snapshot...

modules/deeplearning/icing_cnn.py

@@ -811,7 +811,7 @@ class IcingIntensityNN:
 
         step = 0
         total_time = 0
-        best_test_loss = np.finfo(dtype=np.float).max
+        best_test_loss = np.finfo(dtype=np.float32).max
         best_test_acc = 0
         best_test_recall = 0
         best_test_precision = 0
@@ -842,7 +842,7 @@ class IcingIntensityNN:
 
                         with self.writer_train.as_default():
                             tf.summary.scalar('loss_trn', loss.numpy(), step=step)
-                            tf.summary.scalar('learning_rate', self.optimizer._decayed_lr('float32').numpy(), step=step)
+                            tf.summary.scalar('learning_rate', self.optimizer.lr.numpy(), step=step)
                             tf.summary.scalar('num_train_steps', step, step=step)
                             tf.summary.scalar('num_epochs', epoch, step=step)
 
@@ -869,7 +869,7 @@ class IcingIntensityNN:
                                 tf.summary.scalar('num_epochs', epoch, step=step)
 
                         print('****** test loss, acc, lr: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
-                              self.optimizer._decayed_lr('float32').numpy())
+                              self.optimizer.lr.numpy())
 
                     step += 1
                     print('train loss: ', loss.numpy())