split out makesession logic

src/backend/tensorflow/graph.jl

@@ -68,3 +68,11 @@ end
 TensorFlow.Tensor(m::Flux.Model, args...) = graph(Dict(), m, args...)
 
 RawTensor(data::Union{Batch,Seq}) = RawTensor(rawbatch(data))
+
+function makesession(model, n)
+  sess = Session(Graph())
+  inputs = [placeholder(Float32) for _ = 1:n]
+  params, output = tograph(model, inputs...)
+  run(sess, initialize_all_variables())
+  sess, params, inputs, output
+end

src/backend/tensorflow/model.jl

@@ -10,13 +10,10 @@ end
 ismultioutput(m::Model) = !isa(m.output, Tensor)
 
 function tf(model)
-  sess = Session(Graph())
+  sess, params, input, output = makesession(model, 1)
-  input = placeholder(Float32)
-  params, output = tograph(model, input)
-  run(sess, initialize_all_variables())
   Model(model, sess, params,
-        [input], output,
+        input, output,
-        [gradients(output, input)])
+        gradients(output, input))
 end
 
 batchone(x) = Batch((x,))
@@ -57,12 +54,12 @@ function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
                      opt = TensorFlow.train.GradientDescentOptimizer(η))
   i = 0
   Y = placeholder(Float32)
-  Loss = loss(m.outputs[1], Y)
+  Loss = loss(m.output, 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.session, vcat(m.outputs[1], Loss, minimize_op),
+      y, cur_loss, _ = run(m.session, vcat(m.output, Loss, minimize_op),
                            Dict(m.inputs[1]=>batchone(x), Y=>batchone(y)))
       if i % 5000 == 0
         @show y