You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
4.4 KiB
113 lines
4.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/adadelta_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class AdadeltaOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext *ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("Param"),
|
|
"Input(Param) of AdadeltaOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Grad"),
|
|
"Input(Grad) of AdadeltaOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("AvgSquaredGrad"),
|
|
"Input(AvgSquaredGrad) of AdadeltaOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"),
|
|
"Input(AvgSquaredUpdate) of AdadeltaOp should not be null.");
|
|
|
|
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
|
|
"Output(ParamOut) of AdadeltaOp should not be null.");
|
|
PADDLE_ENFORCE(
|
|
ctx->HasOutput("AvgSquaredGradOut"),
|
|
"Output(AvgSquaredGradOut) of AdadeltaOp should not be null.");
|
|
PADDLE_ENFORCE(
|
|
ctx->HasOutput("AvgSquaredUpdateOut"),
|
|
"Output(AvgSquaredUpdateOut) of AdadeltaOp should not be null.");
|
|
|
|
auto param_dim = ctx->GetInputDim("Param");
|
|
PADDLE_ENFORCE_EQ(
|
|
param_dim, ctx->GetInputDim("Grad"),
|
|
"param and grad input of AdadeltaOp should have same dimension");
|
|
PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredGrad"),
|
|
"Param and AvgSquaredGrad input of AdadeltaOp "
|
|
"should have same dimension");
|
|
PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredUpdate"),
|
|
"Param and AvgSquaredUpdate input of AdadeltaOp "
|
|
"should have same dimension");
|
|
|
|
ctx->SetOutputDim("ParamOut", param_dim);
|
|
ctx->SetOutputDim("AvgSquaredGradOut", param_dim);
|
|
ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim);
|
|
}
|
|
};
|
|
|
|
class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
AdadeltaOpMaker(OpProto *proto, OpAttrChecker *op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput("Param", "(Tensor) Input parameter");
|
|
AddInput("Grad", "(Tensor) Input gradient");
|
|
AddInput("AvgSquaredGrad", "(Tensor) Input average of squared gradient");
|
|
AddInput("AvgSquaredUpdate",
|
|
"(Tensor) Input average of squared parameter updates");
|
|
|
|
AddOutput("ParamOut", "(Tensor) Output parameter");
|
|
AddOutput("AvgSquaredGradOut",
|
|
"(Tensor) Output average of squared gradient");
|
|
AddOutput("AvgSquaredUpdateOut",
|
|
"(Tensor) Output average of squared parameter updates");
|
|
|
|
AddAttr<float>("rho",
|
|
"(float, default 0.95) Exponential decay rate "
|
|
"for squared gradients.")
|
|
.SetDefault(0.95f);
|
|
AddAttr<float>("epsilon",
|
|
"(float, default 1.0e-6) Constant for "
|
|
"numerical stability")
|
|
.SetDefault(1.0e-6f);
|
|
AddComment(R"DOC(
|
|
Adadelta Optimizer.
|
|
|
|
Adadelta optimizer is implemented as explained in:
|
|
https://arxiv.org/abs/1212.5701
|
|
Adadelta is a per-dimension adaptive learning rate method used
|
|
for gradient descent.
|
|
|
|
Adadelta updates are as follows:
|
|
|
|
$$
|
|
avg\_squared\_grad\_out = \rho * avg\_squared\_grad + (1 - \rho) * grad * grad \\
|
|
param\_update = - \sqrt{\frac{avg\_squared\_update + \epsilon}{avg\_squared\_grad\_out + \epsilon}} * grad \\
|
|
avg\_squared\_update\_out = \rho * avg\_squared\_update + (1 - \rho) * {param\_update}^2 \\
|
|
param\_out = param + param\_update
|
|
$$
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::AdadeltaOpKernel<paddle::platform::CPUDeviceContext, double>);
|