| | |
| | | } |
| | | } |
| | | |
| | | void forward_normalization_layer(const normalization_layer layer, float *in) |
| | | void forward_normalization_layer(const normalization_layer layer, network_state state) |
| | | { |
| | | int i,j,k; |
| | | memset(layer.sums, 0, layer.h*layer.w*sizeof(float)); |
| | | int imsize = layer.h*layer.w; |
| | | for(j = 0; j < layer.size/2; ++j){ |
| | | if(j < layer.c) add_square_array(in+j*imsize, layer.sums, imsize); |
| | | if(j < layer.c) add_square_array(state.input+j*imsize, layer.sums, imsize); |
| | | } |
| | | for(k = 0; k < layer.c; ++k){ |
| | | int next = k+layer.size/2; |
| | | int prev = k-layer.size/2-1; |
| | | if(next < layer.c) add_square_array(in+next*imsize, layer.sums, imsize); |
| | | if(prev > 0) sub_square_array(in+prev*imsize, layer.sums, imsize); |
| | | if(next < layer.c) add_square_array(state.input+next*imsize, layer.sums, imsize); |
| | | if(prev > 0) sub_square_array(state.input+prev*imsize, layer.sums, imsize); |
| | | for(i = 0; i < imsize; ++i){ |
| | | layer.output[k*imsize + i] = in[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta); |
| | | layer.output[k*imsize + i] = state.input[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta); |
| | | } |
| | | } |
| | | } |
| | | |
| | | void backward_normalization_layer(const normalization_layer layer, float *in, float *delta) |
| | | void backward_normalization_layer(const normalization_layer layer, network_state state) |
| | | { |
| | | //TODO! |
| | | // TODO! |
| | | // OR NOT TODO!! |
| | | } |
| | | |
| | | void visualize_normalization_layer(normalization_layer layer, char *window) |