diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp
index a14a767717..7c1ab250dc 100644
--- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp
+++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp
@@ -33,31 +33,25 @@ class TripletMarginLoss
   /**
    * Computes the Triplet Margin Loss function.
    *
-   * @param input The propagated input activation.
-   * @param target The target vector.
+   * @param input The propagated input activation. It should be
+   * concatenated anchor and positive samples.
+   * @param target The target vector. It should be negative samples.
    */
-  template<typename AnchorType, typename PositiveType, typename NegativeType>
-  double Forward(const AnchorType& anchor,
-                 const PositiveType& positive,
-                 const NegativeType& negative);
+  template<typename InputType, typename TargetType>
+  double Forward(const InputType& input, const TargetType& target);
 
   /**
    * Ordinary feed backward pass of a neural network.
    *
-   * @param input The propagated input activation.
-   * @param target The target vector.
+   * @param input The propagated input activation. It should be
+   * concatenated anchor and positive samples.
+   * @param target The target vector. It should be negative samples.
    * @param output The calculated error.
    */
-template <
-    typename AnchorType, 
-    typename PositiveType, 
-    typename NegativeType,
-    typename OutputType
-  >
-  void Backward(const AnchorType& anchor,
-                const PositiveType& positive,
-                const NegativeType& negative,
-                OutputType&& output);
+  template<typename InputType, typename TargetType, typename OutputType>
+  void Backward(const InputType& input,
+                const TargetType& target,
+                OutputType& output);
 
   //! Get the output parameter.
   OutputDataType& OutputParameter() const { return outputParameter; }
diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp
index 58a8b5b8e6..dce0968668 100644
--- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp
+++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp
@@ -26,30 +26,30 @@ TripletMarginLoss<InputDataType, OutputDataType>::TripletMarginLoss(
 }
 
 template<typename InputDataType, typename OutputDataType>
-template<typename AnchorType, typename PositiveType, typename NegativeType>
+template<typename InputType, typename TargetType>
 double TripletMarginLoss<InputDataType, OutputDataType>::Forward(
-    const AnchorType& anchor,
-    const PositiveType& positive,
-    const NegativeType& negative)
+    const InputType& input,
+    const TargetType& target)
 {
+  arma::mat anchor = input.submat(0, 0, input.n_rows / 2 - 1, input.n_cols - 1);
+  arma::mat positive = input.submat(input.n_rows / 2, 0, input.n_rows - 1, input.n_cols - 1);
   return std::max(0.0, arma::accu(arma::pow(anchor - positive, 2)) -
-      arma::accu(arma::pow(anchor - negative, 2)) + margin) / anchor.n_cols;
+      arma::accu(arma::pow(anchor - target, 2)) + margin) / anchor.n_cols;
 }
 
 template<typename InputDataType, typename OutputDataType>
 template <
-    typename AnchorType,
-    typename PositiveType,
-    typename NegativeType,
+    typename InputType,
+    typename TargetType,
     typename OutputType
 >
 void TripletMarginLoss<InputDataType, OutputDataType>::Backward(
-    const AnchorType& anchor,
-    const PositiveType& positive,
-    const NegativeType& negative,
-    OutputType&& output)
+    const InputType& input,
+    const TargetType& target,
+    OutputType& output)
 {
-  output = 2 * (negative - positive) / anchor.n_cols;
+  arma::mat positive = input.submat(input.n_rows / 2, 0, input.n_rows - 1, input.n_cols - 1);
+  output = 2 * (target - positive) / target.n_cols;
 }
 
 template<typename InputDataType, typename OutputDataType>
diff --git a/src/mlpack/tests/loss_functions_test.cpp b/src/mlpack/tests/loss_functions_test.cpp
index 0c2c1bff4b..f277554bc5 100644
--- a/src/mlpack/tests/loss_functions_test.cpp
+++ b/src/mlpack/tests/loss_functions_test.cpp
@@ -904,7 +904,8 @@ TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]")
  */
 BOOST_AUTO_TEST_CASE(TripletMarginLossTest)
 {
-  arma::mat anchor, positive, negative, output;
+  arma::mat anchor, positive, negative;
+  arma::mat input, target, output;
   TripletMarginLoss<> module;
 
   // Test the Forward function on a user generator input and compare it against
@@ -912,13 +913,17 @@ BOOST_AUTO_TEST_CASE(TripletMarginLossTest)
   anchor = arma::mat("2 3 5");
   positive = arma::mat("10 12 13");
   negative = arma::mat("4 5 7");
-  double error = module.Forward(std::move(anchor),
-      std::move(positive), std::move(negative));
+
+  input = {
+            {2, 3, 5},
+            {10, 12, 13}
+          };
+
+  double error = module.Forward(input, negative);
   BOOST_REQUIRE_EQUAL(error, 66);
 
   // Test the Backward function.
-  module.Backward(std::move(anchor),
-      std::move(positive), std::move(negative), std::move(output));
+  module.Backward(input, negative, output);
   // According to the used backward formula:
   // output = 2 * (negative - positive) / anchor.n_cols,
   // output * nofColumns / 2 + positive should be equal to negative.
@@ -930,13 +935,16 @@ BOOST_AUTO_TEST_CASE(TripletMarginLossTest)
   anchor = arma::mat("4");
   positive = arma::mat("7");
   negative = arma::mat("1");
-  error = module.Forward(std::move(anchor),
-      std::move(positive), std::move(negative));
+
+  input = arma::mat(2, 1);
+  input[0] = 4;
+  input[1] = 7;
+
+  error = module.Forward(input, negative);
   BOOST_REQUIRE_EQUAL(error, 1.0);
 
   // Test the Backward function on a single input.
-  module.Backward(std::move(anchor),
-      std::move(positive), std::move(negative), std::move(output));
+  module.Backward(input, negative, output);
   // Test whether the output is negative.
   BOOST_REQUIRE_EQUAL(arma::accu(output), -12);
   BOOST_REQUIRE_EQUAL(output.n_elem, 1);