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probably how maxpool layers should be

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This commit is contained in:
Joseph Redmon
2014-08-08 12:04:15 -07:00
parent b32a287e38
commit d9f1b0b16e
32 changed files with 1044 additions and 746 deletions
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130
src/network.c
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@ -9,6 +9,7 @@
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
network make_network(int n, int batch)
{
@ -25,94 +26,6 @@ network make_network(int n, int batch)
return net;
}
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
fprintf(fp, "[convolutional]\n");
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
l->n, l->size, l->stride,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
{
int i;
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
fprintf(fp, "\n\n");
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
void print_normalization_cfg(FILE *fp, normalization_layer *l, int first)
{
fprintf(fp, "[localresponsenormalization]\n");
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\n"
"alpha=%g\n"
"beta=%g\n"
"kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
}
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
void save_network(network net, char *filename)
{
FILE *fp = fopen(filename, "w");
if(!fp) file_error(filename);
int i;
for(i = 0; i < net.n; ++i)
{
if(net.types[i] == CONVOLUTIONAL)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
else if(net.types[i] == CONNECTED)
print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
else if(net.types[i] == NORMALIZATION)
print_normalization_cfg(fp, (normalization_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
fclose(fp);
}
#ifdef GPU
void forward_network(network net, float *input, int train)
{
@ -169,7 +82,7 @@ void forward_network(network net, float *input, int train)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input, train);
forward_connected_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
@ -187,17 +100,22 @@ void forward_network(network net, float *input, int train)
forward_normalization_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == DROPOUT){
if(!train) continue;
dropout_layer layer = *(dropout_layer *)net.layers[i];
forward_dropout_layer(layer, input);
}
}
}
#endif
void update_network(network net, float step, float momentum, float decay)
void update_network(network net)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer(layer, step, momentum, decay);
update_convolutional_layer(layer);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -210,7 +128,7 @@ void update_network(network net, float step, float momentum, float decay)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, momentum, decay);
update_connected_layer(layer);
}
}
}
@ -226,6 +144,8 @@ float *get_network_output_layer(network net, int i)
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == DROPOUT){
return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@ -251,6 +171,8 @@ float *get_network_delta_layer(network net, int i)
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == DROPOUT){
return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@ -326,17 +248,17 @@ float backward_network(network net, float *input, float *truth)
return error;
}
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
float train_network_datum(network net, float *x, float *y)
{
forward_network(net, x, 1);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
update_network(net);
//return (y[class]?1:0);
return error;
}
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
float train_network_sgd(network net, data d, int n)
{
int batch = net.batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
@ -350,9 +272,9 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
}
float err = train_network_datum(net, X, y, step, momentum, decay);
float err = train_network_datum(net, X, y);
sum += err;
//train_network_datum(net, X, y, step, momentum, decay);
//train_network_datum(net, X, y);
/*
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
@ -382,7 +304,7 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
free(y);
return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
float train_network_batch(network net, data d, int n)
{
int i,j;
float sum = 0;
@ -395,18 +317,18 @@ float train_network_batch(network net, data d, int n, float step, float momentum
forward_network(net, x, 1);
sum += backward_network(net, x, y);
}
update_network(net, step, momentum, decay);
update_network(net);
}
return (float)sum/(n*batch);
}
void train_network(network net, data d, float step, float momentum, float decay)
void train_network(network net, data d)
{
int i;
int correct = 0;
for(i = 0; i < d.X.rows; ++i){
correct += train_network_datum(net, d.X.vals[i], d.y.vals[i], step, momentum, decay);
correct += train_network_datum(net, d.X.vals[i], d.y.vals[i]);
if(i%100 == 0){
visualize_network(net);
cvWaitKey(10);
@ -430,6 +352,9 @@ int get_network_input_size_layer(network net, int i)
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.inputs;
} else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
@ -453,6 +378,9 @@ int get_network_output_size_layer(network net, int i)
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
} else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];