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Maybe backward on activation+loss works
This commit is contained in:
Bausager
2026-08-04 21:27:11 +02:00
parent 28c1976fb1
commit fecb4c70b1
6 changed files with 84 additions and 136 deletions
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include/neural_network/activation_loss/activation_softmax_loss_categorical_crossentropy.hpp
+2 -82
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@@ -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;
/**
* @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);
bool forward(const panic::tensor::real_matrix& y_pred, const panic::tensor::real_matrix& y_true) override;
include/neural_network/loss/loss.hpp
+27
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@@ -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
+2 -2
View File
@@ -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
+23 -50
View File
@@ -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){
// 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){
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)){
@@ -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;
}
src/neural_network/loss/loss.cpp
+28
View File
@@ -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
+1 -1
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@@ -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();