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adds backprop (unfinished)

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vikshar 2025-01-14 18:47:35 -06:00
parent f549f9440c
commit 40a2b072b8
1 changed files with 124 additions and 5 deletions
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127
cnn.c
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@ -46,15 +46,22 @@ typedef struct {
struct {
int output_size;
float (*weights);
float (*biases);
float* weights;
float* biases;
activation type;
} fc_params;
} params;
float *output;
float *delta;
float* output;
float* delta;
float* pre_activation;
float (*activation_g)(float);
} Layer;
typedef struct {
Layer** layers;
int num_layers;
} Network;
float he_init(int fan_in) {
float scale = sqrt(2.0f / fan_in);
float random = (float)rand() / RAND_MAX * 2 - 1;
@ -75,6 +82,15 @@ float sigmoid(float x) {
return 1 / (1 + exp(-x));
}
float relu_g(float x) {
return x > 0 ? 1 : 0;
}
float sigmoid_g(float x) {
float sig = sigmoid(x);
return sig * (1 - sig);
}
void softmax(float* input, float* output, int size) {
float max = input[0];
for(int i = 1; i < size; i++) {
@ -182,22 +198,26 @@ void free_layer(Layer* layer) {
case input:
free(layer->output);
free(layer);
break;
case conv:
free(layer->params.conv_params.weights);
free(layer->params.conv_params.biases);
free(layer->output);
free(layer->delta);
free(layer);
break;
case max_pool:
free(layer->output);
free(layer->delta);
free(layer);
break;
case fully_connected:
free(layer->params.fc_params.weights);
free(layer->params.fc_params.biases);
free(layer->output);
free(layer->delta);
free(layer);
break;
}
}
@ -337,3 +357,102 @@ void forward_propagation(Layer* layer, float* input_fc) {
break;
}
}
void network_forward(Network* network, float* input) {
float* current_input = input;
for (int i = 0; i < network->num_layers; i++) {
forward_propagation(network->layers[i], current_input);
current_input = network->layers[i]->output;
}
}
void fc_backward(Layer* layer, float* prev_delta, float* input, float learning_rate) {
int output_size = layer->params.fc_params.output_size;
int input_size = layer->height * layer->width * layer->channels;
// gradient of weights
for(int o = 0; o < output_size; o++) {
for(int i = 0; i < input_size; i++) {
layer->params.fc_params.weights[o * input_size + i] -= learning_rate * prev_delta[o] * input[i];
}
layer->params.fc_params.biases[o] -= learning_rate * prev_delta[o];
}
// gradient w/respect to inputs
for(int i = 0; i < input_size; i++) {
float sum = 0;
for(int o = 0; o < output_size; o++) {
sum += layer->params.fc_params.weights[o * input_size + i] * prev_delta[o];
}
layer->delta[i] = sum * layer->activation_g(layer->pre_activation[i]);
}
}
void conv_backward(Layer* layer, float* prev_delta, float* input, float learning_rate) {
int num_filters = layer->params.conv_params.num_filters;
int channels = layer->channels;
int filter_size = layer->params.conv_params.filter_size;
int input_height = layer->height;
int input_width = layer->width;
int padding = layer->params.conv_params.zero_padding;
int stride = layer->params.conv_params.stride;
int output_height = (input_height + 2 * padding - filter_size) / stride + 1;
int output_width = (input_width + 2 * padding - filter_size) / stride + 1;
// gradient w/respect to filters
for(int f = 0; f < num_filters; f++) {
for(int c = 0; c < channels; c++) {
for(int fh = 0; fh < filter_size; fh++) {
for(int fw = 0; fw < filter_size; fw++) {
float grad = 0;
for(int oh = 0; oh < output_height; oh++) {
for(int ow = 0; ow < output_width; ow++) {
int ih = oh * stride + fh - padding;
int iw = ow * stride + fw - padding;
if(ih >= 0 && ih < input_height && iw >= 0 && iw < input_width) {
grad += input[c * input_height * input_width + ih * input_width + iw] * prev_delta[f * output_height * output_width + oh * output_width + ow];
}
}
}
int index = f * channels * filter_size * filter_size + c * filter_size * filter_size + fh * filter_size + fw;
layer->params.conv_params.weights[index] -= learning_rate * grad;
}
}
}
}
// gradient w/respect to biases
for(int f = 0; f < num_filters; f++) {
float grad = 0;
for(int oh = 0; oh < output_height; oh++) {
for(int ow = 0; ow < output_width; ow++) {
grad += prev_delta[f * output_height * output_width + oh * output_width + ow];
}
}
layer->params.conv_params.biases[f] -= learning_rate * grad;
}
// gradient with respect to inputs
for(int c = 0; c < channels; c++) {
for(int ih = 0; ih < input_height; ih++) {
for(int iw = 0; iw < input_width; iw++) {
float grad = 0;
for(int f = 0; f < num_filters; f++) {
for(int fh = 0; fh < filter_size; fh++) {
for(int fw = 0; fw < filter_size; fw++) {
int oh = (ih - fh + padding) / stride;
int ow = (iw - fw + padding) / stride;
if((ih - fh + padding) % stride == 0 && (iw - fw + padding) % stride == 0 && oh < output_height && ow < output_width) {
int w_index = f * channels * filter_size * filter_size + c * filter_size * filter_size + fh * filter_size + fw;
grad += layer->params.conv_params.weights[w_index] * prev_delta[f * output_height * output_width + oh * output_width + ow];
}
}
}
}
layer->delta[c * input_height * input_width + ih * input_width + iw] = grad * layer->activation_g(layer->pre_activation[c * input_height * input_width + ih * input_width + iw]);
}
}
}
}