get basic training working

src/backend/tensorflow/tensorflow.jl

@@ -1,6 +1,10 @@
 module TF
 
 using ..Flux, Flow, TensorFlow
+import Juno: info
+import Flux: accuracy
+
+export tf
 
 # Workaround for tensor display bug
 using Juno
@@ -12,10 +16,8 @@ cvalue(v::Vertex) = cvalue(value(v))
 
 graph(x::Tensor) = x
 
-matrixify(xs) = xs
-matrixify(xs::Vector) = xs[:,1:1]
 # TODO: detect variable reuse
-graph{T<:AArray}(p::Flux.Param{T}) = Variable(matrixify(p.x))
+graph{T<:AArray}(p::Flux.Param{T}) = Variable(p.x')
 
 function graph(model::Model, args...)
   g = Flux.graph(model)
@@ -28,8 +30,9 @@ function graph(model::Model, args...)
   end |> value
 end
 
-graph(::typeof(*), args...) = *(args...)
+graph(::typeof(*), args...) = *(reverse(args)...)
 graph(::typeof(+), args...) = +(args...)
+graph(::typeof(softmax), x) = nn.softmax(x)
 
 type Model
   session::Session
@@ -41,7 +44,7 @@ end
 Media.render(::Juno.Clipboard, ::Model) = "Flux.TF.Model()"
 
 function tf(model)
-  sess = Session()
+  sess = Session(Graph())
   input = placeholder(Float64)
   g = graph(model, input)
   run(sess, initialize_all_variables())
@@ -50,7 +53,7 @@ end
 
 function (m::Model)(args...)
   @assert length(args) == length(m.inputs)
-  run(m.session, m.graph, Dict(zip(m.inputs, args)))
+  run(m.session, m.graph, Dict(zip(m.inputs, map(transpose, args))))'
 end
 
 function Flux.back!(m::Model, Δ, args...)
@@ -63,4 +66,22 @@ function Flux.update!(m::Model)
   error("update! is not yet supported on TensorFlow models")
 end
 
+function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
+                     loss = (y, y′) -> reduce_sum((y - y′).^2)/2,
+                     opt = TensorFlow.train.GradientDescentOptimizer(η))
+  i = 0
+  Y = placeholder(Float64)
+  Loss = loss(m.graph, Y)
+  minimize_op = TensorFlow.train.minimize(opt, Loss)
+  run(m.session, initialize_all_variables())
+  for e in 1:epoch
+    info("Epoch $e\n")
+    @progress for (x, y) in train
+      y, cur_loss, _ = run(m.session, vcat(m.graph, Loss, minimize_op), Dict(m.inputs[1]=>x', Y=>y'))
+      i % 1000 == 0 && @show accuracy(m, test)
+      i += 1
+    end
+  end
+end
+
 end

src/utils.jl

@@ -5,7 +5,7 @@ const AArray = AbstractArray
 onehot(label, labels) = [i == label for i in labels]
 onecold(pred, labels = 1:length(pred)) = labels[findfirst(pred, maximum(pred))]
 
-initn(dims...) = randn(dims...)/100
+initn(dims...) = randn(dims...)/1000
 
 function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1)
     i = 0
@@ -24,7 +24,7 @@ function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1)
     return m
 end
 
-function accuracy(m::Model, data)
+function accuracy(m, data)
   correct = 0
   for (x, y) in data
     onecold(m(x)) == onecold(y) && (correct += 1)