wehub-resource-sync
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import get_device, is_custom_device
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import paddle
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from paddle.base import core
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class Optimization_ex1(paddle.nn.Layer):
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def __init__(
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self,
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shape,
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param_attr=paddle.nn.initializer.Uniform(low=-5.0, high=5.0),
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dtype='float32',
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):
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super().__init__()
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self.theta = self.create_parameter(
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shape=shape, attr=param_attr, dtype=dtype, is_bias=False
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)
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self.A = paddle.to_tensor(
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np.random.randn(4, 4) + np.random.randn(4, 4) * 1j
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)
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def forward(self):
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loss = paddle.add(self.theta, self.A)
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return loss.real()
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class TestComplexSimpleNet(unittest.TestCase):
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def setUp(self):
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self.devices = ['cpu']
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if core.is_compiled_with_cuda() or is_custom_device():
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self.devices.append(get_device())
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self.iter = 10
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self.learning_rate = 0.5
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self.theta_size = [4, 4]
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def train(self, device):
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paddle.set_device(device)
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myLayer = Optimization_ex1(self.theta_size)
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optimizer = paddle.optimizer.Adam(
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learning_rate=self.learning_rate, parameters=myLayer.parameters()
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)
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for itr in range(self.iter):
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loss = myLayer()
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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def test_train_success(self):
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for dev in self.devices:
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self.train(dev)
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if __name__ == '__main__':
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unittest.main()
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