diff --git a/tutorials/mnist_dpsgd_tutorial_keras.py b/tutorials/mnist_dpsgd_tutorial_keras.py
index 927cb07..7e78a6d 100644
--- a/tutorials/mnist_dpsgd_tutorial_keras.py
+++ b/tutorials/mnist_dpsgd_tutorial_keras.py
@@ -22,19 +22,17 @@ from absl import flags
 from absl import logging
 
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
 from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
 from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
-from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
-
-GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
 
 flags.DEFINE_boolean(
     'dpsgd', True, 'If True, train with DP-SGD. If False, '
     'train with vanilla SGD.')
 flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
-flags.DEFINE_float('noise_multiplier', 1.1,
+flags.DEFINE_float('noise_multiplier', 0.1,
                    'Ratio of the standard deviation to the clipping norm')
 flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
 flags.DEFINE_integer('batch_size', 250, 'Batch size')
@@ -70,8 +68,8 @@ def load_mnist():
   train_data = np.array(train_data, dtype=np.float32) / 255
   test_data = np.array(test_data, dtype=np.float32) / 255
 
-  train_data = train_data.reshape(train_data.shape[0], 28, 28, 1)
-  test_data = test_data.reshape(test_data.shape[0], 28, 28, 1)
+  train_data = train_data.reshape((train_data.shape[0], 28, 28, 1))
+  test_data = test_data.reshape((test_data.shape[0], 28, 28, 1))
 
   train_labels = np.array(train_labels, dtype=np.int32)
   test_labels = np.array(test_labels, dtype=np.int32)
@@ -114,7 +112,7 @@ def main(unused_argv):
   ])
 
   if FLAGS.dpsgd:
-    optimizer = DPGradientDescentGaussianOptimizer(
+    optimizer = DPKerasSGDOptimizer(
         l2_norm_clip=FLAGS.l2_norm_clip,
         noise_multiplier=FLAGS.noise_multiplier,
         num_microbatches=FLAGS.microbatches,
@@ -123,7 +121,7 @@ def main(unused_argv):
     loss = tf.keras.losses.CategoricalCrossentropy(
         from_logits=True, reduction=tf.losses.Reduction.NONE)
   else:
-    optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)
+    optimizer = tf.keras.optimizers.SGD(learning_rate=FLAGS.learning_rate)
     loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
 
   # Compile model with Keras