#include "connected_layer.h"
|
#include "utils.h"
|
#include "cuda.h"
|
#include "blas.h"
|
#include "gemm.h"
|
|
#include <math.h>
|
#include <stdio.h>
|
#include <stdlib.h>
|
#include <string.h>
|
|
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
|
{
|
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->output = calloc(batch*outputs, sizeof(float*));
|
layer->delta = calloc(batch*outputs, sizeof(float*));
|
|
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
|
layer->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));
|
|
|
float scale = 1./sqrt(inputs);
|
//scale = .01;
|
for(i = 0; i < inputs*outputs; ++i){
|
layer->weights[i] = scale*rand_normal();
|
}
|
|
for(i = 0; i < outputs; ++i){
|
layer->biases[i] = scale;
|
// layer->biases[i] = 1;
|
}
|
|
#ifdef GPU
|
layer->weights_gpu = cuda_make_array(layer->weights, inputs*outputs);
|
layer->biases_gpu = cuda_make_array(layer->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);
|
|
layer->output_gpu = cuda_make_array(layer->output, outputs*batch);
|
layer->delta_gpu = cuda_make_array(layer->delta, outputs*batch);
|
#endif
|
layer->activation = activation;
|
fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
|
return layer;
|
}
|
|
void secret_update_connected_layer(connected_layer *layer)
|
{
|
int n = layer->outputs*layer->inputs;
|
float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
|
float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
|
* sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
|
float cos = dot/mag;
|
if(cos > .3) layer->learning_rate *= 1.1;
|
else if (cos < -.3) layer-> learning_rate /= 1.1;
|
|
scal_cpu(n, layer->momentum, layer->weight_prev, 1);
|
axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
|
scal_cpu(n, 0, layer->weight_updates, 1);
|
|
scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
|
axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
|
scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
|
|
axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
|
|
axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
|
axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
|
}
|
|
void update_connected_layer(connected_layer layer)
|
{
|
axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
|
scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
|
|
axpy_cpu(layer.inputs*layer.outputs, -layer.decay, layer.weights, 1, layer.weight_updates, 1);
|
axpy_cpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates, 1, layer.weights, 1);
|
scal_cpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates, 1);
|
}
|
|
void forward_connected_layer(connected_layer layer, float *input)
|
{
|
int i;
|
for(i = 0; i < layer.batch; ++i){
|
copy_cpu(layer.outputs, layer.biases, 1, layer.output + i*layer.outputs, 1);
|
}
|
int m = layer.batch;
|
int k = layer.inputs;
|
int n = layer.outputs;
|
float *a = input;
|
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);
|
}
|
|
void backward_connected_layer(connected_layer layer, float *input, float *delta)
|
{
|
int i;
|
float alpha = 1./layer.batch;
|
gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
|
for(i = 0; i < layer.batch; ++i){
|
axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
|
}
|
int m = layer.inputs;
|
int k = layer.batch;
|
int n = layer.outputs;
|
float *a = input;
|
float *b = layer.delta;
|
float *c = layer.weight_updates;
|
gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
|
|
m = layer.batch;
|
k = layer.outputs;
|
n = layer.inputs;
|
|
a = layer.delta;
|
b = layer.weights;
|
c = delta;
|
|
if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
|
}
|
|
#ifdef GPU
|
|
void pull_connected_layer(connected_layer layer)
|
{
|
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);
|
}
|
|
void push_connected_layer(connected_layer layer)
|
{
|
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);
|
}
|
|
void update_connected_layer_gpu(connected_layer layer)
|
{
|
/*
|
cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
|
cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
|
printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
|
*/
|
|
axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
|
scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
|
|
axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
|
axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
|
scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
|
}
|
|
void forward_connected_layer_gpu(connected_layer layer, float * input)
|
{
|
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);
|
}
|
int m = layer.batch;
|
int k = layer.inputs;
|
int n = layer.outputs;
|
float * a = input;
|
float * b = layer.weights_gpu;
|
float * c = layer.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);
|
}
|
|
void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
|
{
|
float alpha = 1./layer.batch;
|
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, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
|
}
|
int m = layer.inputs;
|
int k = layer.batch;
|
int n = layer.outputs;
|
float * a = input;
|
float * b = layer.delta_gpu;
|
float * c = layer.weight_updates_gpu;
|
gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
|
|
m = layer.batch;
|
k = layer.outputs;
|
n = layer.inputs;
|
|
a = layer.delta_gpu;
|
b = layer.weights_gpu;
|
c = delta;
|
|
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
|
}
|
#endif
|
