diff --git a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp
index f9020a582f..2b7dcb9fe3 100644
--- a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp
+++ b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp
@@ -35,11 +35,10 @@ NegativeLogLikelihood<InputDataType, OutputDataType>::Forward(
   ElemType output = 0;
   for (size_t i = 0; i < input.n_cols; ++i)
   {
-    size_t currentTarget = target(i) - 1;
-    Log::Assert(currentTarget >= 0 && currentTarget < input.n_rows,
+    Log::Assert(target(i) >= 0 && target(i) < input.n_rows,
         "Target class out of range.");
 
-    output -= input(currentTarget, i);
+    output -= input(target(i), i);
   }
 
   return output;
@@ -55,11 +54,10 @@ void NegativeLogLikelihood<InputDataType, OutputDataType>::Backward(
   output = arma::zeros<OutputType>(input.n_rows, input.n_cols);
   for (size_t i = 0; i < input.n_cols; ++i)
   {
-    size_t currentTarget = target(i) - 1;
-    Log::Assert(currentTarget >= 0 && currentTarget < input.n_rows,
+    Log::Assert(target(i) >= 0 && target(i) < input.n_rows,
         "Target class out of range.");
 
-    output(currentTarget, i) = -1;
+    output(target(i), i) = -1;
   }
 }
 
diff --git a/src/mlpack/tests/ann_layer_test.cpp b/src/mlpack/tests/ann_layer_test.cpp
index 8cd06e56f5..3bba999878 100644
--- a/src/mlpack/tests/ann_layer_test.cpp
+++ b/src/mlpack/tests/ann_layer_test.cpp
@@ -90,7 +90,7 @@ BOOST_AUTO_TEST_CASE(GradientAddLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -422,7 +422,7 @@ BOOST_AUTO_TEST_CASE(GradientLinearLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -498,7 +498,7 @@ BOOST_AUTO_TEST_CASE(GradientNoisyLinearLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -602,7 +602,7 @@ BOOST_AUTO_TEST_CASE(GradientLinearNoBiasLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -648,7 +648,7 @@ BOOST_AUTO_TEST_CASE(JacobianNegativeLogLikelihoodLayerTest)
     init.Initialize(input, inputElements, 1);
 
     arma::mat target(1, 1);
-    target(0) = math::RandInt(1, inputElements - 1);
+    target(0) = math::RandInt(0, inputElements - 2);
 
     double error = JacobianPerformanceTest(module, input, target);
     BOOST_REQUIRE_LE(error, 1e-5);
@@ -704,7 +704,7 @@ BOOST_AUTO_TEST_CASE(GradientFlexibleReLULayerTest)
     GradientFunction()
     {
       input = arma::randu(2, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, RandomInitialization>(
           NegativeLogLikelihood<>(), RandomInitialization(0.1, 0.5));
@@ -883,7 +883,7 @@ BOOST_AUTO_TEST_CASE(LSTMRrhoTest)
 {
   const size_t rho = 5;
   arma::cube input = arma::randu(1, 1, 5);
-  arma::cube target = arma::ones(1, 1, 5);
+  arma::cube target = arma::zeros(1, 1, 5);
   RandomInitialization init(0.5, 0.5);
 
   // Create model with user defined rho parameter.
@@ -924,7 +924,7 @@ BOOST_AUTO_TEST_CASE(GradientLSTMLayerTest)
     GradientFunction()
     {
       input = arma::randu(1, 1, 5);
-      target.ones(1, 1, 5);
+      target.zeros(1, 1, 5);
       const size_t rho = 5;
 
       model = new RNN<NegativeLogLikelihood<> >(rho);
@@ -988,7 +988,7 @@ BOOST_AUTO_TEST_CASE(FastLSTMRrhoTest)
 {
   const size_t rho = 5;
   arma::cube input = arma::randu(1, 1, 5);
-  arma::cube target = arma::ones(1, 1, 5);
+  arma::cube target = arma::zeros(1, 1, 5);
   RandomInitialization init(0.5, 0.5);
 
   // Create model with user defined rho parameter.
@@ -1029,7 +1029,7 @@ BOOST_AUTO_TEST_CASE(GradientFastLSTMLayerTest)
     GradientFunction()
     {
       input = arma::randu(1, 1, 5);
-      target = arma::ones(1, 1, 5);
+      target = arma::zeros(1, 1, 5);
       const size_t rho = 5;
 
       model = new RNN<NegativeLogLikelihood<> >(rho);
@@ -1298,7 +1298,7 @@ BOOST_AUTO_TEST_CASE(GradientGRULayerTest)
     GradientFunction()
     {
       input = arma::randu(1, 1, 5);
-      target = arma::ones(1, 1, 5);
+      target = arma::zeros(1, 1, 5);
       const size_t rho = 5;
 
       model = new RNN<NegativeLogLikelihood<> >(rho);
@@ -1537,7 +1537,7 @@ BOOST_AUTO_TEST_CASE(GradientConcatLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -1606,7 +1606,7 @@ BOOST_AUTO_TEST_CASE(GradientConcatenateLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -1961,7 +1961,7 @@ BOOST_AUTO_TEST_CASE(GradientBatchNormTest)
       {
         input = arma::randn(32, 2048);
         arma::mat target;
-        target.ones(1, 2048);
+        target.zeros(1, 2048);
 
         model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
         model->Predictors() = input;
@@ -2037,7 +2037,7 @@ BOOST_AUTO_TEST_CASE(GradientVirtualBatchNormTest)
       input = arma::randn(5, 256);
       arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 16);
       arma::mat target;
-      target.ones(1, 256);
+      target.zeros(1, 256);
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -2099,7 +2099,7 @@ BOOST_AUTO_TEST_CASE(MiniBatchDiscriminationTest)
     {
       input = arma::randn(5, 4);
       arma::mat target;
-      target.ones(1, 4);
+      target.zeros(1, 4);
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -2278,7 +2278,7 @@ BOOST_AUTO_TEST_CASE(GradientTransposedConvolutionLayerTest)
       GradientFunction()
       {
         input = arma::linspace<arma::colvec>(0, 35, 36);
-        target = arma::mat("1");
+        target = arma::mat("0");
 
         model = new FFN<NegativeLogLikelihood<>, RandomInitialization>();
         model->Predictors() = input;
@@ -2395,7 +2395,7 @@ BOOST_AUTO_TEST_CASE(GradientAtrousConvolutionLayerTest)
     GradientFunction()
     {
       input = arma::linspace<arma::colvec>(0, 35, 36);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, RandomInitialization>();
       model->Predictors() = input;
@@ -2578,7 +2578,7 @@ BOOST_AUTO_TEST_CASE(GradientLayerNormTest)
     {
       input = arma::randn(10, 256);
       arma::mat target;
-      target.ones(1, 256);
+      target.zeros(1, 256);
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -2899,7 +2899,7 @@ BOOST_AUTO_TEST_CASE(GradientReparametrizationLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -2943,7 +2943,7 @@ BOOST_AUTO_TEST_CASE(GradientReparametrizationLayerBetaTest)
     GradientFunction()
     {
       input = arma::randu(10, 2);
-      target = arma::mat("1 1");
+      target = arma::mat("0 0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -3099,7 +3099,7 @@ BOOST_AUTO_TEST_CASE(GradientHighwayLayerTest)
     GradientFunction()
     {
       input = arma::randu(5, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -3151,7 +3151,7 @@ BOOST_AUTO_TEST_CASE(GradientSequentialLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -3202,7 +3202,7 @@ BOOST_AUTO_TEST_CASE(GradientWeightNormLayerTest)
     GradientFunction()
     {
       input = arma::randu(10, 1);
-      target = arma::mat("1");
+      target = arma::mat("0");
 
       model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
       model->Predictors() = input;
@@ -4037,7 +4037,7 @@ BOOST_AUTO_TEST_CASE(GradientBatchNormWithMiniBatchesTest)
       {
         input = arma::randn(16, 1024);
         arma::mat target;
-        target.ones(1, 1024);
+        target.zeros(1, 1024);
 
         model = new FFN<NegativeLogLikelihood<>, NguyenWidrowInitialization>();
         model->Predictors() = input;
diff --git a/src/mlpack/tests/callback_test.cpp b/src/mlpack/tests/callback_test.cpp
index a62ede95a8..6483a11890 100644
--- a/src/mlpack/tests/callback_test.cpp
+++ b/src/mlpack/tests/callback_test.cpp
@@ -92,7 +92,7 @@ BOOST_AUTO_TEST_CASE(RNNCallbackTest)
 {
   const size_t rho = 5;
   arma::cube input = arma::randu(1, 1, 5);
-  arma::cube target = arma::ones(1, 1, 5);
+  arma::cube target = arma::zeros(1, 1, 5);
   RandomInitialization init(0.5, 0.5);
 
   // Create model with user defined rho parameter.
@@ -118,7 +118,7 @@ BOOST_AUTO_TEST_CASE(RNNWithOptimizerCallbackTest)
 {
   const size_t rho = 5;
   arma::cube input = arma::randu(1, 1, 5);
-  arma::cube target = arma::ones(1, 1, 5);
+  arma::cube target = arma::zeros(1, 1, 5);
   RandomInitialization init(0.5, 0.5);
 
   // Create model with user defined rho parameter.
diff --git a/src/mlpack/tests/convolutional_network_test.cpp b/src/mlpack/tests/convolutional_network_test.cpp
index 39a7e161c3..c266e2b28b 100644
--- a/src/mlpack/tests/convolutional_network_test.cpp
+++ b/src/mlpack/tests/convolutional_network_test.cpp
@@ -47,13 +47,13 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
   {
     if (i < nPoints / 2)
     {
-      // Assign label "1" to all samples with digit = 4
-      Y(i) = 1;
+      // Assign label "0" to all samples with digit = 4
+      Y(i) = 0;
     }
     else
     {
-      // Assign label "2" to all samples with digit = 9
-      Y(i) = 2;
+      // Assign label "1" to all samples with digit = 9
+      Y(i) = 1;
     }
   }
 
@@ -111,7 +111,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
     for (size_t i = 0; i < predictionTemp.n_cols; ++i)
     {
       prediction(i) = arma::as_scalar(arma::find(
-            arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
+            arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1));
     }
 
     size_t correct = arma::accu(prediction == Y);
diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp
index 47842204e6..c853962f10 100644
--- a/src/mlpack/tests/feedforward_network_test.cpp
+++ b/src/mlpack/tests/feedforward_network_test.cpp
@@ -52,7 +52,7 @@ void TestNetwork(ModelType& model,
   for (size_t i = 0; i < predictionTemp.n_cols; ++i)
   {
     prediction(i) = arma::as_scalar(arma::find(
-        arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
+        arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1));
   }
 
   size_t correct = arma::accu(prediction == testLabels);
@@ -71,12 +71,14 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // Labels should be from 0 to numClasses - 1.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // Labels should be from 0 to numClasses - 1.
 
   /*
    * Construct a feed forward network with trainData.n_rows input nodes,
@@ -120,7 +122,6 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
   labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-  labels += 1;
 
   FFN<NegativeLogLikelihood<> > model1;
   model1.Add<Linear<> >(dataset.n_rows, 10);
@@ -142,7 +143,6 @@ BOOST_AUTO_TEST_CASE(ForwardBackwardTest)
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
   labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-  labels += 1;
 
   FFN<NegativeLogLikelihood<> > model;
   model.Add<Linear<> >(dataset.n_rows, 50);
@@ -189,7 +189,7 @@ BOOST_AUTO_TEST_CASE(ForwardBackwardTest)
       for (size_t i = 0; i < currentResuls.n_cols; ++i)
       {
         prediction(i) = arma::as_scalar(arma::find(
-            arma::max(currentResuls.col(i)) == currentResuls.col(i), 1)) + 1;
+            arma::max(currentResuls.col(i)) == currentResuls.col(i), 1));
       }
 
       size_t correct = arma::accu(prediction == currentLabels);
@@ -218,12 +218,14 @@ BOOST_AUTO_TEST_CASE(DropoutNetworkTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // Labels should be from 0 to numClasses - 1.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // Labels should be from 0 to numClasses - 1.
 
   /*
    * Construct a feed forward network with trainData.n_rows input nodes,
@@ -269,7 +271,6 @@ BOOST_AUTO_TEST_CASE(DropoutNetworkTest)
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
   labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-  labels += 1;
 
   FFN<NegativeLogLikelihood<> > model1;
   model1.Add<Linear<> >(dataset.n_rows, 10);
@@ -295,7 +296,6 @@ BOOST_AUTO_TEST_CASE(HighwayNetworkTest)
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
   labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-  labels += 1;
 
   FFN<NegativeLogLikelihood<> > model;
   model.Add<Linear<> >(dataset.n_rows, 10);
@@ -319,12 +319,14 @@ BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The range should be between 0 and numClasses - 1.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The range should be between 0 and numClasses - 1.
 
  /*
   *  Construct a feed forward network with trainData.n_rows input nodes,
@@ -370,7 +372,6 @@ BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
   labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-  labels += 1;
 
   FFN<NegativeLogLikelihood<> > model1;
   model1.Add<Linear<> >(dataset.n_rows, 10);
@@ -408,12 +409,14 @@ BOOST_AUTO_TEST_CASE(SerializationTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The labels should be between 0 and numClasses - 1.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The labels should be between 0 and numClasses - 1.
 
   // Vanilla neural net with logistic activation function.
   // Because 92% of the patients are not hyperthyroid the neural
@@ -457,12 +460,14 @@ BOOST_AUTO_TEST_CASE(CustomLayerTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The labels should be between 0 and numClasses - 1.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The labels should be between 0 and numClasses - 1.
 
   FFN<NegativeLogLikelihood<>, RandomInitialization, CustomLayer<> > model;
   model.Add<Linear<> >(trainData.n_rows, 8);
@@ -536,12 +541,14 @@ BOOST_AUTO_TEST_CASE(FFNTrainReturnObjective)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The labels should be between 0 and numClasses.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The labels should be between 0 and numClasses.
 
   // Vanilla neural net with logistic activation function.
   // Because 92% of the patients are not hyperthyroid the neural
@@ -606,12 +613,14 @@ BOOST_AUTO_TEST_CASE(OptimizerTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The labels should be between 0 and numClasses.
 
   arma::mat testData;
   data::Load("thyroid_test.csv", testData, true);
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The labels should be between 0 and numClasses.
 
   FFN<NegativeLogLikelihood<>, RandomInitialization, CustomLayer<> > model;
   model.Add<Linear<> >(trainData.n_rows, 8);
@@ -634,11 +643,12 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
 
   arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
   trainData.shed_row(trainData.n_rows - 1);
+  trainLabels -= 1; // The labels should be between 0 and numClasses.
 
   arma::mat trainLabels1 = arma::zeros(3, trainData.n_cols);
   for (size_t i = 0; i < trainData.n_cols; i++)
   {
-    trainLabels1.col(i).row((trainLabels(i) - 1)) = 1;
+    trainLabels1.col(i).row(trainLabels(i)) = 1;
   }
 
   arma::mat testData;
@@ -646,6 +656,7 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
 
   arma::mat testLabels = testData.row(testData.n_rows - 1);
   testData.shed_row(testData.n_rows - 1);
+  testLabels -= 1; // The labels should be between 0 and numClasses.
 
   /*
    * Construct a feed forward network with trainData.n_rows input nodes,
@@ -681,7 +692,7 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
   }
 
   arma::mat labels = arma::zeros(1, dataset.n_cols);
-  labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
+  labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(0);
 
   arma::mat labels1 = arma::zeros(2, dataset.n_cols);
 
@@ -689,7 +700,6 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
   {
     labels1.col(i).row(labels(i)) = 1;
   }
-  labels += 1;
 
   arma::mat centroids1;
   arma::Row<size_t> assignments;
diff --git a/src/mlpack/tests/recurrent_network_test.cpp b/src/mlpack/tests/recurrent_network_test.cpp
index 1bef406e26..4bd9fa25e1 100644
--- a/src/mlpack/tests/recurrent_network_test.cpp
+++ b/src/mlpack/tests/recurrent_network_test.cpp
@@ -93,7 +93,7 @@ BOOST_AUTO_TEST_CASE(SequenceClassificationBRNNTest)
     for (size_t i = 0; i < labelsTemp.n_cols; ++i)
     {
       const int value = arma::as_scalar(arma::find(
-          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1)) + 1;
+          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1));
       labels.tube(0, i).fill(value);
     }
 
@@ -168,7 +168,7 @@ BOOST_AUTO_TEST_CASE(SequenceClassificationTest)
     for (size_t i = 0; i < labelsTemp.n_cols; ++i)
     {
       const int value = arma::as_scalar(arma::find(
-          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1)) + 1;
+          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1));
       labels.tube(0, i).fill(value);
     }
 
@@ -212,10 +212,10 @@ BOOST_AUTO_TEST_CASE(SequenceClassificationTest)
     {
       const int predictionValue = arma::as_scalar(arma::find(
           arma::max(prediction.slice(rho - 1).col(i)) ==
-          prediction.slice(rho - 1).col(i), 1) + 1);
+          prediction.slice(rho - 1).col(i), 1));
 
       const int targetValue = arma::as_scalar(arma::find(
-          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1)) + 1;
+          arma::max(labelsTemp.col(i)) == labelsTemp.col(i), 1));
 
       if (predictionValue == targetValue)
       {
@@ -1452,15 +1452,15 @@ BOOST_AUTO_TEST_CASE(LargeRhoValueRnnTest)
   {
     const auto strLen = strlen(line);
     // Responses for NegativeLogLikelihood should be
-    // non-one-hot-encoded class IDs (from 1 to num_classes).
+    // non-one-hot-encoded class IDs (from 0 to num_classes - 1).
     MatType result(1, 1, strLen, arma::fill::zeros);
     // The response is the *next* letter in the sequence.
     for (size_t i = 0; i < strLen - 1; ++i)
     {
-      result.at(0, 0, i) = static_cast<arma::uword>(line[i + 1]) + 1.0;
+      result.at(0, 0, i) = static_cast<arma::uword>(line[i + 1]);
     }
     // The final response is empty, so we set it to class 0.
-    result.at(0, 0, strLen - 1) = 1.0;
+    result.at(0, 0, strLen - 1) = 0.0;
     return result;
   };