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probably how maxpool layers should be

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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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22
src/connected_layer.c
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@ -7,15 +7,19 @@
#include <stdlib.h>
#include <string.h>
connected_layer *make_connected_layer(int batch, int inputs, int outputs, float dropout, ACTIVATION activation)
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
{
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
layer->learning_rate = learning_rate;
layer->momentum = momentum;
layer->decay = decay;
layer->inputs = inputs;
layer->outputs = outputs;
layer->batch=batch;
layer->dropout = dropout;
layer->output = calloc(batch*outputs, sizeof(float*));
layer->delta = calloc(batch*outputs, sizeof(float*));
@ -25,8 +29,9 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, float
layer->weight_momentum = calloc(inputs*outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
float scale = 1./inputs;
//scale = .01;
for(i = 0; i < inputs*outputs; ++i)
layer->weights[i] = scale*(rand_uniform());
layer->weights[i] = scale*(rand_uniform()-.5);
layer->bias_updates = calloc(outputs, sizeof(float));
layer->bias_adapt = calloc(outputs, sizeof(float));
@ -40,25 +45,24 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, float
return layer;
}
void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
void update_connected_layer(connected_layer layer)
{
int i;
for(i = 0; i < layer.outputs; ++i){
layer.bias_momentum[i] = step*(layer.bias_updates[i]) + momentum*layer.bias_momentum[i];
layer.bias_momentum[i] = layer.learning_rate*(layer.bias_updates[i]) + layer.momentum*layer.bias_momentum[i];
layer.biases[i] += layer.bias_momentum[i];
}
for(i = 0; i < layer.outputs*layer.inputs; ++i){
layer.weight_momentum[i] = step*(layer.weight_updates[i] - decay*layer.weights[i]) + momentum*layer.weight_momentum[i];
layer.weight_momentum[i] = layer.learning_rate*(layer.weight_updates[i] - layer.decay*layer.weights[i]) + layer.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 forward_connected_layer(connected_layer layer, float *input, int train)
void forward_connected_layer(connected_layer layer, float *input)
{
int i;
if(!train) layer.dropout = 0;
for(i = 0; i < layer.batch; ++i){
memcpy(layer.output+i*layer.outputs, layer.biases, layer.outputs*sizeof(float));
}
@ -69,7 +73,7 @@ void forward_connected_layer(connected_layer layer, float *input, int train)
float *b = layer.weights;
float *c = layer.output;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.dropout);
activate_array(layer.output, layer.outputs*layer.batch, layer.activation);
}
void backward_connected_layer(connected_layer layer, float *input, float *delta)