.join(BASE_DIR, 'embeddings')
# Create dirs
utils.create_dirs(LOGS_DIR)
utils.create_dirs(EMBEDDINGS_DIR)
# Loggers
log_config = utils.load_json(
filepath=os.path.join(BASE_DIR, 'logging.json'))
logging.config.dictConfig(log_config)
logger = logging.getLogger('logger')
pretrained_embeddings=embedding_matrix,
freeze_embeddings=args.freeze_embeddings)
model = model.to(device)
config.logger.info("→ Model:\n" f" {model.named_parameters}")
# Define optimizer & scheduler
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.1,
patience=3)
# Model dir
experiment_id = f'{int(time.time())}-{petname.Generate(2)}'
experiment_dir = os.path.join(config.EXPERIMENTS_DIR, experiment_id)
utils.create_dirs(dirpath=experiment_dir)
model_path = os.path.join(experiment_dir, 'model.h5')
# TensorBoard
tb_log_dir = f'{experiment_dir}/tensorboard/'
writer = SummaryWriter(log_dir=tb_log_dir)
# Train
best_val_loss = train(model=model,
optimizer=optimizer,
scheduler=scheduler,
num_epochs=args.num_epochs,
patience=args.patience,
train_set=train_set,
val_set=val_set,
test_set=test_set,
import os
import sys
sys.path.append(".")
import logging
import logging.config
from text_classification import utils
# Directories
BASE_DIR = os.getcwd() # project root
APP_DIR = os.path.dirname(__file__) # app root
LOGS_DIR = os.path.join(BASE_DIR, 'logs')
EMBEDDINGS_DIR = os.path.join(BASE_DIR, 'embeddings')
EXPERIMENTS_DIR = os.path.join(BASE_DIR, 'experiments')
# Create dirs
utils.create_dirs(LOGS_DIR)
utils.create_dirs(EMBEDDINGS_DIR)
utils.create_dirs(EXPERIMENTS_DIR)
# Loggers
log_config = utils.load_json(
filepath=os.path.join(BASE_DIR, 'logging.json'))
logging.config.dictConfig(log_config)
logger = logging.getLogger('logger')