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116 lines
4.4 KiB
116 lines
4.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/operators/log_loss_op.h"
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namespace paddle {
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namespace operators {
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class LogLossOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Predicted"),
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"Input(Predicted) must be initialized.");
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PADDLE_ENFORCE(ctx->HasInput("Labels"),
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"Input(Labels) must be initialized.");
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auto pred_dims = ctx->GetInputDim("Predicted");
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auto label_dims = ctx->GetInputDim("Labels");
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PADDLE_ENFORCE_EQ(pred_dims, label_dims);
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PADDLE_ENFORCE_EQ(pred_dims.size(), 2,
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"The rank of Input(Predicted) must be 2 and the shape is "
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"[batch_size, 1].");
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PADDLE_ENFORCE_EQ(pred_dims[1], 1,
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"Each row of Input(Predicted) contains a real value, "
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"so the 2nd dimension of Input(X) must be 1.");
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ctx->SetOutputDim("Loss", {pred_dims[0], 1});
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ctx->ShareLoD("Predicted", "Loss");
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}
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};
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template <typename AttrType>
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class LogLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Predicted",
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"The input value (Predicted) of Log loss op."
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"Predicted is a 2-D tensor with shape [batch_size, 1].");
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AddInput("Labels",
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"The target value (Labels) of Log loss op."
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"Labels is a 2-D tensor with shape [batch_size, 1].");
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AddOutput("Loss",
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"The output tensor with shape [batch_size, 1] "
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"which represents the log loss.");
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AddAttr<AttrType>("epsilon", "Epsilon in log loss.");
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AddComment(R"DOC(
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LogLoss Operator.
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Log loss is a loss function used for binary classification. Log Loss quantifies
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the accuracy of a classifier by penalising false classifications. Minimising the
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Log Loss is equivalent to maximising the accuracy of the classifier. We define
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Predicted as the values predicted by our model and Labels as the target ground
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truth value. Log loss can evaluate how close the predicted values are to the
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target. The shapes of Predicted and Labels are both [batch_size, 1].
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The equation is:
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$$
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Loss = - Labels * log(Predicted + \epsilon) -
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(1 - Labels) * log(1 - Predicted + \epsilon)
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$$
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)DOC");
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}
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};
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class LogLossGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Predicted"),
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"Input(Predicted) should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Labels"),
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"Input(Labels) should not be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
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"Input(Loss@GRAD) should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Predicted")),
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"Output(Predicted@GRAD) should not be null.");
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auto pred_dims = ctx->GetInputDim("Predicted");
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auto label_dims = ctx->GetInputDim("Labels");
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auto loss_grad_dims = ctx->GetInputDim(framework::GradVarName("Loss"));
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PADDLE_ENFORCE_EQ(loss_grad_dims, pred_dims);
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auto pred_grad_name = framework::GradVarName("Predicted");
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ctx->SetOutputDim(pred_grad_name, pred_dims);
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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_OPERATOR(log_loss, ops::LogLossOp, ops::LogLossOpMaker<float>,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(log_loss_grad, ops::LogLossGradOp);
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REGISTER_OP_CPU_KERNEL(
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log_loss, ops::LogLossKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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log_loss_grad,
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ops::LogLossGradKernel<paddle::platform::CPUDeviceContext, float>);
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