text: # Copyright 2019 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.
  # ============================================================================
  
  import pytest
  import numpy as np
  import mindspore.nn as nn
  from mindspore.nn.optim import Momentum
  from mindspore.ops import operations as P
  from mindspore.nn import TrainOneStepCell, WithLossCell
  from mindspore.nn import Dense
  from mindspore import Tensor
  from mindspore.common.initializer import initializer
  import mindspore.context as context
  
  context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
  class NetMomentum(nn.Cell):
      def __init__(self):
          super(NetMomentum, self).__init__()
          self.batch_size = 1
          self.reshape = P.Reshape()
          weight = Tensor(np.ones([10, 16]).astype(np.float32) * 0.01)
          self.fc1 = Dense(16, 10, weight_init=weight)
  
      def construct(self, input_x):
          output = self.reshape(input_x, (self.batch_size, -1))
          output = self.fc1(output)
          return output
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_momentum():
      epoch = 3
      net = NetMomentum()
      learning_rate = initializer(Tensor(np.array([0.01]).astype(np.float32)), [1])
      momentum = initializer(Tensor(np.array([0.9]).astype(np.float32)), [1])
  
      optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
      criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
      net_with_criterion = WithLossCell(net, criterion)
      train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
      train_network.set_train()
      losses = []
      for i in range(epoch):
          data = Tensor(np.arange(0, 16).reshape(1, 1, 4, 4).astype(np.float32) * 0.01)
          label = Tensor(np.array([0]).astype(np.int32))
          loss = train_network(data, label)
          losses.append(loss)
  
      """
      expect output:
      [[0.04132498 0.00874167 0.00874167 0.00874167 0.00874167
        0.00874167 0.00874167 0.00874167 0.00874167 0.00874167]]
      """
      error = np.ones(shape=[1, 10]) * 1.0e-6
  
      return losses
  