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73 lines
2.5 KiB
73 lines
2.5 KiB
# Copyright 2019 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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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.nn.optim import Momentum
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from mindspore.ops import operations as P
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn import Dense
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from mindspore import Tensor
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import mindspore.context as context
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class MomentumNet(nn.Cell):
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def __init__(self):
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super(MomentumNet, self).__init__()
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self.batch_size = 1
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self.reshape = P.Reshape()
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weight = Tensor(np.ones([10, 16]).astype(np.float32) * 0.01)
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self.fc1 = Dense(16, 10, weight_init=weight)
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def construct(self, input_x):
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output = self.reshape(input_x, (self.batch_size, -1))
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output = self.fc1(output)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_momentum():
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epoch = 13
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net = MomentumNet()
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learning_rate = 0.1
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momentum = 0.9
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
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train_network.set_train()
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losses = []
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for i in range(epoch):
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data = Tensor(np.arange(0, 16).reshape(1, 1, 4, 4).astype(np.float32)*0.01)
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label = Tensor(np.array([0]).astype(np.int32))
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loss = train_network(data, label)
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losses.append(loss)
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print("================================")
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print(losses)
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"""
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expect output:
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[[0.04132498 0.00874167 0.00874167 0.00874167 0.00874167
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0.00874167 0.00874167 0.00874167 0.00874167 0.00874167]]
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"""
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error = np.ones(shape=[1, 10]) * 1.0e-6
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return losses
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