Ref : New implementation with updated APIs

Author: jaywyawhare

examples/nn_training_example.c

@@ -1,71 +1,38 @@
 #include <stdio.h>
 #include <stdlib.h>
-#include <time.h>
 #include "../include/Core/training.h"
+#include "../include/Core/dataset.h"
 
 int main()
 {
-    srand(time(NULL));
     NeuralNetwork *network = create_neural_network(2);
-
-    build_network(network, OPTIMIZER_ADAM, 0.01f, LOSS_MSE, 0.0f, 0.0f);
+    build_network(network, OPTIMIZER_ADAM, 0.1f, LOSS_MSE, 0.0f, 0.0f);
     model_add(network, LAYER_DENSE, ACTIVATION_RELU, 2, 4, 0.0f, 0, 0);
     model_add(network, LAYER_DENSE, ACTIVATION_TANH, 4, 4, 0.0f, 0, 0);
     model_add(network, LAYER_DENSE, ACTIVATION_SIGMOID, 4, 1, 0.0f, 0, 0);
 
-    int num_samples = 4;
-    float **X_train = (float **)cm_safe_malloc(num_samples * sizeof(float *), __FILE__, __LINE__);
-    float **y_train = (float **)cm_safe_malloc(num_samples * sizeof(float *), __FILE__, __LINE__);
-
-    for (int i = 0; i < num_samples; i++)
-    {
-        X_train[i] = (float *)cm_safe_malloc(2 * sizeof(float), __FILE__, __LINE__);
-        y_train[i] = (float *)cm_safe_malloc(1 * sizeof(float), __FILE__, __LINE__);
-    }
+    float X_data[4][2] = {
+        {0.0f, 0.0f},
+        {0.0f, 1.0f},
+        {1.0f, 0.0f},
+        {1.0f, 1.0f}};
 
-    X_train[0][0] = 0.0f;
-    X_train[0][1] = 0.0f;
-    y_train[0][0] = 0.0f;
-    X_train[1][0] = 0.0f;
-    X_train[1][1] = 1.0f;
-    y_train[1][0] = 1.0f;
+    float y_data[4][1] = {
+        {0.0f},
+        {1.0f},
+        {1.0f},
+        {1.0f}};
 
-    X_train[2][0] = 1.0f;
-    X_train[2][1] = 0.0f;
-    y_train[2][0] = 1.0f;
-
-    X_train[3][0] = 1.0f;
-    X_train[3][1] = 1.0f;
-    y_train[3][0] = 1.0f;
+    Dataset *dataset = dataset_create();
+    dataset_load_arrays(dataset, (float *)X_data, (float *)y_data, 4, 2, 1);
 
     summary(network);
-    train_network(network, X_train, y_train, num_samples, 2, 1, 1, 300);
-
-    MetricType metrics[] = {METRIC_R2_SCORE};
 
-    int num_metrics = sizeof(metrics) / sizeof(metrics[0]);
-    float results[num_metrics];
-
-    test_network(network, X_train, y_train, num_samples, 2, 1, (int *)metrics, num_metrics, results);
-    printf("R2 Score: %.2f\n", results[0]);
-
-    for (int i = 0; i < num_samples; i++)
-    {
-        float prediction = 0.0f;
-        forward_pass(network, X_train[i], &prediction, 2, 1, 0);
-        printf("Input: [%.0f, %.0f], Expected: %.0f, Predicted: %.4f\n",
-               X_train[i][0], X_train[i][1], y_train[i][0], prediction);
-    }
+    train_network(network, dataset, 30);
+    test_network(network, dataset->X, dataset->y, dataset->num_samples, NULL);
 
+    dataset_free(dataset);
     free_neural_network(network);
 
-    for (int i = 0; i < num_samples; i++)
-    {
-        cm_safe_free((void **)&X_train[i]);
-        cm_safe_free((void **)&y_train[i]);
-    }
-    cm_safe_free((void **)&X_train);
-    cm_safe_free((void **)&y_train);
-
     return 0;
 }