def print_status(self, mse):
psnr = util.get_psnr(mse, max_value=self.max_value)
if self.step == 0:
print("Initial MSE:%f PSNR:%f" % (mse, psnr))
else:
processing_time = (time.time() - self.start_time) / self.step
print("%s Step:%d MSE:%f PSNR:%f (Training PSNR:%0.3f)" %
(util.get_now_date(), self.step, mse, psnr,
self.training_psnr_sum / self.training_step))
print("Epoch:%d (Step:%s) LR:%f (%2.3fsec/step) MinPSNR:%0.3f" %
(self.epochs_completed, "{:,}".format(self.step), self.lr,
processing_time, util.get_psnr(self.min_validation_mse)))
def train_batch(self):
_, mse = self.sess.run(
[self.training_optimizer, self.mse],
feed_dict={
self.x: self.batch_input,
self.x2: self.batch_input_quad,
self.y: self.batch_true_quad,
self.lr_input: self.lr,
self.dropout_input: self.dropout
})
self.training_psnr_sum += util.get_psnr(mse, max_value=self.max_value)
self.training_step += 1
self.step += 1
def update_epoch_and_lr(self, mse):
lr_updated = False
if self.min_validation_mse < 0 or self.min_validation_mse > mse:
# update new mse
self.min_validation_epoch = self.epochs_completed
self.min_validation_mse = mse
else:
if self.epochs_completed > self.min_validation_epoch + self.lr_decay_epoch:
# set new learning rate
self.min_validation_epoch = self.epochs_completed
self.lr *= self.lr_decay
lr_updated = True
psnr = util.get_psnr(mse, max_value=self.max_value)
self.csv_epochs.append(self.epochs_completed)
self.csv_psnr.append(psnr)
self.csv_training_psnr.append(self.training_psnr_sum /
self.training_step)
return lr_updated
