diff --git a/tutorials/mnist_dpsgd_tutorial_keras.py b/tutorials/mnist_dpsgd_tutorial_keras.py
index 71f67cb..865fb9f 100644
--- a/tutorials/mnist_dpsgd_tutorial_keras.py
+++ b/tutorials/mnist_dpsgd_tutorial_keras.py
@@ -41,10 +41,10 @@ flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
 flags.DEFINE_float('noise_multiplier', 1.1,
                    'Ratio of the standard deviation to the clipping norm')
 flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
-flags.DEFINE_integer('batch_size', 256, 'Batch size')
+flags.DEFINE_integer('batch_size', 250, 'Batch size')
 flags.DEFINE_integer('epochs', 60, 'Number of epochs')
 flags.DEFINE_integer(
-    'microbatches', 256, 'Number of microbatches '
+    'microbatches', 250, 'Number of microbatches '
     '(must evenly divide batch_size)')
 flags.DEFINE_string('model_dir', None, 'Model directory')
 
@@ -121,9 +121,8 @@ def main(unused_argv):
     optimizer = DPGradientDescentGaussianOptimizer(
         l2_norm_clip=FLAGS.l2_norm_clip,
         noise_multiplier=FLAGS.noise_multiplier,
-        num_microbatches=FLAGS.num_microbatches,
-        learning_rate=FLAGS.learning_rate,
-        unroll_microbatches=True)
+        num_microbatches=FLAGS.microbatches,
+        learning_rate=FLAGS.learning_rate)
     # Compute vector of per-example loss rather than its mean over a minibatch.
     loss = tf.keras.losses.CategoricalCrossentropy(
         from_logits=True, reduction=tf.losses.Reduction.NONE)