initial conv example changes for TF

examples/mnist-conv.jl

@@ -1,4 +1,4 @@
-using Flux, MXNet
+using Flux
 
 # Flux aims to provide high-level APIs that work well across backends, but in
 # some cases you may want to take advantage of features specific to a given
@@ -8,21 +8,21 @@ using Flux, MXNet
 
 # In this example, both things are happening; firstly, Flux doesn't yet support
 # ConvNets in the pure-Julia backend, but this is invisible thanks to the use of
-# a simple "shim" type, `Conv`. This is provided by the library but could easily
+# a simple "shim" type, `Conv2D`. This is provided by the library but could easily
 # have been user-defined.
 
-# Secondly, we want to take advantage of MXNet.jl's training process and
+# Secondly, we want to take advantage of TensorFlow.jl's training process and
 # optimisers. We can simply call `mx.FeedForward` exactly as we would on a
-# regular MXNet model, and the rest of the process is trivial.
+# regular TensorFlow model, and the rest of the process is trivial.
 
 conv1 = Chain(
   Input(28,28),
-  Conv((5,5),20), tanh,
+  Conv2D((5,5), out = 20), tanh,
   MaxPool((2,2), stride = (2,2)))
 
 conv2 = Chain(
   conv1,
-  Conv((5,5),50), tanh,
+  Conv2D((5,5), out = 50), tanh,
   MaxPool((2,2), stride = (2,2)))
 
 lenet = Chain(
@@ -33,15 +33,18 @@ lenet = Chain(
 
 #--------------------------------------------------------------------------------
 
-# Now we can continue exactly as in plain MXNet, following
-#   https://github.com/dmlc/MXNet.jl/blob/master/examples/mnist/lenet.jl
+# Now we can continue exactly as in plain TensorFlow, following
+#   https://github.com/malmaud/TensorFlow.jl/blob/master/examples/mnist_full.jl
 
-batch_size = 100
-include(Pkg.dir("MXNet", "examples", "mnist", "mnist-data.jl"))
-train_provider, eval_provider = get_mnist_providers(batch_size; flat=false)
+using TensorFlow
 
-model = mx.FeedForward(lenet, context = mx.gpu())
+sess = Session(Graph())
 
-optimizer = mx.SGD(lr=0.05, momentum=0.9, weight_decay=0.00001)
+x  = placeholder(Float64)
+y′ = placeholder(Float64)
 
-mx.fit(model, optimizer, train_provider, n_epoch=1, eval_data=eval_provider)
+y = Tensor(lenet, x)
+
+include(Pkg.dir("TensorFlow", "examples", "mnist_loader.jl"))
+
+loader = DataLoader()