init = tf.global_variables_initializer()
sess.run(init)
# In[ ]:
# training
if conf.training == True:
stop_count = 0
itr = 0
max_validloss = 99999
while True:
# prepare training input
batch_xs, batch_ys = next(train_batches)
batch_xs_aug, batch_ys_aug = utils.data_augmentation(batch_xs, batch_ys)
batch_ymap_aug = gen_gaumap(batch_ys_aug)
sess.run([train_op,extra_update_ops],
feed_dict = {image: batch_xs_aug/255,
annotation: batch_ymap_aug,
keep_probability: 0.5,
train_phase:conf.training})
# print training learning curve every 500 iterations
if itr % 500 == 0:
train_loss = sess.run([loss],
feed_dict={image: batch_xs_aug/255,
annotation: batch_ymap_aug,
keep_probability: 0.5,
train_phase:conf.training})
print("[T] Step: %d, loss:%g" % (itr, np.mean(train_loss)))
