| | |
|---|
| | | } |
|---|
| | | return (double)correct/n; |
|---|
| | | } |
|---|
| | | double train_network_batch(network net, data d, int n, double step, double momentum,double 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]; |
|---|
| | | 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; |
|---|
| | | |
|---|
| | | } |
|---|
| | | |
|---|
| | | |
|---|
| | | void train_network(network net, data d, double step, double momentum, double decay) |
|---|
| | | { |
|---|
