| | |
| | | return layer; |
| | | } |
| | | |
| | | void forward_cost_layer(cost_layer layer, network_state state) |
| | | { |
| | | if (!state.truth) return; |
| | | copy_cpu(layer.batch*layer.inputs, state.truth, 1, layer.delta, 1); |
| | | axpy_cpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta, 1); |
| | | *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1); |
| | | //printf("cost: %f\n", *layer.output); |
| | | } |
| | | |
| | | void backward_cost_layer(const cost_layer layer, network_state state) |
| | | { |
| | | copy_cpu(layer.batch*layer.inputs, layer.delta, 1, state.delta, 1); |
| | | } |
| | | |
| | | #ifdef GPU |
| | | |
| | | void pull_cost_layer(cost_layer layer) |
| | | { |
| | | cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs); |
| | | } |
| | | |
| | | void push_cost_layer(cost_layer layer) |
| | | { |
| | | cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs); |
| | | } |
| | | |
| | | void forward_cost_layer(cost_layer layer, float *input, float *truth) |
| | | void forward_cost_layer_gpu(cost_layer layer, network_state state) |
| | | { |
| | | if (!truth) return; |
| | | copy_cpu(layer.batch*layer.inputs, truth, 1, layer.delta, 1); |
| | | axpy_cpu(layer.batch*layer.inputs, -1, input, 1, layer.delta, 1); |
| | | *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1); |
| | | //printf("cost: %f\n", *layer.output); |
| | | } |
| | | |
| | | void backward_cost_layer(const cost_layer layer, float *input, float *delta) |
| | | { |
| | | copy_cpu(layer.batch*layer.inputs, layer.delta, 1, delta, 1); |
| | | } |
| | | |
| | | #ifdef GPU |
| | | |
| | | void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth) |
| | | { |
| | | if (!truth) return; |
| | | if (!state.truth) return; |
| | | |
| | | /* |
| | | float *in = calloc(layer.inputs*layer.batch, sizeof(float)); |
| | | float *t = calloc(layer.inputs*layer.batch, sizeof(float)); |
| | | cuda_pull_array(input, in, layer.batch*layer.inputs); |
| | | cuda_pull_array(truth, t, layer.batch*layer.inputs); |
| | | forward_cost_layer(layer, in, t); |
| | | cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs); |
| | | free(in); |
| | | free(t); |
| | | */ |
| | | |
| | | copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_gpu, 1); |
| | | axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1); |
| | | copy_ongpu(layer.batch*layer.inputs, state.truth, 1, layer.delta_gpu, 1); |
| | | axpy_ongpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta_gpu, 1); |
| | | |
| | | cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs); |
| | | *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1); |
| | | //printf("cost: %f\n", *layer.output); |
| | | } |
| | | |
| | | void backward_cost_layer_gpu(const cost_layer layer, float * input, float * delta) |
| | | void backward_cost_layer_gpu(const cost_layer layer, network_state state) |
| | | { |
| | | copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, delta, 1); |
| | | copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, state.delta, 1); |
| | | } |
| | | #endif |
| | | |