paddlepaddle--paddle
wehub-resource-sync
102 行
2.8 KiB
Python
102 行
2.8 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import OpTest
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from paddle.nn import functional as F
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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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self.python_api = F.log_loss
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self.prim_op_type = "comp"
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self.public_python_api = F.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) - (1 - labels) * np.log(
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1 - predicted + epsilon
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)
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self.outputs = {'Loss': loss}
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['Predicted'],
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'Loss',
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max_relative_error=0.03,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestLogLossOp_ZeroSize(OpTest):
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def init_shape(self):
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self.x_shape = (0, 1)
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self.label_shape = (0, 1)
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def setUp(self):
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self.init_shape()
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self.op_type = 'log_loss'
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self.python_api = F.log_loss
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self.public_python_api = F.log_loss
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x = np.random.random(self.x_shape).astype("float32")
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predicted = sigmoid_array(x)
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labels = np.random.randint(0, 2, self.label_shape).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) - (1 - labels) * np.log(
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1 - predicted + epsilon
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)
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self.outputs = {'Loss': loss}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['Predicted'],
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'Loss',
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check_pir=True,
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)
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if __name__ == '__main__':
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unittest.main()
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