num_serialized_models_to_keep=1,
model_save_interval=1,
serialization_dir=run_name,
histogram_interval=100,
patience=args.patience,
num_epochs=args.epochs,
cuda_device=devicea)
for tid, i in enumerate(train, 1):
print("\nTraining task ", i)
sys.stdout.flush()
if args.diff_class:
model.set_task(i)
trainer._num_epochs = args.epochs
iterator.index_with(vocabulary[i])
trainer.train_data = train_data[i]
trainer._validation_data = dev_data[i]
trainer.model = model
trainer.iterator = iterator
trainer._validation_iterator = iterator
if i == 'cola':
trainer._validation_metric = 'average'
trainer._metric_tracker._should_decrease = False
trainer.validation_metric = '+average'
else:
trainer._validation_metric = 'loss'
trainer._metric_tracker._should_decrease = True
trainer.validation_metric = '-loss'
trainer._metric_tracker.clear()
if not args.majority:
metrics = trainer.train()
trainer._tensorboard.add_train_scalar("restore_checkpoint/" +
