Update Readme.md

Alexey

2018-08-20 8e018980a8eb15e319fec5c28163bf4ef408b1f0

 src/dropout_layer.c

@@ -1,55 +1,60 @@
#include "dropout_layer.h"
#include "params.h"
#include "utils.h"
#include "cuda.h"
#include <stdlib.h>
#include <stdio.h>

dropout_layer *make_dropout_layer(int batch, int inputs, float probability)
dropout_layer make_dropout_layer(int batch, int inputs, float probability)
{
    fprintf(stderr, "Dropout Layer: %d inputs, %f probability\n", inputs, probability);
    dropout_layer *layer = calloc(1, sizeof(dropout_layer));
    layer->probability = probability;
    layer->inputs = inputs;
    layer->batch = batch;
    layer->rand = calloc(inputs*batch, sizeof(float));
    layer->scale = 1./(1.-probability);
    dropout_layer l = {0};
    l.type = DROPOUT;
    l.probability = probability;
    l.inputs = inputs;
    l.outputs = inputs;
    l.batch = batch;
    l.rand = calloc(inputs*batch, sizeof(float));
    l.scale = 1./(1.-probability);
    l.forward = forward_dropout_layer;
    l.backward = backward_dropout_layer;
    #ifdef GPU
    layer->rand_gpu = cuda_make_array(layer->rand, inputs*batch);
    l.forward_gpu = forward_dropout_layer_gpu;
    l.backward_gpu = backward_dropout_layer_gpu;
    l.rand_gpu = cuda_make_array(l.rand, inputs*batch);
    #endif
    return layer;
    fprintf(stderr, "dropout       p = %.2f               %4d  ->  %4d\n", probability, inputs, inputs);
    return l;
} 

void resize_dropout_layer(dropout_layer *layer, int inputs)
void resize_dropout_layer(dropout_layer *l, int inputs)
{
    layer->rand = realloc(layer->rand, layer->inputs*layer->batch*sizeof(float));
    l->rand = realloc(l->rand, l->inputs*l->batch*sizeof(float));
    #ifdef GPU
    cuda_free(layer->rand_gpu);
    cuda_free(l->rand_gpu);

    layer->rand_gpu = cuda_make_array(layer->rand, inputs*layer->batch);
    l->rand_gpu = cuda_make_array(l->rand, inputs*l->batch);
    #endif
}

void forward_dropout_layer(dropout_layer layer, network_state state)
void forward_dropout_layer(dropout_layer l, network_state state)
{
    int i;
    if (!state.train) return;
    for(i = 0; i < layer.batch * layer.inputs; ++i){
        float r = rand_uniform();
        layer.rand[i] = r;
        if(r < layer.probability) state.input[i] = 0;
        else state.input[i] *= layer.scale;
    for(i = 0; i < l.batch * l.inputs; ++i){
        float r = rand_uniform(0, 1);
        l.rand[i] = r;
        if(r < l.probability) state.input[i] = 0;
        else state.input[i] *= l.scale;
    }
}

void backward_dropout_layer(dropout_layer layer, network_state state)
void backward_dropout_layer(dropout_layer l, network_state state)
{
    int i;
    if(!state.delta) return;
    for(i = 0; i < layer.batch * layer.inputs; ++i){
        float r = layer.rand[i];
        if(r < layer.probability) state.delta[i] = 0;
        else state.delta[i] *= layer.scale;
    for(i = 0; i < l.batch * l.inputs; ++i){
        float r = l.rand[i];
        if(r < l.probability) state.delta[i] = 0;
        else state.delta[i] *= l.scale;
    }
}