def main(prj_dir=None, model=None, mode=None):
# Configuration Part #
if mode is 'train':
import path_setting as ps
set_path = ps.PathSetting(prj_dir, model)
logs_dir = initial_logs_dir = set_path.logs_dir
input_dir = set_path.input_dir
output_dir = set_path.output_dir
norm_dir = set_path.norm_dir
valid_file_dir = set_path.valid_file_dir
sys.path.insert(0, prj_dir + '/configure/ACAM')
import config as cg
global initLr, dropout_rate, max_epoch, batch_size, valid_batch_size
initLr = cg.lr
dropout_rate = cg.dropout_rate
max_epoch = cg.max_epoch
batch_size = valid_batch_size = cg.batch_size
global w, u
w = cg.w
u = cg.u
global bdnn_winlen, bdnn_inputsize, bdnn_outputsize
bdnn_winlen = (((w - 1) / u) * 2) + 3
bdnn_inputsize = int(bdnn_winlen * num_features)
bdnn_outputsize = int(bdnn_winlen)
global glimpse_hidden, bp_hidden, glimpse_out, bp_out, nGlimpses,\
lstm_cell_size, action_hidden_1, action_hidden_2
glimpse_hidden = cg.glimpse_hidden
bp_hidden = cg.bp_hidden
glimpse_out = bp_out = cg.glimpse_out
nGlimpses = cg.nGlimpse # 7
lstm_cell_size = cg.lstm_cell_size
action_hidden_1 = cg.action_hidden_1 # default : 256
action_hidden_2 = cg.action_hidden_2 # default : 256
# Graph Part #
mean_acc_list = []
var_acc_list = []
print('Mode : ' + mode)
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(batch_size=batch_size,
reuse=None,
is_training=True)
# m_train(batch_size)
with tf.device(device):
with tf.variable_scope("model", reuse=True):
m_valid = Model(batch_size=valid_batch_size,
reuse=True,
is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
# train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', max_queue=4)
# valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=4)
# summary_dic = summary_generation(valid_file_dir)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(initial_logs_dir)
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/',
sess.graph,
max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/',
max_queue=2)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
print(initial_logs_dir + ckpt_name)
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir + ckpt_name)
saver.save(sess, initial_logs_dir + "/model_ACAM.ckpt",
0) # model save
print("Done")
else:
sess.run(tf.global_variables_initializer()
) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(
input_dir, output_dir, norm_dir, w=w, u=u,
name="train") # training data reader initialization
if mode is 'train':
for itr in range(max_epoch):
start_time = time.time()
train_inputs, train_labels = train_data_set.next_batch(batch_size)
feed_dict = {
m_train.inputs: train_inputs,
m_train.labels: train_labels,
m_train.keep_probability: dropout_rate
}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 10 == 0 and itr >= 0:
train_cost, train_reward, train_avg_b, train_rminusb, train_p_bps, train_lr \
= sess.run([m_train.cost, m_train.reward, m_train.avg_b, m_train.rminusb, m_train.p_bps,
m_train.print_lr]
, feed_dict=feed_dict)
duration = time.time() - start_time
print(
"Step: %d, cost: %.4f, accuracy: %4.4f, b: %4.4f, R-b: %4.4f, p_bps: %4.4f, lr: %7.6f (%.3f sec)"
% (itr, train_cost, train_reward, train_avg_b,
train_rminusb, train_p_bps, train_lr, duration))
train_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(
accuracy_summary_op, feed_dict={summary_ph: train_reward})
train_summary_writer.add_summary(
train_cost_summary_str,
itr) # write the train phase summary to event files
train_summary_writer.add_summary(train_accuracy_summary_str,
itr)
# if train_data_set.eof_checker():
# if itr % val_freq == 0 and itr >= val_start_step:
if itr % 50 == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_file_dir, norm_dir,
type='ACAM')
print("valid_cost: %.4f, valid_accuracy=%4.4f" %
(valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(
accuracy_summary_op,
feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(
valid_cost_summary_str,
itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str,
itr)
# mean_accuracy, var_accuracy = full_evaluation(m_valid, sess, valid_batch_size, valid_file_dir, valid_summary_writer, summary_dic, itr)
# if mean_accuracy >= 0.991:
#
# print('model was saved!')
# model_name = '/model' + str(int(mean_accuracy * 1e4)) + 'and'\
# + str(int(var_accuracy * 1e5)) + '.ckpt'
# saver.save(sess, save_dir + model_name, itr)
# mean_acc_list.append(mean_accuracy)
# var_acc_list.append(var_accuracy)
# train_data_set.initialize()
elif mode == 'test':
final_softout, final_label = utils.vad_test(m_valid, sess,
valid_batch_size,
test_file_dir, norm_dir,
data_len, eval_type)
# if data_len is None:
# return final_softout, final_label
# else:
# final_softout = final_softout[0:data_len, :]
# final_label = final_label[0:data_len, :]
# fpr, tpr, thresholds = metrics.roc_curve(final_label, final_softout, pos_label=1)
# eval_auc = metrics.auc(fpr, tpr)
# print(eval_auc)
# full_evaluation(m_valid, sess, valid_batch_size, test_file_dir, valid_summary_writer, summary_dic, 0)
# if visualization:
# global attention
# attention = np.asarray(attention)
# sio.savemat('attention.mat', {'attention' : attention})
# subprocess.call(['./visualize.sh'])
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]
def main(save_dir, prj_dir=None, model=None, mode=None, dev="/gpu:2"):
# Configuration Part #
# os.environ["CUDA_VISIBLE_DEVICES"] = '3'
device = dev
os.environ["CUDA_VISIBLE_DEVICES"] = device[-1]
if mode is 'train':
import path_setting as ps
set_path = ps.PathSetting(prj_dir, model,save_dir)
logs_dir = initial_logs_dir = set_path.logs_dir
input_dir = set_path.input_dir
output_dir = set_path.output_dir
norm_dir = set_path.norm_dir
valid_file_dir = set_path.valid_file_dir
sys.path.insert(0, prj_dir+'/configure/LSTM')
import config as cg
global seq_size, batch_num
seq_size = cg.seq_len
batch_num = cg.num_batches
global learning_rate, dropout_rate, max_epoch, batch_size, valid_batch_size
learning_rate = cg.lr
dropout_rate = cg.dropout_rate
max_epoch = cg.max_epoch
batch_size = valid_batch_size = batch_num*seq_size
global target_delay
target_delay = cg.target_delay
global lstm_cell_size, num_layers
lstm_cell_size = cg.cell_size
num_layers = cg.num_layers
# Graph Part #
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(is_training=True)
with tf.variable_scope("model", reuse=True):
m_valid = Model(is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logs_dir)
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', sess.graph, max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=2)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir+ckpt_name)
# saver.restore(sess, logs_dir+ckpt_name)
saver.save(sess, logs_dir + "/model_LSTM.ckpt", 0) # model save
print("Done")
else:
sess.run(tf.global_variables_initializer()) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(input_dir, output_dir, norm_dir, target_delay=target_delay, u=u, name="train") # training data reader initialization
if mode is 'train':
file_len = train_data_set.get_file_len()
MAX_STEP = max_epoch * file_len
print(get_num_params())
for itr in range(MAX_STEP):
train_inputs, train_labels = train_data_set.next_batch(seq_size)
one_hot_labels = train_labels.reshape((-1, 1))
one_hot_labels = dense_to_one_hot(one_hot_labels, num_classes=2)
feed_dict = {m_train.inputs: train_inputs, m_train.labels: one_hot_labels,
m_train.keep_probability: dropout_rate}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 100 == 0 and itr >= 0:
train_cost, train_accuracy, train_rate, train_pre = sess.run([m_train.cost, m_train.accuracy, m_train.lr, m_train.pred], feed_dict=feed_dict)
# print(logits)
print("Step: %d, train_cost: %.4f, train_accuracy=%4.4f lr=%.8f" % (itr, train_cost, train_accuracy*100, train_rate))
pass
train_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: train_accuracy})
train_summary_writer.add_summary(train_cost_summary_str, itr) # write the train phase summary to event files
train_summary_writer.add_summary(train_accuracy_summary_str, itr)
if itr % file_len == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_file_dir, norm_dir, type='LSTM')
print("valid_cost: %.4f, valid_accuracy=%4.4f" % (valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(valid_cost_summary_str, itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str, itr)
gs.freeze_graph(prj_dir + '/logs/LSTM', prj_dir + '/saved_model/graph/LSTM',
'model_1/soft_pred,model_1/raw_labels')
elif mode is 'test':
final_softout, final_label = utils.vad_test3(m_valid, sess, valid_batch_size, test_file_dir, norm_dir, data_len,
eval_type)
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]
def main(argv=None):
# Graph Part #
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(is_training=True)
with tf.variable_scope("model", reuse=True):
m_valid = Model(is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/',
max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/',
max_queue=2)
# summary_dic = summary_generation(valid_file_dir)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logs_dir)
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir + ckpt_name)
print("Done")
else:
sess.run(tf.global_variables_initializer()
) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(
input_dir, output_dir, norm_dir, w=w, u=u,
name="train") # training data reader initialization
if mode is 'train':
for itr in range(max_epoch):
train_inputs, train_labels = train_data_set.next_batch(batch_size)
# imgplot = plt.imshow(train_inputs)
# plt.show()
one_hot_labels = train_labels.reshape((-1, 1))
one_hot_labels = dense_to_one_hot(one_hot_labels, num_classes=2)
feed_dict = {
m_train.inputs: train_inputs,
m_train.labels: one_hot_labels,
m_train.keep_probability: dropout_rate
}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 10 == 0 and itr >= 0:
# train_cost, train_softpred, train_raw_labels \
# = sess.run([m_train.cost, m_train.softpred, m_train.raw_labels], feed_dict=feed_dict)
# fpr, tpr, thresholds = metrics.roc_curve(train_raw_labels, train_softpred, pos_label=1)
# train_auc = metrics.auc(fpr, tpr)
train_cost, train_accuracy \
= sess.run([m_train.cost, m_train.accuracy], feed_dict=feed_dict)
print("Step: %d, train_cost: %.4f, train_accuracy=%4.4f" %
(itr, train_cost, train_accuracy * 100))
train_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(
accuracy_summary_op,
feed_dict={summary_ph: train_accuracy})
train_summary_writer.add_summary(
train_cost_summary_str,
itr) # write the train phase summary to event files
train_summary_writer.add_summary(train_accuracy_summary_str,
itr)
# if train_data_set.eof_checker():
if itr % 50 == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_batch_size, valid_file_dir, norm_dir,
model_config, type='DNN')
print("valid_cost: %.4f, valid_accuracy=%4.4f" %
(valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(
accuracy_summary_op,
feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(
valid_cost_summary_str,
itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str,
itr)
# full_evaluation(m_valid, sess, valid_batch_size, valid_file_dir, valid_summary_writer, summary_dic, itr)
elif mode is 'test':
# full_evaluation(m_valid, sess, valid_batch_size, test_file_dir, valid_summary_writer, summary_dic, 0)
final_softout, final_label = utils.vad_test(m_valid, sess,
valid_batch_size,
test_file_dir, norm_dir,
data_len, eval_type)
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]
def main(save_dir, prj_dir=None, model=None, mode=None, dev="/gpu:2"):
# Configuration Part #
# os.environ["CUDA_VISIBLE_DEVICES"] = '3'
device = dev
os.environ["CUDA_VISIBLE_DEVICES"] = device[-1]
if mode is 'train':
import path_setting as ps
set_path = ps.PathSetting(prj_dir, model, save_dir)
logs_dir = initial_logs_dir = set_path.logs_dir
input_dir = set_path.input_dir
output_dir = set_path.output_dir
norm_dir = set_path.norm_dir
valid_file_dir = set_path.valid_file_dir
sys.path.insert(0, prj_dir + '/configure/ACAM')
import config as cg
global initLr, dropout_rate, max_epoch, batch_size, valid_batch_size
initLr = cg.lr
dropout_rate = cg.dropout_rate
max_epoch = cg.max_epoch
batch_size = valid_batch_size = cg.batch_size
global w, u
w = cg.w
u = cg.u
global bdnn_winlen, bdnn_inputsize, bdnn_outputsize
bdnn_winlen = (((w - 1) / u) * 2) + 3
bdnn_inputsize = int(bdnn_winlen * num_features)
bdnn_outputsize = int(bdnn_winlen)
global glimpse_hidden, bp_hidden, glimpse_out, bp_out, nGlimpses,\
lstm_cell_size, action_hidden_1, action_hidden_2
glimpse_hidden = cg.glimpse_hidden
bp_hidden = cg.bp_hidden
glimpse_out = bp_out = cg.glimpse_out
nGlimpses = cg.nGlimpse # 7
lstm_cell_size = cg.lstm_cell_size
action_hidden_1 = cg.action_hidden_1 # default : 256
action_hidden_2 = cg.action_hidden_2 # default : 256
# Graph Part #
mean_acc_list = []
var_acc_list = []
print('Mode : ' + mode)
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(batch_size=batch_size,
reuse=None,
is_training=True)
# m_train(batch_size)
with tf.device(device):
with tf.variable_scope("model", reuse=True):
m_valid = Model(batch_size=valid_batch_size,
reuse=True,
is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
# train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', max_queue=4)
# valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=4)
# summary_dic = summary_generation(valid_file_dir)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(initial_logs_dir)
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/',
sess.graph,
max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/',
max_queue=2)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
print(initial_logs_dir + ckpt_name)
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir + ckpt_name)
saver.save(sess, initial_logs_dir + "/model_ACAM.ckpt",
0) # model save
print("Done")
else:
sess.run(tf.global_variables_initializer()
) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(
input_dir, output_dir, norm_dir, w=w, u=u,
name="train") # training data reader initialization
if mode is 'train':
file_len = train_data_set.get_file_len()
MAX_STEP = max_epoch * file_len
print(get_num_params())
for itr in range(MAX_STEP):
start_time = time.time()
train_inputs, train_labels = train_data_set.next_batch(batch_size)
feed_dict = {
m_train.inputs: train_inputs,
m_train.labels: train_labels,
m_train.keep_probability: dropout_rate
}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 100 == 0 and itr >= 0:
train_cost, train_reward, train_avg_b, train_rminusb, train_p_bps, train_lr, train_res \
= sess.run([m_train.cost, m_train.reward, m_train.avg_b, m_train.rminusb, m_train.p_bps,
m_train.print_lr,m_train.result]
, feed_dict=feed_dict)
duration = time.time() - start_time
print(
"Step: %d, cost: %.4f, accuracy: %4.4f, b: %4.4f, R-b: %4.4f, p_bps: %4.4f, lr: %7.6f (%.3f sec)"
% (itr, train_cost, train_reward, train_avg_b,
train_rminusb, train_p_bps, train_lr, duration))
# np.save('pre/'+train_data_set.get_cur_file_name().split('/')[-1], train_res)
train_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(
accuracy_summary_op, feed_dict={summary_ph: train_reward})
train_summary_writer.add_summary(train_cost_summary_str, itr)
train_summary_writer.add_summary(train_accuracy_summary_str,
itr)
if itr % file_len == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_file_dir, norm_dir,
type='ACAM')
print("valid_cost: %.4f, valid_accuracy=%4.4f" %
(valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(
cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(
accuracy_summary_op,
feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(
valid_cost_summary_str,
itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str,
itr)
gs.freeze_graph(prj_dir + '/logs/ACAM',
prj_dir + '/saved_model/graph/ACAM',
'model_1/logits,model_1/raw_labels')
elif mode == 'test':
final_softout, final_label = utils.vad_test(m_valid, sess,
valid_batch_size,
test_file_dir, norm_dir,
data_len, eval_type)
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]
def main(prj_dir=None, model=None, mode=None):
# Configuration Part #
if mode is 'train':
import path_setting as ps
set_path = ps.PathSetting(prj_dir, model)
logs_dir = initial_logs_dir = set_path.logs_dir
input_dir = set_path.input_dir
output_dir = set_path.output_dir
norm_dir = set_path.norm_dir
valid_file_dir = set_path.valid_file_dir
sys.path.insert(0, prj_dir+'/configure/bDNN')
import config as cg
global initLr, dropout_rate, max_epoch, batch_size, valid_batch_size
initLr = cg.lr
dropout_rate = cg.dropout_rate
max_epoch = cg.max_epoch
batch_size = valid_batch_size = cg.batch_size
global w, u
w = cg.w
u = cg.u
global bdnn_winlen, bdnn_inputsize, bdnn_outputsize
bdnn_winlen = (((w-1) / u) * 2) + 3
bdnn_inputsize = int(bdnn_winlen * num_features)
bdnn_outputsize = int(bdnn_winlen)
global num_hidden_1, num_hidden_2
num_hidden_1 = cg.num_hidden_1
num_hidden_2 = cg.num_hidden_2
# Graph Part #
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(is_training=True)
with tf.variable_scope("model", reuse=True):
m_valid = Model(is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
# train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', max_queue=4)
# valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=4)
# summary_dic = summary_generation(valid_file_dir)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(initial_logs_dir)
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', sess.graph, max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=2)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir+ckpt_name)
# print(initial_logs_dir)
# saver.save(sess, initial_logs_dir + "/model_bDNN.ckpt", 0) # model save
print("Done")
else:
sess.run(tf.global_variables_initializer()) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(input_dir, output_dir, norm_dir, w=w, u=u, name="train") # training data reader initialization
# train_data_set = dr.DataReader(input_dir, output_dir, norm_dir, w=w, u=u, name="train") # training data reader initialization
if mode is 'train':
for itr in range(max_epoch):
train_inputs, train_labels = train_data_set.next_batch(batch_size)
feed_dict = {m_train.inputs: train_inputs, m_train.labels: train_labels,
m_train.keep_probability: dropout_rate}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 10 == 0 and itr >= 0:
train_cost, logits = sess.run([m_train.cost, m_train.logits], feed_dict=feed_dict)
result = bdnn_prediction(batch_size, logits, threshold=th)
raw_indx = int(np.floor(train_labels.shape[1] / 2))
raw_labels = train_labels[:, raw_indx]
raw_labels = raw_labels.reshape((-1, 1))
train_accuracy = np.equal(result, raw_labels)
train_accuracy = train_accuracy.astype(int)
train_accuracy = np.sum(train_accuracy) / batch_size # change to mean...
print("Step: %d, train_cost: %.4f, train_accuracy=%4.4f" % (itr, train_cost, train_accuracy*100))
train_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: train_accuracy})
train_summary_writer.add_summary(train_cost_summary_str, itr) # write the train phase summary to event files
train_summary_writer.add_summary(train_accuracy_summary_str, itr)
# if train_data_set.eof_checker():
if itr % 50 == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_file_dir, norm_dir, type='bDNN')
print("valid_cost: %.4f, valid_accuracy=%4.4f" % (valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(valid_cost_summary_str, itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str, itr)
# full_evaluation(m_valid, sess, valid_batch_size, valid_file_dir, valid_summary_writer, summary_dic, itr)
# train_data_set.reader_initialize()
# print('Train data reader was initialized!') # initialize eof flag & num_file & start index
elif mode is 'test':
# full_evaluation(m_valid, sess, valid_batch_size, test_file_dir, valid_summary_writer, summary_dic, 0)
final_softout, final_label = utils.vad_test(m_valid, sess, valid_batch_size, test_file_dir, norm_dir, data_len,
eval_type)
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]
def main(prj_dir=None, model=None, mode=None):
# Configuration Part #
if mode is 'train':
import path_setting as ps
set_path = ps.PathSetting(prj_dir, model)
logs_dir = initial_logs_dir = set_path.logs_dir
input_dir = set_path.input_dir
output_dir = set_path.output_dir
norm_dir = set_path.norm_dir
valid_file_dir = set_path.valid_file_dir
sys.path.insert(0, prj_dir+'/configure/LSTM')
import config as cg
global seq_size, batch_num
seq_size = cg.seq_len
batch_num = cg.num_batches
global learning_rate, dropout_rate, max_epoch, batch_size, valid_batch_size
learning_rate = cg.lr
dropout_rate = cg.dropout_rate
max_epoch = cg.max_epoch
batch_size = valid_batch_size = batch_num*seq_size
global target_delay
target_delay = cg.target_delay
global lstm_cell_size, num_layers
lstm_cell_size = cg.cell_size
num_layers = cg.num_layers
# Graph Part #
print("Graph initialization...")
with tf.device(device):
with tf.variable_scope("model", reuse=None):
m_train = Model(is_training=True)
with tf.variable_scope("model", reuse=True):
m_valid = Model(is_training=False)
print("Done")
# Summary Part #
print("Setting up summary op...")
summary_ph = tf.placeholder(dtype=tf.float32)
with tf.variable_scope("Training_procedure"):
cost_summary_op = tf.summary.scalar("cost", summary_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", summary_ph)
# train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', max_queue=4)
# valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=4)
# summary_dic = summary_generation(valid_file_dir)
print("Done")
# Model Save Part #
print("Setting up Saver...")
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logs_dir + '/LSTM')
print("Done")
# Session Part #
sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
if mode is 'train':
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/', sess.graph, max_queue=2)
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=2)
if ckpt and ckpt.model_checkpoint_path: # model restore
print("Model restored...")
if mode is 'train':
saver.restore(sess, ckpt.model_checkpoint_path)
else:
saver.restore(sess, initial_logs_dir+ckpt_name)
# saver.restore(sess, logs_dir+ckpt_name)
saver.save(sess, logs_dir + "/model_LSTM.ckpt", 0) # model save
print("Done")
else:
sess.run(tf.global_variables_initializer()) # if the checkpoint doesn't exist, do initialization
if mode is 'train':
train_data_set = dr.DataReader(input_dir, output_dir, norm_dir, target_delay=target_delay, u=u, name="train") # training data reader initialization
# train_data_set = dr.DataReader(input_dir, output_dir, norm_dir, target_delay=target_delay, u=u, name="train") # training data reader initialization
if mode is 'train':
for itr in range(max_epoch):
train_inputs, train_labels = train_data_set.next_batch(batch_size)
one_hot_labels = train_labels.reshape((-1, 1))
one_hot_labels = dense_to_one_hot(one_hot_labels, num_classes=2)
feed_dict = {m_train.inputs: train_inputs, m_train.labels: one_hot_labels,
m_train.keep_probability: dropout_rate}
sess.run(m_train.train_op, feed_dict=feed_dict)
if itr % 10 == 0 and itr >= 0:
train_cost, train_accuracy = sess.run([m_train.cost, m_train.accuracy], feed_dict=feed_dict)
print("Step: %d, train_cost: %.4f, train_accuracy=%4.4f" % (itr, train_cost, train_accuracy*100))
train_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: train_cost})
train_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: train_accuracy})
train_summary_writer.add_summary(train_cost_summary_str, itr) # write the train phase summary to event files
train_summary_writer.add_summary(train_accuracy_summary_str, itr)
# if train_data_set.eof_checker():
if itr % 50 == 0 and itr > 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
print('validation start!')
valid_accuracy, valid_cost = \
utils.do_validation(m_valid, sess, valid_file_dir, norm_dir, type='LSTM')
print("valid_cost: %.4f, valid_accuracy=%4.4f" % (valid_cost, valid_accuracy * 100))
valid_cost_summary_str = sess.run(cost_summary_op, feed_dict={summary_ph: valid_cost})
valid_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={summary_ph: valid_accuracy})
valid_summary_writer.add_summary(valid_cost_summary_str, itr) # write the train phase summary to event files
valid_summary_writer.add_summary(valid_accuracy_summary_str, itr)
# mean_acc = full_evaluation(m_valid, sess, valid_batch_size, valid_file_dir,
# valid_summary_writer, summary_dic, itr)
# if mean_acc > 0.968:
# print('finish!!')
# break
# train_data_set.reader_initialize()
# print('Train data reader was initialized!') # initialize eof flag & num_file & start index
elif mode is 'test':
final_softout, final_label = utils.vad_test3(m_valid, sess, valid_batch_size, test_file_dir, norm_dir, data_len,
eval_type)
if data_len is None:
return final_softout, final_label
else:
return final_softout[0:data_len, :], final_label[0:data_len, :]