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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -81,9 +81,13 @@ class ConvLayer void Forward(const arma::Cube<eT>& input, arma::Cube<eT>& output) { arma::Cube<eT> paddedInput; bool usePaddedInput = false; if (wPad != 0 || hPad != 0) { Pad(input, wPad, hPad, paddedInput); usePaddedInput = true; } const size_t wConv = ConvOutSize(input.n_rows, wfilter, xStride, wPad); const size_t hConv = ConvOutSize(input.n_cols, hfilter, yStride, hPad); output = arma::zeros<arma::Cube<eT> >(wConv, hConv, outMaps); @@ -92,8 +96,12 @@ class ConvLayer for (size_t inMap = 0; inMap < inMaps; inMap++, outMapIdx++) { arma::Mat<eT> convOutput; if (usePaddedInput) ForwardConvolutionRule::Convolution(paddedInput.slice(inMap), weights.slice(outMapIdx), convOutput); else ForwardConvolutionRule::Convolution(input.slice(inMap), weights.slice(outMapIdx), convOutput); output.slice(outMap) += convOutput; } @@ -128,8 +136,10 @@ class ConvLayer arma::Mat<eT> output; BackwardConvolutionRule::Convolution(gy.slice(inMap), rotatedFilter, output); g.slice(outMap) += output.submat(rotatedFilter.n_rows / 2, rotatedFilter.n_cols / 2, rotatedFilter.n_rows / 2 + g.n_rows - 1, rotatedFilter.n_cols / 2 + g.n_cols - 1); } } } @@ -165,25 +175,6 @@ class ConvLayer } } //! Get the weights. OutputDataType const& Weights() const { return weights; } //! Modify the weights. @@ -273,6 +264,26 @@ class ConvLayer return std::floor(size + p * 2 - k) / s + 1; } template<typename eT> void Pad(const arma::Mat<eT>& input, size_t wPad, size_t hPad, arma::Mat<eT>& output) { if (output.n_rows != input.n_rows + wPad * 2 || output.n_cols != input.n_cols + hPad * 2) output = arma::zeros(input.n_rows + wPad * 2, input.n_cols + hPad * 2); output.submat(wPad, hPad, wPad + input.n_rows - 1, hPad + input.n_cols - 1) = input; } template<typename eT> void Pad(const arma::Cube<eT>& input, size_t wPad, size_t hPad, arma::Cube<eT>& output) { output = arma::zeros(input.n_rows + wPad * 2, input.n_cols + hPad * 2, input.n_slices); for (size_t i = 0; i < input.n_slices; ++i) Pad<double>(input.slice(i), wPad, hPad, output.slice(i)); } //! Locally-stored filter/kernel width. size_t wfilter; -
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,341 @@ /** * @file conv_layer.hpp * @author Marcus Edel * * Definition of the ConvLayer class. */ #ifndef MLPACK_METHODS_ANN_LAYER_CONV_LAYER_HPP #define MLPACK_METHODS_ANN_LAYER_CONV_LAYER_HPP #include <mlpack/core.hpp> #include <mlpack/methods/ann/layer/layer_traits.hpp> #include <mlpack/methods/ann/convolution_rules/border_modes.hpp> #include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp> namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** * Implementation of the ConvLayer class. The ConvLayer class represents a * single layer of a neural network. * * @tparam ForwardConvolutionRule Convolution to perform forward process. * @tparam BackwardConvolutionRule Convolution to perform backward process. * @tparam GradientConvolutionRule Convolution to calculate gradient. * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>, typename BackwardConvolutionRule = NaiveConvolution<FullConvolution>, typename GradientConvolutionRule = NaiveConvolution<ValidConvolution>, typename InputDataType = arma::cube, typename OutputDataType = arma::cube > class ConvLayer { public: /** * Create the ConvLayer object using the specified number of input maps, * output maps, filter size, stride and padding parameter. * * @param inMaps The number of input maps. * @param outMaps The number of output maps. * @param wfilter Width of the filter/kernel. * @param hfilter Height of the filter/kernel. * @param xStride Stride of filter application in the x direction. * @param yStride Stride of filter application in the y direction. * @param wPad Spatial padding width of the input. * @param hPad Spatial padding height of the input. */ ConvLayer(const size_t inMaps, const size_t outMaps, const size_t wfilter, const size_t hfilter, const size_t xStride = 1, const size_t yStride = 1, const size_t wPad = 0, const size_t hPad = 0) : wfilter(wfilter), hfilter(hfilter), inMaps(inMaps), outMaps(outMaps), xStride(xStride), yStride(yStride), wPad(wPad), hPad(hPad) { weights.set_size(wfilter, hfilter, inMaps * outMaps); } /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ template<typename eT> void Forward(const arma::Cube<eT>& input, arma::Cube<eT>& output) { arma::Cube<eT> paddedInput; if (wPad != 0 || hPad != 0) Pad(input, wPad, hPad, paddedInput); else paddedInput = input; const size_t wConv = ConvOutSize(input.n_rows, wfilter, xStride, wPad); const size_t hConv = ConvOutSize(input.n_cols, hfilter, yStride, hPad); output = arma::zeros<arma::Cube<eT> >(wConv, hConv, outMaps); for (size_t outMap = 0, outMapIdx = 0; outMap < outMaps; outMap++) { for (size_t inMap = 0; inMap < inMaps; inMap++, outMapIdx++) { arma::Mat<eT> convOutput; ForwardConvolutionRule::Convolution(paddedInput.slice(inMap), weights.slice(outMapIdx), convOutput); output.slice(outMap) += convOutput; } } } /** * Ordinary feed backward pass of a neural network, calculating the function * f(x) by propagating x backwards through f. Using the results from the feed * forward pass. * * @param input The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ template<typename eT> void Backward(const arma::Cube<eT>& /* unused */, const arma::Cube<eT>& gy, arma::Cube<eT>& g) { g = arma::zeros<arma::Cube<eT> >(inputParameter.n_rows, inputParameter.n_cols, inputParameter.n_slices); for (size_t outMap = 0, outMapIdx = 0; outMap < inMaps; outMap++) { for (size_t inMap = 0; inMap < outMaps; inMap++, outMapIdx++) { arma::Mat<eT> rotatedFilter; Rotate180(weights.slice(outMap * outMaps + inMap), rotatedFilter); arma::Mat<eT> output; BackwardConvolutionRule::Convolution(gy.slice(inMap), rotatedFilter, output); g.slice(outMap) += output; } } } /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param d The calculated error. * @param g The calculated gradient. */ template<typename InputType, typename eT> void Gradient(const InputType& input, const arma::Cube<eT>& d, arma::Cube<eT>& g) { g = arma::zeros<arma::Cube<eT> >(weights.n_rows, weights.n_cols, weights.n_slices); for (size_t outMap = 0; outMap < outMaps; outMap++) { for (size_t inMap = 0, s = outMap; inMap < inMaps; inMap++, s += outMaps) { arma::Cube<eT> inputSlices = input.slices(inMap, inMap); arma::Cube<eT> deltaSlices = d.slices(outMap, outMap); arma::Cube<eT> output; GradientConvolutionRule::Convolution(inputSlices, deltaSlices, output); for (size_t i = 0; i < output.n_slices; i++) g.slice(s) += output.slice(i); } } } template<typename eT> void Pad(const arma::Mat<eT>& input, size_t wPad, size_t hPad, arma::Mat<eT>& output) { if (output.n_rows != input.n_rows + wPad * 2 || output.n_cols != input.n_cols + hPad * 2) output = arma::zeros(input.n_rows + wPad * 2, input.n_cols + hPad * 2); output.submat(wPad, hPad, wPad + input.n_rows - 1, hPad + input.n_cols - 1) = input; } template<typename eT> void Pad(const arma::Cube<eT>& input, size_t wPad, size_t hPad, arma::Cube<eT>& output) { output = arma::zeros(input.n_rows + wPad * 2, input.n_cols + hPad * 2, input.n_slices); for (size_t i = 0; i < input.n_slices; ++i) Pad<double>(input.slice(i), wPad, hPad, output.slice(i)); } //! Get the weights. OutputDataType const& Weights() const { return weights; } //! Modify the weights. OutputDataType& Weights() { return weights; } //! Get the input parameter. InputDataType const& InputParameter() const { return inputParameter; } //! Modify the input parameter. InputDataType& InputParameter() { return inputParameter; } //! Get the output parameter. OutputDataType const& OutputParameter() const { return outputParameter; } //! Modify the output parameter. OutputDataType& OutputParameter() { return outputParameter; } //! Get the delta. OutputDataType const& Delta() const { return delta; } //! Modify the delta. OutputDataType& Delta() { return delta; } //! Get the gradient. OutputDataType const& Gradient() const { return gradient; } //! Modify the gradient. OutputDataType& Gradient() { return gradient; } /** * Serialize the layer. */ template<typename Archive> void Serialize(Archive& ar, const unsigned int /* version */) { ar & data::CreateNVP(weights, "weights"); ar & data::CreateNVP(wfilter, "wfilter"); ar & data::CreateNVP(hfilter, "hfilter"); ar & data::CreateNVP(inMaps, "inMaps"); ar & data::CreateNVP(outMaps, "outMaps"); ar & data::CreateNVP(xStride, "xStride"); ar & data::CreateNVP(yStride, "yStride"); ar & data::CreateNVP(wPad, "wPad"); ar & data::CreateNVP(hPad, "hPad"); } private: /* * Rotates a 3rd-order tensor counterclockwise by 180 degrees. * * @param input The input data to be rotated. * @param output The rotated output. */ template<typename eT> void Rotate180(const arma::Cube<eT>& input, arma::Cube<eT>& output) { output = arma::Cube<eT>(input.n_rows, input.n_cols, input.n_slices); // * left-right flip, up-down flip */ for (size_t s = 0; s < output.n_slices; s++) output.slice(s) = arma::fliplr(arma::flipud(input.slice(s))); } /* * Rotates a dense matrix counterclockwise by 180 degrees. * * @param input The input data to be rotated. * @param output The rotated output. */ template<typename eT> void Rotate180(const arma::Mat<eT>& input, arma::Mat<eT>& output) { // * left-right flip, up-down flip */ output = arma::fliplr(arma::flipud(input)); } /* * Return the convolution output size. * * @param size The size of the input (row or column). * @param k The size of the filter (width or height). * @param s The stride size (x or y direction). * @param p The size of the padding (width or height). * @return The convolution output size. */ size_t ConvOutSize(const size_t size, const size_t k, const size_t s, const size_t p) { return std::floor(size + p * 2 - k) / s + 1; } //! Locally-stored filter/kernel width. size_t wfilter; //! Locally-stored filter/kernel height. size_t hfilter; //! Locally-stored number of input maps. size_t inMaps; //! Locally-stored number of output maps. size_t outMaps; //! Locally-stored stride of the filter in x-direction. size_t xStride; //! Locally-stored stride of the filter in y-direction. size_t yStride; //! Locally-stored padding width. size_t wPad; //! Locally-stored padding height. size_t hPad; //! Locally-stored weight object. OutputDataType weights; //! Locally-stored delta object. OutputDataType delta; //! Locally-stored gradient object. OutputDataType gradient; //! Locally-stored input parameter object. InputDataType inputParameter; //! Locally-stored output parameter object. OutputDataType outputParameter; }; // class ConvLayer //! Layer traits for the convolution layer. template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, typename InputDataType, typename OutputDataType > class LayerTraits<ConvLayer<ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, InputDataType, OutputDataType> > { public: static const bool IsBinary = false; static const bool IsOutputLayer = false; static const bool IsBiasLayer = false; static const bool IsLSTMLayer = false; static const bool IsConnection = true; }; } // namespace ann } // namespace mlpack #endif