update for mxnet api

src/backend/mxnet/graph.jl

@@ -20,10 +20,10 @@ node(x::mx.SymbolicNode) = x
 graph(::typeof(tuple), args...) = (args...,)
 graph(::typeof(+), args...) = mx.broadcast_plus(args...)
 graph(::typeof(*), x, W) = mx.dot(W, x) # Adjustments for batching
-graph(::typeof(σ), x) = mx.Activation(data = x, act_type = :sigmoid)
-graph(::typeof(relu), x) = mx.Activation(data = x, act_type = :relu)
-graph(::typeof(tanh), x) = mx.Activation(data = x, act_type = :tanh)
-graph(::typeof(flatten), x) = mx.Flatten(data = x)
+graph(::typeof(σ), x) = mx.Activation(x, act_type = :sigmoid)
+graph(::typeof(relu), x) = mx.Activation(x, act_type = :relu)
+graph(::typeof(tanh), x) = mx.Activation(x, act_type = :tanh)
+graph(::typeof(flatten), x) = mx.Flatten(x)
 
 graph(::typeof(softmax), xs) =
   mx.broadcast_div(exp(xs), mx.Reshape(mx.sum(exp(xs)), shape = (1,1)))
@@ -36,20 +36,20 @@ graph(::Input, x) = x
 graph(ctx::Context, d::Affine, x) =
   !ctx[:feedforward] ? invoke(graph, (Context, Any, typeof(x)), ctx, d, x) :
     register(ctx,
-      mx.FullyConnected(data = x,
+      mx.FullyConnected(x,
                         num_hidden = size(d.W.x, 2),
                         weight = var(ctx, d.W, size(d.W)),
                         bias = var(ctx, d.b, size(d.b, 2))))
 
 # TODO: use actual params}
 graph(ctx::Context, c::Conv2D, x) =
-  mx.Convolution(data = x,
+  mx.Convolution(x,
                  kernel = size(c.filter, 1, 2),
                  num_filter = size(c.filter, 4),
                  stride = c.stride)
 
 graph(ctx::Context, p::MaxPool, x) =
-  mx.Pooling(data = x,
+  mx.Pooling(x,
              pool_type = :max,
              kernel = p.size,
              stride = p.stride)

src/backend/mxnet/model.jl

@@ -83,7 +83,7 @@ type SoftmaxOutput
   name::Symbol
 end
 
-graph(s::SoftmaxOutput, xs) = mx.SoftmaxOutput(data = xs, name = s.name)
+graph(s::SoftmaxOutput, xs) = mx.SoftmaxOutput(xs, name = s.name)
 
 function rewrite_softmax(model, name)
   model == softmax && return SoftmaxOutput(name)