Done with activation softmax backwards

include/neural_network/activation/activation_relu.hpp

@@ -77,7 +77,7 @@ struct activation_relu : public layer{
      * 
      * @Note Calculates -> outputs = inputs * weights + biases
      */
-	bool forward(const panic::tensor::real_matrix& inputs);
+	bool forward(const panic::tensor::real_matrix& input_data);
 
     /**
      * @brief Backward function for layer
@@ -86,7 +86,7 @@ struct activation_relu : public layer{
      * 
      * @Note Calculates derivative of forward function.
      */
-	bool backward(const panic::tensor::real_matrix& dinpus);
+	bool backward(const panic::tensor::real_matrix& dvalues);
 };
 
 	} // namespace tensor

include/neural_network/activation/activation_softmax.hpp

@@ -77,7 +77,7 @@ struct activation_softmax : public layer{
      * 
      * @Note Calculates -> outputs = inputs * weights + biases
      */
-	bool forward(const panic::tensor::real_matrix& inputs);
+	bool forward(const panic::tensor::real_matrix& input_data);
 
     /**
      * @brief Backward function for layer
@@ -86,7 +86,7 @@ struct activation_softmax : public layer{
      * 
      * @Note Calculates derivative of forward function.
      */
-	bool backward(const panic::tensor::real_matrix& dinpus);
+	bool backward(const panic::tensor::real_matrix& dvalues);
 };
 
 	} // namespace tensor

include/neural_network/layer/layer.hpp

@@ -56,6 +56,14 @@ namespace panic{
  */
 struct layer{
 
+     /**
+     * @brief Emphty output matrix to store layer input
+     *
+     * Output shape:
+     * samples x neuron_count
+     */
+    panic::tensor::real_matrix inputs;
+
 	 /**
      * @brief Emphty output matrix to store layer output
      *
@@ -64,6 +72,12 @@ struct layer{
      */
 	panic::tensor::real_matrix outputs;
 
+     /**
+     * @brief Emphty matrix to store output for backward pass
+     *
+     */
+    panic::tensor::real_matrix dinputs;
+
     /**
      * @brief Default de-constructor
      *

include/neural_network/layer/layer_dense.hpp

@@ -58,6 +58,12 @@ namespace panic{
  */
 struct layer_dense : public layer{
 
+     /**
+     * @brief Emphty matrix to store input data
+     *
+     */
+    panic::tensor::real_matrix ipnuts;
+
 	 /**
      * @brief Emphty weight matrix to store layer weights
      *
@@ -65,6 +71,7 @@ struct layer_dense : public layer{
      * input_size x neuron_count
      */
 	panic::tensor::real_matrix weights;
+    panic::tensor::real_matrix dweights;
 
 	 /**
      * @brief Emphty bias vector to store layer bias
@@ -73,6 +80,7 @@ struct layer_dense : public layer{
      * 1 x neuron_count
      */
 	panic::tensor::real_vector biases;
+    panic::tensor::real_vector dbiases;
 
     /**
      * @brief Empthy constructor
@@ -102,7 +110,7 @@ struct layer_dense : public layer{
      * 
      * @Note Calculates -> outputs = inputs * weights + biases
      */
-	bool forward(const panic::tensor::real_matrix& inputs);
+	bool forward(const panic::tensor::real_matrix& input_data);
 
     /**
      * @brief Backward function for layer
@@ -111,7 +119,7 @@ struct layer_dense : public layer{
      * 
      * @Note Calculates derivative of forward function.
      */
-	bool backward(const panic::tensor::real_matrix& dinpus);
+	bool backward(const panic::tensor::real_matrix& dvalues);
 };
 
 	} // namespace tensor

include/neural_network/loss/loss.hpp

@@ -68,6 +68,12 @@ struct loss{
      */
     panic::types::real_t data_loss;
 
+    /**
+     * @brief Matrix for backwards pass
+     */
+    panic::tensor::real_matrix dinputs;
+
+
     /**
      * @brief Default de-constructor
      *

include/neural_network/loss/loss_categorical_crossentropy.hpp

@@ -82,9 +82,35 @@ struct loss_categorical_crossentropy: loss{
         const panic::tensor::real_matrix& y_pred, 
         const panic::tensor::real_matrix& y_true);
 
-};
 
 
+    /**
+     * @brief backward function to calculate from losses
+     *
+     * @param y_pred Matrix of model predection.  
+     * @param y_true Vector of true label of data.
+     * 
+     */
+    bool backward(
+        const panic::tensor::real_matrix& dvalues, 
+        const panic::tensor::uint_vector& y_true);
+
+    /**
+     * @brief backward function to calculate from losses
+     *
+     * @param y_pred Matrix of model predection.  
+     * @param y_true Vector of true label of data.
+     * 
+     * @Note Overloaded if one-shot endcoded
+     *         is used.
+     */
+    bool backward(
+        const panic::tensor::real_matrix& dvalues, 
+        const panic::tensor::real_matrix& y_true);
+
+
+};
+
 
 	} // namespace neural_network
 } // namespace panic