| | |
| | | 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 linear_gradient(float x){return 1;} |
| | | float sigmoid_gradient(float x){return (1-x)*x;} |
| | | float relu_gradient(float x){return (x>0);} |
| | | float ramp_gradient(float x){return (x>0)+.1;} |
| | | float tanh_gradient(float x){return 1-x*x;} |
| | | |
| | | float activate(float x, ACTIVATION a) |
| | | { |
| | | switch(a){ |
| | |
| | | return 0; |
| | | } |
| | | |
| | | __kernel void activate_array(__global float *x, |
| | | const int n, const ACTIVATION a) |
| | | float gradient(float x, ACTIVATION a) |
| | | { |
| | | switch(a){ |
| | | case LINEAR: |
| | | return linear_gradient(x); |
| | | case SIGMOID: |
| | | return sigmoid_gradient(x); |
| | | case RELU: |
| | | return relu_gradient(x); |
| | | case RAMP: |
| | | return ramp_gradient(x); |
| | | case TANH: |
| | | return tanh_gradient(x); |
| | | } |
| | | return 0; |
| | | } |
| | | |
| | | __kernel void activate_array(__global float *x, int n, ACTIVATION a) |
| | | { |
| | | int i = get_global_id(0); |
| | | x[i] = activate(x[i], a); |
| | | } |
| | | |
| | | __kernel void gradient_array(__global float *x, int n, ACTIVATION a, __global float *delta) |
| | | { |
| | | int i = get_global_id(0); |
| | | delta[i] *= gradient(x[i], a); |
| | | } |
| | | |