def do_train(network, optimizer, learning_rate, batch_size, epoch_num,
label_type, num_stack, num_skip):
"""Run training. If target labels are phone, the model is evaluated by PER
with 39 phones.
Args:
network: network to train
optimizer: string, the name of optimizer.
ex.) adam, rmsprop
learning_rate: A float value, the initial learning rate
batch_size: int, the size of mini-batch
epoch_num: int, the number of epochs to train
label_type: string, phone39 or phone48 or phone61 or character
num_stack: int, the number of frames to stack
num_skip: int, the number of frames to skip
"""
# Load dataset
train_data = DataSet(data_type='train',
label_type=label_type,
batch_size=batch_size,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=True)
dev_data = DataSet(data_type='dev',
label_type=label_type,
batch_size=batch_size,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False)
if label_type == 'character':
test_data = DataSet(data_type='test',
label_type='character',
batch_size=1,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False)
else:
test_data = DataSet(data_type='test',
label_type='phone39',
batch_size=1,
num_stack=num_stack,
num_skip=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=optimizer,
learning_rate_init=float(learning_rate),
is_scheduled=False)
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 / batch_size)
train_step = train_data.data_num / batch_size
if train_step != int(train_step):
iter_per_epoch += 1
max_steps = iter_per_epoch * epoch_num
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)
inputs, labels_st, inputs_seq_len, _ = mini_batch_train.__next__(
)
feed_dict_train = {
network.inputs: inputs,
network.labels: labels_st,
network.inputs_seq_len: inputs_seq_len,
network.keep_prob_input: network.dropout_ratio_input,
network.keep_prob_hidden: network.dropout_ratio_hidden,
network.lr: learning_rate
}
# Create feed dictionary for next mini batch (dev)
inputs, labels_st, inputs_seq_len, _ = mini_batch_dev.__next__(
)
feed_dict_dev = {
network.inputs: inputs,
network.labels: labels_st,
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:
# 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()
if label_type == 'character':
print('=== Dev Data Evaluation ===')
cer_dev_epoch = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=dev_data)
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,
train_label_type=label_type)
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,
train_label_type=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('')
def do_eval(network, label_type_second, num_stack, num_skip, epoch=None):
"""Evaluate the model.
Args:
network: model to restore
label_type_second: string, phone39 or phone48 or phone61
num_stack: int, the number of frames to stack
num_skip: int, the number of frames to skip
epoch: int, the epoch to restore
"""
# Load dataset
if label_type_second == 'character':
test_data = DataSet(data_type='test',
label_type_second='character',
batch_size=1,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False,
is_progressbar=True)
else:
test_data = DataSet(data_type='test',
label_type_second='phone39',
batch_size=1,
num_stack=num_stack,
num_skip=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_second_pl = tf.placeholder(tf.int64, name='indices_second')
values_second_pl = tf.placeholder(tf.int32, name='values_second')
shape_second_pl = tf.placeholder(tf.int64, name='shape_second')
network.labels_second = tf.SparseTensor(indices_second_pl,
values_second_pl, shape_second_pl)
network.inputs_seq_len = tf.placeholder(tf.int64,
shape=[None],
name='inputs_seq_len')
# Add to the graph each operation
_, logits_main, logits_second = network.compute_loss(
network.inputs, network.labels, network.labels_second,
network.inputs_seq_len)
decode_op_main, decode_op_second = network.decoder(
logits_main,
logits_second,
network.inputs_seq_len,
decode_type='beam_search',
beam_width=20)
per_op_main, per_op_second = network.compute_ler(decode_op_main,
decode_op_second,
network.labels,
network.labels_second)
# 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_second,
per_op=per_op_second,
network=network,
dataset=test_data,
train_label_type=label_type_second,
is_progressbar=True,
is_multitask=True)
print(' PER: %f %%' % (per_test * 100))
def do_eval(network,
label_type,
num_stack,
num_skip,
train_data_size,
epoch=None):
"""Evaluate the model.
Args:
network: model to restore
label_type: string, phone or character o kanji
num_stack: int, the number of frames to stack
num_skip: int, the number of frames to skip
train_data_size: string, default or large
epoch: int, the epoch to restore
"""
# Load dataset
eval1_data = DataSet(data_type='eval1',
label_type=label_type,
batch_size=1,
train_data_size=train_data_size,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False,
is_progressbar=True)
eval2_data = DataSet(data_type='eval2',
label_type=label_type,
batch_size=1,
train_data_size=train_data_size,
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False,
is_progressbar=True)
eval3_data = DataSet(data_type='eval3',
label_type=label_type,
batch_size=1,
train_data_size=train_data_size,
num_stack=num_stack,
num_skip=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.')
if label_type in ['character', 'kanji']:
print('=== eval1 Evaluation ===')
cer_eval1 = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=eval1_data,
label_type=label_type,
is_test=True,
eval_batch_size=1,
is_progressbar=True)
print(' CER: %f %%' % (cer_eval1 * 100))
print('=== eval2 Evaluation ===')
cer_eval2 = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=eval2_data,
label_type=label_type,
is_test=True,
eval_batch_size=1,
is_progressbar=True)
print(' CER: %f %%' % (cer_eval2 * 100))
print('=== eval3 Evaluation ===')
cer_eval3 = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=eval3_data,
label_type=label_type,
is_test=True,
eval_batch_size=1,
is_progressbar=True)
print(' CER: %f %%' % (cer_eval3 * 100))
else:
print('=== eval1 Evaluation ===')
per_eval1 = do_eval_per(session=sess,
per_op=per_op,
network=network,
dataset=eval1_data,
eval_batch_size=1,
is_progressbar=True)
print(' PER: %f %%' % (per_eval1 * 100))
print('=== eval2 Evaluation ===')
per_eval2 = do_eval_per(session=sess,
per_op=per_op,
network=network,
dataset=eval2_data,
eval_batch_size=1,
is_progressbar=True)
print(' PER: %f %%' % (per_eval2 * 100))
print('=== eval3 Evaluation ===')
per_eval3 = do_eval_per(session=sess,
per_op=per_op,
network=network,
dataset=eval3_data,
eval_batch_size=1,
is_progressbar=True)
print(' PER: %f %%' % (per_eval3 * 100))