def __init__(self, mode = 'chat'): if not os.path.isdir(config.PROCESSED_PATH): data.prepare_raw_data() data.process_data() # create checkpoints folder if there isn't one already data.make_dir(config.CPT_PATH) if(mode == "chat"): self.__chat_init()
def write_results(summaries, train_loss, losses, filepath, gt_summaries_path):
''' write evaluation results to file '''
dir_path = os.path.join(filepath, 'generated_summaries')
data.make_dir(dir_path)
with open(os.path.join(filepath, 'log'), 'w') as f:
f.write('Avg Train loss: ' + str(train_loss))
for loss in losses:
f.write(str(loss) + '\n')
f.write('\n' * 3)
f.write('\n' * 3)
for i, generated in enumerate(summaries):
f.write(str(i) + ' Generated headline: ' + generated + '\n\n')
def main():
#parser = argparse.ArgumentParser()
#parser.add_argument('--mode', choices={'train', 'chat'},
# default='train', help="mode. if not specified, it's in the train mode")
#args = parser.parse_args()
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data ready!')
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
def main(args):
if not os.path.isdir(config.PROCESSED_PATH):
data.process_data()
print('Data is ready!')
data.make_dir(config.CPT_PATH)
mode = args[-1]
if mode == 'train':
train()
elif mode == 'test':
predict()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', choices={'train', 'chat'},
default='train', help="mode. if not specified, it's in the train mode")
args = parser.parse_args()
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data ready!')
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
if args.mode == 'train':
train()
elif args.mode == 'chat':
chat()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--mode',choices={'train','chat'},default='train',help="mode if not specified its in train mode")
args = parser.parse_args()
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data Ready!')
data.make_dir(config.CPT_PATH)
if args.mode == 'train':
train()
elif args.mode == 'chat':
chat()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', choices={'train', 'chat'},
default='train', help="mode. if not specified, it's in the train mode")
args = parser.parse_args()
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print("Data ready, starting application")
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
if args.mode == 'train':
train()
elif args.mode == 'chat':
chat()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('mode',
choices={'train', 'test', 'translate'},
default='train',
help="mode. if not specified, it's in the train mode")
args = parser.parse_args()
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data ready!')
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
if args.mode == 'train':
train()
elif args.mode == 'test':
bleu_scores = test()
elif args.mode == 'translate':
translate()
def main():
"""parser = argparse.ArgumentParser()
parser.add_argument('--mode', choices={'train', 'chat'},
default='train', help="mode. if not specified, it's in the train mode")
args = parser.parse_args()"""
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data ready!')
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
mode = input("Input mode (train|chat): ")
"""if args.mode == 'train':
train()
elif args.mode == 'chat':
chat()"""
if mode == 'train':
train()
else:
chat()
def start_training():
if not os.path.isdir(config.PROCESSED_PATH):
data.prepare_raw_data()
data.process_data()
print('Data ready!')
# create checkpoints folder if there isn't one already
data.make_dir(config.CPT_PATH)
""" Train the bot """
test_buckets, data_buckets, train_buckets_scale = _get_buckets()
# in train mode, we need to create the backward path, so forwrad_only is False
model = Seq2SeqModel(False, config.BATCH_SIZE)
model.build_graph()
saver = tf.train.Saver()
with tf.Session() as sess:
print('Running session')
sess.run(tf.global_variables_initializer())
_check_restore_parameters(sess, saver)
iteration = model.global_step.eval()
total_loss = 0
# Infinite loop
print('Start training ...')
train_record_file = open(
os.path.join(config.PROCESSED_PATH, config.TRAINING_RECORD_FILE),
'a+')
test_record_file = open(
os.path.join(config.PROCESSED_PATH, config.TESTING_RECORD_FILE),
'a+')
while True:
try:
skip_step = _get_skip_step(iteration)
bucket_id = _get_random_bucket(train_buckets_scale)
encoder_inputs, decoder_inputs, decoder_masks = data.get_batch(
data_buckets[bucket_id],
bucket_id,
batch_size=config.BATCH_SIZE)
start = time.time()
_, step_loss, _ = run_step(sess, model, encoder_inputs,
decoder_inputs, decoder_masks,
bucket_id, False)
total_loss += step_loss
iteration += 1
if iteration % skip_step == 0:
_train_info = 'Iter {}: loss {}, time {}'.format(
iteration, total_loss / skip_step,
time.time() - start)
print(_train_info)
train_record_file.write(_train_info + '\n')
start = time.time()
total_loss = 0
saver.save(sess,
os.path.join(config.CPT_PATH, 'chatbot'),
global_step=model.global_step)
if iteration % (10 * skip_step) == 0:
# Run evals on development set and print their loss
_test_info = _eval_test_set(sess, model, test_buckets)
for item in _test_info:
print(item)
test_record_file.write("%s\n" % item)
start = time.time()
sys.stdout.flush()
except KeyboardInterrupt:
print('Interrupted by user at iteration {}'.format(iteration))
train_record_file.close()
test_record_file.close()
def make_dirs(name):
data.make_dir(os.path.join(path, name + '_summaries'))
summary_path = os.path.join(path, name + '_summaries/reference')
data.make_dir(summary_path)
data.make_dir(os.path.join(path, name + '_summaries/system'))
return summary_path
def _setup_results(self):
'''Initialize path to results'''
print 'Setting up results directory'
self.results_path = os.path.join(self.sess_dir, 'results')
data.make_dir(self.results_path)
def _setup_checkpoints(self):
'''Initialize path to checkpoints'''
print 'Setting up checkpoints directory'
self.checkpoint_path = os.path.join(self.sess_dir, 'checkpoint')
data.make_dir(self.checkpoint_path)
def _setup_sess_dir(self):
'''Set up session directory. All outputs from the program go here'''
print 'Setting up directory for session'
self.sess_dir = os.path.join('', self.sess_name)
data.make_dir(self.sess_dir)
def evaluate(self, sess, train_losses, iteration, test=False):
''' Evaluate the model's current state on dev or test set '''
bucket_loss_texts = []
bucket_losses = []
summaries = []
eval_iter = 0
avg_loss = np.sum(train_losses) / len(train_losses)
print 'Average train loss', avg_loss
eval_start = time.time()
for bucket_index in xrange(len(config.BUCKETS)):
bucket_count = len(self.test_data[bucket_index]['enc_input'])
if bucket_count == 0:
print 'Test: empty bucket', bucket_index
continue
bucket_loss = []
while True:
# get data
eval_data = self.test_data if test else self.dev_data
batch_data = data.get_batch(eval_data, bucket_index,
config.BUCKETS, config.BATCH_SIZE,
eval_iter)
encoder_inputs = batch_data[0]
decoder_inputs = batch_data[1]
decoder_masks = batch_data[2]
done = batch_data[3]
# run model
_, step_loss, output_logits = self.run_step(
sess, encoder_inputs, decoder_inputs, decoder_masks,
config.BATCH_SIZE, bucket_index, False)
bucket_loss.append(step_loss)
output_logits = np.array(output_logits)
# get summaries
for i in xrange(config.BATCH_SIZE):
summaries.append(
self._construct_seq(output_logits[:, i, :]))
eval_iter += 1
if done:
eval_iter = 0
break
loss_text = 'Test bucket:', bucket_index, 'Avg Loss:', \
(sum(bucket_loss) / len(bucket_loss))
print loss_text
bucket_losses.append(sum(bucket_loss))
bucket_loss_texts.append(loss_text)
path = os.path.join(self.results_path, 'iter_' + str(iteration))
# adjust learning rate if dev loss increases
# if self.dev_loss is not None:
# if sum([self.dev_loss[i] < bucket_losses[i]
# for i in range(len(bucket_losses))]) > 0:
# self.lr /= 2
# print 'Learning rate adjusted'
self.dev_loss = bucket_losses
# log results
if test:
path += '_test'
data.make_dir(path)
gt_path = self.test_headlines_path if test else self.dev_headlines_path
utils.write_results(summaries, avg_loss, bucket_loss_texts, path,
gt_path)
print 'Wrote results to', path
print 'Evaluation took', time.time() - eval_start
