diff --git a/nn.lua b/nn.lua
index edd9391..467bc2a 100644
--- a/nn.lua
+++ b/nn.lua
@@ -290,11 +290,13 @@ local Layer = Base:extend()
 local Model = Base:extend()
 local Input = Layer:extend()
 local Merge = Layer:extend()
+local Reshape = Layer:extend()
 local Relu = Layer:extend()
 local Gelu = Layer:extend()
 local Cos = Layer:extend()
 local Tanh = Layer:extend()
 local Dense = Layer:extend()
+local DenseBroadcast = Layer:extend()
 local Softmax = Layer:extend()
 local Embed = Layer:extend()
 local LayerNorm = Layer:extend()
@@ -436,6 +438,29 @@ function Merge:_propagate(edges, deterministic)
     return Y
 end
 
+function Reshape:init(shape)
+    Layer.init(self, "Reshape")
+    self.size = 0
+    self.shape_out = shape
+end
+
+function Reshape:make_shape(parent)
+    self.shape_in = parent.shape_out
+    -- TODO: allow a single dummy dimension like numpy.
+    assert(prod(self.shape_in) == prod(self.shape_out),
+        "input shape does not fit into given shape.")
+end
+
+function Reshape:forward(X)
+    local bs = checkshape(X, self.shape_in)
+    if bs ~= self.bs then self:reset_cache(bs) end
+    local Y = self.cache
+
+    for i, v in ipairs(X) do Y[i] = v end
+
+    return Y
+end
+
 function Relu:init()
     Layer.init(self, "Relu")
 end
@@ -520,10 +545,36 @@ function Dense:forward(X)
     local Y = self.cache
 
     dot(X, self.coeffs, 2, 1, Y)
+    for i, v in ipairs(Y) do Y[i] = v + self.biases[i] end
 
-    for i = 1, self.dim do
-        Y[i] = Y[i] + self.biases[i]
-    end
+    checkshape(Y, self.shape_out)
+    return Y
+end
+
+function DenseBroadcast:init(dim)
+    -- same as Dense but applies the same to every m of (m, n).
+    Layer.init(self, "DenseBroadcast")
+    assert(type(dim) == "number")
+    self.dim = dim
+    self.coeffs = self:_new_weights(init_he_normal) -- should be normal, but...
+    self.biases = self:_new_weights(init_zeros)
+end
+
+function DenseBroadcast:make_shape(parent)
+    self.shape_in = parent.shape_out
+    assert(#self.shape_in == 2)
+    self.shape_out = {self.shape_in[1], self.dim}
+    self.coeffs.shape = {self.shape_in[#self.shape_in], self.dim}
+    self.biases.shape = {1, self.dim}
+end
+
+function DenseBroadcast:forward(X)
+    local bs = checkshape(X, self.shape_in)
+    if self.bs ~= bs then self:reset_cache(bs) end
+    local Y = self.cache
+
+    dot(X, self.coeffs, 3, 1, Y)
+    for i, v in ipairs(Y) do Y[i] = v + self.biases[(i - 1) % self.dim + 1] end
 
     checkshape(Y, self.shape_out)
     return Y
@@ -763,11 +814,13 @@ return {
     Model       = Model,
     Input       = Input,
     Merge       = Merge,
+    Reshape     = Reshape,
     Relu        = Relu,
     Gelu        = Gelu,
     Cos         = Cos,
     Tanh        = Tanh,
     Dense       = Dense,
+    DenseBroadcast = DenseBroadcast,
     Softmax     = Softmax,
     Embed       = Embed,
     LayerNorm   = LayerNorm,