diff --git a/src/mlpack/methods/ann/rbm/rbm_impl.hpp b/src/mlpack/methods/ann/rbm/rbm_impl.hpp
index 67846f65fc..9a4459dedf 100644
--- a/src/mlpack/methods/ann/rbm/rbm_impl.hpp
+++ b/src/mlpack/methods/ann/rbm/rbm_impl.hpp
@@ -111,8 +111,11 @@ typename std::enable_if<std::is_same<Policy, BinaryRBM>::value, double>::type
 RBM<InitializationRuleType, DataType, PolicyType>::FreeEnergy(
     arma::Mat<ElemType>&& input)
 {
-  preActivation = arma::log(1 + arma::trunc_exp((weight * input) + hiddenBias));
-  return -(arma::accu(preActivation) + arma::dot(input, visibleBias));
+  preActivation = (weight * input);
+  preActivation.each_col() += hiddenBias;
+  preActivation = arma::log(1 + arma::trunc_exp(preActivation));
+  return -(arma::accu(preActivation) + arma::dot(input, arma::repmat(
+      visibleBias, 1, input.n_cols)));
 }
 
 template<
@@ -204,7 +207,8 @@ typename std::enable_if<std::is_same<Policy, BinaryRBM>::value, void>::type
 RBM<InitializationRuleType, DataType, PolicyType>::VisibleMean(DataType&& input,
     DataType&& output)
 {
-  output = weight.t() * input + visibleBias;
+  output = weight.t() * input;
+  output.each_col() += visibleBias;
   LogisticFunction::Fn(output, output);
 }
 
@@ -218,7 +222,8 @@ typename std::enable_if<std::is_same<Policy, BinaryRBM>::value, void>::type
 RBM<InitializationRuleType, DataType, PolicyType>::HiddenMean(DataType&& input,
     DataType&& output)
 {
-  output = weight * input + hiddenBias;
+  output = weight * input;
+  output.each_col() += hiddenBias;
   LogisticFunction::Fn(output, output);
 }
 
@@ -281,6 +286,7 @@ void RBM<InitializationRuleType, DataType, PolicyType>::Gradient(
 
     negativeGradient += tempNegativeGradient;
   }
+
   gradient = ((negativeGradient / negSteps) - positiveGradient);
 }
 
@@ -313,6 +319,8 @@ void RBM<InitializationRuleType, DataType, PolicyType>::serialize(
   ar & BOOST_SERIALIZATION_NVP(negSteps);
   ar & BOOST_SERIALIZATION_NVP(persistence);
   ar & BOOST_SERIALIZATION_NVP(poolSize);
+  ar & BOOST_SERIALIZATION_NVP(visibleBias);
+  ar & BOOST_SERIALIZATION_NVP(hiddenBias);
   ar & BOOST_SERIALIZATION_NVP(weight);
   ar & BOOST_SERIALIZATION_NVP(weightCube);
   ar & BOOST_SERIALIZATION_NVP(spikeBias);
diff --git a/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp b/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp
index 0f602c874d..9c1fdf1427 100644
--- a/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp
+++ b/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp
@@ -41,9 +41,8 @@ RBM<InitializationRuleType, DataType, PolicyType>::Reset()
   slabMean.set_size(poolSize, hiddenSize);
 
   // Weight shape D * K * N
-  weightCube = arma::Cube<ElemType>(parameter.memptr(),
-      visibleSize, poolSize, hiddenSize,
-      false, false);
+  weightCube = arma::Cube<ElemType>(parameter.memptr(), visibleSize, poolSize,
+      hiddenSize, false, false);
   // spike bias shape N * 1
   spikeBias = DataType(parameter.memptr() + weight.n_elem, hiddenSize, 1,
       false, false);
@@ -70,8 +69,7 @@ typename std::enable_if<std::is_same<Policy, SpikeSlabRBM>::value, double>::type
 RBM<InitializationRuleType, DataType, PolicyType>::FreeEnergy(
     arma::Mat<ElemType>&& input)
 {
-  ElemType freeEnergy = 0.5 * arma::as_scalar(visiblePenalty(0) * input.t() *
-      input);
+  ElemType freeEnergy = 0.5 * visiblePenalty(0) * arma::accu(input.t() * input);
 
   freeEnergy -= 0.5 * hiddenSize * poolSize *
       std::log((2.0 * M_PI) / slabPenalty);
@@ -112,11 +110,15 @@ RBM<InitializationRuleType, DataType, PolicyType>::Phase(
   SlabMean(std::move(input), std::move(spikeSamples), std::move(slabMean));
 
   for (size_t i = 0 ; i < hiddenSize; i++)
-    weightGrad.slice(i) = input * slabMean.col(i).t() * spikeMean(i);
+  {
+    weightGrad.slice(i) = input * arma::repmat(slabMean.col(i).t(),
+        input.n_cols, 1) * spikeMean(i);
+  }
 
   spikeBiasGrad = spikeMean;
 
-  visiblePenaltyGrad = -0.5 * input.t() * input;
+  visiblePenaltyGrad = -0.5 * arma::accu(input.t() * input)
+      / std::pow(input.n_cols, 2);
 }
 
 template<
@@ -166,7 +168,9 @@ RBM<InitializationRuleType, DataType, PolicyType>::SampleVisible(
       output(i) = math::RandNormal(visibleMean(i), 1.0 / visiblePenalty(0));
     }
     if (arma::norm(output, 2) < radius)
+    {
       break;
+    }
   }
 
   if (k == numMaxTrials)
@@ -197,7 +201,9 @@ RBM<InitializationRuleType, DataType, PolicyType>::VisibleMean(
       false);
 
   for (size_t i = 0; i < hiddenSize; i++)
+  {
     output += weightCube.slice(i) * slab.col(i) * spike(i);
+  }
 
   output = ((1.0 / visiblePenalty(0)) * output);
 }
@@ -237,9 +243,9 @@ RBM<InitializationRuleType, DataType, PolicyType>::SpikeMean(
 {
   for (size_t i = 0; i < hiddenSize; i++)
   {
-    spikeMean(i) = LogisticFunction::Fn(0.5 * (1.0 / slabPenalty) *
-        arma::as_scalar(visible.t() * weightCube.slice(i) *
-        weightCube.slice(i).t() * visible) + spikeBias(i));
+    spikeMean(i) = LogisticFunction::Fn(0.5 * (1.0 / slabPenalty) * arma::accu(
+        visible.t() * (weightCube.slice(i) * weightCube.slice(i).t()) * visible)
+        / std::pow(visible.n_cols, 2) + spikeBias(i));
   }
 }
 
@@ -255,7 +261,9 @@ RBM<InitializationRuleType, DataType, PolicyType>::SampleSpike(
     DataType&& spike)
 {
   for (size_t i = 0; i < hiddenSize; i++)
+  {
     spike(i) = math::RandBernoulli(spikeMean(i));
+  }
 }
 
 template<
@@ -272,8 +280,8 @@ RBM<InitializationRuleType, DataType, PolicyType>::SlabMean(
 {
   for (size_t i = 0; i < hiddenSize; i++)
   {
-    slabMean.col(i) = (1.0 / slabPenalty) * spike(i) *
-        weightCube.slice(i).t() * visible;
+    slabMean.col(i) = arma::mean((1.0 / slabPenalty) * spike(i) *
+        weightCube.slice(i).t() * visible, 1);
   }
 }
 
@@ -296,6 +304,7 @@ RBM<InitializationRuleType, DataType, PolicyType>::SampleSlab(
     }
   }
 }
+
 } // namespace ann
 } // namespace mlpack
 
diff --git a/src/mlpack/tests/rbm_network_test.cpp b/src/mlpack/tests/rbm_network_test.cpp
index 3944de4c8b..e01d2059bc 100644
--- a/src/mlpack/tests/rbm_network_test.cpp
+++ b/src/mlpack/tests/rbm_network_test.cpp
@@ -38,7 +38,7 @@ using namespace mlpack::regression;
 
 BOOST_AUTO_TEST_SUITE(RBMNetworkTest);
 
-BOOST_AUTO_TEST_CASE(ClassificationTest)
+BOOST_AUTO_TEST_CASE(BinaryRBMClassificationTest)
 {
   // Normalised dataset.
   int hiddenLayerSize = 100;
@@ -74,7 +74,7 @@ BOOST_AUTO_TEST_CASE(ClassificationTest)
 
   size_t numRBMIterations = trainData.n_cols * numEpoches;
   numRBMIterations /= batchSize;
-  optimization::StandardSGD msgd(0.06, batchSize, numRBMIterations, 0, true);
+  optimization::StandardSGD msgd(0.03, batchSize, numRBMIterations, 0, true);
   model.Reset();
   model.VisibleBias().ones();
   model.HiddenBias().ones();