| | |
| | | layer->probability = probability; |
| | | layer->inputs = inputs; |
| | | layer->batch = batch; |
| | | layer->output = calloc(inputs*batch, sizeof(float)); |
| | | layer->rand = calloc(inputs*batch, sizeof(float)); |
| | | layer->scale = 1./(1.-probability); |
| | | #ifdef GPU |
| | | layer->output_cl = cl_make_array(layer->output, inputs*batch); |
| | | layer->rand_cl = cl_make_array(layer->rand, inputs*batch); |
| | | #endif |
| | | return layer; |
| | |
| | | for(i = 0; i < layer.batch * layer.inputs; ++i){ |
| | | float r = rand_uniform(); |
| | | layer.rand[i] = r; |
| | | if(r < layer.probability) input[i] = 0; |
| | | else input[i] *= layer.scale; |
| | | if(r < layer.probability) layer.output[i] = 0; |
| | | else layer.output[i] = input[i]*layer.scale; |
| | | } |
| | | } |
| | | |
| | | void backward_dropout_layer(dropout_layer layer, float *delta) |
| | | { |
| | | int i; |
| | | if(!delta) return; |
| | | for(i = 0; i < layer.batch * layer.inputs; ++i){ |
| | | float r = layer.rand[i]; |
| | | if(r < layer.probability) delta[i] = 0; |
| | |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl); |
| | | check_error(cl); |
| | | |
| | | const size_t global_size[] = {size}; |
| | |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale); |
| | | cl.error = clSetKernelArg(kernel, i++, sizeof(delta), (void*) &delta); |
| | | check_error(cl); |
| | | |
| | | const size_t global_size[] = {size}; |