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86 lines
3.1 KiB
86 lines
3.1 KiB
7 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/adagrad_op.h"
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namespace paddle {
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namespace operators {
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class AdagradOp : 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(framework::InferShapeContextBase *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("param"),
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"Input(param) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("grad"),
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"Input(grad) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("moment"),
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"Input(moment) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("param_out"),
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"Output(param_out) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("moment_out"),
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"Output(moment_out) of AdagradOp should not be null.");
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auto param_dim = ctx->GetInputDim("param");
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PADDLE_ENFORCE_EQ(
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param_dim, ctx->GetInputDim("grad"),
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"Param and grad input of AdagradOp should have the same dimension.");
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PADDLE_ENFORCE_EQ(
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param_dim, ctx->GetInputDim("moment"),
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"Param and moment input of AdagradOp should have the same dimension.");
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ctx->SetOutputDim("param_out", param_dim);
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ctx->SetOutputDim("moment_out", param_dim);
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}
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};
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class AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AdagradOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("param", "Input parameter");
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AddInput("grad", "Input gradient");
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AddInput("moment", "Second moment");
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AddOutput("param_out", "Output parameter");
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AddOutput("moment_out", "Output second moment");
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AddAttr<float>("learning_rate", "Learning rate");
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AddAttr<float>("epsilon", "Constant for numerical stability");
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AddComment(R"DOC(
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Adaptive Gradient Algorithm (Adagrad).
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moment_out = moment + grad * grad
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param_out = param - learning_rate * grad / (sqrt(moment_out) + epsilon)
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The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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does not have the epsilon attribute. It is added here for numerical stability
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by avoiding division by zero.
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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(adagrad, ops::AdagradOp, ops::AdagradOpMaker);
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REGISTER_OP_CPU_KERNEL(adagrad,
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ops::AdagradOpKernel<paddle::platform::CPUPlace, float>);
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