| | |
| | | |
| | | convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation, batch_normalize, binary); |
| | | layer.flipped = option_find_int_quiet(options, "flipped", 0); |
| | | layer.dot = option_find_float_quiet(options, "dot", 0); |
| | | |
| | | char *weights = option_find_str(options, "weights", 0); |
| | | char *biases = option_find_str(options, "biases", 0); |
| | |
| | | fread(l.scales, sizeof(float), l.n, fp); |
| | | fread(l.rolling_mean, sizeof(float), l.n, fp); |
| | | fread(l.rolling_variance, sizeof(float), l.n, fp); |
| | | /* |
| | | int i; |
| | | for(i = 0; i < l.n; ++i){ |
| | | if(l.rolling_mean[i] > 1 || l.rolling_mean[i] < -1 || l.rolling_variance[i] > 1 || l.rolling_variance[i] < -1) |
| | | printf("%f %f\n", l.rolling_mean[i], l.rolling_variance[i]); |
| | | } |
| | | */ |
| | | } |
| | | fflush(stdout); |
| | | fread(l.filters, sizeof(float), num, fp); |
| | | if (l.flipped) { |
| | | transpose_matrix(l.filters, l.c*l.size*l.size, l.n); |