text: #  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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 __future__ import print_function
  
  import unittest
  import numpy as np
  
  import paddle
  
  import paddle.fluid.core as core
  
  
  class Optimization_ex1(paddle.nn.Layer):
      def __init__(self,
                   shape,
                   param_attr=paddle.nn.initializer.Uniform(
                       low=-5., high=5.),
                   dtype='float32'):
          super(Optimization_ex1, self).__init__()
  
          self.theta = self.create_parameter(
              shape=shape, attr=param_attr, dtype=dtype, is_bias=False)
          self.A = paddle.to_tensor(
              np.random.randn(4, 4) + np.random.randn(4, 4) * 1j)
  
      def forward(self):
          loss = paddle.add(self.theta, self.A)
          return loss.real()
  
  
  class TestComplexSimpleNet(unittest.TestCase):
      def setUp(self):
          self.devices = ['cpu']
          if core.is_compiled_with_cuda():
              self.devices.append('gpu')
          self.iter = 10
          self.learning_rate = 0.5
          self.theta_size = [4, 4]
  
      def train(self, device):
          paddle.set_device(device)
  
          myLayer = Optimization_ex1(self.theta_size)
          optimizer = paddle.optimizer.Adam(
              learning_rate=self.learning_rate, parameters=myLayer.parameters())
  
          for itr in range(self.iter):
              loss = myLayer()
              loss.backward()
  
              optimizer.step()
              optimizer.clear_grad()
  
      def test_train_success(self):
          for dev in self.devices:
              self.train(dev)
  
  
  if __name__ == '__main__':
      unittest.main()
  