53 lines
1.7 KiB
53 lines
1.7 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, randomize_probability
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class TestBprLossOp1(OpTest):
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"""Test BprLoss with discrete one-hot labels.
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"""
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def setUp(self):
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self.op_type = "bpr_loss"
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batch_size = 40
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class_num = 5
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X = randomize_probability(batch_size, class_num, dtype='float64')
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label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64")
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bpr_loss_result = []
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for i in range(batch_size):
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sum = 0.0
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for j in range(class_num):
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if j == label[i][0]:
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continue
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sum += (-np.log(1.0 + np.exp(X[i][j] - X[i][label[i][0]])))
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bpr_loss_result.append(-sum / (class_num - 1))
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bpr_loss = np.asmatrix([[x] for x in bpr_loss_result], dtype="float64")
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self.inputs = {"X": X, "Label": label}
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self.outputs = {"Y": bpr_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(["X"], "Y", numeric_grad_delta=0.001)
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if __name__ == "__main__":
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unittest.main()
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