| | |
| | | fill_ongpu(layer.outputs*layer.batch, 0, layer.output_gpu, 1); |
| | | |
| | | for(i = 0; i < layer.batch; ++i){ |
| | | float *a = layer.filters_gpu; |
| | | float *a = layer.weights_gpu; |
| | | float *b = state.input + i*layer.c*layer.h*layer.w; |
| | | float *c = layer.col_image_gpu; |
| | | |
| | |
| | | |
| | | float *a = state.input + i*m*n; |
| | | float *b = layer.col_image_gpu; |
| | | float *c = layer.filter_updates_gpu; |
| | | float *c = layer.weight_updates_gpu; |
| | | |
| | | im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w, |
| | | layer.size, layer.stride, 0, b); |
| | |
| | | int n = layer.h*layer.w; |
| | | int k = layer.size*layer.size*layer.n; |
| | | |
| | | float *a = layer.filters_gpu; |
| | | float *a = layer.weights_gpu; |
| | | float *b = layer.col_image_gpu; |
| | | float *c = state.delta + i*n*m; |
| | | |
| | |
| | | |
| | | extern "C" void pull_deconvolutional_layer(deconvolutional_layer layer) |
| | | { |
| | | cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); |
| | | cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); |
| | | } |
| | | |
| | | extern "C" void push_deconvolutional_layer(deconvolutional_layer layer) |
| | | { |
| | | cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_push_array(layer.biases_gpu, layer.biases, layer.n); |
| | | cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); |
| | | cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); |
| | | } |
| | | |
| | |
| | | axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); |
| | | scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1); |
| | | |
| | | axpy_ongpu(size, -decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1); |
| | | axpy_ongpu(size, learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1); |
| | | scal_ongpu(size, momentum, layer.filter_updates_gpu, 1); |
| | | axpy_ongpu(size, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); |
| | | axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); |
| | | scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); |
| | | } |
| | | |