mark 2

NeuralLayer.cpp

@@ -6,25 +6,21 @@
 NeuralLayer::NeuralLayer(int input_size, int layer_size, double (*activation)(double)) : input_size(input_size),
                                                                                          layer_size(layer_size),
                                                                                          activation(activation) {
-    neurons = new Neuron *[input_size];
-    for (int i = 0; i < input_size; i++) {
-        neurons[i] = new Neuron(layer_size);
+    neurons = new Neuron *[layer_size];
+    for (int i = 0; i < layer_size; i++) {
+        neurons[i] = new Neuron(input_size);
     }
 }
 
 double *NeuralLayer::forward(double *input) {
-    std::cout << "starting forward neural layer input: " << input_size << " layer_size: " << layer_size << std::endl;
     auto output = new double[layer_size];
 
     for (int i = 0; i < layer_size; i++) {
-        std::cout << "neuron: " << i << " started" << std::endl;
-
         Neuron *neuron = neurons[i];
         double value = neuron->forward(input);
         value += neuron->bias;
 
         output[i] = activation(value);
-        std::cout << "neuron: " << i << " completed" << std::endl;
     }
 
     if (activation == Activation::softmax) {

NeuralNetwork.cpp

@@ -45,14 +45,13 @@ void NeuralNetwork::loadParameters(const std::string path) {
             in >> layer_biases[j];
         }
 
-        for (int i = 0; i < input_size; i++) {
+        for (int i = 0; i < hidden_size; i++) {
             auto neuron = layers[layer_index].neurons[i];
-            for (int j = 0; j < hidden_size; j++) {
-                neuron->weights[j] = layer_weights[i][j];
-            }
-            for (int j = 0; j < hidden_size; j++) {
-                neuron->bias = layer_biases[j];
+            for (int j = 0; j < input_size; j++) {
+                double weight = layer_weights[j][i];
+                neuron->weights[j] = weight;
             }
+            neuron->bias = layer_biases[i];
         }
     }
     in.close();

main.cpp

@@ -56,13 +56,17 @@ int main(void) {
     // Constructing the neural network
     auto *network = new NeuralNetwork();
     network->addNeuralLayer(INPUT_SIZE, HIDDEN_LAYER_1_SIZE, Activation::relu);
-    network->addNeuralLayer(HIDDEN_LAYER_1_SIZE, HIDDEN_LAYER_2_SIZE, Activation::relu);
-    network->addNeuralLayer(HIDDEN_LAYER_2_SIZE, OUTPUT_SIZE, Activation::softmax);
+    network->addNeuralLayer(HIDDEN_LAYER_2_SIZE, Activation::relu);
+    network->addNeuralLayer(OUTPUT_SIZE, Activation::softmax);
 
     network->loadParameters("network.txt");
     std::cout << "loaded params" << std::endl;
 
     // Evaluating the neural network with a set of inputs
-    auto *evaluationInput = new double[INPUT_SIZE] { 1.5, 1.2, 5.3, 7.3 };
-    network->evaluate(evaluationInput);
+//    auto *evaluationInput = new double[INPUT_SIZE] { 5.1, 3.5, 1.4, 0.2 };
+//    auto *evaluationInput = new double[INPUT_SIZE] { 4.9, 3.0, 1.4, 0.2 };
+    auto *evaluationInput = new double[INPUT_SIZE] { 5.9, 3.0, 5.1, 1.8 };
+    double *output = network->evaluate(evaluationInput);
+    for (int i = 0; i < OUTPUT_SIZE; i++)
+        std::cout << output[i] << " ";
 }