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modified SSE code building

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This commit is contained in:
Tomáš Černík
2016-02-07 22:54:43 +01:00
parent 5f43fb8cfb
commit bec7a4f3ae
10 changed files with 28 additions and 43 deletions
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6
include/NeuralNetwork/ActivationFunction/ActivationFunction.h
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@@ -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

4
include/NeuralNetwork/ActivationFunction/Heaviside.h
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@@ -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);

4
include/NeuralNetwork/ActivationFunction/HyperbolicTangent.h
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@@ -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);
}

4
include/NeuralNetwork/ActivationFunction/Linear.h
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@@ -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);

7
include/NeuralNetwork/ActivationFunction/Sigmoid.h
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@@ -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)));
}

4
include/NeuralNetwork/ActivationFunction/StreamingActivationFunction.h
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@@ -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;
};
}
}

9
include/NeuralNetwork/BasisFunction/Linear.h
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@@ -17,7 +17,9 @@ namespace BasisFunction {
public:
Linear() {}
inline virtual float computeStreaming(const std::vector<float>& weights, const std::vector<float>& input) const override {
inline virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const override {
#ifdef USE_SSE
size_t inputSize=input.size();
size_t alignedPrev=inputSize-inputSize%4;
@@ -42,17 +44,16 @@ namespace BasisFunction {
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
partialSolution.sse = _mm_hadd_ps(partialSolution.sse, partialSolution.sse);
#endif
return partialSolution.f[0];
}
#else
inline virtual float compute(const std::vector<float>& weights, const std::vector<float>& input) const override {
register float tmp = 0;
size_t inputSize=input.size();
for(size_t k=0;k<inputSize;k++) {
tmp+=input[k]*weights[k];
}
return tmp;
#endif
}
virtual BasisFunction* clone() const override {

8
include/NeuralNetwork/BasisFunction/StreamingBasisFunction.h
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@@ -13,13 +13,7 @@ namespace BasisFunction {
float f[4];
};
virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const override {
return computeStreaming(weights,input);
}
virtual float computeStreaming(const std::vector<float>& weights, const std::vector<float>& input) const =0;
virtual float compute(const std::vector<float>& weights, const std::vector<float>& input) const =0;
virtual float operator()(const std::vector<float>& weights, const std::vector<float>& input) const =0;
};
}
}

12
tests/basis.cpp
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@@ -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;

7
tests/feedforward.cpp
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@@ -3,13 +3,6 @@
#include <cassert>
#include <iostream>
void printVec(const std::vector<float> &v) {
for(int i=0;i<v.size();i++) {
std::cout << v[i] << ", ";
}
std::cout<< "\n";
}
int main() {
{ // XOR problem
NeuralNetwork::FeedForward::Network n(2);