diff --git a/examples/mnist_cnn.py b/examples/mnist_cnn.py
index 25aa07b6a58..22a95048364 100644
--- a/examples/mnist_cnn.py
+++ b/examples/mnist_cnn.py
@@ -6,12 +6,12 @@
 '''
 
 from __future__ import print_function
-import keras
-from keras.datasets import mnist
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten
-from keras.layers import Conv2D, MaxPooling2D
-from keras import backend as K
+import tensorflow.keras
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import Conv2D, MaxPooling2D
+from tensorflow.keras import backend as K
 
 batch_size = 128
 num_classes = 10
@@ -41,8 +41,8 @@
 print(x_test.shape[0], 'test samples')
 
 # convert class vectors to binary class matrices
-y_train = keras.utils.to_categorical(y_train, num_classes)
-y_test = keras.utils.to_categorical(y_test, num_classes)
+y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
+y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)
 
 model = Sequential()
 model.add(Conv2D(32, kernel_size=(3, 3),
@@ -56,8 +56,8 @@
 model.add(Dropout(0.5))
 model.add(Dense(num_classes, activation='softmax'))
 
-model.compile(loss=keras.losses.categorical_crossentropy,
-              optimizer=keras.optimizers.Adadelta(),
+model.compile(loss=tensorflow.keras.losses.categorical_crossentropy,
+              optimizer=tensorflow.keras.optimizers.Adadelta(),
               metrics=['accuracy'])
 
 model.fit(x_train, y_train,