def build_model():
  model = keras.Sequential([
    keras.layers.Conv2D(16, kernel_size=(2, 2), strides=(1,1), activation=tf.nn.relu6,
                        input_shape=input_shape),
    keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
    keras.layers.Conv2D(128, (2, 2), activation=tf.nn.relu6),
    keras.layers.MaxPooling2D(pool_size=(2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(1000, activation=tf.nn.relu6),
    keras.layers.Dense(1,activation=tf.nn.relu)
  ])

  model.compile(loss=keras.losses.kullback_leibler_divergence,
              optimizer=tf.train.AdamOptimizer(0.001, beta1=0.9, beta2=0.99, epsilon=1e-8),
              metrics=['accuracy'])
  return model

model = build_model()
model.summary()