diff --git a/ops/opset13/batch_normalization.go b/ops/opset13/batch_normalization.go
index c548fb5..6caadbd 100644
--- a/ops/opset13/batch_normalization.go
+++ b/ops/opset13/batch_normalization.go
@@ -7,8 +7,10 @@ import (
 )
 
 const (
-	MinBatchNormalizationInputs = 5
-	MaxBatchNormalizationInputs = 5
+	MinBatchNormalizationInputs       = 5
+	MaxBatchNormalizationInputs       = 5
+	BatchNormalizationDefaultEpsilon  = 1e-5
+	BatchNormalizationDefaultMomentum = 0.9
 )
 
 // BatchNormalization represents the ONNX batchNormalization operator.
@@ -21,14 +23,15 @@ type BatchNormalization struct {
 // newBatchNormalization creates a new batchNormalization operator.
 func newBatchNormalization() ops.Operator {
 	return &BatchNormalization{
-		epsilon:  1e-5,
-		momentum: 0.9,
+		epsilon:  BatchNormalizationDefaultEpsilon,
+		momentum: BatchNormalizationDefaultMomentum,
 	}
 }
 
 // Init initializes the batchNormalization operator.
 func (b *BatchNormalization) Init(n *onnx.NodeProto) error {
 	hasMomentum := false
+
 	for _, attr := range n.GetAttribute() {
 		switch attr.GetName() {
 		case "epsilon":
@@ -102,7 +105,9 @@ func (b *BatchNormalization) String() string {
 }
 
 func (b *BatchNormalization) reshapeTensors(X, scale, bias, mean, variance tensor.Tensor) (newScale, newBias, newMean, newVariance tensor.Tensor, err error) {
-	nSpatialDims := len(X.Shape()) - 2
+	nNonSpatialDims := 2
+
+	nSpatialDims := len(X.Shape()) - nNonSpatialDims
 	if nSpatialDims <= 0 {
 		return scale, bias, mean, variance, nil
 	}