diff --git a/vignettes/examples/mnist_irnn.py b/vignettes/examples/mnist_irnn.py
deleted file mode 100755
index 07f630a79..000000000
--- a/vignettes/examples/mnist_irnn.py
+++ /dev/null
@@ -1,73 +0,0 @@
-'''This is a reproduction of the IRNN experiment
-with pixel-by-pixel sequential MNIST in
-"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units"
-by Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
-
-arxiv:1504.00941v2 [cs.NE] 7 Apr 2015
-http://arxiv.org/pdf/1504.00941v2.pdf
-
-Optimizer is replaced with RMSprop which yields more stable and steady
-improvement.
-
-Reaches 0.93 train/test accuracy after 900 epochs
-(which roughly corresponds to 1687500 steps in the original paper.)
-'''
-
-from __future__ import print_function
-
-import keras
-from keras.datasets import mnist
-from keras.models import Sequential
-from keras.layers import Dense, Activation
-from keras.layers import SimpleRNN
-from keras import initializers
-from keras.optimizers import RMSprop
-
-batch_size = 32
-num_classes = 10
-epochs = 200
-hidden_units = 100
-
-learning_rate = 1e-6
-clip_norm = 1.0
-
-# the data, shuffled and split between train and test sets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-x_train = x_train.reshape(x_train.shape[0], -1, 1)
-x_test = x_test.reshape(x_test.shape[0], -1, 1)
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-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)
-
-print('Evaluate IRNN...')
-model = Sequential()
-model.add(SimpleRNN(hidden_units,
-                    kernel_initializer=initializers.RandomNormal(stddev=0.001),
-                    recurrent_initializer=initializers.Identity(gain=1.0),
-                    activation='relu',
-                    input_shape=x_train.shape[1:]))
-model.add(Dense(num_classes))
-model.add(Activation('softmax'))
-rmsprop = RMSprop(lr=learning_rate)
-model.compile(loss='categorical_crossentropy',
-              optimizer=rmsprop,
-              metrics=['accuracy'])
-
-model.fit(x_train, y_train,
-          batch_size=batch_size,
-          epochs=epochs,
-          verbose=1,
-          validation_data=(x_test, y_test))
-
-scores = model.evaluate(x_test, y_test, verbose=0)
-print('IRNN test score:', scores[0])
-print('IRNN test accuracy:', scores[1])