def train(train_data, val_data, data_parser, args):
input_dict, output_dict = data_parser.input_dict, data_parser.output_dict
net = get_model(input_dict,
output_dict,
args.model,
args.hidden_size,
args.max_len,
args.layers,
args.dropout,
kernel_size=args.kernel_size,
dilate=args.dilate,
ignore_pad=args.ignore_pad,
multilinear=(not args.single_linear),
input_len=data_parser.input_max_len,
stride=args.stride,
attn_heads=args.attn_heads,
cuda=args.cuda)
net.initialize_params(args.init_params)
print(args)
print()
print(net)
print('Number of Elements: %d' %
(sum([x.view(-1).size(0) for x in net.parameters()])))
trainer = Trainer(net,
optim_type=args.optim,
print_every=args.print_every,
cuda=args.cuda,
save_dir=args.save_dir,
save_every=args.save_every,
improve_wait=args.improve_wait,
batch_size=args.batch_size)
if args.train_from:
trainer.load_checkpoint(args.train_from)
trainer.continue_training(data_parser,
train_data,
val_data,
args.epochs,
lr=args.lr,
weight_decay=args.weight_decay)
else:
trainer.train_epochs(data_parser,
train_data,
val_data,
args.epochs,
lr=args.lr,
weight_decay=args.weight_decay)
return trainer
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', required=True, help='see example at ')
parser.add_argument('--checkpoint', required=False, help='your checkpoint')
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config)
trainer = Trainer(config)
if args.checkpoint:
trainer.load_checkpoint(args.checkpoint)
trainer.train()
