diff --git a/examples/cifar10_nasnet.py b/examples/cifar10_nasnet.py
new file mode 100644
index 0000000..8eee651
--- /dev/null
+++ b/examples/cifar10_nasnet.py
@@ -0,0 +1,97 @@
+"""
+Adapted from keras example cifar10_cnn.py
+Train NASNet-CIFAR on the CIFAR10 small images dataset.
+
+GPU run command with Theano backend (with TensorFlow, the GPU is automatically used):
+    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_nasnet.py
+"""
+from __future__ import print_function
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.utils import np_utils
+from keras.callbacks import ModelCheckpoint
+from keras.callbacks import ReduceLROnPlateau
+from keras.callbacks import CSVLogger
+from keras_contrib.applications.nasnet import NASNetCIFAR
+
+import numpy as np
+
+
+weights_file = 'NASNet-CIFAR-10.h5'
+lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.5), cooldown=0, patience=5, min_lr=0.5e-6)
+csv_logger = CSVLogger('NASNet-CIFAR-10.csv')
+model_checkpoint = ModelCheckpoint(weights_file, monitor='val_predictions_acc', save_best_only=True,
+                                   save_weights_only=True, mode='max')
+
+batch_size = 128
+nb_classes = 10
+nb_epoch = 200
+data_augmentation = True
+
+# input image dimensions
+img_rows, img_cols = 32, 32
+# The CIFAR10 images are RGB.
+img_channels = 3
+
+# The data, shuffled and split between train and test sets:
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+
+# Convert class vectors to binary class matrices.
+Y_train = np_utils.to_categorical(y_train, nb_classes)
+Y_test = np_utils.to_categorical(y_test, nb_classes)
+
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# subtract mean and normalize
+mean_image = np.mean(X_train, axis=0)
+X_train -= mean_image
+X_test -= mean_image
+X_train /= 128.
+X_test /= 128.
+
+# For training, the auxilary branch must be used to correctly train NASNet
+model = NASNetCIFAR((img_rows, img_cols, img_channels), dropout=0.5,
+                    use_auxilary_branch=True)
+model.compile(loss=['categorical_crossentropy', 'categorical_crossentropy'],
+              optimizer='adam',
+              loss_weights=[1.0, 0.4],
+              metrics=['accuracy'])
+
+if not data_augmentation:
+    print('Not using data augmentation.')
+    model.fit(X_train, Y_train,
+              batch_size=batch_size,
+              nb_epoch=nb_epoch,
+              validation_data=(X_test, Y_test),
+              shuffle=True,
+              callbacks=[lr_reducer, csv_logger, model_checkpoint])
+else:
+    print('Using real-time data augmentation.')
+    # This will do preprocessing and realtime data augmentation:
+    datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
+        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=False)  # randomly flip images
+
+    # Compute quantities required for featurewise normalization
+    # (std, mean, and principal components if ZCA whitening is applied).
+    datagen.fit(X_train)
+
+    # Fit the model on the batches generated by datagen.flow().
+    model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
+                        steps_per_epoch=X_train.shape[0] // batch_size,
+                        validation_data=(X_test, Y_test),
+                        epochs=nb_epoch, verbose=2,
+                        callbacks=[lr_reducer, csv_logger, model_checkpoint])
+
+scores = model.evaluate(X_test, Y_test, batch_size=batch_size)
+for score, metric_name in zip(scores, model.metrics_names):
+    print("%s : %0.4f" % (metric_name, score))
diff --git a/keras_contrib/applications/nasnet.py b/keras_contrib/applications/nasnet.py
index cddc682..072172d 100644
--- a/keras_contrib/applications/nasnet.py
+++ b/keras_contrib/applications/nasnet.py
@@ -215,7 +215,7 @@ def NASNet(input_shape=None,
             auxilary_x = Activation('relu')(auxilary_x)
 
             auxilary_x = GlobalAveragePooling2D()(auxilary_x)
-            auxilary_x = Dense(classes, activation='softmax')(auxilary_x)
+            auxilary_x = Dense(classes, activation='softmax', name='aux_predictions')(auxilary_x)
 
     x, p0 = _reduction_A(x, p, filters * filters_multiplier ** 2, id='reduce_%d' % (2 * nb_blocks))