Fittet new functions to everying in neural networks. Still need to optimise for uint64_t vs int64_t and vec vs mat in some places.
This commit is contained in:
@@ -1,9 +1,9 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
|
||||
|
||||
namespace neural_networks{
|
||||
@@ -18,7 +18,7 @@ namespace neural_networks{
|
||||
|
||||
void forward(const utils::Matrix<T>& inputs){
|
||||
_inputs = inputs;
|
||||
outputs = numerics::matclip_low(inputs, T{0});
|
||||
outputs = numerics::clip_low(inputs, T{0});
|
||||
}
|
||||
void backward(const utils::Matrix<T>& dvalues){
|
||||
// Since we need to modify the original variable,
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
|
||||
#include "./numerics/matmax.h"
|
||||
#include "./numerics/matsubtract.h"
|
||||
#include "./numerics/matexp.h"
|
||||
#include "./numerics/matdiv.h"
|
||||
#include "numerics/max.h"
|
||||
#include "numerics/sub.h"
|
||||
#include "numerics/exp.h"
|
||||
#include "numerics/div.h"
|
||||
|
||||
|
||||
|
||||
@@ -26,10 +26,10 @@ namespace neural_networks{
|
||||
void forward(const utils::Matrix<T>& inputs){
|
||||
|
||||
// Get unnormalized probabilities
|
||||
utils::Matrix<T> exp_values = numerics::matexp(numerics::matsubtract(inputs, numerics::matmax(inputs, "rows"), "col"));
|
||||
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::matdiv(exp_values, numerics::matsum(exp_values, "col"), "col");
|
||||
utils::Matrix<T> probabilities = numerics::div_colwise(exp_values, numerics::sum_colwise(exp_values));
|
||||
outputs = probabilities;
|
||||
}
|
||||
|
||||
|
||||
+9
-9
@@ -1,17 +1,17 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
|
||||
#include "./numerics/matmax.h"
|
||||
#include "./numerics/matsubtract.h"
|
||||
#include "./numerics/matexp.h"
|
||||
#include "./numerics/matdiv.h"
|
||||
#include "numerics/max.h"
|
||||
#include "numerics/sub.h"
|
||||
#include "numerics/exp.h"
|
||||
#include "numerics/div.h"
|
||||
|
||||
#include "./modules/neural_networks/activation_functions/Activation_Softmax.h"
|
||||
#include "./modules/neural_networks/loss/Loss_CategoricalCrossentrophy.h"
|
||||
#include "modules/neural_networks/activation_functions/Activation_Softmax.h"
|
||||
#include "modules/neural_networks/loss/Loss_CategoricalCrossentrophy.h"
|
||||
|
||||
|
||||
namespace neural_networks{
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "./utils/random.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
#include "utils/random.h"
|
||||
|
||||
|
||||
namespace neural_networks{
|
||||
@@ -40,20 +40,23 @@ namespace neural_networks{
|
||||
Dense_Layer(const uint64_t n_inputs, const uint64_t n_neurons){
|
||||
|
||||
weights.random(n_inputs, n_neurons, -1, 1);
|
||||
//weights = numerics::random_matrix(n_inputs, n_neurons, -1, 1);
|
||||
biases.resize(n_neurons, T{0});
|
||||
|
||||
}
|
||||
|
||||
void forward(const utils::Matrix<T>& inputs){
|
||||
_inputs = inputs;
|
||||
outputs = numerics::matadd(numerics::matmul_auto(inputs, weights), biases, "row");
|
||||
//std::cout << "HERE" << std::endl;
|
||||
outputs = numerics::add_rowwise(numerics::matmul(inputs, weights), biases);
|
||||
}
|
||||
|
||||
void backward(const utils::Matrix<T>& dvalues){
|
||||
// Gradients on parameters
|
||||
dweights = numerics::matmul(numerics::transpose(_inputs), dvalues);
|
||||
dbiases = numerics::matsum(dvalues, "row");
|
||||
dbiases = numerics::sum_rowwise(dvalues);
|
||||
//Gradient on values
|
||||
|
||||
dinputs = numerics::matmul(dvalues, numerics::transpose(weights));
|
||||
|
||||
}
|
||||
|
||||
@@ -1,13 +1,12 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
|
||||
#include "numerics/vecmean.h"
|
||||
#include "numerics/matabs.h"
|
||||
#include "numerics/matmean.h"
|
||||
#include "numerics/mean.h"
|
||||
#include "numerics/abs.h"
|
||||
|
||||
namespace neural_networks{
|
||||
|
||||
@@ -28,7 +27,7 @@ namespace neural_networks{
|
||||
sample_losses = forward(output, y);
|
||||
|
||||
// Calculate mean loss
|
||||
data_loss = numerics::vecmean(sample_losses);
|
||||
data_loss = numerics::mean(sample_losses);
|
||||
|
||||
return data_loss;
|
||||
|
||||
@@ -42,12 +41,12 @@ namespace neural_networks{
|
||||
// L1 regularization - weights
|
||||
// calculate only when factor greater than 0
|
||||
if (layer.weight_regularizer_l1){
|
||||
regularization_losss += layer.weight_regularizer_l1 * numerics::matsum_coeff(numerics::matabs(layer.weights));
|
||||
regularization_losss += layer.weight_regularizer_l1 * numerics::sum(numerics::abs(layer.weights));
|
||||
}
|
||||
|
||||
// L2 regularization - weights
|
||||
if (layer.weight_regularizer_l2){
|
||||
regularization_losss += layer.weight_regularizer_l2 * numerics::matsum_coeff(numerics::matmul(layer.weights,layer.weights)); // elementwise!
|
||||
regularization_losss += layer.weight_regularizer_l2 * numerics::sum(numerics::mul(layer.weights,layer.weights)); // elementwise!
|
||||
}
|
||||
|
||||
// L1 regularization - biases
|
||||
|
||||
@@ -1,15 +1,15 @@
|
||||
#pragma once
|
||||
|
||||
#include "./core/omp_config.h"
|
||||
#include "core/omp_config.h"
|
||||
|
||||
#include "./utils/vector.h"
|
||||
#include "./utils/matrix.h"
|
||||
#include "./utils/matcast.h"
|
||||
#include "utils/vector.h"
|
||||
#include "utils/matrix.h"
|
||||
#include "utils/matcast.h"
|
||||
|
||||
#include "./numerics/matclip.h"
|
||||
#include "./numerics/veclog.h"
|
||||
#include "numerics/clip.h"
|
||||
#include "numerics/log.h"
|
||||
|
||||
#include "./Loss.h"
|
||||
#include "Loss.h"
|
||||
|
||||
|
||||
namespace neural_networks{
|
||||
@@ -30,7 +30,7 @@ namespace neural_networks{
|
||||
|
||||
// Clip data to prevent dividning by 0
|
||||
// Clip both sides to not drag mean towards any value
|
||||
utils::Matrix<Td> y_pred_clipped = numerics::matclip(y_pred, Td{1e-7}, Td{1.0} - Td{1e-7});
|
||||
utils::Matrix<Td> y_pred_clipped = numerics::clip(y_pred, Td{1e-7}, Td{1.0} - Td{1e-7});
|
||||
|
||||
// Probabilities for taget values
|
||||
// only if categorical labes
|
||||
@@ -40,7 +40,8 @@ namespace neural_networks{
|
||||
correct_confidences[i] = y_pred_clipped(i, idx);
|
||||
}
|
||||
}else{ // Mask values - only for one-hot encoded labels
|
||||
correct_confidences = numerics::matdot_row(y_pred_clipped, cast_y_true);
|
||||
correct_confidences = numerics::sum_rowwise(numerics::mul(y_pred_clipped, cast_y_true));
|
||||
//correct_confidences = numerics::matdot_row(y_pred_clipped, cast_y_true);
|
||||
|
||||
}
|
||||
// Losses
|
||||
@@ -72,9 +73,12 @@ namespace neural_networks{
|
||||
|
||||
|
||||
// Calculate the gradient
|
||||
numerics::inplace_matscalar(y_temp,Ti{-1});
|
||||
dinputs = numerics::matdiv(utils::matcast<Td, Ti>(y_temp), dvalues);
|
||||
numerics::inplace_matdiv(dinputs, samples);
|
||||
//numerics::inplace_matscalar(y_temp,Ti{-1});
|
||||
y_temp = numerics::neg(y_temp);
|
||||
//dinputs = numerics::matdiv(utils::matcast<Td, Ti>(y_temp), dvalues);
|
||||
dinputs = numerics::div(utils::matcast<Td, Ti>(y_temp), dvalues);
|
||||
//numerics::inplace_matdiv(dinputs, samples);
|
||||
dinputs = numerics::div(dinputs, samples);
|
||||
|
||||
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user