diff --git a/data/iris.mlp b/data/iris.mlp
index 77badc2..dce3f72 100644
Binary files a/data/iris.mlp and b/data/iris.mlp differ
diff --git a/src/Layer.h b/src/Layer.h
index a6efa01..8a36bfd 100644
--- a/src/Layer.h
+++ b/src/Layer.h
@@ -5,9 +5,6 @@
 #ifndef LAYER_H
 #define LAYER_H
 
-#include "Utils.h"
-#include "Node.h"
-
 #include <stdio.h>
 #include <stdlib.h>
 #include <iostream>
@@ -16,6 +13,8 @@
 #include <vector>
 #include <algorithm>
 #include <cassert> // for assert()
+#include "Node.h"
+#include "Utils.h"
 
 class Layer {
 public:
@@ -82,7 +81,7 @@ public:
 
     output->resize(m_num_nodes);
 
-    for (int i = 0; i < m_num_nodes; ++i) {
+    for (size_t i = 0; i < m_num_nodes; ++i) {
       m_nodes[i].GetOutputAfterActivationFunction(input,
                                                   m_activation_function,
                                                   &((*output)[i]));
@@ -111,7 +110,7 @@ public:
       dE_doj = deriv_error[i];
       doj_dnetj = m_deriv_activation_function(net_sum);
 
-      for (int j = 0; j < m_num_inputs_per_node; j++) {
+      for (size_t j = 0; j < m_num_inputs_per_node; j++) {
         (*deltas)[j] += dE_doj * doj_dnetj * m_nodes[i].GetWeights()[j];
 
         dnetj_dwij = input_layer_activation[j];
@@ -180,8 +179,8 @@ public:
   };
 
 protected:
-  int m_num_inputs_per_node{ 0 };
-  int m_num_nodes{ 0 };
+  size_t m_num_inputs_per_node{ 0 };
+  size_t m_num_nodes{ 0 };
   std::vector<Node> m_nodes;
 
   std::string m_activation_function_str;
diff --git a/src/MLP.cpp b/src/MLP.cpp
index ca54320..4770edc 100644
--- a/src/MLP.cpp
+++ b/src/MLP.cpp
@@ -3,6 +3,7 @@
 // Author : David Nogueira
 //============================================================================
 #include "MLP.h"
+
 #include <stdio.h>
 #include <stdlib.h>
 #include <iostream>
@@ -10,6 +11,7 @@
 #include <fstream>
 #include <vector>
 #include <algorithm>
+
 #include "easylogging++.h"
 
 
diff --git a/src/MLP.h b/src/MLP.h
index 2a1cc5e..551f0fe 100644
--- a/src/MLP.h
+++ b/src/MLP.h
@@ -5,10 +5,6 @@
 #ifndef MLP_H
 #define MLP_H
 
-#include "Layer.h"
-#include "Sample.h"
-#include "Utils.h"
-
 #include <stdio.h>
 #include <stdlib.h>
 #include <iostream>
@@ -17,6 +13,9 @@
 #include <vector>
 #include <algorithm>
 #include <exception>
+#include "Layer.h"
+#include "Sample.h"
+#include "Utils.h"
 
 class MLP {
 public:
@@ -54,7 +53,7 @@ private:
                  const std::vector<std::string> & layers_activfuncs,
                  bool use_constant_weight_init,
                  double constant_weight_init = 0.5);
-  int m_num_inputs{ 0 };
+  size_t m_num_inputs{ 0 };
   int m_num_outputs{ 0 };
   int m_num_hidden_layers{ 0 };
   std::vector<uint64_t> m_layers_nodes;
diff --git a/src/MLPTest.cpp b/src/MLPTest.cpp
index ad237f7..3fb0c07 100644
--- a/src/MLPTest.cpp
+++ b/src/MLPTest.cpp
@@ -347,6 +347,17 @@ UNIT(GetWeightsSetWeights) {
   // get layer weights
   std::vector<std::vector<double>> weights = my_mlp.GetLayerWeights( 1 );
 
+  for (const auto & training_sample : training_sample_set_with_bias) {
+    std::vector<double>  output;
+    my_mlp.GetOutput(training_sample.input_vector(), &output);
+    for (size_t i = 0; i < num_outputs; i++) {
+      bool predicted_output = output[i] > 0.5 ? true : false;
+      std::cout << "PREDICTED OUTPUT IS NOW: " << output[i] << std::endl;
+      bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
+      ASSERT_TRUE(predicted_output == correct_output);
+    }
+  }
+
   // the expected value of the internal weights
   // after training are 1.65693 -0.538749
   ASSERT_TRUE(  1.6 <= weights[0][0] && weights[0][0] <=  1.7 );
@@ -358,24 +369,14 @@ UNIT(GetWeightsSetWeights) {
 
   my_mlp.SetLayerWeights( 1, zeroWeights );
 
-  /*
-   *
-   * PREDICTED OUTPUT IS NOW: 0.335394
-PREDICTED OUTPUT IS NOW: 1.13887
-PREDICTED OUTPUT IS NOW: 0.180468
-PREDICTED OUTPUT IS NOW: 1.00535
-   *
-   */
   for (const auto & training_sample : training_sample_set_with_bias) {
     std::vector<double>  output;
     my_mlp.GetOutput(training_sample.input_vector(), &output);
     for (size_t i = 0; i < num_outputs; i++) {
-      bool predicted_output = output[i] > 0.5 ? true : false;
-      std::cout << "PREDICTED OUTPUT IS NOW: " << output[i] << std::endl;
-      bool correct_output = training_sample.output_vector()[i] > 0.5 ? true : false;
-      ASSERT_TRUE(predicted_output == correct_output);
+      ASSERT_TRUE( -0.0001L <= output[i] && output[i] <= 0.0001L );
     }
   }
+
   LOG(INFO) << "Trained with success." << std::endl;
 }
 
diff --git a/src/Node.h b/src/Node.h
index d145d51..2fea944 100644
--- a/src/Node.h
+++ b/src/Node.h
@@ -5,8 +5,6 @@
 #ifndef NODE_H
 #define NODE_H
 
-#include "Utils.h"
-
 #include <stdio.h>
 #include <stdlib.h>
 #include <iostream>
@@ -16,6 +14,7 @@
 #include <algorithm>
 #include <cassert> // for assert()
 #include <exception>
+#include "Utils.h"
 
 #define CONSTANT_WEIGHT_INITIALIZATION 0
 
@@ -150,7 +149,7 @@ public:
   };
 
 protected:
-  int m_num_inputs{ 0 };
+  size_t m_num_inputs{ 0 };
   double m_bias{ 0.0 };
   std::vector<double> m_weights;
 };
diff --git a/src/Utils.h b/src/Utils.h
index 9a8eb30..229d04b 100644
--- a/src/Utils.h
+++ b/src/Utils.h
@@ -5,7 +5,6 @@
 #ifndef UTILS_H
 #define UTILS_H
 
-#include "Chrono.h"
 #include <stdlib.h>
 #include <math.h>
 #include <numeric>
@@ -22,6 +21,8 @@
 #include <typeinfo>
 #include <typeindex>
 #include <cassert>
+
+#include "Chrono.h"
 #ifdef _WIN32
 #include <time.h>
 #else