diff --git a/src/mlpack/methods/ann/layer/linear_no_bias.hpp b/src/mlpack/methods/ann/layer/linear_no_bias.hpp
index 5426f1ff4f..1e85933135 100644
--- a/src/mlpack/methods/ann/layer/linear_no_bias.hpp
+++ b/src/mlpack/methods/ann/layer/linear_no_bias.hpp
@@ -51,6 +51,18 @@ class LinearNoBias
                const size_t outSize,
                RegularizerType regularizer = RegularizerType());
 
+  //! Copy constructor.
+  LinearNoBias(const LinearNoBias& layer);
+
+  //! Move constructor.
+  LinearNoBias(LinearNoBias&&);
+
+  //! Copy assignment operator.
+  LinearNoBias& operator=(const LinearNoBias& layer);
+
+  //! Move assignment operator.
+  LinearNoBias& operator=(LinearNoBias&& layer);
+
   /*
    * Reset the layer parameter.
    */
diff --git a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp
index dbea56e94f..ed26cd59fa 100644
--- a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp
+++ b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp
@@ -41,6 +41,62 @@ LinearNoBias<InputDataType, OutputDataType, RegularizerType>::LinearNoBias(
   weights.set_size(WeightSize(), 1);
 }
 
+template<typename InputDataType, typename OutputDataType,
+    typename RegularizerType>
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>::LinearNoBias(
+    const LinearNoBias& layer) :
+    inSize(layer.inSize),
+    outSize(layer.outSize),
+    weights(layer.weights),
+    regularizer(layer.regularizer)
+{
+  // Nothing to do here.
+}
+
+template<typename InputDataType, typename OutputDataType,
+    typename RegularizerType>
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>::LinearNoBias(
+    LinearNoBias&& layer) :
+    inSize(0),
+    outSize(0),
+    weights(std::move(layer.weights)),
+    regularizer(std::move(layer.regularizer))
+{
+  // Nothing to do here.
+}
+
+template<typename InputDataType, typename OutputDataType,
+    typename RegularizerType>
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>&
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>::
+operator=(const LinearNoBias& layer)
+{
+  if (this != &layer)
+  {
+    inSize = layer.inSize;
+    outSize = layer.outSize;
+    weights = layer.weights;
+    regularizer = layer.regularizer;
+  }
+  return *this;
+}
+
+template<typename InputDataType, typename OutputDataType,
+    typename RegularizerType>
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>&
+LinearNoBias<InputDataType, OutputDataType, RegularizerType>::
+operator=(LinearNoBias&& layer)
+{
+  if (this != &layer)
+  {
+    inSize = layer.inSize;
+    outSize = layer.outSize;
+    weights = std::move(layer.weights);
+    regularizer = std::move(layer.regularizer);
+  }
+  return *this;
+}
+
 template<typename InputDataType, typename OutputDataType,
     typename RegularizerType>
 void LinearNoBias<InputDataType, OutputDataType, RegularizerType>::Reset()
diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp
index bed64a3846..6fd5af4e30 100644
--- a/src/mlpack/tests/feedforward_network_test.cpp
+++ b/src/mlpack/tests/feedforward_network_test.cpp
@@ -386,6 +386,89 @@ TEST_CASE("CheckCopyMovingDropoutNetworkTest", "[FeedForwardNetworkTest]")
   CheckMoveFunction<>(model1, trainData, trainLabels, 1);
 }
 
+/**
+ * Check whether copying and moving Vanila network is working or not.
+ */
+TEST_CASE("CheckCopyMovingVanillaNetworkTestNoBias", "[FeedForwardNetworkTest]")
+{
+  // Load the dataset.
+  arma::mat trainData;
+  if (!data::Load("thyroid_train.csv", trainData))
+    FAIL("Cannot open thyroid_train.csv");
+
+  // Normalize labels to [0, 2].
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1;
+  trainData.shed_row(trainData.n_rows - 1);
+
+  /*
+   * Construct a feed forward network with trainData.n_rows input nodes,
+   * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
+   * network structure looks like:
+   *
+   *  Input         Hidden        Output
+   *  Layer         Layer         Layer
+   * +-----+       +-----+       +-----+
+   * |     |       |     |       |     |
+   * |     +------>|     +------>|     |
+   * |     |       |     |       |     |
+   * +-----+       +--+--+       +-----+
+   */
+
+  FFN<NegativeLogLikelihood<> > *model = new FFN<NegativeLogLikelihood<> >;
+  model->Add<LinearNoBias<> >(trainData.n_rows, 8);
+  model->Add<SigmoidLayer<> >();
+  model->Add<LinearNoBias<> >(8, 3);
+  model->Add<LogSoftMax<> >();
+
+  FFN<NegativeLogLikelihood<> > *model1 = new FFN<NegativeLogLikelihood<> >;
+  model1->Add<LinearNoBias<> >(trainData.n_rows, 8);
+  model1->Add<SigmoidLayer<> >();
+  model1->Add<LinearNoBias<> >(8, 3);
+  model1->Add<LogSoftMax<> >();
+
+  // Check whether copy constructor is working or not.
+  CheckCopyFunction<>(model, trainData, trainLabels, 1);
+
+  // Check whether move constructor is working or not.
+  CheckMoveFunction<>(model1, trainData, trainLabels, 1);
+}
+
+/**
+ * Check whether copying and moving network with Reparametrization is working or not.
+ */
+TEST_CASE("CheckCopyMovingReparametrizationNetworkTestNoBias",
+          "[FeedForwardNetworkTest]")
+{
+  // Load the dataset.
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
+
+  // Normalize labels to [0, 2].
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1;
+  trainData.shed_row(trainData.n_rows - 1);
+
+  /*
+   * Construct a feed forward network with trainData.n_rows input nodes,
+   * followed by a linear layer and then a reparametrization layer.
+   */
+
+  FFN<NegativeLogLikelihood<> > *model = new FFN<NegativeLogLikelihood<> >;
+  model->Add<LinearNoBias<> >(trainData.n_rows, 8);
+  model->Add<Reparametrization<> >(4, false, true, 1);
+  model->Add<LogSoftMax<> >();
+
+  FFN<NegativeLogLikelihood<> > *model1 = new FFN<NegativeLogLikelihood<> >;
+  model1->Add<LinearNoBias<> >(trainData.n_rows, 8);
+  model1->Add<Reparametrization<> >(4, false, true, 1);
+  model1->Add<LogSoftMax<> >();
+
+  // Check whether copy constructor is working or not.
+  CheckCopyFunction<>(model, trainData, trainLabels, 1);
+
+  // Check whether move constructor is working or not.
+  CheckMoveFunction<>(model1, trainData, trainLabels, 1);
+}
+
 /**
  * Train the vanilla network on a larger dataset.
  */