new_contents: #
  # Copyright 2018 Analytics Zoo Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  from zoo.pipeline.api.autograd import *
  from zoo.pipeline.api.keras.layers import *
  from zoo.pipeline.api.keras.models import *
  
  
  def mean_absolute_error(y_true, y_pred):
      result = mean(abs(y_true - y_pred), axis=1)
      return result
  
  if __name__ == "__main__":
      data_len = 1000
      X_ = np.random.uniform(0, 1, (1000, 2))
      Y_ = ((2 * X_).sum(1) + 0.4).reshape([data_len, 1])
      model = Sequential()
      model.add(Dense(1, input_shape=(2,)))
      model.compile(optimizer=SGD(learningrate=1e-2),
                    loss=mean_absolute_error,
                    metrics=None)
      model.fit(x=X_,
                y=Y_,
                batch_size=32,
                nb_epoch=500,
                validation_data=None,
                distributed=False)
      w = model.get_weights()
      print(w)
      pred = model.predict_local(X_)
subject: fix (#164)