text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """ test ADA_GRAD """
  
  import numpy as np
  
  import mindspore.nn as nn
  from mindspore import Tensor, Parameter, context
  from mindspore.common.api import _executor
  from mindspore.nn import TrainOneStepCell, WithLossCell
  from mindspore.nn.optim import Adagrad
  from mindspore.ops import operations as P
  
  context.set_context(enable_sparse=True)
  
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name='weight')
          self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name='bias')
          self.matmul = P.MatMul()
          self.biasAdd = P.BiasAdd()
  
      def construct(self, x):
          x = self.biasAdd(self.matmul(x, self.weight), self.bias)
          return x
  
  
  def test_ada_grad():
      """ test_ada_grad """
      inputs = Tensor(np.ones([1, 64]).astype(np.float32))
      label = Tensor(np.zeros([1, 10]).astype(np.float32))
      net = Net()
      net.set_train()
      loss = nn.SoftmaxCrossEntropyWithLogits()
      optimizer = Adagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
      net_with_loss = WithLossCell(net, loss)
      train_network = TrainOneStepCell(net_with_loss, optimizer)
      _executor.compile(train_network, inputs, label)
  