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71 lines
2.6 KiB
71 lines
2.6 KiB
# Copyright 2020 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_lr_schedule """
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import numpy as np
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from mindspore.nn import Cell
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from mindspore.ops.operations import BiasAdd, MatMul
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from mindspore import Parameter, ParameterTuple, Tensor
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from mindspore.nn import WithLossCell
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from mindspore.nn.optim import Momentum
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from mindspore.nn import SoftmaxCrossEntropyWithLogits
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from mindspore.ops.composite import grad_by_list
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from mindspore.ops import functional as F
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from mindspore.nn.optim import Optimizer
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class Net(Cell):
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""" Net definition """
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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])), name="weight")
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self.bias = Parameter(Tensor(np.ones([10])), name="bias")
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self.matmul = MatMul()
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self.biasAdd = BiasAdd()
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def construct(self, x):
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x = self.biasAdd(self.matmul(x, self.weight), self.bias)
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return x
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class _TrainOneStepCell(Cell):
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""" _TrainOneStepCell definition """
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def __init__(self, network, optimizer):
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"""
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Append an optimizer to the training network after that the construct
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function can be called to create the backward graph.
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Arguments:
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network: The training network.
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Note that loss function should have been added.
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optimizer: optimizer for updating the weights
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"""
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super(_TrainOneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.weights = ParameterTuple(network.get_parameters())
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if not isinstance(optimizer, Optimizer):
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raise TypeError('{} is not an optimizer'.format(
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type(optimizer).__name__))
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self.has_lr_schedule = False
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self.optimizer = optimizer
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def construct(self, data, label, *args):
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weights = self.weights
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grads = grad_by_list(self.network, weights)(data, label)
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if self.lr_schedule:
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self.schedule.update_lr(*args)
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return self.optimizer(grads)
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