| | |
| | | } |
| | | |
| | | |
| | | __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) |
| | | { |
| | | int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; |
| | | if (index >= N) return; |
| | | |
| | | x[index] = x[index] - (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps)); |
| | | //if(index == 0) printf("%f %f %f %f\n", m[index], v[index], (rate * sqrt(1.-pow(B2, t)) / (1.-pow(B1, t)) * m[index] / (sqrt(v[index]) + eps))); |
| | | } |
| | | |
| | | extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) |
| | | { |
| | | adam_kernel<<<cuda_gridsize(n), BLOCK>>>(n, x, m, v, B1, B2, rate, eps, t); |
| | | check_error(cudaPeekAtLastError()); |
| | | } |
| | | |
| | | __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) |
| | | { |
| | | int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; |