def run_train(args):
"""
Function to train a model.
:param args: command line arguments
"""
# Create directories for font, models and plots if they do not exist
if not os.path.exists(args.font_dir):
os.mkdir(args.font_dir)
if not os.path.exists(args.models_dir):
os.mkdir(args.models_dir)
if not os.path.exists(args.plots_dir):
os.mkdir(args.plots_dir)
# Create objects for training and visualization
visualizer = Visualizer(args.font_dir, args.plots_dir)
trainer = Trainer(args)
for epoch in range(trainer.num_epochs):
# Train and validate a model to predict values for both valence and arousal
if trainer.dimension == 'both':
trainer.train_2d()
trainer.validate_2d()
# Train and validate a model to predict values for valence or arousal, according to `dimension`
else:
trainer.train_1d()
trainer.validate_1d()
# Display epoch every `log_interval`
if (epoch + 1) % trainer.log_interval == 0 or (epoch + 1) == trainer.num_epochs:
print_epoch(epoch + 1, trainer.train_dict, trainer.test_dict, trainer.dimension)
# Update the learing rate every `decay_interval`
if (epoch + 1) % args.decay_interval == 0:
trainer.update_learning_rate()
# Visualize train and validation losses
visualizer.plot_losses(trainer.train_dict, trainer.test_dict, trainer.dimension)
# Save the trained model
trainer.save_model()
