LambdaNet is an artificial neural network library written in Haskell that abstracts network creation, training, and use as higher order functions. The benefit of this approach is that it provides a framework in which users can:

• quickly iterate through network designs by using different functional components
• experiment by writing small functional components to extend the library

The library comes with a pre-defined set of functions that can be composed in many ways to operate on real-world data. These will be enumerated later in the documentation.

The code from this repo doesn't reflect the current release of LambdaNet. The README for the current release on Hackage can be found here.

The first step is to follow the HMatrix installation instructions. After that, LambdaNet can be installed through Cabal:

cabal update
cabal install LambdaNet

Alternatively, you can use the nightly. The API may be different than what is covered in the README, but the examples/ folder will always contain a working file using all the features of the current commit.

To install the nightly build, simply run:

git clone https://github.com/jbarrow/LambdaNet.git && cd LambdaNet
cabal install

Using LambdaNet to rapidly prototype networks using built-in functions requires only a minimal level of Haskell knowledge (although getting the data into the right form may be more difficult). However, extending the library may require a more in-depth knowledge of Haskell and functional programming techniques.

You can find a quick example of using the network in XOR.hs. Once LambdaNet is installed, download XOR.hs, and then you can run the file in your REPL to see the results:

runhaskell examples/XOR.hs

The rest of this section dissects the XOR network in order to talk about the design of LambdaNet.

Before you can train or use a network, you must have training data. The training data is a tuple of vectors, the first value being the input to the network, and the second value being the expected output.

For the XOR network, the data is easily hardcoded:

let trainData = [
  (fromList [0.0, 0.0], fromList [0.0]),
  (fromList [0.0, 1.0], fromList [1.0]),
  (fromList [1.0, 0.0], fromList [1.0]),
  (fromList [1.0, 1.0], fromList [0.0])
]

However, for any non-trivial application the most difficult work will be getting the data in this form. Unfortunately, LambdaNet does not currently have tools to support data handling.

The first step in creating a network is to define a list of layer definitions. The type layer definition takes a neuron type, a count of neurons in the layer, and a connectivity function.

Creating the layer definitions for a three-layer XOR network, with 2 neurons in the input layer, 2 hidden neurons, and 1 output neuron can be done as:

let l = LayerDefinition sigmoidNeuron 2 connectFully
let l' = LayerDefinition sigmoidNeuron 2 connectFully
let l'' = LayerDefinition sigmoidNeuron 1 connectFully

A neuron is simply defined as an activation function and its derivative, and the LambdaNet library provides three built-in neuron types:

• sigmoidNeuron - A neuron with a sigmoid activation function
• tanhNeuron - A neuron with a hyperbolic tangent activation function
• recluNeuron - A neuron with a rectified linear activation function

By passing one of these functions into a LayerDefinition, you can create a layer with neurons of that type.

A connectivity function is a bit more opaque. Currently, the library only provides connectFully, a function which creates a fully connected feed-forward network.

Simply, the connectivity function takes in the number of neurons in layer l and the number of neurons in layer l + 1, and returns a boolean matrix of integers (0/1) that represents the connectivity graph of the layers -- a 0 means two neurons are not connected and a 1 means they are. The starting weights are defined later.

The createNetwork function takes in a random transform, an entropy generator, and a list of layer definitions, and returns a network.

For the XOR network, the createNetwork function is:

let n = createNetwork normals (mkStdGen 4) [l, l', l'']

Our source of entropy is the very random: mkStdGen 4, which will always result in the same generator.

The random transform function is a transform that operates on a stream of uniformly distributed random numbers and returns a stream of floating point numbers.

Currently, the two defined distributions are:

• uniforms - A trivial function that returns a stream of uniformly distributed random numbers
• normals - A slightly less-trivial function that uses the Box-Muller transform to create a stream of numbers ~ N(0, 1)

Work is being done to offer a student t-distribution, which would require support for a chi-squared distribution transformation.

In order to train a network, you must create a new trainer:

let t = BackpropTrainer (3 :: Float) quadraticCost quadraticCost'

The BackpropTrainer type takes in a learning rate, a cost function, and its derivative.

The actual training of the network, the fit function uses the trainer, a network, and the training data, and returns a new, trained network. For the XOR network, this is:

let n' = trainUntilErrorLessThan n t online dat 0.01

LambdaNet provides three training methods:

• trainUntil
• trainUntilErrorLessThan
• trainNTimes

The trainUntil function takes a StopCondition (check Network/Trainer.hs) for more information, and the last two are simply wrappers for the first one that provide specific predicates.

The calculated error is what is returned by the cost function.

Currently, the only provided cost function is the quadratic error cost function, quadraticCost and its derivative, quadraticCost'. I am about to add the cross-entropy cost function.

Selection functions break up a dataset for each round of training. The currently provided selection functions are:

• minibatch n - You must provide an n and partially apply it to minibatch to get a valid selection function. This function updates the network after every n passes.
• online - Using this function means that the network updates after every training example.

For small data sets, it's better to use online, while for larger data sets, the training can occur much faster if you use a reasonably sized minibatch.

Once the network is trained, you can use it with your test data or production data:

predict (fromList [1, 0]) n'

LambdaNet at least attempts to follow a Scikit-Learn style naming scheme with fit and predict functions.

Once a network has been trained, the weights and biases can be stored in a file:

saveNetwork "xor.ann" n'

By calling saveNetwork with a file path, you can save the state of the network.

Loading a network requires passing in a list of layer definitions for the original network, but will load all the weights and biases of the saved network:

n'' <- loadNetwork "xor.ann" [l, l', l'']

Note that the loadNetwork function returns an IO (Network), you can't simply call predict or train on the object returned by loadNetwork. Using the approach in XOR.hs should allow you to work with the returned object.

What has been outlined above is only the first stages of LambdaNet. I intend to support some additional features, such as:

• Unit testing
• Self-organizing maps
• Regularization functions
• Additional trainer types (RProp, RMSProp)
• Additional cost functions

In order to develop more complex network architectures, it is important to ensure that all of the basics are working -- especially as the API undergoes changes. To run the unit tests:

git clone https://github.com/jbarrow/LambdaNet.git && cd LambdaNet
cabal install
cd test
runhaskell Main.hs

This will download the most recent version of LambdaNet and run all the unit tests.

SOMs were chosen as the next architecture to develop because they make different assumptions than FeedForward networks. This allows us to see how the current library handles building out new architectures. Already this has forced a change in the Neuron model and spurred the development of a visualizations package (in order to usefully understand the outputs of the SOMs).

Standard backprop training is subject to overfitting and falling into local minima. By providing support for regularization and momentum, LambdaNet will be able to provide more extensible and robust training.

The future goals are:

• Convolutional Networks
• Data handling for Neural Networks

All the documentation for the network was generated in the following manner. In the docs folder, run:

runhaskell docs.hs
python analysis.py

Note that I am currently working on removing the Python image analysis from the library, and switching it with Haskell and gnuplot. I'm also working on using the generated images in network documentation.