diff --git a/tutorials/mnist_dpsgd_tutorial_keras.py b/tutorials/mnist_dpsgd_tutorial_keras.py
index 0638412..b3e0845 100644
--- a/tutorials/mnist_dpsgd_tutorial_keras.py
+++ b/tutorials/mnist_dpsgd_tutorial_keras.py
@@ -11,37 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Training a CNN on MNIST with Keras and the DP SGD optimizer.
-
-**************************** PLEASE READ ME ************************************
-
-A modification to Keras needed for this tutorial to work as it is currently
-written is *being* pushed. While this modification is in the works, you can
-make this tutorial work by making the following change to the TensorFlow source
-code (disabling the reduction of the loss used to compile a model):
-
-Diff for file: tensorflow/python/keras/engine/training_utils.py
-
-```
-+ from tensorflow.python.ops.losses import losses_impl
-
-  def get_loss_function():
-
-    ...
-
--   return losses.LossFunctionWrapper(loss_fn, name=loss_fn.__name__)
-+   return losses.LossFunctionWrapper(loss_fn,
-+                                     name=loss_fn.__name__,
-+                                     reduction=losses_impl.Reduction.NONE)
-```
-
-This allows the DP-SGD optimizer to have access to the loss defined per
-example rather than the mean of the loss for the entire minibatch. This is
-needed to compute gradients for each microbatch contained in a minibatch.
-
-**************************** END OF PLEASE READ ME *****************************
-
-"""
+"""Training a CNN on MNIST with Keras and the DP SGD optimizer."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -138,16 +108,15 @@ def main(unused_argv):
         FLAGS.microbatches,
         learning_rate=FLAGS.learning_rate,
         unroll_microbatches=True)
+    # 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)
   else:
     optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)
-
-  def keras_loss_fn(labels, logits):
-    """This removes the mandatory named arguments for this loss fn."""
-    return tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels,
-                                                      logits=logits)
+    loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
 
   # Compile model with Keras
-  model.compile(optimizer=optimizer, loss=keras_loss_fn, metrics=['accuracy'])
+  model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
 
   # Train model with Keras
   model.fit(train_data, train_labels,