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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15""" test ADA_GRAD """
16
17import pytest
18import numpy as np
19
20import mindspore.nn as nn
21from mindspore import Tensor, Parameter, context
22from mindspore.common.api import _cell_graph_executor
23from mindspore.nn import TrainOneStepCell, WithLossCell
24from mindspore.nn.optim import Adagrad
25from mindspore.ops import operations as P
26
27
28@pytest.fixture(scope="module", autouse=True)
29def setup_teardown():
30    context.set_context(enable_sparse=True)
31    yield
32    context.set_context(enable_sparse=False)
33
34
35class Net(nn.Cell):
36    def __init__(self):
37        super(Net, self).__init__()
38        self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name='weight')
39        self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name='bias')
40        self.matmul = P.MatMul()
41        self.biasAdd = P.BiasAdd()
42
43    def construct(self, x):
44        x = self.biasAdd(self.matmul(x, self.weight), self.bias)
45        return x
46
47
48def test_ada_grad():
49    """ test_ada_grad """
50    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
51    label = Tensor(np.zeros([1, 10]).astype(np.float32))
52    net = Net()
53    net.set_train()
54    loss = nn.SoftmaxCrossEntropyWithLogits()
55    optimizer = Adagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
56    net_with_loss = WithLossCell(net, loss)
57    train_network = TrainOneStepCell(net_with_loss, optimizer)
58    _cell_graph_executor.compile(train_network, inputs, label)
59