Binomial_CrossEnthophy
fixed rowwise/colswise mean/sum and implemented binomial_corssentrhopy. Next up is regression.
This commit is contained in:
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|
||||
);
|
||||
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;
|
||||
}
|
||||
@@ -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;
|
||||
}
|
||||
@@ -20,9 +20,9 @@
|
||||
int main(int argc, char const *argv[])
|
||||
{
|
||||
|
||||
uint64_t number_of_classes = 3;
|
||||
uint64_t number_of_samples = 1000;
|
||||
uint64_t number_of_epochs = 500;
|
||||
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;
|
||||
@@ -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
|
||||
//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)));
|
||||
// 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;
|
||||
@@ -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);
|
||||
dense3.backward(loss_activation.dinputs);
|
||||
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);
|
||||
@@ -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){
|
||||
class_targets = numerics::argmax_rowwise(y_test);
|
||||
}else{
|
||||
class_targets = utils::veccast <uint64_t, int64_t> (y_test.get_col(0));
|
||||
}
|
||||
accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, 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;
|
||||
|
||||
@@ -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
|
||||
@@ -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;
|
||||
}
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
@@ -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"
|
||||
|
||||
@@ -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;
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
@@ -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
|
||||
|
||||
@@ -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) {
|
||||
|
||||
@@ -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) {
|
||||
|
||||
@@ -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;
|
||||
|
||||
+25
-2
@@ -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) {
|
||||
|
||||
@@ -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"
|
||||
|
||||
@@ -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) {
|
||||
|
||||
@@ -31,5 +31,8 @@ namespace utils{
|
||||
return B;
|
||||
}
|
||||
|
||||
// utils::matcast<float, uint64_t> (A);
|
||||
|
||||
|
||||
} // end namespace utils
|
||||
|
||||
|
||||
Reference in New Issue
Block a user