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Small updates

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This commit is contained in:
Joseph Redmon
2014-04-30 16:17:40 -07:00
parent 354b0cbdcb
commit 00d483697a
12 changed files with 53 additions and 189 deletions
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60
src/connected_layer.c
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@ -39,27 +39,6 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
return layer;
}
/*
void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
{
int i;
for(i = 0; i < layer.outputs; ++i){
float delta = layer.bias_updates[i];
layer.bias_adapt[i] += delta*delta;
layer.bias_momentum[i] = step/sqrt(layer.bias_adapt[i])*(layer.bias_updates[i]) + momentum*layer.bias_momentum[i];
layer.biases[i] += layer.bias_momentum[i];
}
for(i = 0; i < layer.outputs*layer.inputs; ++i){
float delta = layer.weight_updates[i];
layer.weight_adapt[i] += delta*delta;
layer.weight_momentum[i] = step/sqrt(layer.weight_adapt[i])*(layer.weight_updates[i] - decay*layer.weights[i]) + momentum*layer.weight_momentum[i];
layer.weights[i] += layer.weight_momentum[i];
}
memset(layer.bias_updates, 0, layer.outputs*sizeof(float));
memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(float));
}
*/
void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
{
int i;
@ -89,7 +68,6 @@ void forward_connected_layer(connected_layer layer, float *input)
for(i = 0; i < layer.outputs*layer.batch; ++i){
layer.output[i] = activate(layer.output[i], layer.activation);
}
//for(i = 0; i < layer.outputs; ++i) if(i%(layer.outputs/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
}
void learn_connected_layer(connected_layer layer, float *input)
@ -110,8 +88,6 @@ void learn_connected_layer(connected_layer layer, float *input)
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
memset(delta, 0, layer.inputs*sizeof(float));
int m = layer.inputs;
int k = layer.outputs;
int n = layer.batch;
@ -120,40 +96,6 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
float *b = layer.delta;
float *c = delta;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
gemm(0,0,m,n,k,1,a,k,b,n,0,c,n);
}
/*
void forward_connected_layer(connected_layer layer, float *input)
{
int i, j;
for(i = 0; i < layer.outputs; ++i){
layer.output[i] = layer.biases[i];
for(j = 0; j < layer.inputs; ++j){
layer.output[i] += input[j]*layer.weights[i*layer.inputs + j];
}
layer.output[i] = activate(layer.output[i], layer.activation);
}
}
void learn_connected_layer(connected_layer layer, float *input)
{
int i, j;
for(i = 0; i < layer.outputs; ++i){
layer.delta[i] *= gradient(layer.output[i], layer.activation);
layer.bias_updates[i] += layer.delta[i];
for(j = 0; j < layer.inputs; ++j){
layer.weight_updates[i*layer.inputs + j] += layer.delta[i]*input[j];
}
}
}
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
int i, j;
for(j = 0; j < layer.inputs; ++j){
delta[j] = 0;
for(i = 0; i < layer.outputs; ++i){
delta[j] += layer.delta[i]*layer.weights[i*layer.inputs + j];
}
}
}
*/