resuscitate mxnet integration example

examples/integration-mx.jl

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+using Flux, MXNet
+
+Flux.loadmx()
+
+# 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
+# backend (or alternatively, Flux may simply not have an implementation of that
+# feature yet). In these cases it's easy to "drop down" and use the backend's
+# API directly, where appropriate.
+
+# 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
+# have been user-defined.
+
+# Secondly, we want to take advantage of MXNet.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.
+
+conv1 = Chain(
+  Input(28,28),
+  Conv2D((5,5), out = 20), tanh,
+  MaxPool((2,2), stride = (2,2)))
+
+conv2 = Chain(
+  conv1,
+  Conv2D((5,5), in = 20, out = 50), tanh,
+  MaxPool((2,2), stride = (2,2)))
+
+lenet = Chain(
+  conv2,
+  flatten,
+  Affine(500), tanh,
+  Affine(10), softmax)
+
+#--------------------------------------------------------------------------------
+
+# Now we can continue exactly as in plain MXNet, following
+#   https://github.com/dmlc/MXNet.jl/blob/master/examples/mnist/lenet.jl
+
+batch_size = 100
+include(Pkg.dir("MXNet", "examples", "mnist", "mnist-data.jl"))
+train_provider, eval_provider = get_mnist_providers(batch_size; flat=false)
+
+model = mx.FeedForward(lenet)
+
+mx.infer_shape(model.arch, data = (28, 28, 1, 100))
+
+optimizer = mx.SGD(lr=0.05, momentum=0.9, weight_decay=0.00001)
+
+mx.fit(model, optimizer, train_provider, n_epoch=1, eval_data=eval_provider)