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48 lines
1.8 KiB
48 lines
1.8 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 mindspore.context as context
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import mindspore.nn as nn
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
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self.variable = Parameter(initializer(
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'normal', [2, 3, 3, 4]), name='variable')
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self.accumulation = Parameter(initializer(
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'normal', [2, 3, 3, 4]), name='accumulation')
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self.learning_rate = Parameter(initializer(
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'normal', [1,]), name='learning_rate')
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self.gradient = Parameter(initializer(
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'normal', [2, 3, 3, 4]), name='gradient')
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self.momentum = Parameter(initializer(
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'normal', [1,]), name='momentum')
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def construct(self):
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return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
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def test_net():
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apply_momentum = Net()
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output = apply_momentum()
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print(output.asnumpy())
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