handle state in training

src/backend/tensorflow/recurrent.jl

@@ -44,15 +44,17 @@ 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))
+  state = batchone.(m.m.model.state)
   Y = placeholder(Float32)
   Loss = loss(m.m.output[end], Y)
   minimize_op = TensorFlow.train.minimize(opt, Loss)
   for e in 1:epoch
     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]=>x, Y=>y),
-                                 Dict(zip(m.m.inputs[1:end-1], m.state))))
+      out = run(m.m.session, vcat(m.m.output..., Loss, minimize_op),
+                merge(Dict(m.m.inputs[end]=>batchone(x), Y=>batchone(y)),
+                      Dict(zip(m.m.inputs[1:end-1], state))))
+      state = out[1:length(state)]
     end
   end
 end