# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core import gradient_checker from decorator_helper import prog_scope class TestReluDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 3, 7, 9] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.relu(x) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) x_arr[np.abs(x_arr) < 0.005] = 0.02 gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestLeakyReluDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 3, 7, 9] eps = 0.005 alpha = 0.2 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.leaky_relu(x, alpha=alpha) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) x_arr[np.abs(x_arr) < 0.005] = 0.02 gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places = [fluid.CUDAPlace(0)] for p in places: self.func(p) class TestELUDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 4, 4] eps = 1e-6 alpha = 0.2 dtype = np.float64 SEED = 0 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.elu(x, alpha=alpha) np.random.RandomState(SEED) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestSqrtDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 3, 7, 9] eps = 0.0001 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.sqrt(x) x_arr = np.random.uniform(0.1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places = [fluid.CUDAPlace(0)] for p in places: self.func(p) class TestSquareDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): # the shape of input variable should be clearly specified, not inlcude -1. shape = [2, 3, 7, 9] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.square(x) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestAbsDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): # the shape of input variable should be clearly specified, not inlcude -1. shape = [2, 3, 7, 9] eps = 1e-6 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.abs(x) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestLogDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 3, 7, 9] eps = 1e-6 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = layers.log(x) x_arr = np.random.uniform(0.1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) if __name__ == "__main__": unittest.main()