def vad_test(m_eval, sess_eval, batch_size_eval, eval_file_dir, norm_dir,
data_len, eval_type):
eval_input_dir = eval_file_dir
eval_output_dir = eval_file_dir + '/Labels'
pad_size = batch_size_eval - data_len % batch_size_eval
if eval_type != 2:
eval_data_set = dr.DataReader(eval_input_dir,
eval_output_dir,
norm_dir,
w=19,
u=9,
name="eval",
pad=pad_size)
else:
eval_data_set = dnn_dr.DataReader(eval_input_dir,
eval_output_dir,
norm_dir,
w=19,
u=9,
name="eval",
pad=pad_size)
final_softout, final_label = evaluation(m_eval, eval_data_set, sess_eval,
batch_size_eval, eval_type)
return final_softout, final_label
def full_evaluation(m_eval, sess_eval, batch_size_eval, eval_file_dir, summary_writer, summary_dic, itr):
mean_cost = []
mean_accuracy = []
mean_auc = []
print("-------- Performance for each of noise types --------")
noise_list = os.listdir(eval_file_dir)
noise_list = sorted(noise_list)
summary_ph = summary_dic["summary_ph"]
for i in range(len(noise_list)):
noise_name = '/' + noise_list[i]
eval_input_dir = eval_file_dir + noise_name
eval_output_dir = eval_file_dir + noise_name + '/Labels'
eval_data_set = dr.DataReader(eval_input_dir, eval_output_dir, norm_dir, w=w, u=u, name="eval")
eval_cost, eval_accuracy, eval_list, eval_auc, eval_auc_list = evaluation(m_eval, eval_data_set, sess_eval, batch_size_eval)
print("--noise type : " + noise_list[i])
print("cost: %.4f, accuracy across all SNRs: %.4f" % (eval_cost, eval_accuracy*100))
print('accuracy wrt SNR:')
print('SNR_-5 : %.4f, SNR_0 : %.4f, SNR_5 : %.4f, SNR_10 : %.4f' % (eval_list[0]*100, eval_list[1]*100,
eval_list[2]*100, eval_list[3]*100))
print('AUC wrt SNR:')
print('SNR_-5 : %.4f, SNR_0 : %.4f, SNR_5 : %.4f, SNR_10 : %.4f' % (eval_auc_list[0]*100, eval_auc_list[1]*100,
eval_auc_list[2]*100, eval_auc_list[3]*100))
print('')
eval_summary_list = [eval_cost] + eval_list + [eval_accuracy]
for j, summary_name in enumerate(summary_list):
summary_str = sess_eval.run(summary_dic[noise_list[i]+"_"+summary_name], feed_dict={summary_ph: eval_summary_list[j]})
summary_writer.add_summary(summary_str, itr)
mean_cost.append(eval_cost)
mean_accuracy.append(eval_accuracy)
mean_auc.append(eval_auc)
mean_cost = np.mean(np.asarray(mean_cost))
var_accuracy = np.var(np.asarray(mean_accuracy))
mean_accuracy = np.mean(np.asarray(mean_accuracy))
mean_auc = np.mean(np.asarray(mean_auc))
summary_writer.add_summary(sess_eval.run(summary_dic["cost_across_all_noise_types"],
feed_dict={summary_ph: mean_cost}), itr)
summary_writer.add_summary(sess_eval.run(summary_dic["accuracy_across_all_noise_types"],
feed_dict={summary_ph: mean_accuracy}), itr)
summary_writer.add_summary(sess_eval.run(summary_dic["variance_across_all_noise_types"],
feed_dict={summary_ph: var_accuracy}), itr)
print("-------- Performance across all of noise types --------")
print("cost : %.4f" % mean_cost)
print("******* averaged accuracy across all noise_types : %.4f *******" % (mean_accuracy*100))
print("******* averaged auc across all noise_types : %.7f *******" % (mean_auc*100))
print("******* variance of accuracies across all noise_types : %6.6f *******" % var_accuracy)
return mean_auc, var_accuracy
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...")
cost_ph = tf.placeholder(dtype=tf.float32)
accuracy_ph = tf.placeholder(dtype=tf.float32)
cost_summary_op = tf.summary.scalar("cost", cost_ph)
accuracy_summary_op = tf.summary.scalar("accuracy", accuracy_ph)
train_summary_writer = tf.summary.FileWriter(logs_dir + '/train/')
valid_summary_writer = tf.summary.FileWriter(logs_dir + '/valid/', max_queue=2)
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...")
saver.restore(sess, ckpt.model_checkpoint_path)
print("Done")
else:
sess.run(tf.global_variables_initializer()) # if the checkpoint doesn't exist, do initialization
data_set = dr.DataReader(input_dir, output_dir, norm_dir, w=w, u=u, name="train") # training data reader initialization
valid_data_set = dr.DataReader(valid_input_dir, valid_output_dir, norm_dir, w=w, u=u, name="valid") # validation data reader initialization
if FLAGS.mode is 'train':
for itr in range(max_epoch):
train_inputs, train_labels = 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 % 50 == 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: %.3f, train_accuracy=%3.3f" % (itr, train_cost, train_accuracy))
train_cost_summary_str = sess.run(cost_summary_op, feed_dict={cost_ph: train_cost})
train_accuracy_summary_str = sess.run(accuracy_summary_op, feed_dict={accuracy_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 % 100 == 0 and itr >= 0:
saver.save(sess, logs_dir + "/model.ckpt", itr) # model save
valid_cost, valid_accuracy = evaluation(m_valid, valid_data_set, sess, valid_batch_size)
#
print('')
print("valid_cost: %.3f, valid_accuracy: %.3f" % (valid_cost, valid_accuracy))
print('')
valid_summary_str_cost = sess.run(cost_summary_op, feed_dict={cost_ph: valid_cost})
valid_summary_str_accuracy = sess.run(accuracy_summary_op, feed_dict={accuracy_ph: valid_accuracy})
valid_summary_writer.add_summary(valid_summary_str_cost, itr)
valid_summary_writer.add_summary(valid_summary_str_accuracy, itr)
elif FLAGS.mode is 'test':
_, valid_accuracy = evaluation(m_valid, valid_data_set, sess, valid_batch_size)
print("valid_accuracy = %.3f" % valid_accuracy)