OnlineAI.jl

Machine learning for sequential/streaming data
Popularity
31 Stars
Updated Last
11 Months Ago
Started In
June 2015

OnlineAI.jl

Machine learning for sequential/streaming data

This is a work in progress... use at your own risk!

Example usage:

# solve for the "xor" problem using a simple neural net with 1 hidden layer with 2 nodes
inputs = [0 0; 0 1; 1 0; 1 1]
targets = float(sum(inputs,2) .== 1)

# build train/validation/test sets, all with the same data
data = buildSolverData(float(inputs), targets)
datasets = DataSets(data, data, data)

# create the network with one 2-node hidden layer
# the params defines some hyperparameters:
#   η := gradient descent speed
#   μ := momentum term
#   λ := L2-penalty param
#   dropoutStrategy
#   mloss
hiddenLayerNodes = [2]
net = buildRegressionNet(ncols(inputs),
                         ncols(targets),
                         hiddenLayerNodes;
                         params = NetParams(η=0.3, μ=0.1, λ=1e-5))

# some extra params for the solve iterations
params = SolverParams(maxiter=maxiter, minerror=1e-6)

# fit the net
solve!(net, params, datasets)

# now predict the output
output = predict(net, float(inputs))

# show it
for (o, d) in zip(output, data)
  println("Result: input=$(d.input) target=$(d.target) output=$o")
end

Implementation progress:

NNet:

  • Basic feedforward network
  • Backprop working
  • Standard activations/layers (Identity, Sigmoid, Tanh, Softsign)
  • Other activations/layers (Softmax, ReLU, LReLU)
  • Dropout regularization
  • Basic data management (train/validate/test splitting)
  • Advanced data cleaning/transformations (handling NaNs, map multinomal classes to dummies, standardizing)
  • Basic gradient descent params (early stopping, momentum, L2 penalty)
  • Easy network building methods (buildClassificationNet, buildRegressionNet)
  • Advanced network building methods (ReLU + dropout, multinomal classification)
  • Generalized penalty functions
  • Online algo: handle sequential data properly (unbiased validation/test data)
  • Cross-validation framework
  • Visualization tools (network design, connection weights, fit plots)
  • Ensembles

Experimental:

  • Spiking neuron model (Leaky Integrate and Fire Neuron based on Spike Response Model)
  • Gaussian receptive field for input spike train generation
  • Liquid State Machine (LSM) framework
  • LSM visualizations
  • LSTM layer
  • Grid-search for hyperparameters/net design
  • GA for hyperparameters/net design
  • Readout model tuning

Other:

  • Naive Bayes

Roadmap/goals:

  • Neural net framework with plug and play components
  • Simple network building. Give type of problem, desired input/output, and let it figure out a good network design
  • Focus on time series and sequential models.
  • Recurrent networks, time delay networks, LSTM.
  • Mini-batch and single update solvers
  • Spiking neural models
  • Echo state networks / Liquid state machines