diff --git a/examples/variational_autoencoder.py b/examples/variational_autoencoder.py
index a93a47d12..b10e1fee4 100644
--- a/examples/variational_autoencoder.py
+++ b/examples/variational_autoencoder.py
@@ -5,7 +5,7 @@ Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
 import numpy as np
 import matplotlib.pyplot as plt
 
-from keras.layers import Input, Dense, merge
+from keras.layers import Input, Dense, Lambda
 from keras.models import Model
 from keras import backend as K
 from keras import objectives
@@ -14,21 +14,22 @@ from keras.datasets import mnist
 batch_size = 100
 original_dim = 784
 latent_dim = 2
-intermediate_dim = 500
-nb_epoch = 100
+intermediate_dim = 256
+nb_epoch = 50
 
 x = Input(batch_shape=(batch_size, original_dim))
 h = Dense(intermediate_dim, activation='relu')(x)
 z_mean = Dense(latent_dim)(h)
 z_log_var = Dense(latent_dim)(h)
 
+
 def sampling(args):
     z_mean, z_log_var = args
     epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.)
-    return z_mean + K.exp(z_log_var/2) * epsilon
+    return z_mean + K.exp(z_log_var / 2) * epsilon
 
 # note that "output_shape" isn't necessary with the TensorFlow backend
-z = merge([z_mean, z_log_var], mode=sampling, output_shape=(latent_dim,))
+z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
 
 # we instantiate these layers separately so as to reuse them later
 decoder_h = Dense(intermediate_dim, activation='relu')
@@ -36,6 +37,7 @@ decoder_mean = Dense(original_dim, activation='sigmoid')
 h_decoded = decoder_h(z)
 x_decoded_mean = decoder_mean(h_decoded)
 
+
 def vae_loss(x, x_decoded_mean):
     xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
     kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)