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.

include/neural_network/model/model.hpp

@@ -44,6 +44,7 @@
 #include <neural_network/layer/layer_dense.hpp> // fully connected dense layer
 
 #include <neural_network/loss/loss.hpp>
+#include <neural_network/activation_loss/activation_softmax_loss_categorical_crossentropy.hpp>
 
 #include <neural_network/optimizers/optimizer.hpp>
 #include <neural_network/optimizers/optimizer_sgd.hpp>
@@ -71,44 +72,77 @@ namespace panic{
 struct model{
 
     /**
-     * @brief a pointer to a pointer of layers
+     * @brief Array of pointers to all model layers.
      *
-     *      An example:
-     *          layers[0] points to a layer_dense
-     *          layers[1] points to an actication function
-     *          layers[2] points to another layer_dense
-     * 
-     * @note The model owns these layers and deletes them in clear().
-     * 
+     * Example:
+     *     layers[0] points to a layer_dense
+     *     layers[1] points to an activation_ReLU
+     *     layers[2] points to another layer_dense
+     *
+     * @note The model owns every object referenced by this array.
+     *       clear() deletes each layer and then deletes the array.
      */
     layer** layers;
 
+    /**
+     * @brief Number of layers currently stored in layers.
+     */
+    panic::types::uint_t layer_count;
 
+
+    /**
+     * @brief Array of pointers to the trainable layers.
+     *
+     * @note These pointers refer to objects already owned through layers.
+     *       Do not delete the individual objects through this array.
+     *       Only the pointer array itself is owned separately.
+     */
     trainable_layer** trainable_layers;
+
+    /**
+     * @brief Number of trainable-layer pointers.
+     */
     panic::types::uint_t trainable_layer_count;
 
+
+    /**
+     * @brief Configured loss function.
+     *
+     * @note The model owns this object and deletes it in clear().
+     */
+    loss* loss_function;
+
+    /**
+     * @brief Configured optimizer.
+     *
+     * @note The model owns this object and deletes it in clear().
+     */
     optimizer* optimizer_function;
 
 
     /**
-     * @brief a pointer the loss function
+     * @brief Optimized backward helper for the combination of
+     *        Softmax and categorical cross-entropy.
      *
-     * 
-     * @note The model owns these layers and deletes them in clear().
-     * 
+     * This is a normal member object, not a dynamically allocated object.
      */
-    loss* loss_function;
+    activation_softmax_loss_categorical_crossentropy softmax_classifier_output;
 
-    // Number of layers currently stored in the model.
     /**
-     * @brief Stores the number of layers
-     *
+     * @brief Whether the optimized Softmax + categorical
+     *        cross-entropy backward path should be used.
      */
-    panic::types::uint_t layer_count;
+    bool use_softmax_classifier_output;
 
-    // model output (may be deleted and also used for debug)
+
+    /**
+     * @brief Output of the final model layer.
+     */
     panic::tensor::real_matrix outputs;
-    // model dinputs (may be deleted and also used for debug)
+
+    /**
+     * @brief Gradient with respect to the model input.
+     */
     panic::tensor::real_matrix dinputs;
 
     /**
@@ -231,16 +265,32 @@ struct model{
      *
      * Computes:
      * @code
-     * model.bacward(dvalues_data_matrix)
+     * model.backward(model_output, y_true_values)
      * @endcode
      *
-     * @param dvalues diput data.
+     * @param output Model output.
+     * @param y_true True values for data.
      *
      * @return true looped over every layer.
-     * 
      *
      */
-    bool backward(const panic::tensor::real_matrix& dvalues);
+    bool backward(const panic::tensor::real_matrix& output, const panic::tensor::uint_vector& y_true);
+
+    /**
+     * @brief Loops over all layers backward function
+     *
+     * Computes:
+     * @code
+     * model.backward(model_output, y_true_values)
+     * @endcode
+     *
+     * @param output Model output.
+     * @param y_true True values for data.
+     *
+     * @return true looped over every layer.
+     *
+     */
+    bool backward(const panic::tensor::real_matrix& output, const panic::tensor::real_matrix& y_true);
 
 
     /**
@@ -259,24 +309,6 @@ struct model{
     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
      *
@@ -295,7 +327,15 @@ struct model{
 
 
 
-
+    /**
+     * @brief Finalizes the model configuration.
+     *
+     * Detects whether the model can use the optimized
+     * Softmax + categorical-cross-entropy backward pass.
+     *
+     * @return true if the model configuration is valid.
+     */
+    bool finalize();
 
     /**
      * @brief Trains the model with input data