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I've created the optimizer class

main.cpp

@@ -63,6 +63,7 @@
 #include <math/argmax.hpp>
 #include <math/equal.hpp>
 #include <math/mean.hpp>
+#include <neural_network/activation_loss/activation_softmax_loss_categorical_crossentropy.hpp>
 
 #include <math.h>
 
@@ -735,32 +736,15 @@ int main(void) {
     // Create activation softmax layer
     mymodel.add_activation_softmax();
 
-    // create loss function
-    panic::neural_network::loss_categorical_crossentropy loss_function;
-
-    mymodel.forward(X);
-
-    loss_function.calculate(mymodel.outputs, y);
-
-    panic::tensor::uint_vector prediction;
-    prediction = panic::math::argmax_rowwise(mymodel.outputs);
-
-    panic::types::real_t accuracy;
-    panic::tensor::uint_vector comparisons;
-
-    comparisons = panic::math::equal(prediction, y);
-
-    accuracy = panic::math::mean(comparisons);
-
-    std::cout << "loss: " << loss_function.data_loss << std::endl;
-
-    std::cout << "acc: " << accuracy << std::endl;
-
-
+    mymodel.add_loss_categorical_crossentropy();
+    //mymodel.activation_softmax_loss_categorical_crossentropy();
 
+    mymodel.add_optimizer_sgd();
     
+    panic::types::uint_t epochs = 10;
+    panic::types::uint_t print_every = 1;
 
-    
+    mymodel.train(X, y, epochs, print_every);