forked from huawei/mindspore2022
126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
# Copyright 2021 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 Rprop """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.nn.optim import Rprop
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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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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_rpropwithoutparam():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when trainable_params is empty.
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Expectation: ValueError is raised.
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"""
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net = NetWithoutWeight()
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net.set_train()
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with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
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Rprop(net.trainable_params(), learning_rate=0.1)
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def test_rprop_tuple():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when the type of etas and step_sizes is not correct.
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Expectation: TypeError is raised.
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"""
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net = Net()
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with pytest.raises(TypeError):
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Rprop(net.get_parameters(), etas=[0.5, 1.2], learning_rate=0.1)
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with pytest.raises(TypeError):
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Rprop(net.get_parameters(), step_sizes=[1e-6, 50.], learning_rate=0.1)
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def test_rprop_size():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when the size of etas and step_sizes is not correct.
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Expectation: ValueError is raised.
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"""
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net = Net()
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), etas=(0.5, 1.2, 1.3), learning_rate=0.1)
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), step_sizes=(1e-6, 50., 60.), learning_rate=0.1)
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def test_rprop_stepsize():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when the value of step_sizes is not correct.
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Expectation: ValueError is raised.
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"""
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net = Net()
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), step_sizes=(50., 1e-6), learning_rate=0.1)
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def test_rprop_etas():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when the value range of etas is not correct.
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Expectation: ValueError is raised.
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"""
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net = Net()
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), etas=(0.5, 0.9), learning_rate=0.1)
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), etas=(1., 1.2), learning_rate=0.1)
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with pytest.raises(ValueError):
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Rprop(net.get_parameters(), etas=(-0.1, 1.2), learning_rate=0.1)
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def test_rprop_mindspore_with_empty_params():
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"""
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Feature: Test Rprop optimizer.
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Description: Test if error is raised when there is no trainable_params.
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Expectation: ValueError is raised.
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"""
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net = nn.Flatten()
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with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
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Rprop(net.get_parameters())
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