def do_eval(model, params, epoch, beam_width, eval_batch_size):
"""Evaluate the model.
Args:
model: the model to restore
params (dict): A dictionary of parameters
epoch (int): the epoch to restore
beam_width (int): beam_width (int, optional): beam width for beam search.
1 disables beam search, which mean greedy decoding.
eval_batch_size (int): the size of mini-batch when evaluation
"""
# Load dataset
if 'phone' in params['label_type']:
test_data = Dataset(
data_type='test', label_type='phone39',
batch_size=eval_batch_size, splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
shuffle=False, progressbar=True)
else:
test_data = Dataset(
data_type='test', label_type=params['label_type'],
batch_size=eval_batch_size, splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
shuffle=False, progressbar=True)
# Define placeholders
model.create_placeholders()
# Add to the graph each operation (including model definition)
_, logits = model.compute_loss(model.inputs_pl_list[0],
model.labels_pl_list[0],
model.inputs_seq_len_pl_list[0],
model.keep_prob_pl_list[0])
decode_op = model.decoder(logits,
model.inputs_seq_len_pl_list[0],
beam_width=beam_width)
per_op = model.compute_ler(decode_op, model.labels_pl_list[0])
# Create a saver for writing training checkpoints
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(model.save_path)
# If check point exists
if ckpt:
# Use last saved model
model_path = ckpt.model_checkpoint_path
if epoch != -1:
# Use the best model
# NOTE: In the training stage, parameters are saved only when
# accuracies are improved
model_path = model_path.split('/')[:-1]
model_path = '/'.join(model_path) + '/model.ckpt-' + str(epoch)
saver.restore(sess, model_path)
print("Model restored: " + model_path)
else:
raise ValueError('There are not any checkpoints.')
print('Test Data Evaluation:')
if 'char' in params['label_type']:
cer_test, wer_test = do_eval_cer(
session=sess,
decode_op=decode_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
is_test=True,
eval_batch_size=eval_batch_size,
progressbar=True)
print(' CER: %f %%' % (cer_test * 100))
print(' WER: %f %%' % (wer_test * 100))
else:
per_test = do_eval_per(
session=sess,
decode_op=decode_op,
per_op=per_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
is_test=True,
eval_batch_size=eval_batch_size,
progressbar=True)
print(' PER: %f %%' % (per_test * 100))
def do_train(network, param):
"""Run multi-task training. The target labels in the main task is
characters and those in the sub task is 61 phones. The model is
evaluated by CER and PER with 39 phones.
Args:
network: network to train
param: A dictionary of parameters
"""
# Load dataset
train_data = Dataset(data_type='train',
label_type_main='character',
label_type_sub=param['label_type_sub'],
batch_size=param['batch_size'],
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=True)
dev_data = Dataset(data_type='dev',
label_type_main='character',
label_type_sub=param['label_type_sub'],
batch_size=param['batch_size'],
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=False)
test_data = Dataset(data_type='test',
label_type_main='character',
label_type_sub='phone39',
batch_size=1,
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=False)
# Tell TensorFlow that the model will be built into the default graph
with tf.Graph().as_default():
# Define placeholders
network.inputs = tf.placeholder(tf.float32,
shape=[None, None, network.input_size],
name='input')
indices_pl = tf.placeholder(tf.int64, name='indices')
values_pl = tf.placeholder(tf.int32, name='values')
shape_pl = tf.placeholder(tf.int64, name='shape')
network.labels = tf.SparseTensor(indices_pl, values_pl, shape_pl)
indices_sub_pl = tf.placeholder(tf.int64, name='indices_sub')
values_sub_pl = tf.placeholder(tf.int32, name='values_sub')
shape_sub_pl = tf.placeholder(tf.int64, name='shape_sub')
network.labels_sub = tf.SparseTensor(indices_sub_pl, values_sub_pl,
shape_sub_pl)
network.inputs_seq_len = tf.placeholder(tf.int64,
shape=[None],
name='inputs_seq_len')
network.keep_prob_input = tf.placeholder(tf.float32,
name='keep_prob_input')
network.keep_prob_hidden = tf.placeholder(tf.float32,
name='keep_prob_hidden')
# Add to the graph each operation
loss_op, logits_main, logits_sub = network.compute_loss(
network.inputs, network.labels, network.labels_sub,
network.inputs_seq_len, network.keep_prob_input,
network.keep_prob_hidden)
train_op = network.train(loss_op,
optimizer=param['optimizer'],
learning_rate_init=float(
param['learning_rate']),
decay_steps=param['decay_steps'],
decay_rate=param['decay_rate'])
decode_op_main, decode_op_sub = network.decoder(
logits_main,
logits_sub,
network.inputs_seq_len,
decode_type='beam_search',
beam_width=20)
ler_op_main, ler_op_sub = network.compute_ler(decode_op_main,
decode_op_sub,
network.labels,
network.labels_sub)
# Build the summary tensor based on the TensorFlow collection of
# summaries
summary_train = tf.summary.merge(network.summaries_train)
summary_dev = tf.summary.merge(network.summaries_dev)
# Add the variable initializer operation
init_op = tf.global_variables_initializer()
# Create a saver for writing training checkpoints
saver = tf.train.Saver(max_to_keep=None)
# Count total parameters
parameters_dict, total_parameters = count_total_parameters(
tf.trainable_variables())
for parameter_name in sorted(parameters_dict.keys()):
print("%s %d" % (parameter_name, parameters_dict[parameter_name]))
print("Total %d variables, %s M parameters" %
(len(parameters_dict.keys()), "{:,}".format(
total_parameters / 1000000)))
csv_steps, csv_loss_train, csv_loss_dev = [], [], []
csv_cer_train, csv_cer_dev = [], []
csv_per_train, csv_per_dev = [], []
# Create a session for running operation on the graph
with tf.Session() as sess:
# Instantiate a SummaryWriter to output summaries and the graph
summary_writer = tf.summary.FileWriter(network.model_dir,
sess.graph)
# Initialize parameters
sess.run(init_op)
# Make mini-batch generator
mini_batch_train = train_data.next_batch()
mini_batch_dev = dev_data.next_batch()
# Train model
iter_per_epoch = int(train_data.data_num / param['batch_size'])
train_step = train_data.data_num / param['batch_size']
if train_step != int(train_step):
iter_per_epoch += 1
max_steps = iter_per_epoch * param['num_epoch']
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
cer_dev_best = 1
for step in range(max_steps):
# Create feed dictionary for next mini batch (train)
with tf.device('/cpu:0'):
inputs, labels_char, labels_phone, inputs_seq_len, _ = mini_batch_train.__next__(
)
feed_dict_train = {
network.inputs:
inputs,
network.labels:
list2sparsetensor(labels_char, padded_value=-1),
network.labels_sub:
list2sparsetensor(labels_phone, padded_value=-1),
network.inputs_seq_len:
inputs_seq_len,
network.keep_prob_input:
network.dropout_ratio_input,
network.keep_prob_hidden:
network.dropout_ratio_hidden
}
# Update parameters
sess.run(train_op, feed_dict=feed_dict_train)
if (step + 1) % 10 == 0:
# Create feed dictionary for next mini batch (dev)
with tf.device('/cpu:0'):
inputs, labels_char, labels_phone, inputs_seq_len, _ = mini_batch_dev.__next__(
)
feed_dict_dev = {
network.inputs:
inputs,
network.labels:
list2sparsetensor(labels_char, padded_value=-1),
network.labels_sub:
list2sparsetensor(labels_phone, padded_value=-1),
network.inputs_seq_len:
inputs_seq_len,
network.keep_prob_input:
network.dropout_ratio_input,
network.keep_prob_hidden:
network.dropout_ratio_hidden
}
# Compute loss
loss_train = sess.run(loss_op, feed_dict=feed_dict_train)
loss_dev = sess.run(loss_op, feed_dict=feed_dict_dev)
csv_steps.append(step)
csv_loss_train.append(loss_train)
csv_loss_dev.append(loss_dev)
# Change to evaluation mode
feed_dict_train[network.keep_prob_input] = 1.0
feed_dict_train[network.keep_prob_hidden] = 1.0
feed_dict_dev[network.keep_prob_input] = 1.0
feed_dict_dev[network.keep_prob_hidden] = 1.0
# Compute accuracy & update event file
cer_train, per_train, summary_str_train = sess.run(
[ler_op_main, ler_op_sub, summary_train],
feed_dict=feed_dict_train)
cer_dev, per_dev, summary_str_dev = sess.run(
[ler_op_main, ler_op_sub, summary_dev],
feed_dict=feed_dict_dev)
csv_cer_train.append(cer_train)
csv_cer_dev.append(cer_dev)
csv_per_train.append(per_train)
csv_per_dev.append(per_dev)
summary_writer.add_summary(summary_str_train, step + 1)
summary_writer.add_summary(summary_str_dev, step + 1)
summary_writer.flush()
duration_step = time.time() - start_time_step
print(
"Step % d: loss = %.3f (%.3f) / cer = %.4f (%.4f) / per = % .4f (%.4f) (%.3f min)"
% (step + 1, loss_train, loss_dev, cer_train, cer_dev,
per_train, per_dev, duration_step / 60))
sys.stdout.flush()
start_time_step = time.time()
# Save checkpoint and evaluate model per epoch
if (step + 1) % iter_per_epoch == 0 or (step + 1) == max_steps:
duration_epoch = time.time() - start_time_epoch
epoch = (step + 1) // iter_per_epoch
print('-----EPOCH:%d (%.3f min)-----' %
(epoch, duration_epoch / 60))
# Save model (check point)
checkpoint_file = join(network.model_dir, 'model.ckpt')
save_path = saver.save(sess,
checkpoint_file,
global_step=epoch)
print("Model saved in file: %s" % save_path)
if epoch >= 10:
start_time_eval = time.time()
print('=== Dev Data Evaluation ===')
cer_dev_epoch = do_eval_cer(session=sess,
decode_op=decode_op_main,
network=network,
dataset=dev_data,
eval_batch_size=1,
is_multitask=True)
print(' CER: %f %%' % (cer_dev_epoch * 100))
per_dev_epoch = do_eval_per(
session=sess,
decode_op=decode_op_sub,
per_op=ler_op_sub,
network=network,
dataset=dev_data,
label_type=param['label_type_sub'],
eval_batch_size=1,
is_multitask=True)
print(' PER: %f %%' % (per_dev_epoch * 100))
if cer_dev_epoch < cer_dev_best:
cer_dev_best = cer_dev_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
print('=== Test Data Evaluation ===')
cer_test = do_eval_cer(session=sess,
decode_op=decode_op_main,
network=network,
dataset=test_data,
eval_batch_size=1,
is_multitask=True)
print(' CER: %f %%' % (cer_test * 100))
per_test = do_eval_per(
session=sess,
decode_op=decode_op_sub,
per_op=ler_op_sub,
network=network,
dataset=test_data,
label_type=param['label_type_sub'],
eval_batch_size=1,
is_multitask=True)
print(' PER: %f %%' % (per_test * 100))
duration_eval = time.time() - start_time_eval
print('Evaluation time: %.3f min' %
(duration_eval / 60))
start_time_epoch = time.time()
start_time_step = time.time()
duration_train = time.time() - start_time_train
print('Total time: %.3f hour' % (duration_train / 3600))
# Save train & dev loss, ler
save_loss(csv_steps,
csv_loss_train,
csv_loss_dev,
save_path=network.model_dir)
save_ler(csv_steps,
csv_cer_train,
csv_cer_dev,
save_path=network.model_dir)
save_ler(csv_steps,
csv_per_train,
csv_per_dev,
save_path=network.model_dir)
# Training was finished correctly
with open(join(network.model_dir, 'complete.txt'), 'w') as f:
f.write('')
def do_eval(network, param, epoch=None):
"""Evaluate the model.
Args:
network: model to restore
param: A dictionary of parameters
epoch: int, the epoch to restore
"""
# Load dataset
test_data = Dataset(data_type='test',
label_type_main='character',
label_type_sub='phone39',
batch_size=1,
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=False,
is_progressbar=True)
# Define placeholders
network.inputs = tf.placeholder(tf.float32,
shape=[None, None, network.input_size],
name='input')
indices_pl = tf.placeholder(tf.int64, name='indices')
values_pl = tf.placeholder(tf.int32, name='values')
shape_pl = tf.placeholder(tf.int64, name='shape')
network.labels = tf.SparseTensor(indices_pl, values_pl, shape_pl)
indices_sub_pl = tf.placeholder(tf.int64, name='indices_sub')
values_sub_pl = tf.placeholder(tf.int32, name='values_sub')
shape_sub_pl = tf.placeholder(tf.int64, name='shape_sub')
network.labels_sub = tf.SparseTensor(indices_sub_pl, values_sub_pl,
shape_sub_pl)
network.inputs_seq_len = tf.placeholder(tf.int64,
shape=[None],
name='inputs_seq_len')
# Add to the graph each operation
_, logits_main, logits_sub = network.compute_loss(network.inputs,
network.labels,
network.labels_sub,
network.inputs_seq_len)
decode_op_main, decode_op_sub = network.decoder(logits_main,
logits_sub,
network.inputs_seq_len,
decode_type='beam_search',
beam_width=20)
per_op_main, per_op_sub = network.compute_ler(decode_op_main,
decode_op_sub,
network.labels,
network.labels_sub)
# Create a saver for writing training checkpoints
saver = tf.train.Saver()
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(network.model_dir)
# If check point exists
if ckpt:
# Use last saved model
model_path = ckpt.model_checkpoint_path
if epoch is not None:
model_path = model_path.split('/')[:-1]
model_path = '/'.join(model_path) + '/model.ckpt-' + str(epoch)
saver.restore(sess, model_path)
print("Model restored: " + model_path)
else:
raise ValueError('There are not any checkpoints.')
print('=== Test Data Evaluation ===')
cer_test = do_eval_cer(session=sess,
decode_op=decode_op_main,
network=network,
dataset=test_data,
is_progressbar=True,
is_multitask=True)
print(' CER: %f %%' % (cer_test * 100))
per_test = do_eval_per(session=sess,
decode_op=decode_op_sub,
per_op=per_op_sub,
network=network,
dataset=test_data,
train_label_type=param['label_type_sub'],
is_progressbar=True,
is_multitask=True)
print(' PER: %f %%' % (per_test * 100))
def do_train(model, params):
"""Run multi-task CTC training. The target labels in the main task is
characters and those in the sub task is 61 phones. The model is
evaluated by CER and PER with 39 phones.
Args:
model: the model to train
params (dict): A dictionary of parameters
"""
# Load dataset
train_data = Dataset(data_type='train',
label_type_main=params['label_type_main'],
label_type_sub=params['label_type_sub'],
batch_size=params['batch_size'],
max_epoch=params['num_epoch'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
sort_stop_epoch=params['sort_stop_epoch'])
dev_data = Dataset(data_type='dev',
label_type_main=params['label_type_main'],
label_type_sub=params['label_type_sub'],
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False)
test_data = Dataset(data_type='test',
label_type_main=params['label_type_main'],
label_type_sub='phone39',
batch_size=1,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False)
# Tell TensorFlow that the model will be built into the default graph
with tf.Graph().as_default():
# Define placeholders
model.create_placeholders()
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
# Add to the graph each operation
loss_op, logits_main, logits_sub = model.compute_loss(
model.inputs_pl_list[0], model.labels_pl_list[0],
model.labels_sub_pl_list[0], model.inputs_seq_len_pl_list[0],
model.keep_prob_input_pl_list[0],
model.keep_prob_hidden_pl_list[0],
model.keep_prob_output_pl_list[0])
train_op = model.train(loss_op,
optimizer=params['optimizer'],
learning_rate=learning_rate_pl)
decode_op_character, decode_op_phone = model.decoder(
logits_main,
logits_sub,
model.inputs_seq_len_pl_list[0],
beam_width=params['beam_width'])
cer_op, per_op = model.compute_ler(decode_op_character,
decode_op_phone,
model.labels_pl_list[0],
model.labels_sub_pl_list[0])
# Define learning rate controller
lr_controller = Controller(
learning_rate_init=params['learning_rate'],
decay_start_epoch=params['decay_start_epoch'],
decay_rate=params['decay_rate'],
decay_patient_epoch=params['decay_patient_epoch'],
lower_better=True)
# Build the summary tensor based on the TensorFlow collection of
# summaries
summary_train = tf.summary.merge(model.summaries_train)
summary_dev = tf.summary.merge(model.summaries_dev)
# Add the variable initializer operation
init_op = tf.global_variables_initializer()
# Create a saver for writing training checkpoints
saver = tf.train.Saver(max_to_keep=None)
# Count total parameters
parameters_dict, total_parameters = count_total_parameters(
tf.trainable_variables())
for parameter_name in sorted(parameters_dict.keys()):
print("%s %d" % (parameter_name, parameters_dict[parameter_name]))
print("Total %d variables, %s M parameters" %
(len(parameters_dict.keys()), "{:,}".format(
total_parameters / 1000000)))
csv_steps, csv_loss_train, csv_loss_dev = [], [], []
csv_cer_train, csv_cer_dev = [], []
csv_per_train, csv_per_dev = [], []
# Create a session for running operation on the graph
with tf.Session() as sess:
# Instantiate a SummaryWriter to output summaries and the graph
summary_writer = tf.summary.FileWriter(model.save_path, sess.graph)
# Initialize parameters
sess.run(init_op)
# Train model
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
cer_dev_best = 1
learning_rate = float(params['learning_rate'])
for step, (data, is_new_epoch) in enumerate(train_data):
# Create feed dictionary for next mini batch (train)
inputs, labels_char, labels_phone, inputs_seq_len, _ = data
feed_dict_train = {
model.inputs_pl_list[0]:
inputs,
model.labels_pl_list[0]:
list2sparsetensor(labels_char,
padded_value=train_data.padded_value),
model.labels_sub_pl_list[0]:
list2sparsetensor(labels_phone,
padded_value=train_data.padded_value),
model.inputs_seq_len_pl_list[0]:
inputs_seq_len,
model.keep_prob_input_pl_list[0]:
params['dropout_input'],
model.keep_prob_hidden_pl_list[0]:
params['dropout_hidden'],
model.keep_prob_output_pl_list[0]:
params['dropout_output'],
learning_rate_pl:
learning_rate
}
# Update parameters
sess.run(train_op, feed_dict=feed_dict_train)
if (step + 1) % params['print_step'] == 0:
# Create feed dictionary for next mini batch (dev)
(inputs, labels_char, labels_phone, inputs_seq_len,
_), _ = dev_data.next()
feed_dict_dev = {
model.inputs_pl_list[0]:
inputs,
model.labels_pl_list[0]:
list2sparsetensor(labels_char,
padded_value=dev_data.padded_value),
model.labels_sub_pl_list[0]:
list2sparsetensor(labels_phone,
padded_value=dev_data.padded_value),
model.inputs_seq_len_pl_list[0]:
inputs_seq_len,
model.keep_prob_input_pl_list[0]:
1.0,
model.keep_prob_hidden_pl_list[0]:
1.0,
model.keep_prob_output_pl_list[0]:
1.0
}
# Compute loss
loss_train = sess.run(loss_op, feed_dict=feed_dict_train)
loss_dev = sess.run(loss_op, feed_dict=feed_dict_dev)
csv_steps.append(step)
csv_loss_train.append(loss_train)
csv_loss_dev.append(loss_dev)
# Change to evaluation mode
feed_dict_train[model.keep_prob_input_pl_list[0]] = 1.0
feed_dict_train[model.keep_prob_hidden_pl_list[0]] = 1.0
feed_dict_train[model.keep_prob_output_pl_list[0]] = 1.0
# Compute accuracy & update event files
cer_train, per_train, summary_str_train = sess.run(
[cer_op, per_op, summary_train],
feed_dict=feed_dict_train)
cer_dev, per_dev, summary_str_dev = sess.run(
[cer_op, per_op, summary_dev], feed_dict=feed_dict_dev)
csv_cer_train.append(cer_train)
csv_cer_dev.append(cer_dev)
csv_per_train.append(per_train)
csv_per_dev.append(per_dev)
summary_writer.add_summary(summary_str_train, step + 1)
summary_writer.add_summary(summary_str_dev, step + 1)
summary_writer.flush()
duration_step = time.time() - start_time_step
print(
"Step %d (epoch: %.3f): loss = %.3f (%.3f) / cer = %.3f (%.3f) / per = % .3f (%.3f) / lr = %.5f (%.3f min)"
% (step + 1, train_data.epoch_detail, loss_train,
loss_dev, cer_train, cer_dev, per_train, per_dev,
learning_rate, duration_step / 60))
sys.stdout.flush()
start_time_step = time.time()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
print('-----EPOCH:%d (%.3f min)-----' %
(train_data.epoch, duration_epoch / 60))
# Save fugure of loss & ler
plot_loss(csv_loss_train,
csv_loss_dev,
csv_steps,
save_path=model.save_path)
plot_ler(csv_cer_train,
csv_cer_dev,
csv_steps,
label_type=params['label_type_main'],
save_path=model.save_path)
plot_ler(csv_per_train,
csv_per_dev,
csv_steps,
label_type=params['label_type_sub'],
save_path=model.save_path)
if train_data.epoch >= params['eval_start_epoch']:
start_time_eval = time.time()
print('=== Dev Data Evaluation ===')
cer_dev_epoch, wer_dev_epoch = do_eval_cer(
session=sess,
decode_op=decode_op_character,
model=model,
dataset=dev_data,
label_type=params['label_type_main'],
eval_batch_size=1,
is_multitask=True)
print(' WER: %f %%' % (wer_dev_epoch * 100))
print(' CER: %f %%' % (cer_dev_epoch * 100))
per_dev_epoch = do_eval_per(
session=sess,
decode_op=decode_op_phone,
per_op=per_op,
model=model,
dataset=dev_data,
label_type=params['label_type_sub'],
eval_batch_size=1,
is_multitask=True)
print(' PER: %f %%' % (per_dev_epoch * 100))
if cer_dev_epoch < cer_dev_best:
cer_dev_best = cer_dev_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
# Save model only when best accuracy is obtained
# (check point)
checkpoint_file = join(model.save_path,
'model.ckpt')
save_path = saver.save(
sess,
checkpoint_file,
global_step=train_data.epoch)
print("Model saved in file: %s" % save_path)
print('=== Test Data Evaluation ===')
cer_test, wer_test = do_eval_cer(
session=sess,
decode_op=decode_op_character,
model=model,
dataset=test_data,
label_type=params['label_type_main'],
eval_batch_size=1,
is_multitask=True)
print(' WER: %f %%' % (wer_test * 100))
print(' CER: %f %%' % (cer_test * 100))
per_test = do_eval_per(
session=sess,
decode_op=decode_op_phone,
per_op=per_op,
model=model,
dataset=test_data,
label_type=params['label_type_sub'],
eval_batch_size=1,
is_multitask=True)
print(' PER: %f %%' % (per_test * 100))
duration_eval = time.time() - start_time_eval
print('Evaluation time: %.3f min' %
(duration_eval / 60))
# Update learning rate
learning_rate = lr_controller.decay_lr(
learning_rate=learning_rate,
epoch=train_data.epoch,
value=cer_dev_epoch)
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
print('Total time: %.3f hour' % (duration_train / 3600))
# Training was finished correctly
with open(join(model.save_path, 'complete.txt'), 'w') as f:
f.write('')
