diff --git a/nn.lua b/nn.lua
index a460d46..862d8c6 100644
--- a/nn.lua
+++ b/nn.lua
@@ -554,25 +554,31 @@ function Tanh:forward(X)
     return Y
 end
 
-function Dense:init(dim, norm_in)
+function Dense:init(dim, norm_in, biasing)
     Layer.init(self, "Dense")
     assert(type(dim) == "number")
     self.dim = dim
     self.shape_out = {dim}
-    if norm_in then
+    self.norm_in = norm_in and true or false
+    self.biasing = biasing == nil or biasing
+
+    if self.norm_in then
         self.coeffs = self:_new_weights(init_normal)
     else
         self.coeffs = self:_new_weights(init_he_normal)
     end
-    self.biases = self:_new_weights(init_zeros)
-    self.norm_in = norm_in and true or false
+    if self.biasing then
+        self.biases = self:_new_weights(init_zeros)
+    end
     self.c = 1.0
 end
 
 function Dense:make_shape(parent)
     self.shape_in = parent.shape_out
     self.coeffs.shape = {self.shape_in[#self.shape_in], self.dim}
-    self.biases.shape = {1, self.dim}
+    if self.biasing then
+        self.biases.shape = {1, self.dim}
+    end
     if self.norm_in then
         self.c = 1 / sqrt(prod(self.shape_in))
     end
@@ -584,7 +590,11 @@ function Dense:forward(X)
     local Y = self.cache
 
     dot(X, self.coeffs, 2, 1, Y)
-    for i, v in ipairs(Y) do Y[i] = self.c * v + self.biases[i] end
+    if self.biasing then
+        for i, v in ipairs(Y) do Y[i] = self.c * v + self.biases[i] end
+    elseif self.norm_in then
+        for i, v in ipairs(Y) do Y[i] = self.c * v end
+    end
 
     checkshape(Y, self.shape_out)
     return Y