Soa network implimentation by eliminating layer struct

include/cneuron/cneuron.h

@@ -108,26 +108,21 @@ void vector_apply_activation(const float *a, float *b, size_t length, float (*ac
  */
 void hadamard_product(const float *restrict a, const float *restrict b, float *restrict c, size_t length);
 
-/**
- * @brief Represents a single layer in a neural network.
- */
-typedef struct layer {
-    float *delta;          /**< Error delta for backpropagation. */
-    float *weighted_input; /**< Weighted input values for the layer. */
-    float *weights;        /**< Weights of the layer in column-major format. */
-    float *bias;           /**< Bias values for the layer. */
-    float *output;         /**< Output values from the layer. */
-    size_t length;         /**< Number of neurons in this layer. */
-} layer;
-
 /**
  * @brief Represents a neural network with multiple layers.
  */
 typedef struct {
-    layer *layers;                             /**< Array of struct to layers in the network. */
     size_t length;                             /**< Number of layers in the network. */
     size_t inputs_length;                      /**< Number of inputs to the network. */
+    size_t *layer_lengths;                     /**< Number of neuron in each layer. */
+    size_t *prev_lengths_sums;                 /**< Number of neuron from all previous layer. */
+    size_t *prev_weights_sums;                 /**< Number of weights from all previous layer. */
     float (*activation_function)(float, bool); /**< Pointer to the activation function used in the network. */
+    float *delta;                              /**< Error delta for backpropagation. */
+    float *weighted_input;                     /**< Weighted input values for the layer. */
+    float *output;                             /**< Output values from the layer. */
+    float *bias;                               /**< Bias values for the layer. */
+    float *weights;                            /**< Weights of the layer in column-major format. */
 } neural_network;
 
 /**

src/main.c

@@ -130,20 +130,17 @@ int main(int argc, char **argv) {
 
     dataset *train_dataset = get_mnist(false);
     dataset *test_dataset = get_mnist(true);
-    size_t network_length = 3;
-    size_t *layers_length = malloc(sizeof(size_t) * network_length);
-    layers_length[0] = 100;
-    layers_length[1] = 16;
-    layers_length[2] = 10;
+    const size_t network_length = 3;
+    const size_t layer_lengths[] = {100, 16, 10};
 
-    neural_network *nn = get_neural_network(network_length, layers_length, train_dataset->inputs_length, &sigmoid);
+    neural_network *nn = get_neural_network(network_length, layer_lengths, train_dataset->inputs_length, &sigmoid);
 
     // Parameters
-    float learn_rate = 1.5f;
-    size_t batch_size = 30;
-    int learn_amount = 4800000;
-    int batch_amount = learn_amount / batch_size;
-    int log_amount = 1000;  // Log once reached a number of batch
+    const float learn_rate = 1.5f;
+    const size_t batch_size = 30;
+    const int learn_amount = 50000000;
+    const int batch_amount = learn_amount / batch_size;
+    const int log_amount = 1000;  // Log once reached a number of batch
 
     char cmd[100];
     FILE *fp;
@@ -225,6 +222,5 @@ int main(int argc, char **argv) {
     free(train_dataset);
     free(test_dataset);
     free(nn);
-    free(layers_length);
     return 0;
 }