def run(args):
# TODO: save the results of processing data for faster inference load
if exists(args.data_reader_path):
print 'Loading data reader...'
with open(args.data_reader_path, 'rb') as f:
data_reader = Unpickler(f).load()
print 'Loaded'
vocab = data_reader.get_vocab()
else:
print 'Creating data reader...'
data_reader = DataReader(args.train_dir)
vocab = data_reader.get_vocab()
# Save the data reader
with open(args.data_reader_path, 'wb') as f:
Pickler(f).dump(data_reader)
print 'Init model...'
model = WordModel(args, vocab)
if args.inference:
model.generate(primer=args.primer)
else:
global_step = 0
while global_step < args.max_steps:
inputs, targets = data_reader.get_train_batch(
args.batch_size, args.seq_len)
global_step = model.train_step(inputs, targets)
