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Generalizing conv layer so deconv is easier

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This commit is contained in:
Joseph Redmon
2015-02-09 13:27:58 -08:00
parent 7ee45082f1
commit 979d02126b
4 changed files with 38 additions and 82 deletions
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42
src/convolutional_layer.c
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@@ -111,27 +111,37 @@ void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
layer->batch*out_h * out_w * layer->n*sizeof(float));
}
void bias_output(const convolutional_layer layer)
void bias_output(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.n; ++i){
for(j = 0; j < out_h*out_w; ++j){
layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] = biases[i];
}
}
}
}
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
{
float alpha = 1./batch;
int i,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
bias_updates[i] += alpha * sum_array(delta+size*(i+b*n), size);
}
}
}
void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
int i;
bias_output(layer);
bias_output(layer.output, layer.biases, layer.batch, layer.n, out_h*out_w);
int m = layer.n;
int k = layer.size*layer.size*layer.c;
@@ -151,19 +161,6 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
activate_array(layer.output, m*n*layer.batch, layer.activation);
}
void learn_bias_convolutional_layer(convolutional_layer layer)
{
float alpha = 1./layer.batch;
int i,b;
int size = convolutional_out_height(layer)
*convolutional_out_width(layer);
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.n; ++i){
layer.bias_updates[i] += alpha * sum_array(layer.delta+size*(i+b*layer.n), size);
}
}
}
void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
{
float alpha = 1./layer.batch;
@@ -174,8 +171,7 @@ void backward_convolutional_layer(convolutional_layer layer, float *in, float *d
convolutional_out_width(layer);
gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
learn_bias_convolutional_layer(layer);
backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, k);
if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));