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103 lines
3.5 KiB
103 lines
3.5 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.backward import calc_gradient
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class TestCalcGradient(unittest.TestCase):
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def test_calc_gradient(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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x = layers.create_parameter(dtype="float32", shape=[5, 10])
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y = layers.create_parameter(dtype="float32", shape=[10, 8])
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mul_out = layers.mul(x=x, y=y)
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mean_out = layers.mean(mul_out)
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a = calc_gradient(mean_out, mul_out)
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b = calc_gradient(mean_out, x)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup)
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exe.run(main, feed={}, fetch_list=[a, b])
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class TestDoubleGrad(unittest.TestCase):
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def test1(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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net = lambda x: x * x
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x = fluid.layers.create_parameter(
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name='x',
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shape=[1],
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dtype='float32',
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default_initializer=fluid.initializer.Constant(3))
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grad1, = fluid.gradients(net(x), x) # 2x = 6
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z = net(x - grad1)
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grad2, = fluid.gradients(z, x) # gradients( (x - 2x)^2) = 2x = 6
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup)
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out = exe.run(main, fetch_list=[grad1.name, grad2.name])
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self.assertEqual(6, out[0][0])
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self.assertEqual(6, out[1][0])
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def test2(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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x = fluid.layers.create_parameter(
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name='x',
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shape=[1],
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dtype='float32',
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default_initializer=fluid.initializer.Constant(1))
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y = x * x
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dx1, = fluid.gradients(y, x)
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z = dx1 * dx1 + y * y
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dx2, = fluid.gradients(z, x)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup)
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out, = exe.run(main, fetch_list=[dx2])
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self.assertEqual(12, out[0])
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class TestGradientWithPrune(unittest.TestCase):
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def test_prune(self):
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x = fluid.data(name='x', shape=[3], dtype='float32')
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x.stop_gradient = False
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x1, x2, x3 = fluid.layers.split(x, dim=0, num_or_sections=3)
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y = x1 * 2
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x1_grad = fluid.gradients(y, x)
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exe = fluid.Executor(fluid.CPUPlace())
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main = fluid.default_main_program()
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exe.run(fluid.default_startup_program())
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out = exe.run(main,
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feed={'x': np.ones([3]).astype('float32')},
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fetch_list=[x1_grad])
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self.assertTrue(np.array_equal(out[0], [2., 0., 0.]))
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if __name__ == "__main__":
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unittest.main()
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