# Copyright (c) 2018 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. import unittest import numpy as np from op_test import OpTest def modified_huber_loss_forward(val): if val < -1: return -4. * val elif val < 1: return (1. - val) * (1. - val) else: return 0. class TestModifiedHuberLossOp(OpTest): def setUp(self): self.op_type = 'modified_huber_loss' samples_num = 32 x_np = np.random.uniform(-2., 2., (samples_num, 1)).astype('float32') y_np = np.random.choice([0, 1], samples_num).reshape( (samples_num, 1)).astype('float32') product_res = x_np * (2. * y_np - 1.) # keep away from the junction of piecewise function for pos, val in np.ndenumerate(product_res): while abs(val - 1.) < 0.05: x_np[pos] = np.random.uniform(-2., 2.) y_np[pos] = np.random.choice([0, 1]) product_res[pos] = x_np[pos] * (2 * y_np[pos] - 1) val = product_res[pos] self.inputs = {'X': x_np, 'Y': y_np} loss = np.vectorize(modified_huber_loss_forward)(product_res) self.outputs = { 'IntermediateVal': product_res.astype('float32'), 'Out': loss.reshape((samples_num, 1)).astype('float32') } def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out', max_relative_error=0.01) if __name__ == '__main__': unittest.main()