Binomial_CrossEnthophy

fixed rowwise/colswise mean/sum and implemented binomial_corssentrhopy. Next up is regression.

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@@ -492,8 +492,8 @@ events:
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-      source: "/home/atlas/Documents/Git/Flux/build/CMakeFiles/CMakeScratch/TryCompile-8cCtst"
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-      binary: "/home/atlas/Documents/Git/Flux/build/CMakeFiles/CMakeScratch/TryCompile-8cCtst"
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-        Change Dir: '/home/atlas/Documents/Git/Flux/build/CMakeFiles/CMakeScratch/TryCompile-8cCtst'
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-        Run Build Command(s): /usr/bin/cmake -E env VERBOSE=1 /usr/bin/gmake -f Makefile cmTC_9bda3/fast
+        Run Build Command(s): /usr/bin/cmake -E env VERBOSE=1 /usr/bin/gmake -f Makefile cmTC_d43db/fast
-        /usr/bin/gmake  -f CMakeFiles/cmTC_9bda3.dir/build.make CMakeFiles/cmTC_9bda3.dir/build
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-        Building CXX object CMakeFiles/cmTC_9bda3.dir/OpenMPCheckVersion.cpp.o
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build/examples/dense-neural-network/CMakeFiles/dense-neural-network.dir/main.cpp.o.d

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  /home/atlas/Documents/Git/Flux/include/./numerics/argmin.h \
  /home/atlas/Documents/Git/Flux/include/./numerics/detail/argmin_serial.h \
+ /home/atlas/Documents/Git/Flux/include/./numerics/binary_threshold.h \
+ /home/atlas/Documents/Git/Flux/include/./numerics/detail/binary_threshold_serial.h \
  /home/atlas/Documents/Git/Flux/include/./numerics/clip.h \
  /home/atlas/Documents/Git/Flux/include/./numerics/detail/clip_serial.h \
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  /home/atlas/Documents/Git/Flux/include/modules/neural_networks/loss/Loss_CategoricalCrossentrophy.h \
  /home/atlas/Documents/Git/Flux/include/modules/neural_networks/loss/Loss.h \
+ /home/atlas/Documents/Git/Flux/include/modules/neural_networks/activation_functions/Activation_Sigmoid.h \
+ /home/atlas/Documents/Git/Flux/include/modules/neural_networks/loss/Loss_BinaryCrossentropy.h \
  /home/atlas/Documents/Git/Flux/include/modules/neural_networks/optimizers/Optimizer_SGD.h \
  /home/atlas/Documents/Git/Flux/include/modules/neural_networks/optimizers/Optimizer_Adagrad.h \
  /usr/include/c++/13/math.h \

examples/dense-neural-network/Activation_Softmax_Loss_CategoricalCrossentropy-Example.txt

@@ -0,0 +1,233 @@
+#include "core/omp_config.h"
+
+#include "utils/utils.h"
+#include "numerics/numerics.h"
+#include "decomp/decomp.h"
+
+#include "modules/neural_networks/neural_networks.h"
+#include "random/random.h"
+
+
+
+//#include <iostream>
+//#include <stdexcept>
+//#include <chrono>
+
+
+
+
+
+int main(int argc, char const *argv[])
+{   
+
+    uint64_t number_of_classes = 3;
+    uint64_t number_of_samples = 150;
+    uint64_t number_of_epochs = 500;
+
+    utils::Mf X;
+    utils::Mf X_test;
+    utils::Matrix<int64_t> y;
+    utils::Matrix<int64_t> y_test;
+    float data_loss;
+    float regularization_loss;
+    float loss;
+    float accuracy;
+
+    utils::Vector<uint64_t> class_targets;
+    utils::Vector<uint64_t> predections;
+
+
+    // Create dataset
+    neural_networks::create_spital_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
+    //neural_networks::create_vertical_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
+
+    // Create Dense layer with 2 input featues and 3 output values
+    neural_networks::Dense_Layer<float> dense1(
+                                            2, 16,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+
+    // Create ReLU activation (to be used with Dense layer)
+    neural_networks::Activation_ReLU<float> activation1;
+    neural_networks::Dropout_Layer<float> dropout1(0.1);
+
+
+
+    // Create a second Dense layer with 16 inputs (as we take the vlaues from the last layer)
+    // and 16 output values
+    neural_networks::Dense_Layer<float> dense2(
+                                            16, 16,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+    neural_networks::Activation_Softmax<float> activation2;
+
+
+    // Create a second Dense layer with 3 inputs (as we take the vlaues from the last layer)
+    // and 3 output values
+    neural_networks::Dense_Layer<float> dense3(
+                                            16, 16,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+    neural_networks::Activation_Sigmoid<float> activation3;
+
+    neural_networks::Dense_Layer<float> dense4(
+                                            16, number_of_classes,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+
+    // Create a Sfotmax classifier's combined loss and activation
+    neural_networks::Activation_Softmax_Loss_CategoricalCrossentropy<float, int64_t> loss_activation;
+
+    // Create optimizer
+    //neural_networks::Optimizer_SGD<float> optimizer(1, 1e-3, 0.5);
+    //neural_networks::Optimizer_Adagrad<float> optimizer(1, 1e-3, 1e-6);
+    //neural_networks::Optimizer_RMSprop<float> optimizer(1, 1e-3, 1e-6, 0.9);
+    neural_networks::Optimizer_Adam<float> optimizer(
+                                                    0.05,      // Learning-rate
+                                                    5e-5,   // Learning-rate decay
+                                                    1e-6,   // epsilons
+                                                    0.9,    // beta 1 
+                                                    0.999   // beta 2
+                                                    );
+    
+
+
+    // Train in loop
+    for (uint64_t epoch = 0; epoch < number_of_epochs+1; ++epoch){
+
+        // Perform a forward pass of our training data through this layer
+        dense1.forward(X);
+        activation1.forward(dense1.outputs);
+        dropout1.forward(activation1.outputs);
+
+        dense2.forward(dropout1.outputs);
+        activation2.forward(dense2.outputs);
+
+        dense3.forward(activation2.outputs);
+        activation3.forward(dense3.outputs);
+
+        dense4.forward(activation3.outputs);
+
+        // Perform a foard pass through the activation/loss function
+        // takes the output of the second dense layer here and returns loss
+        data_loss = loss_activation.forward(dense4.outputs, y);
+
+        // Calculate regularization penalty
+        regularization_loss = loss_activation.loss.regularization_loss(dense1) + 
+                            loss_activation.loss.regularization_loss(dense2) + 
+                            loss_activation.loss.regularization_loss(dense3) + 
+                            loss_activation.loss.regularization_loss(dense4);
+
+        loss = data_loss + regularization_loss;
+
+        // Calculate accuracy from output of activation2 and targets
+        //predections = numerics::matargmax_row <int64_t, float>(loss_activation.outputs);
+        predections = numerics::argmax_rowwise(loss_activation.outputs);
+
+        if (y.cols() > 1){
+            class_targets = numerics::argmax_rowwise(y);
+        }else{
+            class_targets = utils::veccast <uint64_t, int64_t> (y.get_col(0));
+        }
+
+
+        accuracy = numerics::mean( utils::veccast<float, uint64_t> (numerics::equal_elementwise_serial(predections, class_targets)));
+
+
+        if (!(epoch%100)){
+            std::cout << "epoch: " << epoch;
+            std::cout << ", acc: " << accuracy;
+            std::cout << ", loss: " << loss;
+            std::cout << ", data_loss: " << data_loss;
+            std::cout << ", regularization_loss: " << regularization_loss;
+            std::cout << ", lr: " << optimizer.current_learning_rate;
+            std::cout << std::endl;
+        }
+
+        // Backward pass
+        loss_activation.backward(loss_activation.outputs, y);
+        dense4.backward(loss_activation.dinputs);
+
+        activation3.backward(dense4.dinputs);
+        dense3.backward(activation3.dinputs);
+
+        activation2.backward(dense3.dinputs);
+        dense2.backward(activation2.dinputs);
+
+        dropout1.backward(dense2.dinputs);
+        activation1.backward(dropout1.dinputs);
+        dense1.backward(activation1.dinputs);
+
+
+        // Update weights and biases
+        optimizer.pre_update_params();
+        optimizer.update_params(dense1);
+        optimizer.update_params(dense2);
+        optimizer.update_params(dense3);
+        optimizer.update_params(dense4);
+        optimizer.post_update_params();
+
+    }
+
+    // Validate the model
+
+    // Create dataset
+    neural_networks::create_spital_data<float, int64_t>(100, number_of_classes, X_test, y_test);
+
+    // Perform a forward pass of our training data through this layer
+    dense1.forward(X_test);
+    activation1.forward(dense1.outputs);
+    //dropout1.forward(activation1.outputs);
+
+    dense2.forward(activation1.outputs);
+    activation2.forward(dense2.outputs);
+
+    dense3.forward(activation2.outputs);
+    activation3.forward(dense3.outputs);
+
+    dense4.forward(activation3.outputs);
+
+    // Perform a foard pass through the activation/loss function
+    // takes the output of the second dense layer here and returns loss
+    data_loss = loss_activation.forward(dense4.outputs, y_test);
+
+    // Calculate regularization penalty
+    regularization_loss = loss_activation.loss.regularization_loss(dense1) + 
+                        loss_activation.loss.regularization_loss(dense2) + 
+                        loss_activation.loss.regularization_loss(dense3) + 
+                        loss_activation.loss.regularization_loss(dense4);
+
+    loss = data_loss + regularization_loss;
+
+    // Calculate accuracy from output of activation2 and targets
+    predections = numerics::argmax_rowwise(loss_activation.outputs);
+
+
+
+
+    if (y.cols() > 1){
+        class_targets = numerics::argmax_rowwise(y_test);
+    }else{
+        class_targets = utils::veccast <uint64_t, int64_t> (y_test.get_col(0));
+    }
+
+
+    accuracy = numerics::mean( utils::veccast<float, uint64_t> (numerics::equal_elementwise_serial(predections, class_targets)));
+
+
+    std::cout << "validation, acc: " << accuracy << ", loss: " << loss << std::endl;
+
+    return 0;
+}

examples/dense-neural-network/Loss_BinaryCrossentropy_example.txt

@@ -0,0 +1,205 @@
+#include "core/omp_config.h"
+
+#include "utils/utils.h"
+#include "numerics/numerics.h"
+#include "decomp/decomp.h"
+
+#include "modules/neural_networks/neural_networks.h"
+#include "random/random.h"
+
+
+
+//#include <iostream>
+//#include <stdexcept>
+//#include <chrono>
+
+
+
+
+
+int main(int argc, char const *argv[])
+{   
+
+    uint64_t number_of_classes = 2;
+    uint64_t number_of_samples = 150;
+    uint64_t number_of_epochs = 1000;
+
+    utils::Mf X;
+    utils::Mf X_test;
+    utils::Matrix<int64_t> y;
+    utils::Matrix<int64_t> y_test;
+    float data_loss;
+    float regularization_loss;
+    float loss;
+    float accuracy;
+
+    utils::Vector<uint64_t> class_targets;
+    utils::Vector<float> predictions;
+
+
+    // Create dataset
+    neural_networks::create_spital_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
+    //neural_networks::create_vertical_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
+
+    
+    // Create Dense layer with 2 input featues and 3 output values
+    neural_networks::Dense_Layer<float> dense1(
+                                            2, 16,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+
+    // Create ReLU activation (to be used with Dense layer)
+    neural_networks::Activation_ReLU<float> activation1;
+    neural_networks::Dropout_Layer<float> dropout1(0.1);
+
+
+
+    // Create a second Dense layer with 16 inputs (as we take the vlaues from the last layer)
+    // and 16 output values
+    neural_networks::Dense_Layer<float> dense2(
+                                            16, 16,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+    neural_networks::Activation_Softmax<float> activation2;
+
+
+    // Create a second Dense layer with 3 inputs (as we take the vlaues from the last layer)
+    // and 3 output values
+    neural_networks::Dense_Layer<float> dense3(
+                                            16, 1,  // input/output
+                                            0.0f,  // weight L1
+                                            5e-4f,  // weight L2
+                                            0.0f,   // bias L1
+                                            5e-4f    // bias L2
+                                            );
+    neural_networks::Activation_Sigmoid<float> activation3;
+
+
+    // Create a Sfotmax classifier's combined loss and activation
+    //neural_networks::Activation_Softmax_Loss_CategoricalCrossentropy<float, int64_t> loss_activation;
+    neural_networks::Loss_BinaryCrossentropy<float, int64_t> loss_activation;
+
+    // Create optimizer
+    //neural_networks::Optimizer_SGD<float> optimizer(1, 1e-3, 0.5);
+    //neural_networks::Optimizer_Adagrad<float> optimizer(1, 1e-3, 1e-6);
+    //neural_networks::Optimizer_RMSprop<float> optimizer(1, 1e-3, 1e-6, 0.9);
+    neural_networks::Optimizer_Adam<float> optimizer(
+                                                    0.05,   // Learning-rate
+                                                    5e-5,   // Learning-rate decay
+                                                    1e-6,   // epsilons
+                                                    0.9,    // beta 1 
+                                                    0.999   // beta 2
+                                                    );
+    
+
+
+    // Train in loop
+    for (uint64_t epoch = 0; epoch < number_of_epochs+1; ++epoch){
+
+        // Perform a forward pass of our training data through this layer
+        dense1.forward(X);
+        activation1.forward(dense1.outputs);
+        dropout1.forward(activation1.outputs);
+
+        dense2.forward(dropout1.outputs);
+        activation2.forward(dense2.outputs);
+
+        dense3.forward(activation2.outputs);
+        activation3.forward(dense3.outputs);
+
+        // Perform a foard pass through the activation/loss function
+        // takes the output of the second dense layer here and returns loss
+        data_loss = loss_activation.calculate(activation3.outputs, y);
+
+        // Calculate regularization penalty
+        regularization_loss = loss_activation.regularization_loss(dense1) + 
+                            loss_activation.regularization_loss(dense2) + 
+                            loss_activation.regularization_loss(dense3);
+
+        loss = data_loss + regularization_loss;
+
+        // Calculate accuracy from output of activation3 and targets
+        // Part in the brackets returns a binary mask - array consisting
+        // of True/False values, multiplying it by 1 changes it into array
+        // of 1s and 0s
+        predictions = numerics::greater_than(activation3.outputs, 0.5f).get_col(0);
+        accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y.get_col(0))));
+
+
+        if (!(epoch%100)){
+            
+            std::cout << "epoch: " << epoch;
+            std::cout << ", acc: " << accuracy;
+            std::cout << ", loss: " << loss;
+            std::cout << ", data_loss: " << data_loss;
+            std::cout << ", regularization_loss: " << regularization_loss;
+            std::cout << ", lr: " << optimizer.current_learning_rate;
+            std::cout << std::endl;
+
+        }
+
+        // Backward pass
+        loss_activation.backward(activation3.outputs, y);
+
+        activation3.backward(loss_activation.dinputs);
+        dense3.backward(activation3.dinputs);
+
+        activation2.backward(dense3.dinputs);
+        dense2.backward(activation2.dinputs);
+
+        dropout1.backward(dense2.dinputs);
+        activation1.backward(dropout1.dinputs);
+        dense1.backward(activation1.dinputs);
+
+        // Update weights and biases
+        optimizer.pre_update_params();
+        optimizer.update_params(dense1);
+        optimizer.update_params(dense2);
+        optimizer.update_params(dense3);
+        optimizer.post_update_params();
+
+    }
+
+    // Validate the model
+
+    // Create dataset
+    neural_networks::create_spital_data<float, int64_t>(100, number_of_classes, X_test, y_test);
+
+    // Perform a forward pass of our training data through this layer
+    dense1.forward(X_test);
+    activation1.forward(dense1.outputs);
+    //dropout1.forward(activation1.outputs);
+
+    dense2.forward(activation1.outputs);
+    activation2.forward(dense2.outputs);
+
+    dense3.forward(activation2.outputs);
+    activation3.forward(dense3.outputs);
+
+    // Perform a foard pass through the activation/loss function
+    // takes the output of the second dense layer here and returns loss
+    data_loss = loss_activation.calculate(activation3.outputs, y_test);
+
+    // Calculate regularization penalty
+    regularization_loss = loss_activation.regularization_loss(dense1) + 
+                        loss_activation.regularization_loss(dense2) + 
+                        loss_activation.regularization_loss(dense3);
+
+    loss = data_loss + regularization_loss;
+
+    // Calculate accuracy from output of activation2 and targets
+    predictions = numerics::greater_than(activation3.outputs, 0.5f).get_col(0);
+
+    accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y_test.get_col(0))));
+
+
+    std::cout << "validation, acc: " << accuracy << ", loss: " << loss << std::endl;
+
+    return 0;
+}

examples/dense-neural-network/main.cpp

@@ -20,9 +20,9 @@
 int main(int argc, char const *argv[])
 {   
 
-    uint64_t number_of_classes = 3;
+    uint64_t number_of_classes = 2;
-    uint64_t number_of_samples = 1000;
+    uint64_t number_of_samples = 150;
-    uint64_t number_of_epochs = 500;
+    uint64_t number_of_epochs = 1000;
 
     utils::Mf X;
     utils::Mf X_test;
@@ -34,13 +34,14 @@ int main(int argc, char const *argv[])
     float accuracy;
 
     utils::Vector<uint64_t> class_targets;
-    utils::Vector<uint64_t> predections;
+    utils::Vector<float> predictions;
 
 
     // Create dataset
     neural_networks::create_spital_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
     //neural_networks::create_vertical_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
 
+    
     // Create Dense layer with 2 input featues and 3 output values
     neural_networks::Dense_Layer<float> dense1(
                                             2, 16,  // input/output
@@ -65,29 +66,31 @@ int main(int argc, char const *argv[])
                                             0.0f,   // bias L1
                                             5e-4f    // bias L2
                                             );
-    // Create Softmax activation (to be used with Dense layer)
     neural_networks::Activation_Softmax<float> activation2;
 
 
     // Create a second Dense layer with 3 inputs (as we take the vlaues from the last layer)
     // and 3 output values
     neural_networks::Dense_Layer<float> dense3(
-                                            16, number_of_classes,  // input/output
+                                            16, 1,  // input/output
                                             0.0f,  // weight L1
                                             5e-4f,  // weight L2
                                             0.0f,   // bias L1
                                             5e-4f    // bias L2
                                             );
+    neural_networks::Activation_Sigmoid<float> activation3;
+
 
     // Create a Sfotmax classifier's combined loss and activation
-    neural_networks::Activation_Softmax_Loss_CategoricalCrossentropy<float, int64_t> loss_activation;
+    //neural_networks::Activation_Softmax_Loss_CategoricalCrossentropy<float, int64_t> loss_activation;
+    neural_networks::Loss_BinaryCrossentropy<float, int64_t> loss_activation;
 
     // Create optimizer
     //neural_networks::Optimizer_SGD<float> optimizer(1, 1e-3, 0.5);
     //neural_networks::Optimizer_Adagrad<float> optimizer(1, 1e-3, 1e-6);
     //neural_networks::Optimizer_RMSprop<float> optimizer(1, 1e-3, 1e-6, 0.9);
     neural_networks::Optimizer_Adam<float> optimizer(
-                                                    0.05,      // Learning-rate
+                                                    0.05,   // Learning-rate
                                                     5e-5,   // Learning-rate decay
                                                     1e-6,   // epsilons
                                                     0.9,    // beta 1 
@@ -101,51 +104,36 @@ int main(int argc, char const *argv[])
 
         // Perform a forward pass of our training data through this layer
         dense1.forward(X);
-
-        // Perform a forward pass thourgh activation function
-        // takes the output fo the first layer here
         activation1.forward(dense1.outputs);
         dropout1.forward(activation1.outputs);
 
-        // Perform a forward pass through second Dense layer
-        // takes output of activation function of the first layer as input
         dense2.forward(dropout1.outputs);
-
-
-        // Perform a forward pass thourgh activation function
-        // takes the output fo the first layer here
         activation2.forward(dense2.outputs);
 
-        // Perform a forward pass through second Dense layer
-        // takes output of activation function of the first layer as input
         dense3.forward(activation2.outputs);
-
+        activation3.forward(dense3.outputs);
-
 
         // Perform a foard pass through the activation/loss function
         // takes the output of the second dense layer here and returns loss
-        data_loss = loss_activation.forward(dense3.outputs, y);
+        data_loss = loss_activation.calculate(activation3.outputs, y);
 
         // Calculate regularization penalty
-        regularization_loss = loss_activation.loss.regularization_loss(dense1) + loss_activation.loss.regularization_loss(dense2) + loss_activation.loss.regularization_loss(dense3);
+        regularization_loss = loss_activation.regularization_loss(dense1) + 
+                            loss_activation.regularization_loss(dense2) + 
+                            loss_activation.regularization_loss(dense3);
 
         loss = data_loss + regularization_loss;
 
-        // Calculate accuracy from output of activation2 and targets
+        // Calculate accuracy from output of activation3 and targets
-        //predections = numerics::matargmax_row <int64_t, float>(loss_activation.outputs);
+        // Part in the brackets returns a binary mask - array consisting
-        predections = numerics::argmax_rowwise(loss_activation.outputs);
+        // of True/False values, multiplying it by 1 changes it into array
-
+        // of 1s and 0s
-        if (y.cols() > 1){
+        predictions = numerics::greater_than(activation3.outputs, 0.5f).get_col(0);
-            class_targets = numerics::argmax_rowwise(y);
+        accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y.get_col(0))));
-        }else{
-            class_targets = utils::veccast <uint64_t, int64_t> (y.get_col(0));
-        }
-
-
-        accuracy = numerics::mean( utils::veccast<float, uint64_t> (numerics::equal_elementwise_serial(predections, class_targets)));
 
 
         if (!(epoch%100)){
+            
             std::cout << "epoch: " << epoch;
             std::cout << ", acc: " << accuracy;
             std::cout << ", loss: " << loss;
@@ -153,18 +141,22 @@ int main(int argc, char const *argv[])
             std::cout << ", regularization_loss: " << regularization_loss;
             std::cout << ", lr: " << optimizer.current_learning_rate;
             std::cout << std::endl;
+
         }
 
         // Backward pass
-        loss_activation.backward(loss_activation.outputs, y);
+        loss_activation.backward(activation3.outputs, y);
-        dense3.backward(loss_activation.dinputs);
+
+        activation3.backward(loss_activation.dinputs);
+        dense3.backward(activation3.dinputs);
+
         activation2.backward(dense3.dinputs);
         dense2.backward(activation2.dinputs);
+
         dropout1.backward(dense2.dinputs);
         activation1.backward(dropout1.dinputs);
         dense1.backward(activation1.dinputs);
 
-
         // Update weights and biases
         optimizer.pre_update_params();
         optimizer.update_params(dense1);
@@ -179,48 +171,34 @@ int main(int argc, char const *argv[])
     // Create dataset
     neural_networks::create_spital_data<float, int64_t>(100, number_of_classes, X_test, y_test);
 
-    // Perform a forward pass of our testing data through this layer
+    // Perform a forward pass of our training data through this layer
     dense1.forward(X_test);
-
-
-    // Perform a forward pass thourgh activation function
-    // takes the output fo the first layer here
     activation1.forward(dense1.outputs);
+    //dropout1.forward(activation1.outputs);
 
-    // Perform a forward pass through second Dense layer
-    // takes output of activation function of the first layer as input
     dense2.forward(activation1.outputs);
-
-    // Perform a forward pass thourgh activation function
-    // takes the output fo the first layer here
     activation2.forward(dense2.outputs);
 
-    // Perform a forward pass through second Dense layer
-    // takes output of activation function of the first layer as input
     dense3.forward(activation2.outputs);
-
+    activation3.forward(dense3.outputs);
 
     // Perform a foard pass through the activation/loss function
     // takes the output of the second dense layer here and returns loss
-    data_loss = loss_activation.forward(dense3.outputs, y_test);
+    data_loss = loss_activation.calculate(activation3.outputs, y_test);
 
     // Calculate regularization penalty
-    regularization_loss = loss_activation.loss.regularization_loss(dense1) + loss_activation.loss.regularization_loss(dense2) + loss_activation.loss.regularization_loss(dense3);
+    regularization_loss = loss_activation.regularization_loss(dense1) + 
+                        loss_activation.regularization_loss(dense2) + 
+                        loss_activation.regularization_loss(dense3);
 
     loss = data_loss + regularization_loss;
 
     // Calculate accuracy from output of activation2 and targets
-    predections = numerics::argmax_rowwise(loss_activation.outputs);
+    predictions = numerics::greater_than(activation3.outputs, 0.5f).get_col(0);
 
-    if (y.cols() > 1){
+    accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y_test.get_col(0))));
-        class_targets = numerics::argmax_rowwise(y_test);
-    }else{
-        class_targets = utils::veccast <uint64_t, int64_t> (y_test.get_col(0));
-    }
 
 
-    accuracy = numerics::mean( utils::veccast<float, uint64_t> (numerics::equal_elementwise_serial(predections, class_targets)));
-
     std::cout << "validation, acc: " << accuracy << ", loss: " << loss << std::endl;
 
     return 0;

include/modules/neural_networks/activation_functions/Activation_Sigmoid.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include "core/omp_config.h"
+
+#include "utils/vector.h"
+#include "utils/matrix.h"
+
+#include "numerics/neg.h"
+#include "numerics/exp.h"
+#include "numerics/add.h"
+#include "numerics/div.h"
+#include "numerics/sub.h"
+#include "numerics/mul.h"
+
+namespace neural_networks{
+
+	template <typename T>
+	struct Activation_Sigmoid{
+
+		utils::Matrix<T> _inputs;
+		utils::Matrix<T> outputs;
+
+		utils::Matrix<T> dinputs;
+
+        void forward(const utils::Matrix<T>& inputs){
+        	_inputs = inputs;
+
+        	outputs = numerics::neg(inputs);
+        	outputs = numerics::exp(outputs);
+        	outputs = numerics::add(outputs, T{1});
+        	outputs = numerics::div(T{1}, outputs);
+        }
+        void backward(const utils::Matrix<T>& dvalues){
+        	
+        	dinputs = numerics::sub(T{1}, outputs);
+        	dinputs = numerics::mul(dvalues, dinputs);
+        	dinputs = numerics::mul(dinputs, outputs);
+
+
+
+        }
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/activation_functions/Activation_Softmax.h

@@ -29,7 +29,7 @@ namespace neural_networks{
 			    utils::Matrix<T> exp_values = numerics::exp(numerics::sub_colwise(inputs, numerics::max_rowwise(inputs)));
 
 			    // Normalize them for each sample
-			    utils::Matrix<T> probabilities = numerics::div_colwise(exp_values, numerics::sum_colwise(exp_values));
+			    utils::Matrix<T> probabilities = numerics::div_colwise(exp_values, numerics::sum_rowwise(exp_values));
 			    outputs = probabilities;
 	        }
 

include/modules/neural_networks/layers/Dense_Layer.h

@@ -63,7 +63,7 @@ namespace neural_networks{
         void backward(const utils::Matrix<T>& dvalues){
         	// Gradients on parameters
         	dweights  = numerics::matmul(numerics::transpose(_inputs), dvalues);
-        	dbiases = numerics::sum_rowwise(dvalues);
+        	dbiases = numerics::sum_colwise(dvalues);
 
 
         	// Gradients on regularization 

include/modules/neural_networks/loss/Loss_BinaryCrossentropy.h

@@ -0,0 +1,81 @@
+#pragma once
+
+#include "core/omp_config.h"
+
+#include "utils/vector.h"
+#include "utils/matrix.h"
+#include "utils/matcast.h"
+
+#include "numerics/clip.h"
+#include "numerics/log.h"
+#include "numerics/sub.h"
+
+#include "Loss.h"
+
+
+namespace neural_networks{
+
+	template <typename Td, typename Ti>
+	struct Loss_BinaryCrossentropy : Loss<Td, Ti>  {
+
+		utils::Matrix<Td> dinputs;
+		utils::Matrix<Td> y_true;
+
+	
+		utils::Vector<Td> forward(const utils::Matrix<Td>& y_pred, const utils::Matrix<Ti>& y_true) override{
+			
+			this->y_true = utils::matcast<Td, Ti>(y_true);
+
+			// Clip daa to prevent division by 0
+			// Clip both sides not to drag mean towards any value
+			utils::Matrix<Td> y_pred_clipped = numerics::clip(y_pred, Td{1e-7}, Td{1.0} - Td{1e-7});
+
+			// Calculate sample-wise loss
+			utils::Matrix<Td> sample_losses_temp = numerics::log(numerics::sub(Td{1}, y_pred_clipped));
+			sample_losses_temp = numerics::mul(sample_losses_temp, numerics::sub(Td{1}, this->y_true));
+			sample_losses_temp = numerics::add(sample_losses_temp, numerics::mul(this->y_true, numerics::log(y_pred_clipped)));
+			sample_losses_temp = numerics::neg(sample_losses_temp);
+
+
+
+
+
+			utils::Vector<Td> sample_losses = numerics::mean_rowwise(sample_losses_temp);
+
+			// Return losses
+			return sample_losses;
+		}
+		
+		void backward(const utils::Matrix<Td>& dvalues, const utils::Matrix<Ti>& y_true) override{
+
+				/*std::cout << "BCE backward y_true: "
+				          << y_true.rows() << " x " << y_true.cols()
+				          << std::endl;*/
+
+
+			// Number of samples
+			const Td samples = static_cast<Td> (this->y_true.rows());
+			// Number of outputs in every sample
+			const Td outputs = static_cast<Td> (dvalues.cols());
+
+			// Clip data to prevent division by 0
+			// Clip both sides to not drag mean towards any value
+			utils::Matrix<Td> clipped_dvalues = numerics::clip(dvalues, Td{1e-7}, Td{1.0} - Td{1e-7});
+
+
+			// Calculate gradient
+			dinputs = numerics::div(numerics::neg(numerics::sub(numerics::div(this->y_true, clipped_dvalues), numerics::div(numerics::sub(Td{1}, this->y_true), numerics::sub(Td{1}, clipped_dvalues)))), outputs);
+			// Normalize gradients
+			dinputs = numerics::div(dinputs, samples);
+
+			/*
+			std::cout << "BCE backward dinputs: "
+          << dinputs.rows() << " x " << dinputs.cols()
+          << std::endl;*/
+
+		}
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/neural_networks.h

@@ -12,10 +12,12 @@
 #include "activation_functions/Activation_ReLU.h"
 #include "activation_functions/Activation_Softmax.h"
 #include "activation_functions/Activation_Softmax_Loss_CategoricalCrossentropy.h"
+#include "activation_functions/Activation_Sigmoid.h"
 
 
 #include "loss/Loss.h" // Base
 #include "loss/Loss_CategoricalCrossentrophy.h"
+#include "loss/Loss_BinaryCrossentropy.h"
 
 
 #include "optimizers/Optimizer_SGD.h"

include/numerics/binary_threshold.h

@@ -0,0 +1,51 @@
+#pragma once
+
+#include "core/omp_config.h"
+#include "detail/binary_threshold_serial.h"
+
+
+namespace numerics{
+
+	// ---------------- Elementwise ----------------
+	template <typename T>
+	inline void inplace_greater_than(T& a, const T c) {
+		detail::inplace_greater_than_serial(a, c);
+	}
+
+	template <typename T>
+	inline T greater_than(const T a, const T c) {
+		T out = a;
+		inplace_greater_than(out, c);
+		return out;
+	}
+
+	template <typename T>
+	inline void inplace_greater_than(utils::Vector<T>& v, const T c) {
+		detail::inplace_greater_than_serial(v, c);
+	}
+
+	template <typename T>
+	inline utils::Vector<T> greater_than(const utils::Vector<T>& v, const T c) {
+	    utils::Vector<T> out = v;
+	    inplace_greater_than(out, c);
+	    return out;
+	}
+
+
+
+	template <typename T>
+	inline void inplace_greater_than(utils::Matrix<T>& A, const T c) {
+		detail::inplace_greater_than_serial(A, c);
+	}
+
+	template <typename T>
+	inline utils::Matrix<T> greater_than(const utils::Matrix<T>& A, const T c) {
+	    utils::Matrix<T> out = A;
+	    inplace_greater_than(out, c);
+	    return out;
+	}
+
+
+
+
+}

include/numerics/detail/binary_threshold_serial.h

@@ -0,0 +1,44 @@
+#pragma once
+
+#include <cstdint> //uint64_t
+#include <cmath> // std::abs
+
+#include "utils/vector.h"
+#include "utils/matrix.h"
+
+namespace numerics::detail{
+	
+
+	// ---------------- Elemenwise ----------------
+	template <typename T>
+	void inplace_greater_than_serial(T& a, const T c) {
+		if (a > c){
+			a = T{1};
+		}
+		else{
+			a = T{0};
+		}
+	}
+		
+	template <typename T>
+	void inplace_greater_than_serial(utils::Vector<T>& v, const T c) {
+		for (uint64_t i = 0; i < v.size(); ++i){
+			inplace_greater_than_serial(v[i], c);
+		}
+	}
+
+
+	template <typename T>
+	void inplace_greater_than_serial(utils::Matrix<T>& A, const T c) {
+	    const uint64_t rows = A.rows();
+	    const uint64_t cols = A.cols();
+		for (uint64_t i = 0; i < rows; ++i){
+			for (uint64_t j = 0; j < cols; ++j){
+				inplace_greater_than_serial(A(i,j), c);
+			}
+		}
+	}
+
+
+} // namespace numerics
+

include/numerics/detail/div_serial.h

@@ -20,12 +20,31 @@ namespace numerics::detail{
 		}
 	}
 
+	template <typename T>
+	void inplace_div_scalar_serial(const T c, utils::Matrix<T>& A) {
+	    const uint64_t rows = A.rows();
+	    const uint64_t cols = A.cols();
+		for (uint64_t i = 0; i < rows; ++i){
+			for (uint64_t j = 0; j < cols; ++j){
+				A(i,j) = c / A(i,j);
+			}
+		}
+	}
+
 	template <typename T>
 	void inplace_div_scalar_serial(utils::Vector<T>& v, const T c) {
 		for (uint64_t i = 0; i < v.size(); ++i){
 			v[i] /= c;
 		}
 	}
+
+	template <typename T>
+	void inplace_div_scalar_serial(const T c, utils::Vector<T>& v) {
+		for (uint64_t i = 0; i < v.size(); ++i){
+			v[i] = c / v[i];
+		}
+	}
+
 	// ---------------- Elemenwise ----------------
 	template <typename T>
 	void inplace_div_elementwise_serial(utils::Matrix<T>& A, const utils::Matrix<T>& B) {

include/numerics/detail/sub_serial.h

@@ -20,12 +20,33 @@ namespace numerics::detail{
 		}
 	}
 
+	template <typename T>
+	void inplace_sub_scalar_serial(const T c, utils::Matrix<T>& A) {
+	    const uint64_t rows = A.rows();
+	    const uint64_t cols = A.cols();
+		for (uint64_t i = 0; i < rows; ++i){
+			for (uint64_t j = 0; j < cols; ++j){
+				A(i,j) = c - A(i,j);
+			}
+		}
+	}
+
+
+
 	template <typename T>
 	void inplace_sub_scalar_serial(utils::Vector<T>& v, const T c) {
 		for (uint64_t i = 0; i < v.size(); ++i){
 			v[i] -= c;
 		}
 	}
+
+	template <typename T>
+	void inplace_sub_scalar_serial(const T c, utils::Vector<T>& v) {
+		for (uint64_t i = 0; i < v.size(); ++i){
+			v[i] = c - v[i];
+		}
+	}
+
 	// ---------------- Elemenwise ----------------
 	template <typename T>
 	void inplace_sub_elementwise_serial(utils::Matrix<T>& A, const utils::Matrix<T>& B) {

include/numerics/detail/sum_serial.h

@@ -38,10 +38,10 @@ namespace numerics::detail{
 	utils::Vector<T> sum_rowwise_serial(const utils::Matrix<T>& A) {
 	    const uint64_t rows = A.rows();
 	    const uint64_t cols = A.cols();
-		utils::Vector<T> sum(cols, T{0});
+		utils::Vector<T> sum(rows, T{0});
 		for (uint64_t i = 0; i < rows; ++i){
 			for (uint64_t j = 0; j < cols; ++j){
-				sum[j] += A(i,j);
+				sum[i] += A(i,j);
 			}
 		}
 		return sum;
@@ -51,10 +51,10 @@ namespace numerics::detail{
 	utils::Vector<T> sum_colwise_serial(const utils::Matrix<T>& A) {
 	    const uint64_t rows = A.rows();
 	    const uint64_t cols = A.cols();
-		utils::Vector<T> sum(rows, T{0});
+		utils::Vector<T> sum(cols, T{0});
 		for (uint64_t i = 0; i < rows; ++i){
 			for (uint64_t j = 0; j < cols; ++j){
-				sum[i] += A(i,j);
+				sum[j] += A(i,j);
 			}
 		}
 		return sum;

include/numerics/div.h

@@ -11,7 +11,6 @@ namespace numerics{
 	inline void inplace_div(utils::Matrix<T>& A, const T b) {
 		detail::inplace_div_scalar_serial(A,b);
 	}
-
 	template <typename T>
 	inline utils::Matrix<T> div(const utils::Matrix<T>& A, const T b) {
 	    utils::Matrix<T> out = A;
@@ -19,17 +18,41 @@ namespace numerics{
 	    return out;
 	}
 
+	template <typename T>
+	inline void inplace_div(const T b, utils::Matrix<T>& A) {
+		detail::inplace_div_scalar_serial(b, A);
+	}
+	template <typename T>
+	inline utils::Matrix<T> div(const T b, const utils::Matrix<T>& A) {
+	    utils::Matrix<T> out = A;
+	    inplace_div(b, out);
+	    return out;
+	}
+
+
 	template <typename T>
 	inline void inplace_div(utils::Vector<T>& v, const T b) {
 		detail::inplace_div_scalar_serial(v,b);
 	}
-
 	template <typename T>
 	inline utils::Vector<T> div(const utils::Vector<T>& v, const T b) {
 	    utils::Vector<T> out = v;
 	    inplace_div(out, b);
 	    return out;
 	}
+
+	template <typename T>
+	inline void inplace_div(const T b, utils::Vector<T>& v) {
+		detail::inplace_div_scalar_serial(b, v);
+	}
+	template <typename T>
+	inline utils::Vector<T> div(const T b, const utils::Vector<T>& v) {
+	    utils::Vector<T> out = v;
+	    inplace_div(b, out);
+	    return out;
+	}
+
+
 	// ---------------- Elementwise ----------------
 	template <typename T>
 	inline void inplace_div(utils::Matrix<T>& A, const utils::Matrix<T>& B) {

include/numerics/numerics.h

@@ -4,6 +4,7 @@
 #include "./numerics/add.h"
 #include "./numerics/argmax.h"
 #include "./numerics/argmin.h"
+#include "./numerics/binary_threshold.h"
 #include "./numerics/clip.h"
 #include "./numerics/div.h"
 #include "./numerics/dot.h"

include/numerics/sub.h

@@ -11,6 +11,11 @@ namespace numerics{
 	inline void inplace_sub(utils::Matrix<T>& A, const T b) {
 		detail::inplace_sub_scalar_serial(A,b);
 	}
+	template <typename T>
+	inline void inplace_sub(const T b, utils::Matrix<T>& A) {
+		detail::inplace_sub_scalar_serial(b,A);
+	}
+
 
 	template <typename T>
 	inline utils::Matrix<T> sub(const utils::Matrix<T>& A, const T b) {
@@ -18,11 +23,22 @@ namespace numerics{
 	    inplace_sub(out, b);
 	    return out;
 	}
+	template <typename T>
+	inline utils::Matrix<T> sub(const T b, const utils::Matrix<T>& A) {
+	    utils::Matrix<T> out = A;
+	    inplace_sub(b, out);
+	    return out;
+	}
+
 
 	template <typename T>
 	inline void inplace_sub(utils::Vector<T>& v, const T b) {
 		detail::inplace_sub_scalar_serial(v,b);
 	}
+	template <typename T>
+	inline void inplace_sub(const T b, utils::Vector<T>& v) {
+		detail::inplace_sub_scalar_serial(b,v);
+	}
 
 	template <typename T>
 	inline utils::Vector<T> sub(const utils::Vector<T>& v, const T b) {
@@ -30,6 +46,14 @@ namespace numerics{
 	    inplace_sub(out, b);
 	    return out;
 	}
+	template <typename T>
+	inline utils::Vector<T> sub(const T b, const utils::Vector<T>& v) {
+	    utils::Vector<T> out = v;
+	    inplace_sub(b, out);
+	    return out;
+	}
+
+
 	// ---------------- Elementwise ----------------
 	template <typename T>
 	inline void inplace_sub(utils::Matrix<T>& A, const utils::Matrix<T>& B) {

include/utils/matcast.h

@@ -31,5 +31,8 @@ namespace utils{
         return B;
     }
 
+// utils::matcast<float, uint64_t> (A);
+
+
 } // end namespace utils