diff --git a/examples/mnist_irnn_rmsprop.py b/examples/mnist_irnn.py
similarity index 65%
rename from examples/mnist_irnn_rmsprop.py
rename to examples/mnist_irnn.py
index 874fcf490..5178937e1 100644
--- a/examples/mnist_irnn_rmsprop.py
+++ b/examples/mnist_irnn.py
@@ -7,12 +7,13 @@ from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers.core import Dense, Activation
 from keras.initializations import normal, identity
-from keras.layers.recurrent import SimpleRNN
+from keras.layers.recurrent import SimpleRNN, LSTM
 from keras.optimizers import RMSprop
 from keras.utils import np_utils
 
 '''
-    This is a variant of IRNN experiment with sequential MNIST in
+    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
 
@@ -22,24 +23,20 @@ from keras.utils import np_utils
     Optimizer is replaced with RMSprop which give more stable and steady
     improvement.
 
-    Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_irnn_rmsprop.py
-
-    Reaches to 80% train/test accuracy and 0.55 train/test loss after 70 epochs
+    0.80 train/test accuracy and 0.55 train/test loss after 70 epochs
     (it's still underfitting at that point, though).
-
-    About 15 minuts per epoch on a GRID K520 GPU.
 '''
 
-batch_size = 16
+batch_size = 32
 nb_classes = 10
 nb_epochs = 200
 hidden_units = 100
 
 learning_rate = 1e-6
 clip_norm = 1.0
-BPTT_trancate = 28*28
+BPTT_truncate = 28*28
 
-# the data, shuffled and split between tran and test sets
+# 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)
@@ -56,11 +53,12 @@ print(X_test.shape[0], 'test samples')
 Y_train = np_utils.to_categorical(y_train, nb_classes)
 Y_test = np_utils.to_categorical(y_test, nb_classes)
 
+print('Evaluate IRNN...')
 model = Sequential()
 model.add(SimpleRNN(input_dim=1, output_dim=hidden_units,
                     init=lambda shape: normal(shape, scale=0.001),
                     inner_init=lambda shape: identity(shape, scale=1.0),
-                    activation='relu', truncate_gradient=BPTT_trancate))
+                    activation='relu', truncate_gradient=BPTT_truncate))
 model.add(Dense(hidden_units, nb_classes))
 model.add(Activation('softmax'))
 rmsprop = RMSprop(lr=learning_rate)
@@ -69,6 +67,21 @@ model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
 model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
           show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
 
-score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
-print('Test score:', score[0])
-print('Test accuracy:', score[1])
+scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
+print('IRNN test score:', scores[0])
+print('IRNN test accuracy:', scores[1])
+
+print('Compare to LSTM...')
+model = Sequential()
+model.add(LSTM(1, hidden_units))
+model.add(Dense(hidden_units, nb_classes))
+model.add(Activation('softmax'))
+rmsprop = RMSprop(lr=learning_rate)
+model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
+
+model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
+          show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
+
+scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
+print('LSTM test score:', scores[0])
+print('LSTM test accuracy:', scores[1])
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