batched training for char-rnn

REQUIRE

@@ -1,2 +1,3 @@
 julia 0.5-
 TensorFlow
+Iterators

examples/char-rnn.jl

@@ -1,20 +1,22 @@
 using Flux
 
+using Juno
+
+getseqs(chars, alphabet) = sequences((onehot(Float32, char, alphabet) for char in chars), 50)
+getbatches(chars, alphabet) = batches((getseqs(part, alphabet) for part in chunk(chars, 50))...)
+
 input = readstring("$(homedir())/Downloads/shakespeare_input.txt")
+const alphabet = unique(input)
 
-alphabet = unique(input)
-
-getseqs(data, n) = (Seq(onehot(Float32, char, alphabet) for char in chunk) for chunk in chunks(data, n))
-
-data = zip(getseqs(input, 50), getseqs(input[2:end], 50))
+train = zip(getbatches(input, alphabet), getbatches(input[2:end], alphabet))
 
 model = Chain(
   Input(length(alphabet)),
   Flux.Recurrent(length(alphabet), 128, length(alphabet)),
   softmax)
 
-unrolled = unroll(model, 50)
+m = tf(unroll(model, 50))
 
-m = tf(unrolled)
+Flux.train!(m, train, η = 0.1/50, epoch = 5)
 
-Flux.train!(m, data)
+map(c->onecold(c, alphabet), m(train[1][1][1]))

src/Flux.jl

@@ -10,6 +10,7 @@ import Juno: Tree, Row
 
 include("model.jl")
 include("utils.jl")
+include("data.jl")
 
 include("compiler/graph.jl")
 include("compiler/diff.jl")

src/backend/tensorflow/recurrent.jl

@@ -44,7 +44,6 @@ end
 function Flux.train!(m::SeqModel, train; epoch = 1, η = 0.1,
                      loss = (y, y′) -> reduce_sum((y - y′).^2)/2,
                      opt = TensorFlow.train.GradientDescentOptimizer(η))
-  i = 0
   Y = placeholder(Float32)
   Loss = loss(m.m.output[end], Y)
   minimize_op = TensorFlow.train.minimize(opt, Loss)
@@ -52,12 +51,8 @@ function Flux.train!(m::SeqModel, train; epoch = 1, η = 0.1,
     info("Epoch $e\n")
     @progress for (x, y) in train
       y, cur_loss, _ = run(m.m.session, vcat(m.m.output[end], Loss, minimize_op),
-                           merge(Dict(m.m.inputs[end]=>batchone(x), Y=>batchone(y)),
+                           merge(Dict(m.m.inputs[end]=>x, Y=>y),
                                  Dict(zip(m.m.inputs[1:end-1], m.state))))
-      if i % 5000 == 0
-        @show y
-      end
-      i += 1
     end
   end
 end

src/compiler/code.jl

@@ -61,6 +61,8 @@ function build_backward(body, x, params = [])
   syntax(cse(back))
 end
 
+import Lazy: groupby
+
 function process_type(ex)
   @capture(ex, type T_ fs__ end)
   @destruct [params = false || [],

src/data.jl

@@ -0,0 +1,18 @@
+export onehot, onecold, chunk, partition, batches, sequences
+
+onehot(T::Type, label, labels) = T[i == label for i in labels]
+onehot(label, labels) = onehot(Int, label, labels)
+onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))]
+
+using Iterators
+import Iterators: partition
+
+export partition
+
+_partition(r::UnitRange, step::Integer) = (step*(i-1)+1:step*i for i in 1:(r.stop÷step))
+_partition(xs, step) = (xs[i] for i in _partition(1:length(xs), step))
+
+chunk(xs, n) = _partition(xs, length(xs)÷n)
+
+batches(xs...) = (Batch(x) for x in zip(xs...))
+sequences(xs, len) = (Seq(x) for x in partition(xs, len))

src/utils.jl

@@ -1,14 +1,7 @@
-export AArray, onehot, onecold, chunks
+export AArray
 
 const AArray = AbstractArray
 
-onehot(T::Type, label, labels) = T[i == label for i in labels]
-onehot(label, labels) = onehot(Int, label, labels)
-onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))]
-
-chunks(r::UnitRange, step::Integer) = (step*(i-1)+1:step*i for i in 1:(r.stop÷step))
-chunks(xs, step) = (xs[i] for i in chunks(1:length(xs), step))
-
 initn(dims...) = randn(Float32, dims...)/1000
 
 function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1)