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main.cpp

@@ -705,7 +705,7 @@ int main(void) {
     panic::tensor::real_matrix X;
     panic::tensor::uint_vector y;
 
-    panic::types::uint_t samples = 10;
+    panic::types::uint_t samples = 100;
     panic::types::uint_t classes = 3;
 
 
@@ -716,7 +716,7 @@ int main(void) {
     panic::neural_network::model mymodel;
 
     // Create Dense layer with 2 input features and 3 output values
-    if (!mymodel.add_layer_dense(2,3)){
+    if (!mymodel.add_layer_dense(2,64)){
         return false;
     }
     
@@ -726,7 +726,7 @@ int main(void) {
     }
     
     // Create a second dense layer with 3 inputs and 3 outputs
-    if (!mymodel.add_layer_dense(3, 3)){
+    if (!mymodel.add_layer_dense(64, 3)){
         return false;
     }
     
@@ -740,15 +740,19 @@ int main(void) {
     if (!mymodel.add_loss_categorical_crossentropy()){
         return false;
     }
+
+    if (! mymodel.add_optimizer_sgd()){
+        return false;
+    }
    
     if (!mymodel.finalize()){
         return false;
     }
 
-    //mymodel.add_optimizer_sgd();
     
-    panic::types::uint_t epochs = 10;
-    panic::types::uint_t print_every = 1;
+    
+    panic::types::uint_t epochs = 10000;
+    panic::types::uint_t print_every = 100;
 
     if (!mymodel.train(X, y, epochs, print_every)){
         std::cout << "Training failed" << std::endl;