root.setLevel(logging.DEBUG)
dictionary, rev_dict = utils.get_dictionary(args.text)
num_classes = len(dictionary)
iterator = utils.tokenize(args.text,
dictionary,
batch_size=args.batch_size,
seq_len=args.seq_len)
sess = tf.Session()
model = SeqGAN(sess,
num_classes,
logdir=args.logdir,
learn_phase=args.learn_phase,
only_cpu=args.only_cpu)
model.build()
model.load(ignore_missing=True)
for epoch in xrange(1, args.num_epochs + 1):
for step in xrange(1, args.num_steps + 1):
logging.info('epoch %d, step %d', epoch, step)
model.train_batch(iterator.next())
# Generates a sample from the model.
g = model.generate(1000)
print(utils.detokenize(g, rev_dict))
# Saves the model to the logdir.
model.save()
root.setLevel(logging.DEBUG)
dictionary, rev_dict = utils.get_dictionary(args.text, args.dictionary)
num_classes = len(dictionary)
iterator = utils.tokenize(args.text,
dictionary,
batch_size=args.batch_size,
seq_len=args.seq_len)
sess = tf.Session()
model = SeqGAN(sess,
num_classes,
logdir=args.logdir,
learn_phase=args.learn_phase,
only_cpu=args.only_cpu)
model.build()
model.load(ignore_missing=True)
for epoch in range(1, args.num_epochs + 1):
for step in range(1, args.num_steps + 1):
logging.info('epoch %d, step %d', epoch, step)
model.train_batch(next(iterator))
# Generates a sample from the model.
g = model.generate(1000)
print(utils.detokenize(g, rev_dict))
# Saves the model to the logdir.
model.save()