char-rnn example

docs/make.jl

@@ -16,7 +16,8 @@ makedocs(modules=[Flux],
                     "Batching" => "apis/batching.md",
                     "Backends" => "apis/backends.md"],
                   "In Action" => [
-                    "Logistic Regression" => "examples/logreg.md"],
+                    "Logistic Regression" => "examples/logreg.md"
+                    "Char RNN" => "examples/char-rnn.md"],
                   "Contributing & Help"   => "contributing.md",
                   "Internals" => "internals.md"])
 

docs/src/examples/char-rnn.md

@@ -0,0 +1,40 @@
+# Char RNN
+
+```julia
+using Flux
+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))...)
+
+input = readstring("$(homedir())/Downloads/shakespeare_input.txt")
+alphabet = unique(input)
+N = length(alphabet)
+
+Xs, Ys = getbatches(input, alphabet), getbatches(input[2:end], alphabet)
+
+model = Chain(
+  Input(N),
+  LSTM(N, 256),
+  LSTM(256, 256),
+  Affine(256, N),
+  softmax)
+
+m = tf(unroll(model, nunroll))
+
+@time Flux.train!(m, Xs, Ys, η = 0.1, epoch = 1)
+
+function sample(model, n, temp = 1)
+  s = [rand(alphabet)]
+  m = tf(unroll(model, 1))
+  for i = 1:n
+    push!(s, wsample(alphabet, softmax(m(Seq((onehot(Float32, s[end], alphabet),)))[1]./temp)))
+  end
+  return string(s...)
+end
+
+sample(model, 100)
+```