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81 lines
3.1 KiB
81 lines
3.1 KiB
8 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/operators/auc_op.h"
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namespace paddle {
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namespace operators {
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class AccuracyOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
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"Input of Inference must be initialized.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
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"Input of Inference must be initialized.");
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auto *inference = ctx.Input<framework::Tensor>("Inference");
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auto *inference_prob = ctx.Input<framework::Tensor>("InferenceProb");
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auto *label = ctx.Input<framework::Tensor>("Label");
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PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
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PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
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"inference size must be the same as label size");
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PADDLE_ENFORCE_EQ(inference->dims(), inference_prob->dims());
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ctx.Output<Tensor>("Accuracy")->Resize({1});
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}
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};
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class AucOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Inference",
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"Topk(indices) the network output, float value indicating "
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"probabilities of classification");
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AddInput("InferenceProb",
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"Topk(values) the network output, float value indicating "
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"probabilities of classification");
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AddInput("Label", "Label of the training data");
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// TODO(typhoonzero): support weight
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AddOutput("AUC", "Area Under Curve caculations");
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AddAttr<std::string>("curve", "Possible curves are ROC and PR")
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.SetDefault("ROC");
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AddAttr<int>("num_thresholds",
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"The number of thresholds to use when discretizing the"
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" roc curve.")
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.SetDefault(200);
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AddComment(
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R"DOC(Computes the AUC according forward output and label.
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You can find the definations here:
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https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
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Possible curves are:
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ROC: Receiver operating characteristic
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PR: Precision Recall
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AccuracyOp, ops::AccuracyOpMaker);
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REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);
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