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I've created the optimizer class

include/neural_network/model/model.hpp

@@ -40,8 +40,14 @@
 #include <tensor/matrix.hpp>                    // panic::tensor::real_matrix
 
 #include <neural_network/layer/layer.hpp>       // Base layer struct
+#include <neural_network/layer/trainable_layer.hpp>
 #include <neural_network/layer/layer_dense.hpp> // fully connected dense layer
 
+#include <neural_network/loss/loss.hpp>
+
+#include <neural_network/optimizers/optimizer.hpp>
+#include <neural_network/optimizers/optimizer_sgd.hpp>
+
 //---------------------------------------------------------------------------------------------------------------------------
 // TYPE DESCRIPTION
 //---------------------------------------------------------------------------------------------------------------------------
@@ -77,6 +83,22 @@ struct model{
      */
     layer** layers;
 
+
+    trainable_layer** trainable_layers;
+    panic::types::uint_t trainable_layer_count;
+
+    optimizer* optimizer_function;
+
+
+    /**
+     * @brief a pointer the loss function
+     *
+     * 
+     * @note The model owns these layers and deletes them in clear().
+     * 
+     */
+    loss* loss_function;
+
     // Number of layers currently stored in the model.
     /**
      * @brief Stores the number of layers
@@ -86,6 +108,8 @@ struct model{
 
     // model output (may be deleted and also used for debug)
     panic::tensor::real_matrix outputs;
+    // model dinputs (may be deleted and also used for debug)
+    panic::tensor::real_matrix dinputs;
 
     /**
      * @brief Empthy constructor
@@ -116,7 +140,21 @@ struct model{
      *  @note it adds an already-inplemented layer to the model.
      *
      */
-    bool add(layer* new_layer);
+    bool add_layer(layer* new_layer);
+
+    /**
+     * @brief Helper function for adding layers
+     * 
+     * Computes:
+     * @code
+     * layer_dense* new_layer = new layer_dense(3, 4);
+     * add(new_layer)
+     * @endcode
+     * 
+     *  @note it adds an already-inplemented layer to the model.
+     *
+     */
+    bool add_trainable_layer(trainable_layer* new_layer);
 
     /**
      * @brief Adds a dense layer to the model.
@@ -187,8 +225,99 @@ struct model{
      */
     bool forward(const panic::tensor::real_matrix& inputs);
 
-    // Delete all layers and reset the model.
 
+    /**
+     * @brief Loops over all layers backward function
+     *
+     * Computes:
+     * @code
+     * model.bacward(dvalues_data_matrix)
+     * @endcode
+     *
+     * @param dvalues diput data.
+     *
+     * @return true looped over every layer.
+     * 
+     *
+     */
+    bool backward(const panic::tensor::real_matrix& dvalues);
+
+
+    /**
+     * @brief Add loss for categorical crossentropy.
+     *
+     * Computes:
+     * @code
+     * model.add_loss_categorical_crossentropy();
+     * @endcode
+     *
+     *
+     * @return true if loss is added
+     *
+     * @note This function is convenient, but it allocates a new layer.
+     */
+    bool add_loss_categorical_crossentropy();
+
+
+
+    /**
+     * @brief Adds activation softmax AND loss for categorical crossentropy.
+     *
+     * Computes:
+     * @code
+     * model.activation_softmax_loss_categorical_crossentropy();
+     * @endcode
+     *
+     *
+     * @return true if activation and loss is added
+     *
+     * @note This function is convenient, but it allocates a new layer.
+     */
+    bool activation_softmax_loss_categorical_crossentropy();
+
+
+
+    /**
+     * @brief Adds optimizer_sgd to the model
+     *
+     * Computes:
+     * @code
+     * model.optimizer_sgd(1e-4)
+     * @endcode
+     *
+     * @param learning_rate Learning rate for update_param (default 1e-3).
+     *
+     * @return true If looped and optimized every trainable layer.
+     *  
+     *
+     */
+    bool add_optimizer_sgd(const panic::types::real_t learning_rate = static_cast<panic::types::real_t>(1e-3));
+
+
+
+
+
+    /**
+     * @brief Trains the model with input data
+     *
+     * Computes:
+     * @code
+     * model.backward(input_data_matrix)
+     * @endcode
+     *
+     * @param X_train Input X data for training.
+     * @param epochs Number of training iterations.
+     * @param print_every Prints every n iteration.
+     * 
+     *
+     * @return true if training is done correctly.
+     * 
+     *
+     */
+    bool train(const panic::tensor::real_matrix& X_train, 
+                    const panic::tensor::uint_vector& y_train,
+                    const panic::types::uint_t epochs, 
+                    const panic::types::uint_t print_every);
     /**
      * @brief Clears and deletes all layers and resets the model
      *