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Convolutional layers working w/ matrices

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This commit is contained in:
Joseph Redmon
2014-01-28 16:28:42 -08:00
parent b2b7137b6f
commit f7a17f82eb
26 changed files with 459 additions and 363 deletions
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70
src/network.c
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@ -21,7 +21,7 @@ network make_network(int n)
return net;
}
void forward_network(network net, double *input)
void forward_network(network net, float *input)
{
int i;
for(i = 0; i < net.n; ++i){
@ -48,7 +48,7 @@ void forward_network(network net, double *input)
}
}
void update_network(network net, double step, double momentum, double decay)
void update_network(network net, float step, float momentum, float decay)
{
int i;
for(i = 0; i < net.n; ++i){
@ -69,7 +69,7 @@ void update_network(network net, double step, double momentum, double decay)
}
}
double *get_network_output_layer(network net, int i)
float *get_network_output_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@ -86,12 +86,12 @@ double *get_network_output_layer(network net, int i)
}
return 0;
}
double *get_network_output(network net)
float *get_network_output(network net)
{
return get_network_output_layer(net, net.n-1);
}
double *get_network_delta_layer(network net, int i)
float *get_network_delta_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@ -109,16 +109,16 @@ double *get_network_delta_layer(network net, int i)
return 0;
}
double *get_network_delta(network net)
float *get_network_delta(network net)
{
return get_network_delta_layer(net, net.n-1);
}
double calculate_error_network(network net, double *truth)
float calculate_error_network(network net, float *truth)
{
double sum = 0;
double *delta = get_network_delta(net);
double *out = get_network_output(net);
float sum = 0;
float *delta = get_network_delta(net);
float *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
delta[i] = truth[i] - out[i];
@ -129,17 +129,17 @@ double calculate_error_network(network net, double *truth)
int get_predicted_class_network(network net)
{
double *out = get_network_output(net);
float *out = get_network_output(net);
int k = get_network_output_size(net);
return max_index(out, k);
}
double backward_network(network net, double *input, double *truth)
float backward_network(network net, float *input, float *truth)
{
double error = calculate_error_network(net, truth);
float error = calculate_error_network(net, truth);
int i;
double *prev_input;
double *prev_delta;
float *prev_input;
float *prev_delta;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
prev_input = input;
@ -152,7 +152,7 @@ double backward_network(network net, double *input, double *truth)
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
learn_convolutional_layer(layer);
//learn_convolutional_layer(layer);
//if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
if(i != 0) backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -171,49 +171,49 @@ double backward_network(network net, double *input, double *truth)
return error;
}
double train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
forward_network(net, x);
int class = get_predicted_class_network(net);
double error = backward_network(net, x, y);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
//return (y[class]?1:0);
return error;
}
double train_network_sgd(network net, data d, int n, double step, double momentum,double decay)
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
int i;
double error = 0;
float error = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
//if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (double)correct/(i+1));
// printf("%d: %f\n", (i+1), (float)correct/(i+1));
//}
}
return error/n;
}
double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
int i;
int correct = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
double *x = d.X.vals[index];
double *y = d.y.vals[index];
float *x = d.X.vals[index];
float *y = d.y.vals[index];
forward_network(net, x);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
}
update_network(net, step, momentum, decay);
return (double)correct/n;
return (float)correct/n;
}
void train_network(network net, data d, double step, double momentum, double decay)
void train_network(network net, data d, float step, float momentum, float decay)
{
int i;
int correct = 0;
@ -226,7 +226,7 @@ void train_network(network net, data d, double step, double momentum, double dec
}
visualize_network(net);
cvWaitKey(100);
printf("Accuracy: %f\n", (double)correct/d.X.rows);
printf("Accuracy: %f\n", (float)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@ -294,10 +294,10 @@ void visualize_network(network net)
}
}
double *network_predict(network net, double *input)
float *network_predict(network net, float *input)
{
forward_network(net, input);
double *out = get_network_output(net);
float *out = get_network_output(net);
return out;
}
@ -307,7 +307,7 @@ matrix network_predict_data(network net, data test)
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
for(i = 0; i < test.X.rows; ++i){
double *out = network_predict(net, test.X.vals[i]);
float *out = network_predict(net, test.X.vals[i]);
for(j = 0; j < k; ++j){
pred.vals[i][j] = out[j];
}
@ -319,7 +319,7 @@ void print_network(network net)
{
int i,j;
for(i = 0; i < net.n; ++i){
double *output = 0;
float *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@ -343,8 +343,8 @@ void print_network(network net)
output = layer.output;
n = layer.inputs;
}
double mean = mean_array(output, n);
double vari = variance_array(output, n);
float mean = mean_array(output, n);
float vari = variance_array(output, n);
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
if(n > 100) n = 100;
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
@ -353,10 +353,10 @@ void print_network(network net)
}
}
double network_accuracy(network net, data d)
float network_accuracy(network net, data d)
{
matrix guess = network_predict_data(net, d);
double acc = matrix_accuracy(d.y, guess);
float acc = matrix_accuracy(d.y, guess);
free_matrix(guess);
return acc;
}