basic sequence model training

src/backend/tensorflow/recurrent.jl

@@ -19,7 +19,7 @@ function tf(model::Flux.Unrolled)
     Model(model, sess, params,
           [instates..., input], [outstates..., output],
           [gradients(output, input)]),
-    [])
+    batchone.(model.states))
 end
 
 function batchseq(xs)
@@ -40,3 +40,24 @@ function (m::SeqModel)(x::BatchSeq)
 end
 
 (m::SeqModel)(x::Seq) = first(m(batchone(x)))
+
+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)
+  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]=>batchone(x), Y=>batchone(y)),
+                                 Dict(zip(m.m.inputs[1:end-1], m.state))))
+      if i % 5000 == 0
+        @show y
+      end
+      i += 1
+    end
+  end
+end