diff --git a/src/model/RecurrentFWNetwork.lua b/src/model/RecurrentFWNetwork.lua
index 3cea038..d15bfe1 100644
--- a/src/model/RecurrentFWNetwork.lua
+++ b/src/model/RecurrentFWNetwork.lua
@@ -15,6 +15,8 @@ return function(opt, params)
    local eps = eps or 1e-5
    local outputType = opt.outputType or 'last' -- 'last' or 'all'
    local relu = nn.ReLU()
+   local mm  = nn.MM(false, false) -- A * B
+   local mmT = nn.MM(false,  true) -- A * B'
 
    -- container:
    params = params or {}
@@ -34,7 +36,6 @@ return function(opt, params)
          x = torch.view(x, 1, torch.size(x, 1), torch.size(x, 2))
       end
       local batch = torch.size(x, 1)
-      assert(batch == 1, 'Batch mode not yet supprted.')
       local steps = torch.size(x, 2)
 
       -- hiddens:
@@ -44,7 +45,8 @@ return function(opt, params)
       local hp = prevState.h or torch.zero(x.new(batch, hiddenFeatures))
 
       -- fast weights
-      local A = prevState.A or torch.zero(x.new(hiddenFeatures, hiddenFeatures))
+      local A = prevState.A or
+                torch.zero(x.new(batch, hiddenFeatures, hiddenFeatures))
 
       local hs = {}
       -- go over time:
@@ -55,8 +57,11 @@ return function(opt, params)
          -- prev h
          hp = hs[t-1] or hp
 
+         -- vector to matrix
+         local hpMat = torch.view(hp, batch, -1, 1)
+
          -- fast weights update
-         A = l * A + e * (torch.t(hp) * hp)
+         A = l * A + e * mmT{hpMat, hpMat}
 
          -- pack all dot products:
          local dot = torch.cat(xt, hp, 2) * p.W
@@ -64,8 +69,13 @@ return function(opt, params)
 
          hs[t] = torch.zero(x.new(batch, hiddenFeatures))
          for s = 0, S do
+
+            -- vector to matrix
+            local hstMat = torch.view(hs[t], batch, -1, 1)
+
             -- next h:
-            hs[t] = dot + hs[t] * A
+            hs[t] = dot + torch.view(mm{A, hstMat}, batch, -1)
+
             if LayerNorm then
                local h = hs[t]
                if torch.nDimension(hs[t]) == 1 then
@@ -78,7 +88,10 @@ return function(opt, params)
                torch.size(h))
                hs[t] = torch.view(torch.cdiv(h, std), torch.size(hs[t]))
             end
+
+            -- apply non-linearity
             hs[t] = relu(hs[t])
+
          end
       end