340 lines
10 KiB
C
340 lines
10 KiB
C
// convolutional neural network c header library
|
|
// inspired by euske's nn1
|
|
// meant to be synthesized into RTL through Vitus HLS for an FPGA implementation
|
|
|
|
#include <stdlib.h>
|
|
#include <math.h>
|
|
|
|
typedef enum {
|
|
input,
|
|
conv,
|
|
max_pool,
|
|
fully_connected
|
|
} ltype;
|
|
|
|
typedef enum {
|
|
fc_input,
|
|
fc_hidden,
|
|
fc_output,
|
|
} fcpos;
|
|
|
|
typedef enum {
|
|
a_sigmoid,
|
|
a_softmax,
|
|
} activation;
|
|
|
|
typedef struct {
|
|
ltype type;
|
|
int height;
|
|
int width;
|
|
int channels; // in this case, "channels" are the number of filters that are coming in
|
|
|
|
union {
|
|
struct {
|
|
int num_filters;
|
|
int filter_size; // single integer b/c filter will usually be square shaped
|
|
int stride;
|
|
int zero_padding; // single integer for how many layers of zero padding
|
|
float (*weights);
|
|
float (*biases);
|
|
} conv_params;
|
|
|
|
struct {
|
|
int pool_size; // single integer again
|
|
int stride;
|
|
} pool_params;
|
|
|
|
struct {
|
|
int output_size;
|
|
float (*weights);
|
|
float (*biases);
|
|
activation type;
|
|
} fc_params;
|
|
} params;
|
|
float *output;
|
|
float *delta;
|
|
} Layer;
|
|
|
|
float he_init(int fan_in) {
|
|
float scale = sqrt(2.0f / fan_in);
|
|
float random = (float)rand() / RAND_MAX * 2 - 1;
|
|
return random * scale;
|
|
}
|
|
|
|
float glorot_init(int fan_in, int fan_out) {
|
|
float limit = sqrt(6.0f / (fan_in + fan_out));
|
|
float random = (float)rand() / RAND_MAX;
|
|
return random * 2 * limit - limit;
|
|
}
|
|
|
|
float relu(float x) {
|
|
return x > 0 ? x : 0;
|
|
}
|
|
|
|
float sigmoid(float x) {
|
|
return 1 / (1 + exp(-x));
|
|
}
|
|
|
|
void softmax(float* input, float* output, int size) {
|
|
float max = input[0];
|
|
for(int i = 1; i < size; i++) {
|
|
if(input[i] > max) {
|
|
max = input[i];
|
|
}
|
|
}
|
|
float sum = 0;
|
|
for(int i = 0; i < size; i++) {
|
|
output[i] = exp(input[i] - max);
|
|
sum += output[i];
|
|
}
|
|
for(int i = 0; i < size; i++) {
|
|
output[i] /= sum;
|
|
}
|
|
}
|
|
|
|
Layer* create_input(int height, int width, int channels) {
|
|
Layer* layer = (Layer*)malloc(sizeof(Layer));
|
|
layer->type = input;
|
|
layer->height = height;
|
|
layer->width = width;
|
|
layer->channels = channels;
|
|
layer->output = (float*)calloc(height * width * channels, sizeof(float));
|
|
return layer;
|
|
}
|
|
|
|
Layer* create_conv(int input_height, int input_width, int input_channels, int num_filters, int filter_size, int stride, int padding) {
|
|
Layer* layer = (Layer*)malloc(sizeof(Layer));
|
|
layer->type = conv;
|
|
layer->params.conv_params.num_filters = num_filters;
|
|
layer->params.conv_params.filter_size = filter_size;
|
|
layer->params.conv_params.stride = stride;
|
|
layer->params.conv_params.zero_padding = padding;
|
|
|
|
// output dimensions
|
|
// https://cs231n.github.io/convolutional-networks/
|
|
int output_h = (input_height + 2 * padding - filter_size) / stride + 1;
|
|
int output_w = (input_width + 2 * padding - filter_size) / stride + 1;
|
|
layer->height = output_h;
|
|
layer->width = output_w;
|
|
layer->channels = num_filters;
|
|
|
|
// conv layer uses relu, use HE init
|
|
int weights_size = num_filters * input_channels * filter_size * filter_size;
|
|
int fan_in = input_channels * filter_size * filter_size;
|
|
layer->params.conv_params.weights = (float*)calloc(weights_size, sizeof(float));
|
|
for (int i = 0; i < weights_size; i++) {
|
|
layer->params.conv_params.weights[i] = he_init(fan_in);
|
|
}
|
|
|
|
layer->params.conv_params.biases = (float*)calloc(num_filters, sizeof(float));
|
|
|
|
layer->output = (float*) calloc(output_h * output_w * num_filters, sizeof(float));
|
|
layer->delta = (float*) calloc(output_h * output_w * num_filters, sizeof(float));
|
|
|
|
return layer;
|
|
}
|
|
|
|
Layer* create_maxpool(int input_height, int input_width, int input_channels, int pool_size, int stride) {
|
|
Layer* layer = (Layer*)malloc(sizeof(Layer));
|
|
layer->type = max_pool;
|
|
layer->params.pool_params.pool_size = pool_size;
|
|
layer->params.pool_params.stride = stride;
|
|
|
|
// output dimensions
|
|
// https://cs231n.github.io/convolutional-networks/
|
|
int output_h = (input_height - pool_size) / stride + 1;
|
|
int output_w = (input_width - pool_size) / stride + 1;
|
|
layer->height = output_h;
|
|
layer->width = output_w;
|
|
layer->channels = input_channels;
|
|
|
|
layer->output = (float*) calloc(output_h * output_w * input_channels, sizeof(float));
|
|
layer->delta = (float*) calloc(output_h * output_w * input_channels, sizeof(float));
|
|
|
|
return layer;
|
|
}
|
|
|
|
Layer* create_fc(int output_size, int input_size, activation type) {
|
|
Layer* layer = (Layer*)malloc(sizeof(Layer));
|
|
layer->type = fully_connected;
|
|
layer->params.fc_params.output_size = output_size;
|
|
layer->params.fc_params.type = type; // activation type can either be sigmoid or softmax (output layer)
|
|
|
|
// use glorot initalization
|
|
layer->params.fc_params.weights = (float*)calloc(output_size * input_size, sizeof(float));
|
|
for (int i = 0; i < (output_size * input_size); i++) {
|
|
layer->params.fc_params.weights[i] = glorot_init(input_size, output_size);
|
|
}
|
|
|
|
layer->params.fc_params.biases = (float*)calloc(output_size, sizeof(float));
|
|
|
|
layer->height = 1;
|
|
layer->width = 1;
|
|
layer->channels = output_size;
|
|
layer->output = (float*) calloc(output_size, sizeof(float));
|
|
layer->delta = (float*) calloc(output_size, sizeof(float));
|
|
|
|
return layer;
|
|
}
|
|
|
|
void free_layer(Layer* layer) {
|
|
switch (layer->type) {
|
|
case input:
|
|
free(layer->output);
|
|
free(layer);
|
|
case conv:
|
|
free(layer->params.conv_params.weights);
|
|
free(layer->params.conv_params.biases);
|
|
free(layer->output);
|
|
free(layer->delta);
|
|
free(layer);
|
|
case max_pool:
|
|
free(layer->output);
|
|
free(layer->delta);
|
|
free(layer);
|
|
case fully_connected:
|
|
free(layer->params.fc_params.weights);
|
|
free(layer->params.fc_params.biases);
|
|
free(layer->output);
|
|
free(layer->delta);
|
|
free(layer);
|
|
}
|
|
}
|
|
|
|
void conv_forward(Layer* layer, float* input) {
|
|
int padding = layer->params.conv_params.zero_padding;
|
|
int stride = layer->params.conv_params.stride;
|
|
int filter_size = layer->params.conv_params.filter_size;
|
|
int num_filters = layer->params.conv_params.num_filters;
|
|
int input_height = layer->height; // from previous layer
|
|
int input_width = layer->width;
|
|
int input_channels = layer->channels;
|
|
|
|
int padded_height = input_height + 2 * padding;
|
|
int padded_width = input_width + 2 * padding;
|
|
float* padded_input = (float*) calloc(padded_height * padded_width * input_channels, sizeof(float));
|
|
|
|
for (int c = 0; c < input_channels; c++) {
|
|
for (int h = 0; h < input_height; h++) {
|
|
for (int w = 0; w < input_width; w++) {
|
|
padded_input[c * padded_height * padded_width + (h + padding) * padded_width + (w + padding)] = input[c * input_height * input_width + h * input_width + w];
|
|
}
|
|
}
|
|
}
|
|
|
|
int output_height = (padded_height - filter_size) / stride + 1;
|
|
int output_width = (padded_width - filter_size) / stride + 1;
|
|
int output_size = output_height * output_width * num_filters;
|
|
|
|
// for every filter
|
|
for(int f = 0; f < num_filters; f++) {
|
|
// for height and width
|
|
for(int oh = 0; oh < output_height; oh++) {
|
|
for(int ow = 0; ow < output_width; ow++) {
|
|
float sum = 0;
|
|
// for each "channel (feature maps coming in)", and filter size.
|
|
for(int c = 0; c < input_channels; c++) {
|
|
for(int fh = 0; fh < filter_size; fh++) {
|
|
for(int fw = 0; fw < filter_size; fw++) {
|
|
int ph = oh * stride + fh;
|
|
int pw = ow * stride + fw;
|
|
sum += padded_input[c * padded_height * padded_width + ph * padded_width + pw] * layer->params.conv_params.weights[f * input_channels * filter_size * filter_size + c * filter_size * filter_size + fh * filter_size + fw];
|
|
}
|
|
}
|
|
}
|
|
sum += layer->params.conv_params.biases[f];
|
|
layer->output[f * output_height * output_width + oh * output_width + ow] = relu(sum);
|
|
}
|
|
}
|
|
}
|
|
|
|
free(padded_input);
|
|
}
|
|
|
|
void maxpool_forward(Layer* layer, float* input) {
|
|
int pool_size = layer->params.pool_params.pool_size;
|
|
int stride = layer->params.pool_params.stride;
|
|
// prev layer
|
|
int input_height = layer->height;
|
|
int input_width = layer->width;
|
|
int input_channels = layer->channels;
|
|
|
|
int output_height = (input_height - pool_size) / stride + 1;
|
|
int output_width = (input_width - pool_size) / stride + 1;
|
|
int output_size = output_height * output_width * input_channels;
|
|
|
|
for(int c = 0; c < input_channels; c++) {
|
|
for(int oh = 0; oh < output_height; oh++) {
|
|
for(int ow = 0; ow < output_width; ow++) {
|
|
float max_val = -INFINITY;
|
|
for(int ph = 0; ph < pool_size; ph++) {
|
|
for(int pw = 0; pw < pool_size; pw++) {
|
|
int ih = oh * stride + ph;
|
|
int iw = ow * stride + pw;
|
|
float val = input[c * input_height * input_width + ih * input_width + iw];
|
|
if(val > max_val) {
|
|
max_val = val;
|
|
}
|
|
}
|
|
}
|
|
layer->output[c * output_height * output_width + oh * output_width + ow] = max_val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void fc_forward(Layer* layer, float* input) {
|
|
int output_size = layer->params.fc_params.output_size;
|
|
int input_size = layer->height * layer->width * layer->channels;
|
|
|
|
// flatten
|
|
float* flattened_input = (float*) calloc(input_size, sizeof(float));
|
|
for(int i = 0; i < input_size; i++) {
|
|
flattened_input[i] = input[i];
|
|
}
|
|
|
|
// matmul (output = bias + (input * weight))
|
|
float* temp_output = (float*) calloc(output_size, sizeof(float));
|
|
for(int o = 0; o < output_size; o++) {
|
|
float sum = 0;
|
|
for(int i = 0; i < input_size; i++) {
|
|
sum += flattened_input[i] * layer->params.fc_params.weights[o * input_size + i];
|
|
}
|
|
sum += layer->params.fc_params.biases[o];
|
|
temp_output[o] = sum;
|
|
}
|
|
|
|
// apply the correct activation (sigmoid for non output layers, softmax for output)
|
|
if(layer->params.fc_params.type == a_sigmoid) {
|
|
for(int o = 0; o < output_size; o++) {
|
|
layer->output[o] = sigmoid(temp_output[o]);
|
|
}
|
|
} else if(layer->params.fc_params.type == a_softmax) {
|
|
softmax(temp_output, layer->output, output_size);
|
|
}
|
|
|
|
free(temp_output);
|
|
free(flattened_input);
|
|
}
|
|
|
|
void forward_propagation(Layer* layer, float* input_fc) {
|
|
switch(layer->type) {
|
|
case input:
|
|
// input to layer->output
|
|
int input_size = layer->height * layer->width * layer->channels;
|
|
for(int i = 0; i < input_size; i++) {
|
|
layer->output[i] = input_fc[i];
|
|
}
|
|
break;
|
|
case conv:
|
|
conv_forward(layer, input);
|
|
break;
|
|
case max_pool:
|
|
maxpool_forward(layer, input);
|
|
break;
|
|
case fully_connected:
|
|
fc_forward(layer, input);
|
|
break;
|
|
}
|
|
}
|