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84 lines
2.5 KiB
84 lines
2.5 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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from op_test import OpTest
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import paddle.fluid as fluid
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def sigmoid_array(x):
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return 1 / (1 + np.exp(-x))
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class TestLogLossOp(OpTest):
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def setUp(self):
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self.op_type = 'log_loss'
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samples_num = 100
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x = np.random.random((samples_num, 1)).astype("float32")
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predicted = sigmoid_array(x)
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labels = np.random.randint(0, 2, (samples_num, 1)).astype("float32")
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epsilon = 1e-7
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self.inputs = {
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'Predicted': predicted,
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'Labels': labels,
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}
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self.attrs = {'epsilon': epsilon}
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loss = -labels * np.log(predicted + epsilon) - (
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1 - labels) * np.log(1 - predicted + epsilon)
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self.outputs = {'Loss': loss}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['Predicted'], 'Loss', max_relative_error=0.03)
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class TestLogLossOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program()):
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def test_x_type():
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input_data = np.random.random(100, 1).astype("float32")
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fluid.layers.log_loss(input_data)
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self.assertRaises(TypeError, test_x_type)
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def test_x_dtype():
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x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
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fluid.layers.log_loss(x2)
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self.assertRaises(TypeError, test_x_dtype)
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def test_label_type():
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input_data = np.random.random(100, 1).astype("float32")
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fluid.layers.log_loss(input_data)
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self.assertRaises(TypeError, test_label_type)
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def test_label_dtype():
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x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
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fluid.layers.log_loss(x2)
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self.assertRaises(TypeError, test_label_dtype)
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if __name__ == '__main__':
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unittest.main()
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