Softmax + Categorical Crossentropy

I fixed the activation+loss function, you can't select it directly, it automaticly uses it if it can. I also fixed one-hot generator. Still haven't tested the activation + loss nor any other backward function. I'll do that when I get to the optimizers which is next.

main.cpp

@@ -88,15 +88,6 @@
 // #define TEST_FALG              1
 
 
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 //---------------------------------------------------------------------------------------------------------------------------
 // Function Name : benchmark_omp_min_work
 //
@@ -725,28 +716,46 @@ int main(void) {
     panic::neural_network::model mymodel;
 
     // Create Dense layer with 2 input features and 3 output values
-    mymodel.add_layer_dense(2,3);
+    if (!mymodel.add_layer_dense(2,3)){
+        return false;
+    }
     
     // Create an activation ReLU layer 
-    mymodel.add_activation_relu();
-
+    if (!mymodel.add_activation_relu()){
+        return false;
+    }
+    
     // Create a second dense layer with 3 inputs and 3 outputs
-    mymodel.add_layer_dense(3, 3);
+    if (!mymodel.add_layer_dense(3, 3)){
+        return false;
+    }
+    
 
     // Create activation softmax layer
-    mymodel.add_activation_softmax();
+    if (!mymodel.add_activation_softmax()){
+        return false;
+    }
+    
 
-    mymodel.add_loss_categorical_crossentropy();
-    //mymodel.activation_softmax_loss_categorical_crossentropy();
+    if (!mymodel.add_loss_categorical_crossentropy()){
+        return false;
+    }
+   
+    if (!mymodel.finalize()){
+        return false;
+    }
 
-    mymodel.add_optimizer_sgd();
+    //mymodel.add_optimizer_sgd();
     
     panic::types::uint_t epochs = 10;
     panic::types::uint_t print_every = 1;
 
-    mymodel.train(X, y, epochs, print_every);
+    if (!mymodel.train(X, y, epochs, print_every)){
+        std::cout << "Training failed" << std::endl;
+        return false;
+    }
 
-   
+    
 
 
 	return 0;