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route handles input images well....ish

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This commit is contained in:
Joseph Redmon
2015-05-11 13:46:49 -07:00
parent dc0d7bb8a8
commit 516f019ba6
31 changed files with 1250 additions and 1819 deletions
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182
src/connected_layer.c
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@ -9,99 +9,97 @@
#include <stdlib.h>
#include <string.h>
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
{
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
connected_layer l = {0};
l.type = CONNECTED;
layer->inputs = inputs;
layer->outputs = outputs;
layer->batch=batch;
l.inputs = inputs;
l.outputs = outputs;
l.batch=batch;
layer->output = calloc(batch*outputs, sizeof(float*));
layer->delta = calloc(batch*outputs, sizeof(float*));
l.output = calloc(batch*outputs, sizeof(float*));
l.delta = calloc(batch*outputs, sizeof(float*));
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
layer->bias_updates = calloc(outputs, sizeof(float));
l.weight_updates = calloc(inputs*outputs, sizeof(float));
l.bias_updates = calloc(outputs, sizeof(float));
layer->weight_prev = calloc(inputs*outputs, sizeof(float));
layer->bias_prev = calloc(outputs, sizeof(float));
layer->weights = calloc(inputs*outputs, sizeof(float));
layer->biases = calloc(outputs, sizeof(float));
l.weights = calloc(inputs*outputs, sizeof(float));
l.biases = calloc(outputs, sizeof(float));
float scale = 1./sqrt(inputs);
for(i = 0; i < inputs*outputs; ++i){
layer->weights[i] = 2*scale*rand_uniform() - scale;
l.weights[i] = 2*scale*rand_uniform() - scale;
}
for(i = 0; i < outputs; ++i){
layer->biases[i] = scale;
l.biases[i] = scale;
}
#ifdef GPU
layer->weights_gpu = cuda_make_array(layer->weights, inputs*outputs);
layer->biases_gpu = cuda_make_array(layer->biases, outputs);
l.weights_gpu = cuda_make_array(l.weights, inputs*outputs);
l.biases_gpu = cuda_make_array(l.biases, outputs);
layer->weight_updates_gpu = cuda_make_array(layer->weight_updates, inputs*outputs);
layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, outputs);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, inputs*outputs);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
layer->output_gpu = cuda_make_array(layer->output, outputs*batch);
layer->delta_gpu = cuda_make_array(layer->delta, outputs*batch);
l.output_gpu = cuda_make_array(l.output, outputs*batch);
l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
#endif
layer->activation = activation;
l.activation = activation;
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
return layer;
return l;
}
void update_connected_layer(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
void update_connected_layer(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
axpy_cpu(layer.outputs, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
scal_cpu(layer.outputs, momentum, layer.bias_updates, 1);
axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
scal_cpu(l.outputs, momentum, l.bias_updates, 1);
axpy_cpu(layer.inputs*layer.outputs, -decay*batch, layer.weights, 1, layer.weight_updates, 1);
axpy_cpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates, 1, layer.weights, 1);
scal_cpu(layer.inputs*layer.outputs, momentum, layer.weight_updates, 1);
axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
}
void forward_connected_layer(connected_layer layer, network_state state)
void forward_connected_layer(connected_layer l, network_state state)
{
int i;
for(i = 0; i < layer.batch; ++i){
copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
for(i = 0; i < l.batch; ++i){
copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
}
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
int m = l.batch;
int k = l.inputs;
int n = l.outputs;
float *a = state.input;
float *b = layer.weights;
float *c = layer.output;
float *b = l.weights;
float *c = l.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);
activate_array(l.output, l.outputs*l.batch, l.activation);
}
void backward_connected_layer(connected_layer layer, network_state state)
void backward_connected_layer(connected_layer l, network_state state)
{
int i;
gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
for(i = 0; i < layer.batch; ++i){
axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
for(i = 0; i < l.batch; ++i){
axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
}
int m = layer.inputs;
int k = layer.batch;
int n = layer.outputs;
int m = l.inputs;
int k = l.batch;
int n = l.outputs;
float *a = state.input;
float *b = layer.delta;
float *c = layer.weight_updates;
float *b = l.delta;
float *c = l.weight_updates;
gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
n = layer.inputs;
m = l.batch;
k = l.outputs;
n = l.inputs;
a = layer.delta;
b = layer.weights;
a = l.delta;
b = l.weights;
c = state.delta;
if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
@ -109,69 +107,69 @@ void backward_connected_layer(connected_layer layer, network_state state)
#ifdef GPU
void pull_connected_layer(connected_layer layer)
void pull_connected_layer(connected_layer l)
{
cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
cuda_pull_array(layer.biases_gpu, layer.biases, layer.outputs);
cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
}
void push_connected_layer(connected_layer layer)
void push_connected_layer(connected_layer l)
{
cuda_push_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
cuda_push_array(layer.biases_gpu, layer.biases, layer.outputs);
cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
cuda_push_array(l.biases_gpu, l.biases, l.outputs);
cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
}
void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay)
void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay)
{
axpy_ongpu(layer.outputs, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.outputs, momentum, layer.bias_updates_gpu, 1);
axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
axpy_ongpu(layer.inputs*layer.outputs, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
axpy_ongpu(layer.inputs*layer.outputs, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
scal_ongpu(layer.inputs*layer.outputs, momentum, layer.weight_updates_gpu, 1);
axpy_ongpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
axpy_ongpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
scal_ongpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
}
void forward_connected_layer_gpu(connected_layer layer, network_state state)
void forward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
for(i = 0; i < layer.batch; ++i){
copy_ongpu_offset(layer.outputs, layer.biases_gpu, 0, 1, layer.output_gpu, i*layer.outputs, 1);
for(i = 0; i < l.batch; ++i){
copy_ongpu_offset(l.outputs, l.biases_gpu, 0, 1, l.output_gpu, i*l.outputs, 1);
}
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
int m = l.batch;
int k = l.inputs;
int n = l.outputs;
float * a = state.input;
float * b = layer.weights_gpu;
float * c = layer.output_gpu;
float * b = l.weights_gpu;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation);
activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
}
void backward_connected_layer_gpu(connected_layer layer, network_state state)
void backward_connected_layer_gpu(connected_layer l, network_state state)
{
int i;
gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
for(i = 0; i < layer.batch; ++i){
axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
for(i = 0; i < l.batch; ++i){
axpy_ongpu_offset(l.outputs, 1, l.delta_gpu, i*l.outputs, 1, l.bias_updates_gpu, 0, 1);
}
int m = layer.inputs;
int k = layer.batch;
int n = layer.outputs;
int m = l.inputs;
int k = l.batch;
int n = l.outputs;
float * a = state.input;
float * b = layer.delta_gpu;
float * c = layer.weight_updates_gpu;
float * b = l.delta_gpu;
float * c = l.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
n = layer.inputs;
m = l.batch;
k = l.outputs;
n = l.inputs;
a = layer.delta_gpu;
b = layer.weights_gpu;
a = l.delta_gpu;
b = l.weights_gpu;
c = state.delta;
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);