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/layer/layer.hpp

@@ -44,6 +44,14 @@ namespace panic{
 
 
 
+enum struct layer_type {
+    unknown,
+    layer_dense,
+    activation_relu,
+    activation_softmax
+};
+
+
 /**
  * @brief Base layer for the rest of the neural network library to use
  *
@@ -85,6 +93,16 @@ struct layer{
 	virtual ~layer() = default;
 
 
+    /**
+     * @brief Virtual layer_type function for derivative layers
+     *
+     * @Note This returns the type of layer it is
+     *    unknown be default
+     */
+     virtual layer_type get_type() const {
+          return layer_type::unknown;
+     }
+
     /**
      * @brief Virtual forward function for derivative layers
      *
@@ -103,7 +121,7 @@ struct layer{
      * @Note It's equal to 0 because it make the derivative 
      * 		object NEEDS to have these function to work.
      */
-	virtual bool backward(const panic::tensor::real_matrix& dinputs) = 0;
+	virtual bool backward(const panic::tensor::real_matrix& dvalues) = 0;
 
 };
 

include/neural_network/layer/layer_dense.hpp

@@ -58,30 +58,6 @@ namespace panic{
  */
 struct layer_dense : trainable_layer{
 
-     /**
-     * @brief Emphty matrix to store input data
-     *
-     */
-    panic::tensor::real_matrix ipnuts;
-
-	 /**
-     * @brief Emphty weight matrix to store layer weights
-     *
-     * Weight shape:
-     * input_size x neuron_count
-     */
-	panic::tensor::real_matrix weights;
-    panic::tensor::real_matrix dweights;
-
-	 /**
-     * @brief Emphty bias vector to store layer bias
-     *
-     * Bias shape:
-     * 1 x neuron_count
-     */
-	panic::tensor::real_vector biases;
-    panic::tensor::real_vector dbiases;
-
     /**
      * @brief Empthy constructor
      *
@@ -103,6 +79,16 @@ struct layer_dense : trainable_layer{
      */
 	~layer_dense() = default;
 
+    /**
+     * @brief get_type function for layer
+     *
+     * @returns the layer type
+     * 
+     */
+    layer_type get_type() const override {
+        return layer_type::layer_dense;
+    }
+
     /**
      * @brief Forward function for layer
      *