diff --git a/include/NeuralNetwork/ActivationFunction/ActivationFunction.h b/include/NeuralNetwork/ActivationFunction/ActivationFunction.h index 9de6cc4..2c13104 100644 --- a/include/NeuralNetwork/ActivationFunction/ActivationFunction.h +++ b/include/NeuralNetwork/ActivationFunction/ActivationFunction.h @@ -18,7 +18,7 @@ namespace ActivationFunction { * @brief Returns derivation of output, it is slower than version with output as it needs to compute output * @param input is input of function */ - inline float derivatedOutput(const float &input) { + inline float derivatedOutput(const float &input) const { return derivatedOutput(input,operator()(input)); }; @@ -28,13 +28,13 @@ namespace ActivationFunction { * @param output is output of function * @see derivatedOutput */ - virtual float derivatedOutput(const float &input, const float &output) =0; + virtual float derivatedOutput(const float &input, const float &output) const=0; /** * @brief Returns value of output * @param x is input of function */ - virtual float operator()(const float &x)=0; + virtual float operator()(const float &x) const=0; /** * @brief Function returns clone of object diff --git a/include/NeuralNetwork/ActivationFunction/Heaviside.h b/include/NeuralNetwork/ActivationFunction/Heaviside.h index 2531b97..d787293 100644 --- a/include/NeuralNetwork/ActivationFunction/Heaviside.h +++ b/include/NeuralNetwork/ActivationFunction/Heaviside.h @@ -9,8 +9,8 @@ namespace ActivationFunction { public: Heaviside(const float &lambdaP=1.0): lambda(lambdaP) {} - inline virtual float derivatedOutput(const float &,const float &) override { return 1.0; } - inline virtual float operator()(const float &x) override { return x>lambda ? 1.0f : 0.0f; }; + inline virtual float derivatedOutput(const float &,const float &) const override { return 1.0; } + inline virtual float operator()(const float &x) const override { return x>lambda ? 1.0f : 0.0f; }; virtual ActivationFunction* clone() const override { return new Heaviside(lambda); diff --git a/include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h b/include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h index 561413b..61c36f5 100644 --- a/include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h +++ b/include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h @@ -11,9 +11,9 @@ namespace ActivationFunction { public: HyperbolicTangent(const float& lam=1):lambda(lam) {} - inline virtual float derivatedOutput(const float &,const float &output) override { return lambda*(1-output*output); } + inline virtual float derivatedOutput(const float &,const float &output) const override { return lambda*(1-output*output); } - inline virtual float operator()(const float &x) override { return tanh(lambda*x); }; + inline virtual float operator()(const float &x) const override { return tanh(lambda*x); }; virtual ActivationFunction* clone() const override { return new HyperbolicTangent(lambda); } diff --git a/include/NeuralNetwork/ActivationFunction/Linear.h b/include/NeuralNetwork/ActivationFunction/Linear.h index 65cebcc..68ad136 100644 --- a/include/NeuralNetwork/ActivationFunction/Linear.h +++ b/include/NeuralNetwork/ActivationFunction/Linear.h @@ -9,9 +9,9 @@ namespace ActivationFunction { public: Linear(const float &lambdaP=1.0): lambda(lambdaP) {} - inline virtual float derivatedOutput(const float &,const float &) override { return lambda; } + inline virtual float derivatedOutput(const float &,const float &) const override { return lambda; } - inline virtual float operator()(const float &x) override { return x*lambda; }; + inline virtual float operator()(const float &x) const override { return x*lambda; }; virtual ActivationFunction* clone() const override { return new Linear(lambda); diff --git a/include/NeuralNetwork/ActivationFunction/Sigmoid.h b/include/NeuralNetwork/ActivationFunction/Sigmoid.h index 0a0be7e..d3f1862 100644 --- a/include/NeuralNetwork/ActivationFunction/Sigmoid.h +++ b/include/NeuralNetwork/ActivationFunction/Sigmoid.h @@ -16,10 +16,11 @@ namespace ActivationFunction { public: Sigmoid(const float lambdaP = -0.5): lambda(lambdaP) {} - inline virtual float derivatedOutput(const float &, const float &output) override { return -lambda*output*(1.0f-output); } + inline virtual float derivatedOutput(const float &, const float &output) const override { return -lambda*output*(1.0f-output); } - inline virtual float operator()(const float &x) override { return 1.0f / (1.0f +exp(lambda*x) ); }; - inline virtual __m128 operator()(const __m128 &x) override { + inline virtual float operator()(const float &x) const override { return 1.0f / (1.0f +exp(lambda*x) ); }; + + inline virtual __m128 operator()(const __m128 &x) const override { // exp_ps is extremly slow! return _mm_div_ps(_mm_set1_ps(1.0),_mm_add_ps(exp_ps(_mm_mul_ps(_mm_set1_ps(lambda),x)),_mm_set1_ps(1.0))); } diff --git a/include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h b/include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h index 736ab75..fa2a270 100644 --- a/include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h +++ b/include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h @@ -14,13 +14,13 @@ namespace ActivationFunction { class StreamingActivationFunction : public ActivationFunction { public: - virtual float operator()(const float &x)=0; + virtual float operator()(const float &x) const=0; /** * @brief Returns value of four outputs * @param x is float[4], in every array value can be stored */ - virtual __m128 operator()(const __m128 &x)=0; + virtual __m128 operator()(const __m128 &x) const=0; }; } } \ No newline at end of file diff --git a/include/NeuralNetwork/BasisFunction/Linear.h b/include/NeuralNetwork/BasisFunction/Linear.h index 1c01166..e4a007d 100644 --- a/include/NeuralNetwork/BasisFunction/Linear.h +++ b/include/NeuralNetwork/BasisFunction/Linear.h @@ -17,7 +17,9 @@ namespace BasisFunction { public: Linear() {} - inline virtual float computeStreaming(const std::vector& weights, const std::vector& input) const override { + inline virtual float operator()(const std::vector& weights, const std::vector& input) const override { + +#ifdef USE_SSE size_t inputSize=input.size(); size_t alignedPrev=inputSize-inputSize%4; @@ -35,24 +37,23 @@ namespace BasisFunction { partialSolution.sse=_mm_add_ps(partialSolution.sse,_mm_mul_ps(_mm_load_ss(weightsData+k),_mm_load_ss(inputData+k))); } -#ifdef USE_SSE2 //pre-SSE3 solution + #ifdef USE_SSE2 //pre-SSE3 solution partialSolution.sse= _mm_add_ps(_mm_movehl_ps(partialSolution.sse, partialSolution.sse), partialSolution.sse); partialSolution.sse=_mm_add_ss(partialSolution.sse, _mm_shuffle_ps(partialSolution.sse,partialSolution.sse, 1)); -#else + #else partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse); partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse); -#endif - + #endif return partialSolution.f[0]; - } +#else - inline virtual float compute(const std::vector& weights, const std::vector& input) const override { register float tmp = 0; size_t inputSize=input.size(); for(size_t k=0;k& weights, const std::vector& input) const override { - return computeStreaming(weights,input); - } - - virtual float computeStreaming(const std::vector& weights, const std::vector& input) const =0; - - virtual float compute(const std::vector& weights, const std::vector& input) const =0; + virtual float operator()(const std::vector& weights, const std::vector& input) const =0; }; } } \ No newline at end of file diff --git a/tests/basis.cpp b/tests/basis.cpp index 20a0104..f5b8141 100644 --- a/tests/basis.cpp +++ b/tests/basis.cpp @@ -8,13 +8,11 @@ int main() { { NeuralNetwork::BasisFunction::Linear l; - assert(39.0==l.compute({1,2,3,5},{1,2,3,5})); - assert(39.0==l.computeStreaming({1,2,3,5},{1,2,3,5})); + assert(39.0==l({1,2,3,5},{1,2,3,5})); } { NeuralNetwork::BasisFunction::Linear l; - assert(88.0==l.computeStreaming({1,2,3,5,7},{1,2,3,5,7})); - assert(88.0==l.compute({1,2,3,5,7},{1,2,3,5,7})); + assert(88.0==l({1,2,3,5,7},{1,2,3,5,7})); } { NeuralNetwork::BasisFunction::Linear l; @@ -22,8 +20,7 @@ int main() { for(int in=0;in<100;in++) { w.push_back(2); } - assert(400.0==l.computeStreaming(w,w)); - assert(400.0==l.compute(w,w)); + assert(400.0==l(w,w)); } { NeuralNetwork::BasisFunction::Linear l; @@ -31,8 +28,7 @@ int main() { for(int in=0;in<55;in++) { w.push_back(2); } - assert(220.0==l.computeStreaming(w,w)); - assert(220.0==l.compute(w,w)); + assert(220.0==l(w,w)); } { NeuralNetwork::BasisFunction::Product l; diff --git a/tests/feedforward.cpp b/tests/feedforward.cpp index 560144a..e47eb78 100644 --- a/tests/feedforward.cpp +++ b/tests/feedforward.cpp @@ -3,13 +3,6 @@ #include #include -void printVec(const std::vector &v) { - for(int i=0;i