def check_loading(self, label_type, num_gpu, is_sorted):
print('----- label_type: ' + label_type + ', num_gpu: ' +
str(num_gpu) + ', is_sorted: ' + str(is_sorted) + ' -----')
batch_size = 64
dataset = Dataset(data_type='train',
label_type=label_type,
batch_size=batch_size,
num_stack=3,
num_skip=3,
is_sorted=is_sorted,
is_progressbar=True,
num_gpu=num_gpu)
tf.reset_default_graph()
with tf.Session().as_default() as sess:
print('=> Loading mini-batch...')
if label_type == 'character':
map_file_path = '../metrics/mapping_files/ctc/char2num.txt'
map_fn = num2char
else:
map_file_path = '../metrics/mapping_files/ctc/phone2num_' + \
label_type[5:7] + '.txt'
map_fn = num2phone
mini_batch = dataset.next_batch(session=sess)
iter_per_epoch = int(dataset.data_num / (batch_size * num_gpu)) + 1
for i in range(iter_per_epoch + 1):
return_tuple = mini_batch.__next__()
inputs = return_tuple[0]
labels_st = return_tuple[1]
if num_gpu > 1:
for inputs_gpu in inputs:
print(inputs_gpu.shape)
labels_st = labels_st[0]
labels = sparsetensor2list(labels_st, batch_size=len(inputs))
if num_gpu == 1:
for inputs_i, labels_i in zip(inputs, labels):
if len(inputs_i) < len(labels_i):
print(len(inputs_i))
print(len(labels_i))
raise ValueError
str_true = map_fn(labels[0], map_file_path)
str_true = re.sub(r'_', ' ', str_true)
def do_decode(model, params, epoch, beam_width, eval_batch_size):
"""Decode the CTC outputs.
Args:
model: the model to restore
params (dict): A dictionary of parameters
epoch (int): the epoch to restore
beam_width (int): 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
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)
# 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:
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.')
# Visualize
decode(session=sess,
decode_op=decode_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
is_test=True,
save_path=None)
def do_plot(model, params, epoch):
"""Plot the CTC posteriors.
Args:
model: the model to restore
params (dict): A dictionary of parameters
epoch (int): epoch to restore
"""
# Load dataset
test_data = Dataset(data_type='test',
label_type=params['label_type'],
batch_size=1,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=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_input_pl_list[0],
model.keep_prob_hidden_pl_list[0],
model.keep_prob_output_pl_list[0])
posteriors_op = model.posteriors(logits,
blank_prior=1,
softmax_tempareture=1)
# 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:
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.')
posterior_test(session=sess,
posteriors_op=posteriors_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
num_stack=params['num_stack'],
save_path=model.save_path,
show=True)
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_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='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)
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 (including model definition)
_, logits = network.compute_loss(network.inputs, network.labels,
network.inputs_seq_len,
network.keep_prob_input,
network.keep_prob_hidden)
decode_op = network.decoder(logits,
network.inputs_seq_len,
decode_type='beam_search',
beam_width=20)
per_op = network.compute_ler(decode_op, network.labels)
# 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:')
if param['label_type'] == 'character':
cer_test = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=test_data,
is_progressbar=True)
print(' CER: %f %%' % (cer_test * 100))
else:
per_test = do_eval_per(session=sess,
decode_op=decode_op,
per_op=per_op,
network=network,
dataset=test_data,
label_type=param['label_type'],
is_progressbar=True)
print(' PER: %f %%' % (per_test * 100))
def check_loading(self,
label_type,
data_type='dev',
shuffle=False,
sort_utt=False,
sort_stop_epoch=None,
frame_stacking=False,
splice=1):
print('========================================')
print(' label_type: %s' % label_type)
print(' data_type: %s' % data_type)
print(' shuffle: %s' % str(shuffle))
print(' sort_utt: %s' % str(sort_utt))
print(' sort_stop_epoch: %s' % str(sort_stop_epoch))
print(' frame_stacking: %s' % str(frame_stacking))
print(' splice: %d' % splice)
print('========================================')
num_stack = 3 if frame_stacking else 1
num_skip = 3 if frame_stacking else 1
dataset = Dataset(data_type=data_type,
label_type=label_type,
batch_size=64,
max_epoch=2,
splice=splice,
num_stack=num_stack,
num_skip=num_skip,
shuffle=shuffle,
sort_utt=sort_utt,
sort_stop_epoch=sort_stop_epoch,
progressbar=True)
print('=> Loading mini-batch...')
if label_type in ['character', 'character_capital_divide']:
map_fn = Idx2char(
map_file_path='../../metrics/mapping_files/ctc/' + label_type +
'.txt')
else:
map_fn = Idx2phone(
map_file_path='../../metrics/mapping_files/ctc/' + label_type +
'.txt')
for data, is_new_epoch in dataset:
inputs, labels, inputs_seq_len, input_names = data
# length check
for i_batch, l_batch in zip(inputs, labels):
if len(np.where(l_batch == dataset.padded_value)[0]) > 0:
if i_batch.shape[0] < np.where(
l_batch == dataset.padded_value)[0][0]:
raise ValueError(
'input length must be longer than label length.')
else:
if i_batch.shape[0] < len(l_batch):
raise ValueError(
'input length must be longer than label length.')
str_true = map_fn(labels[0])
str_true = re.sub(r'_', ' ', str_true)
print('----- %s ----- (epoch: %.3f)' %
(input_names[0], dataset.epoch_detail))
print(inputs[0].shape)
print(str_true)
def do_train(model, params):
"""Run training. If target labels are phone, the model is evaluated by 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=params['label_type'],
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=params['label_type'],
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False)
if 'char' in params['label_type']:
test_data = Dataset(data_type='test',
label_type=params['label_type'],
batch_size=1,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False)
else:
test_data = Dataset(data_type='test',
label_type='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 (including model definition)
loss_op, logits = model.compute_loss(model.inputs_pl_list[0],
model.labels_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 = model.decoder(logits,
model.inputs_seq_len_pl_list[0],
beam_width=params['beam_width'])
ler_op = model.compute_ler(decode_op, model.labels_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_ler_train, csv_ler_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()
ler_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, inputs_seq_len, _ = data
feed_dict_train = {
model.inputs_pl_list[0]:
inputs,
model.labels_pl_list[0]:
list2sparsetensor(labels,
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, inputs_seq_len, _), _ = dev_data.next()
feed_dict_dev = {
model.inputs_pl_list[0]:
inputs,
model.labels_pl_list[0]:
list2sparsetensor(labels,
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
ler_train, summary_str_train = sess.run(
[ler_op, summary_train], feed_dict=feed_dict_train)
ler_dev, summary_str_dev = sess.run(
[ler_op, summary_dev], feed_dict=feed_dict_dev)
csv_ler_train.append(ler_train)
csv_ler_dev.append(ler_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) / ler = %.3f (%.3f) / lr = %.5f (%.3f min)"
% (step + 1, train_data.epoch_detail, loss_train,
loss_dev, ler_train, ler_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_ler_train,
csv_ler_dev,
csv_steps,
label_type=params['label_type'],
save_path=model.save_path)
if train_data.epoch >= params['eval_start_epoch']:
start_time_eval = time.time()
if 'char' in params['label_type']:
print('=== Dev Data Evaluation ===')
ler_dev_epoch, wer_dev_epoch = do_eval_cer(
session=sess,
decode_op=decode_op,
model=model,
dataset=dev_data,
label_type=params['label_type'],
eval_batch_size=1)
print(' WER: %f %%' % (wer_dev_epoch * 100))
print(' CER: %f %%' % (ler_dev_epoch * 100))
if ler_dev_epoch < ler_dev_best:
ler_dev_best = ler_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 ===')
ler_test, wer_test = do_eval_cer(
session=sess,
decode_op=decode_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
eval_batch_size=1)
print(' WER: %f %%' % (wer_test * 100))
print(' CER: %f %%' % (ler_test * 100))
else:
print('=== Dev Data Evaluation ===')
ler_dev_epoch = do_eval_per(
session=sess,
decode_op=decode_op,
per_op=ler_op,
model=model,
dataset=dev_data,
label_type=params['label_type'],
eval_batch_size=1)
print(' PER: %f %%' % (ler_dev_epoch * 100))
if ler_dev_epoch < ler_dev_best:
ler_dev_best = ler_dev_epoch
print('■■■ ↑Best Score (PER)↑ ■■■')
# 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 ===')
ler_test = do_eval_per(
session=sess,
decode_op=decode_op,
per_op=ler_op,
model=model,
dataset=test_data,
label_type=params['label_type'],
eval_batch_size=1)
print(' PER: %f %%' % (ler_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=ler_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('')
def do_plot(network, param, epoch=None):
"""Plot the CTC posteriors.
Args:
network: model to restore
param: A dictionary of parameters
epoch: epoch to restore
"""
# Load dataset
test_data = Dataset(data_type='test', label_type=param['label_type'],
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)
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 (including model definition)
_, logits = network.compute_loss(network.inputs,
network.labels,
network.inputs_seq_len,
network.keep_prob_input,
network.keep_prob_hidden)
posteriors_op = network.posteriors(logits)
# 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.')
posterior_test(session=sess,
posteriors_op=posteriors_op,
network=network,
dataset=test_data,
label_type=param['label_type'],
save_path=network.model_dir,
show=True)
def do_train(network, param):
"""Run training. If target labels are phone, the model is evaluated by PER
with 39 phones.
Args:
network: network to train
param: A dictionary of parameters
"""
# Load dataset
train_data = Dataset(data_type='train', label_type=param['label_type'],
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=param['label_type'],
batch_size=param['batch_size'],
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=False)
if param['label_type'] == 'character':
test_data = Dataset(data_type='test', label_type='character',
batch_size=1,
num_stack=param['num_stack'],
num_skip=param['num_skip'],
is_sorted=False)
else:
test_data = Dataset(data_type='test', label_type='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)
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 (including model definition)
loss_op, logits = network.compute_loss(network.inputs,
network.labels,
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 = network.decoder(logits,
network.inputs_seq_len,
decode_type='beam_search',
beam_width=20)
ler_op = network.compute_ler(decode_op, network.labels)
# 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)))
# Make mini-batch generator
mini_batch_train = train_data.next_batch()
mini_batch_dev = dev_data.next_batch()
csv_steps, csv_loss_train, csv_loss_dev = [], [], []
csv_ler_train, csv_ler_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)
# 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()
error_best = 1
for step in range(max_steps):
# Create feed dictionary for next mini batch (train)
with tf.device('/cpu:0'):
inputs, labels, inputs_seq_len, _ = mini_batch_train.__next__()
feed_dict_train = {
network.inputs: inputs,
network.labels: list2sparsetensor(labels, 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, inputs_seq_len, _ = mini_batch_dev.__next__()
feed_dict_dev = {
network.inputs: inputs,
network.labels: list2sparsetensor(labels,
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
ler_train, summary_str_train = sess.run(
[ler_op, summary_train], feed_dict=feed_dict_train)
ler_dev, summary_str_dev = sess.run(
[ler_op, summary_dev], feed_dict=feed_dict_dev)
csv_ler_train.append(ler_train)
csv_ler_dev.append(ler_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) / ler = %.4f (%.4f) (%.3f min)" %
(step + 1, loss_train, loss_dev, ler_train,
ler_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()
with tf.device('/cpu:0'):
if param['label_type'] == 'character':
print('=== Dev Data Evaluation ===')
cer_dev_epoch = do_eval_cer(
session=sess,
decode_op=decode_op,
network=network,
dataset=dev_data,
eval_batch_size=1)
print(' CER: %f %%' % (cer_dev_epoch * 100))
if cer_dev_epoch < error_best:
error_best = cer_dev_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
print('=== Test Data Evaluation ===')
cer_test = do_eval_cer(
session=sess,
decode_op=decode_op,
network=network,
dataset=test_data,
eval_batch_size=1)
print(' CER: %f %%' % (cer_test * 100))
else:
print('=== Dev Data Evaluation ===')
per_dev_epoch = do_eval_per(
session=sess,
decode_op=decode_op,
per_op=ler_op,
network=network,
dataset=dev_data,
label_type=param['label_type'],
eval_batch_size=1)
print(' PER: %f %%' % (per_dev_epoch * 100))
if per_dev_epoch < error_best:
error_best = per_dev_epoch
print('■■■ ↑Best Score (PER)↑ ■■■')
print('=== Test Data Evaluation ===')
per_test = do_eval_per(
session=sess,
decode_op=decode_op,
per_op=ler_op,
network=network,
dataset=test_data,
label_type=param['label_type'],
eval_batch_size=1)
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_ler_train, csv_ler_dev,
save_path=network.model_dir)
# Training was finished correctly
with open(join(network.model_dir, 'complete.txt'), 'w') as f:
f.write('')