diff --git a/source/Rbm.cpp b/source/Rbm.cpp
index 5eae51d..cfb919d 100644
--- a/source/Rbm.cpp
+++ b/source/Rbm.cpp
@@ -17,8 +17,8 @@
 Rbm::Rbm(size_t numVisible, size_t numHidden)
 : m_params()
 , m_w(numVisible, numHidden)
-, m_bv(1, numVisible)
 , m_bh(1, numHidden)
+, m_bv(1, numVisible)
 {
 	assert(numVisible > 0);
 	assert(numHidden > 0);
@@ -28,8 +28,8 @@ Rbm::Rbm(size_t numVisible, size_t numHidden)
 Rbm::Rbm(const Rbm& orig)
 : m_params(orig.m_params)
 , m_w(orig.m_w)
-, m_bv(orig.m_bv)
 , m_bh(orig.m_bh)
+, m_bv(orig.m_bv)
 {
 }
 
@@ -41,9 +41,8 @@ Rbm::~Rbm()
 void Rbm::weightsInit(double stddev, double mu)
 {
 	uniform(m_w, stddev, mu);
-	uniform(m_bv, stddev, mu);
 	uniform(m_bh, stddev, mu);
-
+	uniform(m_bv, stddev, mu);
 }
 
 void Rbm::fromJson(Json::Value rbm)
@@ -68,12 +67,12 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 	int lastProgress = -100;
 	int batchRowIndex = 0;
 	
-	arma::mat grad_bias_v(arma::zeros(1, m_w.n_rows));
-	arma::mat grad_bias_h(arma::zeros(1, m_w.n_cols));
+	arma::mat grad_bias_v(arma::zeros(1, m_bv.n_cols));
+	arma::mat grad_bias_h(arma::zeros(1, m_bh.n_cols));
 	arma::mat grad_weight(arma::zeros(m_w.n_rows, m_w.n_cols));
 	arma::mat momentum_weights = arma::zeros(m_w.n_rows, m_w.n_cols);
-	arma::mat momentum_bias_v(arma::zeros(1, m_w.n_rows));
-	arma::mat momentum_bias_h(arma::zeros(1, m_w.n_cols));
+	arma::mat momentum_bias_v(arma::zeros(1, m_bv.n_cols));
+	arma::mat momentum_bias_h(arma::zeros(1, m_bh.n_cols));
 	arma::mat penalty_weights = arma::zeros(m_w.n_rows, m_w.n_cols);
 	
 	int trainingSizeRemain = batch.n_rows;
@@ -89,10 +88,21 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 		double learning_rate = m_params.learningRate/scaler;
 		double weight_decay = m_params.weightDecay/scaler;
 
-		arma::mat vis_state(miniBatchSizeActual, m_w.n_rows);
-		arma::mat vis_probs(miniBatchSizeActual, m_w.n_rows);
-		arma::mat hid_state(miniBatchSizeActual, m_w.n_cols);
-		arma::mat hid_probs(miniBatchSizeActual, m_w.n_cols);
+		arma::mat vis_state(miniBatchSizeActual, m_bv.n_cols);
+		arma::mat vis_probs(miniBatchSizeActual, m_bv.n_cols);
+		arma::mat hid_state(miniBatchSizeActual, m_bh.n_cols);
+		arma::mat hid_probs(miniBatchSizeActual, m_bh.n_cols);
+
+		// Create hidden layer base on training data
+		if (m_params.doSampleBatch)
+		{
+			// When the hidden units are being driven by data, always use stochastic binary states
+			vis_state = sample(miniBatch);
+		}
+		else
+		{
+			vis_state = miniBatch;
+		}
 
 		for (int epoch=0; epoch < m_params.numEpochs; epoch++)
 		{
@@ -110,17 +120,6 @@ void Rbm::train(const arma::mat& batch, IListener* pListener)
 				}
 			}
 
-			// Create hidden layer base on training data
-			if (m_params.doSampleBatch)
-			{
-				// When the hidden units are being driven by data, always use stochastic binary states
-				vis_state = sample(miniBatch);
-			}
-			else
-			{
-				vis_state = miniBatch;
-			}
-			
 			hid_probs = probsLogistic(toHiddenState(vis_state));
 
 			// Sample hidden