71 lines
2.3 KiB
Julia
71 lines
2.3 KiB
Julia
using Flux
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# Flux aims to provide high-level APIs that work well across backends, but in
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# some cases you may want to take advantage of features specific to a given
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# backend (or Flux may simply not have an implementation of that feature yet).
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# In these cases it's easy to "drop down" and use the backend's API directly,
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# where appropriate.
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# In this example, both things are happening; firstly, Flux doesn't yet support
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# ConvNets in the pure-Julia backend, but this is invisible thanks to the use of
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# a simple "shim" type, `Conv2D`. This is provided by the library but could easily
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# have been user-defined.
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# Secondly, we want to take advantage of TensorFlow.jl's training process and
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# optimisers. We can simply call `Tensor` exactly as we would on a regular
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# TensorFlow model, and the rest of the process trivially follows
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# TensorFlow.jl's usual API.
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conv1 = Chain(
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Reshape(28,28,1),
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Conv2D((5,5), out = 20), tanh,
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MaxPool((2,2), stride = (2,2)))
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conv2 = Chain(
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Input(12,12,20),
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Conv2D((5,5), in = 20, out = 50), tanh,
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MaxPool((2,2), stride = (2,2)))
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lenet = Chain(
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conv1, conv2, flatten,
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Dense(500), tanh,
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Dense(10), softmax)
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#--------------------------------------------------------------------------------
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# Now we can continue exactly as in plain TensorFlow, following
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# https://github.com/malmaud/TensorFlow.jl/blob/master/examples/mnist_full.jl
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# (taking only the training and cost logic, not the graph building steps)
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using TensorFlow, Distributions
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include(Pkg.dir("TensorFlow", "examples", "mnist_loader.jl"))
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loader = DataLoader()
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session = Session(Graph())
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x = placeholder(Float32)
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y′ = placeholder(Float32)
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y = Tensor(lenet, x)
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cross_entropy = reduce_mean(-reduce_sum(y′.*log(y), reduction_indices=[2]))
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train_step = train.minimize(train.AdamOptimizer(1e-4), cross_entropy)
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accuracy = reduce_mean(cast(indmax(y, 2) .== indmax(y′, 2), Float32))
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run(session, initialize_all_variables())
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@progress for i in 1:1000
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batch = next_batch(loader, 50)
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if i%100 == 1
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train_accuracy = run(session, accuracy, Dict(x=>batch[1], y′=>batch[2]))
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info("step $i, training accuracy $train_accuracy")
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end
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run(session, train_step, Dict(x=>batch[1], y′=>batch[2]))
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end
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testx, testy = load_test_set()
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test_accuracy = run(session, accuracy, Dict(x=>testx, y′=>testy))
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info("test accuracy $test_accuracy")
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