class Test:
def __init__(self):
conf_path_cnn = '/src/cnn/conf/cnn.yaml'
with open(conf_path_cnn, 'r') as fd:
self.conf = yaml.safe_load(fd)
self.batch_size = self.conf['testing']['batch_size']
self.checkpoint_path = self.conf['misc']['checkpoint_path']
self.logger = Logger
self.preProcessor = PreProcessor(Logger)
self.cnn = Cnn(Logger, conf_path_cnn, testing=True)
self.sess = tf.compat.v1.Session()
self.start()
def start(self):
test_x, test_y = self.preProcessor.get_data(testing=True)
self.logger.info('testing...')
# restore model
self.sess = tf.compat.v1.Session()
saver = tf.compat.v1.train.Saver()
model_path = self.conf['testing']['model']
saver.restore(self.sess, model_path)
self.logger.info('{0} restored'.format(model_path))
# test
self.sess.run(tf.compat.v1.local_variables_initializer())
pred_y, loss, acc = self.cnn.analyze_epoch(self.sess, test_x, test_y)
self.logger.info('test_loss: {0}, test_acc: {1}'.format(loss, acc))
self.sess.close()
return pred_y
class Train:
def __init__(self):
conf_path_cnn = '/src/cnn/conf/cnn.yaml'
with open(conf_path_cnn, 'r') as fd:
self.conf = yaml.safe_load(fd)
# load configuration
self.num_epochs = self.conf['training']['epochs']
self.batch_size = self.conf['training']['batch_size']
self.checkpoint_path = self.conf['misc']['checkpoint_path']
self.logger = Logger
self.preProcessor = PreProcessor(Logger)
self.cnn = Cnn(Logger, conf_path_cnn)
self.sess = tf.compat.v1.Session()
self.start()
def start(self):
train_x, train_y, val_x, val_y = self.preProcessor.get_data()
self.sess.run(tf.compat.v1.global_variables_initializer())
self.sess.run(tf.compat.v1.local_variables_initializer())
saver = tf.compat.v1.train.Saver()
num_iterations = int(len(train_x) / self.batch_size)
self.logger.info('training...')
for epoch in range(self.num_epochs):
for iter_ in range(num_iterations):
batch_x = train_x[iter_ * self.batch_size:(iter_ + 1) *
self.batch_size, :]
batch_y = train_y[iter_ * self.batch_size:(iter_ + 1) *
self.batch_size, :]
self.sess.run(self.cnn.training,
feed_dict={
self.cnn.layer_input: batch_x,
self.cnn.ground_truth: batch_y
})
# train loss
_, loss_train, acc_train = self.cnn.analyze_epoch(
self.sess, train_x, train_y)
# val loss
_, loss_val, acc_val = self.cnn.analyze_epoch(
self.sess, val_x, val_y)
self.logger.info(
'epoch: {0}/{1}, loss_train: {2}, acc_train: {3}, loss_val: {4}, acc_val: {5}'
.format(epoch + 1, self.num_epochs, loss_train, acc_train,
loss_val, acc_val))
# save model
saved_path = saver.save(self.sess,
'{0}model.ckpt'.format(self.checkpoint_path))
self.logger.info('model saved in {0}'.format(saved_path))
self.sess.close()