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Maybe backward on activation+loss works

include/neural_network/activation_loss/activation_softmax_loss_categorical_crossentropy.hpp

@@ -59,19 +59,6 @@ struct activation_softmax_loss_categorical_crossentropy: loss{
      activation_softmax activation;
      loss_categorical_crossentropy loss;
 
-     /**
-     * @brief Emphty matrix to store input data for bacward pass
-     *
-     */
-    panic::tensor::real_matrix dinputs;
-
-     /**
-     * @brief Emphty matrix to store output data
-     *
-     */
-    panic::tensor::real_matrix outputs;
-
-
 
     /**
      * @brief Empthy constructor
@@ -92,7 +79,7 @@ struct activation_softmax_loss_categorical_crossentropy: loss{
      * @param y_true Vector of true label of data.
      * 
      */
-     bool calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::uint_vector& y_true) override;
+     bool forward(const panic::tensor::real_matrix& y_pred, const panic::tensor::uint_vector& y_true) override;
 
     /**
      * @brief forward function to calculate losses
@@ -103,74 +90,7 @@ struct activation_softmax_loss_categorical_crossentropy: loss{
      * @Note Overloaded if one-shot endcoded
      *         is used.
      */
-    bool calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true) override;
+    bool forward(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true) override;
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-    /**
-     * @brief forward function to calculate losses
-     *
-     * @param y_pred Matrix of model predection.  
-     * @param y_true Vector of true label of data.
-     * 
-     */
-     bool calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::uint_vector& y_true);
-
-    /**
-     * @brief forward function to calculate 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 calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true);
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include/neural_network/loss/loss.hpp

@@ -112,6 +112,33 @@ struct loss{
         const panic::tensor::real_matrix& y_pred, 
         const panic::tensor::real_matrix& y_true);
 
+
+    /**
+     * @brief Virtual backward function for derivative loss functions
+     *
+     * @param y_pred Matrix of model predection.  
+     * @param y_true Vector of true label of data.
+     * 
+     * @Note If the derivatived object does not use this,
+     *          it returns false.
+     */
+    virtual bool backward(
+        const panic::tensor::real_matrix& y_pred, 
+        const panic::tensor::uint_vector& y_true);
+
+    /**
+     * @brief Virtual backward function for derivative loss functions
+     *
+     * @param y_pred Matrix of model predection.  
+     * @param y_true Matrix of true label of data.
+     * 
+     * @Note If the derivatived object does not use this,
+     *          it returns false.
+     */
+    virtual bool backward(
+        const panic::tensor::real_matrix& y_pred, 
+        const panic::tensor::real_matrix& y_true);
+
     /**
      * @brief Virtual calculate function that calculates the loss
      *

include/neural_network/loss/loss_categorical_crossentropy.hpp

@@ -67,7 +67,7 @@ struct loss_categorical_crossentropy: loss{
      */
 	bool forward(
         const panic::tensor::real_matrix& y_pred, 
-        const panic::tensor::uint_vector& y_true);
+        const panic::tensor::uint_vector& y_true)override;
 
     /**
      * @brief forward function to calculate losses
@@ -80,7 +80,7 @@ struct loss_categorical_crossentropy: loss{
      */
     bool forward(
         const panic::tensor::real_matrix& y_pred, 
-        const panic::tensor::real_matrix& y_true);
+        const panic::tensor::real_matrix& y_true)override;
 
 
     /**

src/neural_network/activation_loss/activation_softmax_loss_categorical_crossentropy.cpp

@@ -70,39 +70,14 @@ namespace panic{
 activation_softmax_loss_categorical_crossentropy::activation_softmax_loss_categorical_crossentropy() {
 }
 
+
 //--------------------------------------------------------------------------------------------------------------------------
-// Function Name : panic::neural_network::activation_softmax_loss_categorical_crossentropy.calculate
+// Function Name : panic::neural_network::activation_softmax_loss_categorical_crossentropy.forward
 //
 // Description:
-//		Calculated the calculate pass
+//		Calculated the forward pass
 //--------------------------------------------------------------------------------------------------------------------------
-bool activation_softmax_loss_categorical_crossentropy::calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::uint_vector& y_true){
+bool activation_softmax_loss_categorical_crossentropy::forward(const panic::tensor::real_matrix& y_pred, const panic::tensor::uint_vector& y_true){
-
-	// Output layers activation function
-	if (!activation.forward(y_pred)){
-	 	return false;
-	 } 
-
-	// Set the output
-	outputs = activation.outputs;
-
-	// calculate the loss value.
-	if (!loss.calculate(outputs, y_true)){
-		return false;
-	}
-	
-	data_loss = loss.data_loss;
-
-	return true;
-}
-
-//--------------------------------------------------------------------------------------------------------------------------
-// Function Name : panic::neural_network::activation_softmax_loss_categorical_crossentropy.calculate
-//
-// Description:
-//		Calculated the calculate pass
-//--------------------------------------------------------------------------------------------------------------------------
-bool activation_softmax_loss_categorical_crossentropy::calculate(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true){
 
 	// Output layers activation function
 	if (!activation.forward(y_pred)){
@@ -116,33 +91,31 @@ bool activation_softmax_loss_categorical_crossentropy::calculate(const panic::te
 		return false;
 	}
 	
-	data_loss = loss.data_loss;
-	
 	return true;
 }
 
+//--------------------------------------------------------------------------------------------------------------------------
+// Function Name : panic::neural_network::activation_softmax_loss_categorical_crossentropy.forward
+//
+// Description:
+//		Calculated the forward pass
+//--------------------------------------------------------------------------------------------------------------------------
+bool activation_softmax_loss_categorical_crossentropy::forward(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true){
 
+	// Output layers activation function
+	if (!activation.forward(y_pred)){
+	 	return false;
+	 } 
+	// Set the output
+	outputs = activation.outputs;
 
+	// calculate the loss value.
+	if (!loss.calculate(outputs, y_true)){
+		return false;
+	}
 	
-
+	return true;
-
+}
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src/neural_network/loss/loss.cpp

@@ -136,6 +136,34 @@ bool loss::forward(
 }
 
 
+//--------------------------------------------------------------------------------------------------------------------------
+// Function Name : panic::neural_network::loss::backward
+//
+// Description:
+//      Default implementation. Derived classes can override it.
+//--------------------------------------------------------------------------------------------------------------------------
+bool loss::backward(
+    const panic::tensor::real_matrix& y_pred,
+    const panic::tensor::uint_vector& y_true){
+
+    return false;
+}
+
+
+//--------------------------------------------------------------------------------------------------------------------------
+// Function Name : panic::neural_network::loss::backward
+//
+// Description:
+//      Default matrix-target implementation. Derived classes can override it.
+//--------------------------------------------------------------------------------------------------------------------------
+bool loss::backward(
+    const panic::tensor::real_matrix& y_pred,
+    const panic::tensor::real_matrix& y_true){
+
+    return false;
+}
+
+
 	} // namespace tensor
 } // namespace panic
 

src/neural_network/model/model.cpp

@@ -463,7 +463,7 @@ bool model::train(const panic::tensor::real_matrix& X_train,
         }
 
 
-        loss_function.backward(loss_function.outputs, y_train);
+        loss_function->backward(loss_function->outputs, y_train);
 
         //backward();