def do_train(model, params, gpu_indices):
"""Run training.
Args:
model: the model to train
params (dict): A dictionary of parameters
gpu_indices (list): GPU indices
"""
if 'kanji' in params['label_type']:
map_file_path = '../metrics/mapping_files/' + \
params['label_type'] + '_' + params['train_data_size'] + '.txt'
elif 'kana' in params['label_type']:
map_file_path = '../metrics/mapping_files/' + \
params['label_type'] + '.txt'
# Load dataset
train_data = Dataset(
data_type='train', train_data_size=params['train_data_size'],
label_type=params['label_type'], map_file_path=map_file_path,
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'],
num_gpu=len(gpu_indices))
dev_data = Dataset(
data_type='dev', train_data_size=params['train_data_size'],
label_type=params['label_type'], map_file_path=map_file_path,
batch_size=params['batch_size'], splice=params['splice'],
num_stack=params['num_stack'], num_skip=params['num_skip'],
sort_utt=False, num_gpu=len(gpu_indices))
# Tell TensorFlow that the model will be built into the default graph
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to track the global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set optimizer
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
optimizer = model._set_optimizer(
params['optimizer'], learning_rate_pl)
# Calculate the gradients for each model tower
total_grads_and_vars, total_losses = [], []
decode_ops_infer, ler_ops = [], []
all_devices = ['/gpu:%d' % i_gpu for i_gpu in range(len(gpu_indices))]
# NOTE: /cpu:0 is prepared for evaluation
with tf.variable_scope(tf.get_variable_scope()):
for i_gpu in range(len(all_devices)):
with tf.device(all_devices[i_gpu]):
with tf.name_scope('tower_gpu%d' % i_gpu) as scope:
# Define placeholders in each tower
model.create_placeholders()
# Calculate the total loss for the current tower of the
# model. This function constructs the entire model but
# shares the variables across all towers.
tower_loss, tower_logits, tower_decoder_outputs_train, tower_decoder_outputs_infer = model.compute_loss(
model.inputs_pl_list[i_gpu],
model.labels_pl_list[i_gpu],
model.inputs_seq_len_pl_list[i_gpu],
model.labels_seq_len_pl_list[i_gpu],
model.keep_prob_encoder_pl_list[i_gpu],
model.keep_prob_decoder_pl_list[i_gpu],
model.keep_prob_embedding_pl_list[i_gpu],
scope)
tower_loss = tf.expand_dims(tower_loss, axis=0)
total_losses.append(tower_loss)
# Reuse variables for the next tower
tf.get_variable_scope().reuse_variables()
# Calculate the gradients for the batch of data on this
# tower
tower_grads_and_vars = optimizer.compute_gradients(
tower_loss)
# Gradient clipping
tower_grads_and_vars = model._clip_gradients(
tower_grads_and_vars)
# TODO: Optionally add gradient noise
# Keep track of the gradients across all towers
total_grads_and_vars.append(tower_grads_and_vars)
# Add to the graph each operation per tower
_, decode_op_tower_infer = model.decode(
tower_decoder_outputs_train,
tower_decoder_outputs_infer)
decode_ops_infer.append(decode_op_tower_infer)
# ler_op_tower = model.compute_ler(
# decode_op_tower, model.labels_pl_list[i_gpu])
ler_op_tower = model.compute_ler(
model.labels_st_true_pl_list[i_gpu],
model.labels_st_pred_pl_list[i_gpu])
ler_op_tower = tf.expand_dims(ler_op_tower, axis=0)
ler_ops.append(ler_op_tower)
# Aggregate losses, then calculate average loss
total_losses = tf.concat(axis=0, values=total_losses)
loss_op = tf.reduce_mean(total_losses, axis=0)
ler_ops = tf.concat(axis=0, values=ler_ops)
ler_op = tf.reduce_mean(ler_ops, axis=0)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers
average_grads_and_vars = average_gradients(total_grads_and_vars)
# Apply the gradients to adjust the shared variables.
train_op = optimizer.apply_gradients(average_grads_and_vars,
global_step=global_step)
# 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
# NOTE: Start running operations on the Graph. allow_soft_placement
# must be set to True to build towers on GPU, as some of the ops do not
# have GPU implementations.
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)) as sess:
# Instantiate a SummaryWriter to output summaries and the graph
summary_writer = tf.summary.FileWriter(
model.save_path, sess.graph)
# Initialize param
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
not_improved_epoch = 0
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_train, inputs_seq_len, labels_seq_len, _ = data
feed_dict_train = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_train[model.inputs_pl_list[i_gpu]
] = inputs[i_gpu]
feed_dict_train[model.labels_pl_list[i_gpu]
] = labels_train[i_gpu]
feed_dict_train[model.inputs_seq_len_pl_list[i_gpu]
] = inputs_seq_len[i_gpu]
feed_dict_train[model.labels_seq_len_pl_list[i_gpu]
] = labels_seq_len[i_gpu]
feed_dict_train[model.keep_prob_encoder_pl_list[i_gpu]
] = 1 - float(params['dropout_encoder'])
feed_dict_train[model.keep_prob_decoder_pl_list[i_gpu]
] = 1 - float(params['dropout_decoder'])
feed_dict_train[model.keep_prob_embedding_pl_list[i_gpu]
] = 1 - float(params['dropout_embedding'])
feed_dict_train[learning_rate_pl] = learning_rate
# Update parameters
sess.run(train_op, feed_dict=feed_dict_train)
if (step + 1) % int(params['print_step'] / len(gpu_indices)) == 0:
# Create feed dictionary for next mini batch (dev)
inputs, labels_dev, inputs_seq_len, labels_seq_len, _ = dev_data.next()[
0]
feed_dict_dev = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_dev[model.inputs_pl_list[i_gpu]
] = inputs[i_gpu]
feed_dict_dev[model.labels_pl_list[i_gpu]
] = labels_dev[i_gpu]
feed_dict_dev[model.inputs_seq_len_pl_list[i_gpu]
] = inputs_seq_len[i_gpu]
feed_dict_dev[model.labels_seq_len_pl_list[i_gpu]
] = labels_seq_len[i_gpu]
feed_dict_dev[model.keep_prob_encoder_pl_list[i_gpu]
] = 1.0
feed_dict_dev[model.keep_prob_decoder_pl_list[i_gpu]
] = 1.0
feed_dict_dev[model.keep_prob_embedding_pl_list[i_gpu]
] = 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
for i_gpu in range(len(gpu_indices)):
feed_dict_train[model.keep_prob_encoder_pl_list[i_gpu]] = 1.0
feed_dict_train[model.keep_prob_decoder_pl_list[i_gpu]] = 1.0
feed_dict_train[model.keep_prob_embedding_pl_list[i_gpu]] = 1.0
# Predict class ids
predicted_ids_train_list, summary_str_train = sess.run(
[decode_ops_infer, summary_train], feed_dict=feed_dict_train)
predicted_ids_dev_list, summary_str_dev = sess.run(
[decode_ops_infer, summary_dev], feed_dict=feed_dict_dev)
# Convert to sparsetensor to compute LER
feed_dict_ler_train = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_ler_train[model.labels_st_true_pl_list[i_gpu]] = list2sparsetensor(
labels_train[i_gpu],
padded_value=train_data.padded_value),
feed_dict_ler_train[model.labels_st_pred_pl_list[i_gpu]] = list2sparsetensor(
predicted_ids_train_list[i_gpu],
padded_value=train_data.padded_value)
feed_dict_ler_dev = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_ler_dev[model.labels_st_true_pl_list[i_gpu]] = list2sparsetensor(
labels_dev[i_gpu],
padded_value=dev_data.padded_value),
feed_dict_ler_dev[model.labels_st_pred_pl_list[i_gpu]] = list2sparsetensor(
predicted_ids_dev_list[i_gpu],
padded_value=dev_data.padded_value)
# Compute accuracy
# ler_train = sess.run(ler_op, feed_dict=feed_dict_ler_train)
# ler_dev = sess.run(ler_op, feed_dict=feed_dict_ler_dev)
ler_train = 1
ler_dev = 1
csv_ler_train.append(ler_train)
csv_ler_dev.append(ler_dev)
# TODO: fix this
# Update even files
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()
print('=== Dev Data Evaluation ===')
cer_dev_epoch = do_eval_cer(
session=sess,
decode_ops=decode_ops_infer,
model=model,
dataset=dev_data,
label_type=params['label_type'],
train_data_size=params['train_data_size'],
eval_batch_size=1)
print(' CER: %f %%' % (cer_dev_epoch * 100))
if cer_dev_epoch < cer_dev_best:
cer_dev_best = cer_dev_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
# Save model (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)
else:
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
print('Evaluation time: %.3f min' %
(duration_eval / 60))
# Early stopping
if not_improved_epoch == params['not_improved_patient_epoch']:
break
# 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('')
def do_train(model, params, gpu_indices):
"""Run training.
Args:
model: the model to train
params (dict): A dictionary of parameters
gpu_indices (list): GPU indices
"""
if 'kanji' in params['label_type']:
map_file_path = '../metrics/mapping_files/' + \
params['label_type'] + '_' + params['train_data_size'] + '.txt'
elif 'kana' in params['label_type']:
map_file_path = '../metrics/mapping_files/' + \
params['label_type'] + '.txt'
# Load dataset
train_data = Dataset(data_type='train',
train_data_size=params['train_data_size'],
label_type=params['label_type'],
map_file_path=map_file_path,
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'],
num_gpu=len(gpu_indices))
dev_data = Dataset(data_type='dev',
train_data_size=params['train_data_size'],
label_type=params['label_type'],
map_file_path=map_file_path,
batch_size=params['batch_size'],
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=False,
num_gpu=len(gpu_indices))
# Tell TensorFlow that the model will be built into the default graph
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to track the global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set optimizer
learning_rate_pl = tf.placeholder(tf.float32, name='learning_rate')
optimizer = model._set_optimizer(params['optimizer'], learning_rate_pl)
# Calculate the gradients for each model tower
total_grads_and_vars, total_losses = [], []
decode_ops_infer, ler_ops = [], []
all_devices = ['/gpu:%d' % i_gpu for i_gpu in range(len(gpu_indices))]
# NOTE: /cpu:0 is prepared for evaluation
with tf.variable_scope(tf.get_variable_scope()):
for i_gpu in range(len(all_devices)):
with tf.device(all_devices[i_gpu]):
with tf.name_scope('tower_gpu%d' % i_gpu) as scope:
# Define placeholders in each tower
model.create_placeholders()
# Calculate the total loss for the current tower of the
# model. This function constructs the entire model but
# shares the variables across all towers.
tower_loss, tower_logits, tower_decoder_outputs_train, tower_decoder_outputs_infer = model.compute_loss(
model.inputs_pl_list[i_gpu],
model.labels_pl_list[i_gpu],
model.inputs_seq_len_pl_list[i_gpu],
model.labels_seq_len_pl_list[i_gpu],
model.keep_prob_encoder_pl_list[i_gpu],
model.keep_prob_decoder_pl_list[i_gpu],
model.keep_prob_embedding_pl_list[i_gpu], scope)
tower_loss = tf.expand_dims(tower_loss, axis=0)
total_losses.append(tower_loss)
# Reuse variables for the next tower
tf.get_variable_scope().reuse_variables()
# Calculate the gradients for the batch of data on this
# tower
tower_grads_and_vars = optimizer.compute_gradients(
tower_loss)
# Gradient clipping
tower_grads_and_vars = model._clip_gradients(
tower_grads_and_vars)
# TODO: Optionally add gradient noise
# Keep track of the gradients across all towers
total_grads_and_vars.append(tower_grads_and_vars)
# Add to the graph each operation per tower
_, decode_op_tower_infer = model.decode(
tower_decoder_outputs_train,
tower_decoder_outputs_infer)
decode_ops_infer.append(decode_op_tower_infer)
# ler_op_tower = model.compute_ler(
# decode_op_tower, model.labels_pl_list[i_gpu])
ler_op_tower = model.compute_ler(
model.labels_st_true_pl_list[i_gpu],
model.labels_st_pred_pl_list[i_gpu])
ler_op_tower = tf.expand_dims(ler_op_tower, axis=0)
ler_ops.append(ler_op_tower)
# Aggregate losses, then calculate average loss
total_losses = tf.concat(axis=0, values=total_losses)
loss_op = tf.reduce_mean(total_losses, axis=0)
ler_ops = tf.concat(axis=0, values=ler_ops)
ler_op = tf.reduce_mean(ler_ops, axis=0)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers
average_grads_and_vars = average_gradients(total_grads_and_vars)
# Apply the gradients to adjust the shared variables.
train_op = optimizer.apply_gradients(average_grads_and_vars,
global_step=global_step)
# 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
# NOTE: Start running operations on the Graph. allow_soft_placement
# must be set to True to build towers on GPU, as some of the ops do not
# have GPU implementations.
with tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)) as sess:
# Instantiate a SummaryWriter to output summaries and the graph
summary_writer = tf.summary.FileWriter(model.save_path, sess.graph)
# Initialize param
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
not_improved_epoch = 0
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_train, inputs_seq_len, labels_seq_len, _ = data
feed_dict_train = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_train[
model.inputs_pl_list[i_gpu]] = inputs[i_gpu]
feed_dict_train[
model.labels_pl_list[i_gpu]] = labels_train[i_gpu]
feed_dict_train[model.inputs_seq_len_pl_list[
i_gpu]] = inputs_seq_len[i_gpu]
feed_dict_train[model.labels_seq_len_pl_list[
i_gpu]] = labels_seq_len[i_gpu]
feed_dict_train[
model.keep_prob_encoder_pl_list[i_gpu]] = 1 - float(
params['dropout_encoder'])
feed_dict_train[
model.keep_prob_decoder_pl_list[i_gpu]] = 1 - float(
params['dropout_decoder'])
feed_dict_train[
model.keep_prob_embedding_pl_list[i_gpu]] = 1 - float(
params['dropout_embedding'])
feed_dict_train[learning_rate_pl] = learning_rate
# Update parameters
sess.run(train_op, feed_dict=feed_dict_train)
if (step + 1) % int(
params['print_step'] / len(gpu_indices)) == 0:
# Create feed dictionary for next mini batch (dev)
inputs, labels_dev, inputs_seq_len, labels_seq_len, _ = dev_data.next(
)[0]
feed_dict_dev = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_dev[
model.inputs_pl_list[i_gpu]] = inputs[i_gpu]
feed_dict_dev[
model.labels_pl_list[i_gpu]] = labels_dev[i_gpu]
feed_dict_dev[model.inputs_seq_len_pl_list[
i_gpu]] = inputs_seq_len[i_gpu]
feed_dict_dev[model.labels_seq_len_pl_list[
i_gpu]] = labels_seq_len[i_gpu]
feed_dict_dev[
model.keep_prob_encoder_pl_list[i_gpu]] = 1.0
feed_dict_dev[
model.keep_prob_decoder_pl_list[i_gpu]] = 1.0
feed_dict_dev[
model.keep_prob_embedding_pl_list[i_gpu]] = 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
for i_gpu in range(len(gpu_indices)):
feed_dict_train[
model.keep_prob_encoder_pl_list[i_gpu]] = 1.0
feed_dict_train[
model.keep_prob_decoder_pl_list[i_gpu]] = 1.0
feed_dict_train[
model.keep_prob_embedding_pl_list[i_gpu]] = 1.0
# Predict class ids
predicted_ids_train_list, summary_str_train = sess.run(
[decode_ops_infer, summary_train],
feed_dict=feed_dict_train)
predicted_ids_dev_list, summary_str_dev = sess.run(
[decode_ops_infer, summary_dev],
feed_dict=feed_dict_dev)
# Convert to sparsetensor to compute LER
feed_dict_ler_train = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_ler_train[model.labels_st_true_pl_list[
i_gpu]] = list2sparsetensor(
labels_train[i_gpu],
padded_value=train_data.padded_value),
feed_dict_ler_train[model.labels_st_pred_pl_list[
i_gpu]] = list2sparsetensor(
predicted_ids_train_list[i_gpu],
padded_value=train_data.padded_value)
feed_dict_ler_dev = {}
for i_gpu in range(len(gpu_indices)):
feed_dict_ler_dev[model.labels_st_true_pl_list[
i_gpu]] = list2sparsetensor(
labels_dev[i_gpu],
padded_value=dev_data.padded_value),
feed_dict_ler_dev[model.labels_st_pred_pl_list[
i_gpu]] = list2sparsetensor(
predicted_ids_dev_list[i_gpu],
padded_value=dev_data.padded_value)
# Compute accuracy
# ler_train = sess.run(ler_op, feed_dict=feed_dict_ler_train)
# ler_dev = sess.run(ler_op, feed_dict=feed_dict_ler_dev)
ler_train = 1
ler_dev = 1
csv_ler_train.append(ler_train)
csv_ler_dev.append(ler_dev)
# TODO: fix this
# Update even files
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()
print('=== Dev Data Evaluation ===')
cer_dev_epoch = do_eval_cer(
session=sess,
decode_ops=decode_ops_infer,
model=model,
dataset=dev_data,
label_type=params['label_type'],
train_data_size=params['train_data_size'],
eval_batch_size=1)
print(' CER: %f %%' % (cer_dev_epoch * 100))
if cer_dev_epoch < cer_dev_best:
cer_dev_best = cer_dev_epoch
print('■■■ ↑Best Score (CER)↑ ■■■')
# Save model (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)
else:
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
print('Evaluation time: %.3f min' %
(duration_eval / 60))
# Early stopping
if not_improved_epoch == params[
'not_improved_patient_epoch']:
break
# 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('')