| | |
| | | return RELU; |
| | | } |
| | | |
| | | float linear_activate(float x){return x;} |
| | | float sigmoid_activate(float x){return 1./(1. + exp(-x));} |
| | | float relu_activate(float x){return x*(x>0);} |
| | | float ramp_activate(float x){return x*(x>0)+.1*x;} |
| | | float tanh_activate(float x){return (exp(2*x)-1)/(exp(2*x)+1);} |
| | | |
| | | float activate(float x, ACTIVATION a){ |
| | | switch(a){ |
| | | case LINEAR: |
| | | return x; |
| | | return linear_activate(x); |
| | | case SIGMOID: |
| | | return 1./(1.+exp(-x)); |
| | | return sigmoid_activate(x); |
| | | case RELU: |
| | | return x*(x>0); |
| | | return relu_activate(x); |
| | | case RAMP: |
| | | return x*(x>0) + .1*x; |
| | | return ramp_activate(x); |
| | | case TANH: |
| | | return (exp(2*x)-1)/(exp(2*x)+1); |
| | | return tanh_activate(x); |
| | | } |
| | | return 0; |
| | | } |
| | | |
| | | void activate_array(float *x, const int n, const ACTIVATION a) |
| | | { |
| | | int i; |
| | | for(i = 0; i < n; ++i){ |
| | | x[i] = activate(x[i], a); |
| | | } |
| | | } |
| | | |
| | | |
| | | float gradient(float x, ACTIVATION a){ |
| | | switch(a){ |
| | | case LINEAR: |
| | |
| | | return 0; |
| | | } |
| | | |
| | | void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta) |
| | | { |
| | | int i; |
| | | for(i = 0; i < n; ++i){ |
| | | delta[i] *= gradient(x[i], a); |
| | | } |
| | | } |
| | | |