def do_restore(network, label_type, num_stack, num_skip, epoch=None):
"""Restore model.
Args:
network: model to restore
label_type: phone or character o kanji
num_stack: int, the number of frames to stack
num_skip: int, the number of frames to skip
epoch: epoch to restore
"""
# Load dataset
eval1_data = DataSet(data_type='eval1', label_type=label_type,
num_stack=num_stack, num_skip=num_skip,
is_sorted=False, is_progressbar=True)
eval2_data = DataSet(data_type='eval2', label_type=label_type,
num_stack=num_stack, num_skip=num_skip,
is_sorted=False, is_progressbar=True)
eval3_data = DataSet(data_type='eval3', label_type=label_type,
num_stack=num_stack, num_skip=num_skip,
is_sorted=False, is_progressbar=True)
# Define model
network.define()
# Add to the graph each operation
decode_op = network.decoder(decode_type='beam_search',
beam_width=20)
posteriors_op = network.posteriors(decode_op)
per_op = network.ler(decode_op)
# 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:■')
do_eval_cer(session=sess, decode_op=decode_op, network=network,
dataset=eval1_data, eval_batch_size=network.batch_size,
is_progressbar=True)
print('■eval2 Evaluation:■')
do_eval_cer(session=sess, decode_op=decode_op, network=network,
dataset=eval2_data, eval_batch_size=network.batch_size,
is_progressbar=True)
print('■eval3 Evaluation:■')
do_eval_cer(session=sess, decode_op=decode_op, network=network,
dataset=eval3_data, eval_batch_size=network.batch_size,
is_progressbar=True)
else:
print('■eval1 Evaluation:■')
do_eval_per(session=sess, per_op=per_op, network=network,
dataset=eval1_data, eval_batch_size=network.batch_size,
is_progressbar=True)
print('■eval2 Evaluation:■')
do_eval_per(session=sess, per_op=per_op, network=network,
dataset=eval2_data, eval_batch_size=network.batch_size,
is_progressbar=True)
print('■eval3 Evaluation:■')
do_eval_per(session=sess, per_op=per_op, network=network,
dataset=eval3_data, eval_batch_size=network.batch_size,
is_progressbar=True)
# Visualize
decode_test(session=sess, decode_op=decode_op, network=network,
dataset=eval1_data, label_type=label_type)
def do_train(network, optimizer, learning_rate, batch_size, epoch_num, label_type, num_stack, num_skip):
"""Run training.
Args:
network: network to train
optimizer: string, the name of optimizer. ex.) adam, rmsprop
learning_rate: initial learning rate
batch_size: size of mini batch
epoch_num: epoch num to train
label_type: 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,
num_stack=num_stack, num_skip=num_skip,
is_sorted=True)
if label_type == 'character':
dev_data = DataSet(data_type='dev', label_type='character',
num_stack=num_stack, num_skip=num_skip,
is_sorted=False)
test_data = DataSet(data_type='test', label_type='character',
num_stack=num_stack, num_skip=num_skip,
is_sorted=False)
else:
dev_data = DataSet(data_type='dev', label_type='phone39',
num_stack=num_stack, num_skip=num_skip,
is_sorted=False)
test_data = DataSet(data_type='test', label_type='phone39',
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 model
network.define()
# NOTE: define model under tf.Graph()
# Add to the graph each operation
loss_op = network.loss()
train_op = network.train(optimizer=optimizer,
learning_rate_init=learning_rate,
is_scheduled=False)
decode_op = network.decoder(decode_type='beam_search',
beam_width=20)
per_op = network.ler(decode_op)
# 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_train_loss = []
csv_dev_loss = []
# 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)
if (train_data.data_num / batch_size) != int(train_data.data_num / batch_size):
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, seq_len, _ = train_data.next_batch(
batch_size=batch_size)
indices, values, dense_shape = list2sparsetensor(labels)
feed_dict_train = {
network.inputs_pl: inputs,
network.label_indices_pl: indices,
network.label_values_pl: values,
network.label_shape_pl: dense_shape,
network.seq_len_pl: seq_len,
network.keep_prob_input_pl: network.dropout_ratio_input,
network.keep_prob_hidden_pl: network.dropout_ratio_hidden,
network.lr_pl: learning_rate
}
# Create feed dictionary for next mini batch (dev)
inputs, labels, seq_len, _ = dev_data.next_batch(
batch_size=batch_size)
indices, values, dense_shape = list2sparsetensor(labels)
feed_dict_dev = {
network.inputs_pl: inputs,
network.label_indices_pl: indices,
network.label_values_pl: values,
network.label_shape_pl: dense_shape,
network.seq_len_pl: seq_len,
network.keep_prob_input_pl: network.dropout_ratio_input,
network.keep_prob_hidden_pl: network.dropout_ratio_hidden
}
# Update parameters & compute loss
_, loss_train = sess.run(
[train_op, loss_op], feed_dict=feed_dict_train)
loss_dev = sess.run(loss_op, feed_dict=feed_dict_dev)
csv_steps.append(step)
csv_train_loss.append(loss_train)
csv_dev_loss.append(loss_dev)
if (step + 1) % 10 == 0:
# Change feed dict for evaluation
feed_dict_train[network.keep_prob_input_pl] = 1.0
feed_dict_train[network.keep_prob_hidden_pl] = 1.0
feed_dict_dev[network.keep_prob_input_pl] = 1.0
feed_dict_dev[network.keep_prob_hidden_pl] = 1.0
# Compute accuracy & update event file
ler_train, summary_str_train = sess.run([per_op, summary_train],
feed_dict=feed_dict_train)
ler_dev, summary_str_dev, labels_st = sess.run([per_op, summary_dev, decode_op],
feed_dict=feed_dict_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:■')
error_epoch = do_eval_cer(session=sess,
decode_op=decode_op,
network=network,
dataset=dev_data,
eval_batch_size=1)
if error_epoch < error_best:
error_best = error_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
print('■Test Data Evaluation:■')
do_eval_cer(session=sess, decode_op=decode_op,
network=network, dataset=test_data,
eval_batch_size=1)
else:
print('■Dev Data Evaluation:■')
error_epoch = do_eval_per(session=sess,
decode_op=decode_op,
per_op=per_op,
network=network,
dataset=dev_data,
label_type=label_type,
eval_batch_size=1)
if error_epoch < error_best:
error_best = error_epoch
print('■■■ ↑Best Score (PER)↑ ■■■')
print('■Test Data Evaluation:■')
do_eval_per(session=sess, decode_op=decode_op,
per_op=per_op, network=network,
dataset=test_data,
label_type=label_type,
eval_batch_size=1)
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
save_loss(csv_steps, csv_train_loss, csv_dev_loss,
save_path=network.model_dir)
# Training was finished correctly
with open(join(network.model_dir, 'complete.txt'), 'w') as f:
f.write('')
def do_restore(network, label_type, num_stack, num_skip, epoch=None):
"""Restore model.
Args:
network: model to restore
label_type: phone39 or phone48 or phone61
num_stack: int, the number of frames to stack
num_skip: int, the number of frames to skip
epoch: epoch to restore
"""
# Load dataset
if label_type == 'character':
test_data = DataSet(data_type='test',
label_type='character',
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False,
is_progressbar=True)
else:
test_data = DataSet(data_type='test',
label_type='phone39',
num_stack=num_stack,
num_skip=num_skip,
is_sorted=False,
is_progressbar=True)
# Define model
network.define()
# Add to the graph each operation
decode_op1, decode_op2 = network.decoder(decode_type='beam_search',
beam_width=20)
# posteriors_op = network.posteriors(decode_op1)
per_op1, per_op2 = network.ler(decode_op1, decode_op2)
# 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:')
do_eval_cer(session=sess,
decode_op=decode_op1,
network=network,
dataset=test_data,
is_progressbar=True,
is_multitask=True)
do_eval_per(session=sess,
decode_op=decode_op2,
per_op=per_op2,
network=network,
dataset=test_data,
label_type=label_type,
is_progressbar=True,
is_multitask=True)