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#include "nn.h"
static void fill_random_weights(double *weights, double *bias, size_t rows, size_t cols);
void nn_layer_backward(
double *weights, size_t weigths_shape[2],
double *delta, size_t delta_cols,
double *out_prev, size_t out_cols,
Layer layer, double alpha)
{
assert(out_cols == weigths_shape[0] && "out_cols does not match with weight rows");
assert(delta_cols == weigths_shape[1] && "delta_cols does not match with weight cols");
for (size_t i = 0; i < weigths_shape[0]; i++) {
for (size_t j = 0; j < weigths_shape[0]; j++) {
size_t index = weigths_shape[1] * i + j;
double dcost_w = delta[j] * out_prev[i];
weights[index] = layer.weights[index] + alpha * dcost_w;
}
}
}
void nn_layer_hidden_delta(
double *delta, size_t delta_cols,
double *delta_next, size_t delta_next_cols,
double *weigths_next, size_t weigths_shape[2],
double *zout, size_t zout_cols,
double (*activation_derivative)(double))
{
assert(delta_cols == zout_cols);
assert(delta_cols == weigths_shape[0]);
assert(delta_next_cols == weigths_shape[1]);
for (size_t j = 0; j < delta_cols; j++) {
double sum = 0;
for (size_t k = 0; k < delta_next_cols; k++) {
size_t index = j * delta_cols + k;
sum += delta_next[k] * weigths_next[index];
}
delta[j] = sum * activation_derivative(zout[j]);
}
}
void nn_layer_out_delta(
double *delta, size_t delta_cols,
double *error, size_t error_cols,
double *zout, size_t zout_cols,
double (*activation_derivative)(double))
{
assert(delta_cols == error_cols);
assert(zout_cols == error_cols);
for (size_t i = 0; i < delta_cols; i++) {
delta[i] = error[i] * activation_derivative(zout[i]);
}
}
void nn_forward(
double **out, double **zout,
double *X, size_t X_shape[2],
Layer network[], size_t network_size)
{
size_t in_shape[2] = {X_shape[0], X_shape[1]};
size_t out_shape[2];
out_shape[0] = X_shape[0];
double *input = X;
for (size_t l = 0; l < network_size; l++) {
out_shape[1] = network[l].neurons;
nn_layer_forward(network[l], zout[l], out_shape, input, in_shape);
nn_layer_map_activation(network[l].activation, out[l], out_shape, zout[l], out_shape);
in_shape[1] = out_shape[1];
input = out[l];
}
}
void nn_layer_map_activation(
double (*activation)(double),
double *aout, size_t aout_shape[2],
double *zout, size_t zout_shape[2])
{
if (zout_shape[0] != aout_shape[0] || zout_shape[1] != aout_shape[1]) {
fprintf(stderr,
"nn_layer_map_activation() Error: zout must have (%zu x %zu) dimensions not (%zu x %zu)\n",
aout_shape[0], aout_shape[1], zout_shape[0], zout_shape[1]);
exit(1);
}
for (size_t i = 0; i < aout_shape[0]; i++) {
for (size_t j = 0; j < aout_shape[1]; j ++) {
size_t index = aout_shape[1] * i + j;
aout[index] = activation(zout[index]);
}
}
}
void nn_layer_forward(Layer layer, double *zout, size_t zout_shape[2], double *input, size_t input_shape[2])
{
if (zout_shape[0] != input_shape[0] || zout_shape[1] != layer.neurons) {
fprintf(stderr,
"nn_layer_forward() Error: zout must have (%zu x %zu) dimensions not (%zu x %zu)\n",
input_shape[0], layer.neurons, zout_shape[0], zout_shape[1]);
exit(1);
}
for (size_t i = 0; i < input_shape[0]; i++) {
for (size_t j = 0; j < layer.neurons; j++) {
size_t index = layer.neurons * i + j;
zout[index] = layer.bias[j];
}
}
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
input_shape[0], layer.neurons, layer.input_nodes, // m, n, k
1.0, input, input_shape[1], //alpha X
layer.weights, layer.neurons, // W
1.0, zout, layer.neurons); // beta B
}
void nn_network_init_weights(Layer layers[], size_t nmemb, size_t n_inputs)
{
int i;
size_t prev_size = n_inputs;
for (i = 0; i < nmemb; i++) {
layers[i].weights = calloc(prev_size * layers[i].neurons, sizeof(double));
layers[i].bias = calloc(layers[i].neurons, sizeof(double));
if (layers[i].weights == NULL || layers[i].bias == NULL) {
goto nn_layers_calloc_weights_error;
}
fill_random_weights(layers[i].weights, layers[i].bias, prev_size, layers[i].neurons);
layers[i].input_nodes = prev_size;
prev_size = layers[i].neurons;
}
return;
nn_layers_calloc_weights_error:
perror("nn_layers_calloc_weights() Error");
exit(1);
}
void nn_network_free_weights(Layer layers[], size_t nmemb)
{
for (int i = 0; i < nmemb; i++) {
free(layers[i].weights);
free(layers[i].bias);
}
}
double identity(double x)
{
return x;
}
double sigmoid(double x)
{
return 1 / (1 + exp(-x));
}
double relu(double x)
{
return (x > 0) ? x : 0;
}
void fill_random_weights(double *weights, double *bias, size_t rows, size_t cols)
{
FILE *fp = fopen("/dev/random", "rb");
if (fp == NULL) goto nn_fill_random_weights_error;
size_t weights_size = rows * cols;
int64_t *random_weights = calloc(weights_size, sizeof(int64_t));
int64_t *random_bias = calloc(cols, sizeof(int64_t));
fread(random_weights, sizeof(int64_t), weights_size, fp);
fread(random_bias, sizeof(int64_t), cols, fp);
if (!random_weights || !random_bias) goto nn_fill_random_weights_error;
for (size_t i = 0; i < weights_size; i++) {
weights[i] = (double)random_weights[i] / (double)INT64_MAX * 2;
}
for (size_t i = 0; i < cols; i++) {
bias[i] = (double)random_bias[i] / (double)INT64_MAX * 2;
}
free(random_weights);
free(random_bias);
fclose(fp);
return;
nn_fill_random_weights_error:
perror("nn_fill_random_weights Error()");
exit(1);
}
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