diff --git a/examples/char-rnn.jl b/examples/char-rnn.jl
index 2802e232..e8ed72ab 100644
--- a/examples/char-rnn.jl
+++ b/examples/char-rnn.jl
@@ -1,23 +1,22 @@
 using Flux
+using Flux: onehot, logloss, unsqueeze
+using Flux.Batches: Batch, tobatch, seqs, chunk
 import StatsBase: wsample
 
 nunroll = 50
 nbatch = 50
 
-getseqs(chars, alphabet) =
-  sequences((onehot(Float32, char, alphabet) for char in chars), nunroll)
-getbatches(chars, alphabet) =
-  batches((getseqs(part, alphabet) for part in chunk(chars, nbatch))...)
+encode(input) = seqs((onehot(ch, alphabet) for ch in input), nunroll)
 
-input = readstring("$(homedir())/Downloads/shakespeare_input.txt");
+cd(@__DIR__)
+input = readstring("shakespeare_input.txt");
 alphabet = unique(input)
 N = length(alphabet)
 
-train = zip(getbatches(input, alphabet), getbatches(input[2:end], alphabet))
-eval = tobatch.(first(drop(train, 5)))
+Xs = (Batch(ss) for ss in zip(encode.(chunk(input, 50))...))
+Ys = (Batch(ss) for ss in zip(encode.(chunk(input[2:end], 50))...))
 
 model = Chain(
-  Input(N),
   LSTM(N, 256),
   LSTM(256, 256),
   Affine(256, N),
@@ -25,9 +24,10 @@ model = Chain(
 
 m = mxnet(unroll(model, nunroll))
 
+eval = tobatch.(first.(drop.((Xs, Ys), 5)))
 evalcb = () -> @show logloss(m(eval[1]), eval[2])
 
-@time Flux.train!(m, train, η = 0.1, loss = logloss, cb = [evalcb])
+# @time Flux.train!(m, zip(Xs, Ys), η = 0.001, loss = logloss, cb = [evalcb], epoch = 10)
 
 function sample(model, n, temp = 1)
   s = [rand(alphabet)]
@@ -38,4 +38,4 @@ function sample(model, n, temp = 1)
   return string(s...)
 end
 
-s = sample(model[1:end-1], 100)
+# s = sample(model[1:end-1], 100)