forked from mindspore-Ecosystem/mindspore
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test adadelta """
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import pytest
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Adadelta
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE)
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skip_flag = context.get_context("device_target") == "GPU"
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class Net(nn.Cell):
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"""Net."""
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def __init__(self):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name='weight')
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self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name='bias')
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self.matmul = P.MatMul()
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self.bias_add = P.BiasAdd()
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def construct(self, x):
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x = self.bias_add(self.matmul(x, self.weight), self.bias)
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return x
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@pytest.mark.skipif(skip_flag, reason="not support running in GPU")
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def test_adadelta():
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"""
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Feature: Test Adadelta.
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Description: Test Adadelta functional.
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Expectation: Success.
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"""
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Adadelta(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_cell_graph_executor.compile(train_network, inputs, label)
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