Support backward of backward for Relu and add a new gradient checker by comparing theoretical and numerical Jacobian. (#16862)
* Support backward of backward and a new gradient checker * Rename decorators.py to decorator_helper.py, since Python on Windows CI has decorators package. 1. Add ReluDoubleGradMaker when register relu_grad. 2. Add a new gradient checker by comparing theoretical and numerical Jacobian. Check double gradients by double_grad_check.shanyi15-patch-1
qingqing01
6 years ago
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# Copyright (c) 2019 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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import paddle.fluid.core as core
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import gradient_checker
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from decorator_helper import prog_scope
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class TestMulGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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prog = fluid.Program()
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with fluid.program_guard(prog):
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x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
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y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
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z = layers.mul(x=x, y=y)
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gradient_checker.grad_check([x, y], z, place=place)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestReluDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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# the shape of input variable shoule be clearly specified, not inlcude -1.
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shape = [2, 8]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', shape, False, dtype)
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x.persistable = True
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y = layers.relu(x)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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x_arr[np.abs(x_arr) < 0.005] = 0.02
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gradient_checker.double_grad_check(
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[x], y, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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if __name__ == "__main__":
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unittest.main()
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