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
# 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, `Conv2D`. This is provided by the library but could easily
# have been user-defined.

# 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 TensorFlow 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), out = 50), tanh,
  MaxPool((2,2), stride = (2,2)))

lenet = Chain(
  conv2,
  flatten,
  Dense(500), tanh,
  Dense(10), softmax)

#--------------------------------------------------------------------------------

# Now we can continue exactly as in plain TensorFlow, following
#   https://github.com/malmaud/TensorFlow.jl/blob/master/examples/mnist_full.jl

using TensorFlow

sess = Session(Graph())

x  = placeholder(Float64)
y′ = placeholder(Float64)

y = Tensor(lenet, x)

include(Pkg.dir("TensorFlow", "examples", "mnist_loader.jl"))

loader = DataLoader()