julia/examples/mnist/mlp.jl

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using MXNet

#--------------------------------------------------------------------------------
# define MLP
# the following two ways are equivalent

#-- Option 1: explicit composition
# data = mx.Variable(:data)
# fc1  = mx.FullyConnected(data, name=:fc1, num_hidden=128)
# act1 = mx.Activation(fc1, name=:relu1, act_type=:relu)
# fc2  = mx.FullyConnected(act1, name=:fc2, num_hidden=64)
# act2 = mx.Activation(fc2, name=:relu2, act_type=:relu)
# fc3  = mx.FullyConnected(act2, name=:fc3, num_hidden=10)
# mlp  = mx.SoftmaxOutput(fc3, name=:softmax)

#-- Option 2: using the mx.chain macro
# mlp = @mx.chain mx.Variable(:data)             =>
#   mx.FullyConnected(name=:fc1, num_hidden=128) =>
#   mx.Activation(name=:relu1, act_type=:relu)   =>
#   mx.FullyConnected(name=:fc2, num_hidden=64)  =>
#   mx.Activation(name=:relu2, act_type=:relu)   =>
#   mx.FullyConnected(name=:fc3, num_hidden=10)  =>
#   mx.SoftmaxOutput(name=:softmax)

#-- Option 3: using nn-factory
mlp = @mx.chain mx.Variable(:data) =>
  mx.MLP([128, 64, 10])            =>
  mx.SoftmaxOutput(name=:softmax)

# data provider
batch_size = 100
include("mnist-data.jl")
train_provider, eval_provider = get_mnist_providers(batch_size)

# setup model
model = mx.FeedForward(mlp, context=mx.cpu())

# optimizer
optimizer = mx.SGD(η=0.1, μ=0.9, λ=0.00001)

# fit parameters
mx.fit(model, optimizer, train_provider, eval_data=eval_provider, n_epoch=20)

#--------------------------------------------------------------------------------
# Optional, demonstration of the predict API
probs = mx.predict(model, eval_provider)

# collect all labels from eval data
labels = reduce(
  vcat,
  copy(mx.get(eval_provider, batch, :softmax_label)) for batch ∈ eval_provider)
# labels are 0...9
labels .= labels .+ 1

# Now we use compute the accuracy
pred = map(i -> argmax(probs[1:10, i]), 1:size(probs, 2))
correct = sum(pred .== labels)
@printf "Accuracy on eval set: %.2f%%\n" 100correct/length(labels)