| | |
| | | |
| | | // float scale = 1./sqrt(size*size*c); |
| | | float scale = sqrt(2./(size*size*c)); |
| | | for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale; |
| | | for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1); |
| | | int out_h = convolutional_out_height(l); |
| | | int out_w = convolutional_out_width(l); |
| | | l.out_h = out_h; |
| | |
| | | #endif |
| | | } |
| | | |
| | | void bias_output(float *output, float *biases, int batch, int n, int size) |
| | | void add_bias(float *output, float *biases, int batch, int n, int size) |
| | | { |
| | | int i,j,b; |
| | | for(b = 0; b < batch; ++b){ |
| | | for(i = 0; i < n; ++i){ |
| | | for(j = 0; j < size; ++j){ |
| | | output[(b*n + i)*size + j] = biases[i]; |
| | | output[(b*n + i)*size + j] += biases[i]; |
| | | } |
| | | } |
| | | } |
| | | } |
| | | |
| | | void scale_bias(float *output, float *scales, int batch, int n, int size) |
| | | { |
| | | int i,j,b; |
| | | for(b = 0; b < batch; ++b){ |
| | | for(i = 0; i < n; ++i){ |
| | | for(j = 0; j < size; ++j){ |
| | | output[(b*n + i)*size + j] *= scales[i]; |
| | | } |
| | | } |
| | | } |
| | |
| | | int out_w = convolutional_out_width(l); |
| | | int i; |
| | | |
| | | bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w); |
| | | fill_cpu(l.outputs*l.batch, 0, l.output, 1); |
| | | |
| | | int m = l.n; |
| | | int k = l.size*l.size*l.c; |
| | |
| | | } |
| | | |
| | | if(l.batch_normalize){ |
| | | mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean); |
| | | variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance); |
| | | normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w); |
| | | if(state.train){ |
| | | mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean); |
| | | variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance); |
| | | normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w); |
| | | } else { |
| | | normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w); |
| | | } |
| | | scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w); |
| | | } |
| | | add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); |
| | | |
| | | activate_array(l.output, m*n*l.batch, l.activation); |
| | | } |