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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| /** | ||
| * @file methods/ann/layer/softmin.hpp | ||
| * @author Aakash Kaushik | ||
| * | ||
| * Definition of the Softmin class. | ||
| * | ||
| * mlpack is free software; you may redistribute it and/or modify it under the | ||
| * terms of the 3-clause BSD license. You should have received a copy of the | ||
| * 3-clause BSD license along with mlpack. If not, see | ||
| * http://www.opensource.org/licenses/BSD-3-Clause for more information. | ||
| */ | ||
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| #ifndef MLPACK_METHODS_ANN_LAYER_SOFTMIN_HPP | ||
| #define MLPACK_METHODS_ANN_LAYER_SOFTMIN_HPP | ||
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| #include <mlpack/prereqs.hpp> | ||
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| namespace mlpack { | ||
| namespace ann /** Artificial Neural Network. */ { | ||
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| /** | ||
| * Implementation of the Softmin layer. The Softmin function takes as a input | ||
| * a vector of K real numbers, rescaling them so that the elements of the | ||
| * K-dimensional output vector lie in the range [0, 1] and sum to 1. | ||
| * | ||
| * @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 InputDataType = arma::mat, | ||
| typename OutputDataType = arma::mat | ||
| > | ||
| class Softmin | ||
| { | ||
| public: | ||
| /** | ||
| * Create the Softmin object. | ||
| */ | ||
| Softmin(); | ||
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| /** | ||
| * 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 InputType, typename OutputType> | ||
| void Forward(const InputType& input, OutputType& output); | ||
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| /** | ||
| * 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::Mat<eT>& input, | ||
| const arma::Mat<eT>& gy, | ||
| arma::Mat<eT>& g); | ||
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| //! Get the output parameter. | ||
| OutputDataType& OutputParameter() const { return outputParameter; } | ||
| //! Modify the output parameter. | ||
| OutputDataType& OutputParameter() { return outputParameter; } | ||
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| //! Get the delta. | ||
| InputDataType& Delta() const { return delta; } | ||
| //! Modify the delta. | ||
| InputDataType& Delta() { return delta; } | ||
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| /** | ||
| * Serialize the layer. | ||
| */ | ||
| template<typename Archive> | ||
| void serialize(Archive& /* ar */, const unsigned int /* version */); | ||
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| private: | ||
| //! Locally-stored delta object. | ||
| OutputDataType delta; | ||
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| //! Locally stored output parameter object. | ||
| OutputDataType outputParameter; | ||
| }; // class Softmin | ||
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| } // namespace ann | ||
| } // namespace mlpack | ||
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| // Include implementation. | ||
| #include "softmin_impl.hpp" | ||
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| #endif |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| /** | ||
| * @file methods/ann/layer/softmin_impl.hpp | ||
| * @author Aakash Kaushik | ||
| * | ||
| * Implementation of the Softmin class. | ||
| * | ||
| * mlpack is free software; you may redistribute it and/or modify it under the | ||
| * terms of the 3-clause BSD license. You should have received a copy of the | ||
| * 3-clause BSD license along with mlpack. If not, see | ||
| * http://www.opensource.org/licenses/BSD-3-Clause for more information. | ||
| */ | ||
| #ifndef MLPACK_METHODS_ANN_LAYER_SOFTMIN_IMPL_HPP | ||
| #define MLPACK_METHODS_ANN_LAYER_SOFTMIN_IMPL_HPP | ||
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| // In case it hasn't yet been included. | ||
| #include "softmin.hpp" | ||
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| namespace mlpack { | ||
| namespace ann /** Artificial Neural Network. */ { | ||
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| template<typename InputDataType, typename OutputDataType> | ||
| Softmin<InputDataType, OutputDataType>::Softmin() | ||
| { | ||
| // Nothing to do here. | ||
| } | ||
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| template<typename InputDataType, typename OutputDataType> | ||
| template<typename InputType, typename OutputType> | ||
| void Softmin<InputDataType, OutputDataType>::Forward( | ||
| const InputType& input, | ||
| OutputType& output) | ||
| { | ||
| InputType inputMin = arma::repmat(arma::min(input,0), input.n_rows, 1); | ||
| output = arma::repmat(arma::log(arma::sum( | ||
| arma::exp(-(input - inputMin)),0)), input.n_rows, 1); | ||
| output = arma::exp(-(input - inputMin) - output); | ||
| } | ||
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| template<typename InputDataType, typename OutputDataType> | ||
| template<typename eT> | ||
| void Softmin<InputDataType, OutputDataType>::Backward( | ||
| const arma::Mat<eT>& input, | ||
| const arma::Mat<eT>& gy, | ||
| arma::Mat<eT>& g) | ||
| { | ||
| g = input % (gy - arma::repmat(arma::sum(gy % input), input.n_rows, 1)); | ||
| } | ||
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| template<typename InputDataType, typename OutputDataType> | ||
| template<typename Archive> | ||
| void Softmin<InputDataType, OutputDataType>::serialize( | ||
| Archive& /* ar */, | ||
| const unsigned int /* version */) | ||
| { | ||
| // Nothing to do here. | ||
| } | ||
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| } // namespace ann | ||
| } // namespace mlpack | ||
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| #endif |
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