forked from mindspore-Ecosystem/mindspore
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
# 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 lamb """
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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
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Lamb
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from mindspore.ops import operations as P
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class Net(nn.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]).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.biasAdd = P.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 NetWithoutWeight(nn.Cell):
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""" NetWithoutWeight definition """
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def __init__(self):
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super(NetWithoutWeight, self).__init__()
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self.matmul = P.MatMul()
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def construct(self, x):
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x = self.matmul(x, x)
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return x
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def test_lamb_1():
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""" test_Lamb_1 """
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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 = Lamb(net.trainable_params(), decay_steps=10, warmup_steps=5)
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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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_executor.compile(train_network, inputs, label)
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def test_lamb_2():
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""" test_Lamb_2 """
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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 = Lamb(net.trainable_params(), decay_steps=10, warmup_steps=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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_executor.compile(train_network, inputs, label)
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