diff --git a/nn/init.go b/nn/init.go
index b94a180..3d7fdb4 100644
--- a/nn/init.go
+++ b/nn/init.go
@@ -420,3 +420,35 @@ func XavierUniform_(x *ts.Tensor, gainOpt ...float64) {
 
 	src.MustDrop()
 }
+
+// XavierNormal fills the input tensor with values according to the method
+// described in the paper `Understanding the difficulty of training deep feedforward neural networks`
+// using a normal distribution
+//
+// Also known as normal Glorot initialization.
+//
+// Paper: https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
+// Pytorch implementation: https://github.com/pytorch/pytorch/blob/df50f91571891ec3f87977a2bdd4a2b609d70afc/torch/nn/init.py#L337
+func XavierNormal_(x *ts.Tensor, gainOpt ...float64) {
+	gain := 1.0
+	if len(gainOpt) > 0 {
+		gain = gainOpt[0]
+	}
+
+	size := x.MustSize()
+	dtype := x.DType()
+	device := x.MustDevice()
+	fanIn, fanOut, err := CalculateFans(size)
+	if err != nil {
+		panic(err)
+	}
+
+	std := gain * math.Sqrt(2.0/float64(fanIn+fanOut))
+
+	// calculate uniform bounds from standard deviation
+	uniformInit := NewUniformInit(0, std)
+	src := uniformInit.InitTensor(size, device, dtype)
+	x.Copy_(src)
+
+	src.MustDrop()
+}