def run_model(self,
writer,
set_path,
preload_epoch,
epoch,
training=False,
shuffle=False,
pretrain=False):
if not training:
print('VALIDATION')
image_loader = ImageLoader(batch_size=2, image_dir=set_path)
if shuffle:
image_loader.shuffle_data()
batch_gen = image_loader.getImages()
for i, (batch_x, batch_y) in enumerate(batch_gen):
subbatch = 100
feed = {
'tf_x:0': batch_x,
'tf_y:0': batch_y,
'tf_training:0': training
}
if (i % subbatch == 0):
print('batch ' + str(i) + '/' + str(image_loader.batch_count))
loss = self.sess.run(self.merged, feed_dict=feed)
writer.add_summary(loss)
if (training and i % 1000 == 0):
self.save(epoch=preload_epoch + epoch)
if (not pretrain):
_ = self.sess.run('train_op_disc', feed_dict=feed)
_ = self.sess.run('train_op', feed_dict=feed)
else:
loss, _ = self.sess.run([self.mse_loss_summ, 'train_mse_op'],
feed_dict=feed)
writer.add_summary(loss)
