cblas_new
Yu Yang
8 years ago
commit
fa5a5a3acf
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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 "Layer.h"
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namespace paddle {
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/**
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* A layer for clipping the input value by the threshold.
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* \f[
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* out[i] = \min\left(\max\left(in[i],p_{1}\right),p_{2}\right)
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* \f]
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*/
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class ClipLayer : public Layer {
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protected:
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double min_;
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double max_;
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public:
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explicit ClipLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(clip, ClipLayer);
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bool ClipLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1U);
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auto layerConf = config_.inputs(0).clip_conf();
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min_ = layerConf.min();
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max_ = layerConf.max();
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CHECK_LT(min_, max_);
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return true;
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}
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void ClipLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV = getInputValue(0);
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resetOutput(inV->getHeight(), inV->getWidth());
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MatrixPtr outV = getOutputValue();
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outV->copyFrom(*inV);
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outV->clip(min_, max_);
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}
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void ClipLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV = getInputValue(0);
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MatrixPtr inG = getInputGrad(0);
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if (inG) {
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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MatrixPtr tmpMtx;
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Matrix::resizeOrCreate(
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tmpMtx, outG->getHeight(), outG->getWidth(), false, useGpu_);
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tmpMtx->clipDerivative(*inV, min_, max_);
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inG->addDotMul(*outG, *tmpMtx, 1, 1);
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}
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}
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} // namespace paddle
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@ -0,0 +1,98 @@
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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 "Layer.h"
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namespace paddle {
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/**
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* A layer for L2 normalization in each row,
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* \f[
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* out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
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* \f]
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* where the size of \f$in\f$ is (batchSize x dataDim),
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* and the size of \f$out\f$ is (batchSize x dataDim).
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*/
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class RowL2NormLayer : public Layer {
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protected:
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MatrixPtr inSquare_;
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MatrixPtr l2NormReciprocal_;
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MatrixPtr dotSum_;
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public:
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explicit RowL2NormLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(row_l2_norm, RowL2NormLayer);
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bool RowL2NormLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1U);
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return true;
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}
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void RowL2NormLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV = getInputValue(0);
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/* malloc memory for the output_ if necessary */
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size_t batchSize = inV->getHeight();
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size_t dataDim = getSize();
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CHECK_EQ(dataDim, inV->getWidth());
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resetOutput(batchSize, dataDim);
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MatrixPtr outV = getOutputValue();
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Matrix::resizeOrCreate(inSquare_, batchSize, dataDim, false, useGpu_);
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inV->square2(*inSquare_);
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Matrix::resizeOrCreate(l2NormReciprocal_, batchSize, 1, false, useGpu_);
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inSquare_->rowSum(*l2NormReciprocal_);
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l2NormReciprocal_->sqrt2(*l2NormReciprocal_);
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l2NormReciprocal_->scalarDiv(*l2NormReciprocal_, 1.0);
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outV->rowScale(0, *inV, *l2NormReciprocal_);
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}
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void RowL2NormLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV = getInputValue(0);
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MatrixPtr inG = getInputGrad(0);
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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size_t batchSize = inV->getHeight();
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// inG[ij] += outG[ij] / l2NormReciprocal
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// inG[ij] += -inV[ij] * l2NormReciprocal * l2NormReciprocal * DotMul(outG[i],
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// inV[i])
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if (inG) {
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Matrix::resizeOrCreate(dotSum_, batchSize, 1, false, useGpu_);
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dotSum_->zeroMem();
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dotSum_->rowDotMul(0, *outG, *outV);
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dotSum_->dotMul(*dotSum_, *l2NormReciprocal_);
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dotSum_->dotMul(*dotSum_, *l2NormReciprocal_);
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inSquare_->rowScale(0, *inV, *dotSum_);
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inG->sub(*inSquare_);
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inG->addRowScale(0, *outG, *l2NormReciprocal_);
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}
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}
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} // namespace paddle
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@ -1,3 +1,4 @@
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#define EIGEN_USE_GPU
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#include "paddle/operators/cross_entropy_op.h"
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#include "paddle/operators/cross_entropy_op.h"
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REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
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REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
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