diff --git a/examples/frameworks/keras/keras_tensorboard.py b/examples/frameworks/keras/keras_tensorboard.py
index 6762965f..1d944898 100644
--- a/examples/frameworks/keras/keras_tensorboard.py
+++ b/examples/frameworks/keras/keras_tensorboard.py
@@ -106,12 +106,12 @@ task.set_model_label_enumeration(labels)
 output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')
 
 board = TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False)
-model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))
+model_store = ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.keras'))
 
 # load previous model, if it is there
 # noinspection PyBroadException
 try:
-    model.load_weights(os.path.join(output_folder, 'weight.1.hdf5'))
+    model.load_weights(os.path.join(output_folder, 'weight.1.keras'))
 except Exception:
     pass
 
diff --git a/examples/frameworks/tensorflow/tensorflow_mnist.py b/examples/frameworks/tensorflow/tensorflow_mnist.py
index 4eb5333f..26211275 100644
--- a/examples/frameworks/tensorflow/tensorflow_mnist.py
+++ b/examples/frameworks/tensorflow/tensorflow_mnist.py
@@ -93,7 +93,7 @@ train_summary_writer = tf.summary.create_file_writer(train_log_dir)
 test_summary_writer = tf.summary.create_file_writer(test_log_dir)
 
 # Set up checkpoints manager
-ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=optimizer, net=model)
+ckpt = tf.train.Checkpoint(step=tf.Variable(1), net=model)
 manager = tf.train.CheckpointManager(ckpt, os.path.join(gettempdir(), 'tf_ckpts'), max_to_keep=3)
 ckpt.restore(manager.latest_checkpoint)
 if manager.latest_checkpoint:
@@ -129,7 +129,13 @@ for epoch in range(EPOCHS):
                           test_accuracy.result()*100))
 
     # Reset the metrics for the next epoch
-    train_loss.reset_states()
-    train_accuracy.reset_states()
-    test_loss.reset_states()
-    test_accuracy.reset_states()
+    try:
+        train_loss.reset_states()
+        train_accuracy.reset_states()
+        test_loss.reset_states()
+        test_accuracy.reset_states()
+    except AttributeError: 
+        train_loss.reset_state()
+        train_accuracy.reset_state()
+        test_loss.reset_state()
+        test_accuracy.reset_state()