hnn-0.3: A reasonably fast and simple neural network library

AI.HNN.FF.Network

Description

An implementation of feed-forward neural networks in pure Haskell.

It uses weight matrices between each layer to represent the connections between neurons from a layer to the next and exports only the useful bits for a user of the library.

Here is an example of using this module to create a feed-forward neural network with 2 inputs, 2 neurons in a hidden layer and one neuron in the output layer, with random weights, and compute its output for [1,2] using the sigmoid function for activation for all the neurons.

import AI.HNN.FF.Network
import Numeric.LinearAlgebra

main = do
n <- createNetwork 2 [2] 1 :: IO (Network Double)
print \$ output n sigmoid (fromList [1, 1])

Note: Here, I create a Network Double, but you can replace Double with any number type that implements the appropriate typeclasses you can see in the signatures of this module. Having your number type implement the Floating typeclass too is a good idea, since that's what most of the common activation functions require.

Note 2: You can also give some precise weights to initialize the neural network with, with fromWeightMatrices. You can also restore a neural network you had saved using loadNetwork.

Here is an example of how to train a neural network to learn the XOR function. ( for reference: XOR(0, 0) = 0, XOR(0, 1) = 1, XOR(1, 0) = 1, XOR(1, 1) = 0 )

First, let's import hnn's feedforward neural net module, and hmatrix's vector types.

import AI.HNN.FF.Network
import Numeric.LinearAlgebra

Now, we will specify our training set (what the net should try to learn).

samples :: Samples Double
samples = [ fromList [0, 0] --> fromList [0]
, fromList [0, 1] --> fromList [1]
, fromList [1, 0] --> fromList [1]
, fromList [1, 1] --> fromList [0]
]

You can see that this is basically a list of pairs of vectors, the first vector being the input given to the network, the second one being the expected output. Of course, this imply working on a neural network with 2 inputs, and a single neuron on the output layer. Then, let's create one!

main = do
net <- createNetwork 2 [2] 1

You may have noticed we haven't specified a signature this time, unlike in the earlier snippet. Since we gave a signature to samples, specifying we're working with Double numbers, and since we are going to tie net and samples by a call to a learning function, GHC will gladly figure out that net is working with Double.

Now, it's time to train our champion. But first, let's see how bad he is now. The weights are most likely not close to those that will give a good result for simulating XOR. Let's compute the output of the net on the input vectors of our samples, using tanh as the activation function.

  mapM_ (print . output net tanh . fst) samples

Ok, you've tested this, and it gives terrible results. Let's fix this by letting trainNTimes teach our neural net how to behave. Since we're using tanh as our activation function, we will tell it to the training function, and also specify its derivative.

  let smartNet = trainNTimes 1000 0.8 tanh tanh' net samples

So, this tiny piece of code will run the backpropagation algorithm on the samples 1000 times, with a learning rate of 0.8. The learning rate is basically how strongly we should modify the weights when we try to correct the error the net makes on our samples. The bigger it is, the more the weights are going to change significantly. Depending on the case, it can be good, but sometimes it can make the backprop algorithm oscillate around good weight values without actually getting to them. You usually want to test several values and see which ones get you the nicest neural net, which generalizes well to samples that are not in the training set while giving decent results on the training set.

Now, let's see how that worked out for us:

  mapM_ (print . output smartNet tanh . fst) samples

You could even save that neural network's weights to a file, so that you don't need to train it again in the future, using saveNetwork:

  saveNetwork "smartNet.nn" smartNet

Please note that saveNetwork is just a wrapper around zlib compression + serialization using the binary package. AI.HNN.FF.Network also provides a Binary instance for Network, which means you can also simply use encode and decode to have your own saving/restoring routines, or to simply get a bytestring we can send over the network, for example.

Here's a run of the program we described on my machine (with the timing): first set of fromList's is the output of the initial neural network, the second one is the output of smartNet :-)

fromList [0.574915179613429]
fromList [0.767589097192215]
fromList [0.7277396607146663]
fromList [0.8227114080561128]
------------------
fromList [6.763498312099933e-2]
fromList [0.9775186355284375]
fromList [0.9350823296850516]
fromList [-4.400205702560454e-2]

real    0m0.365s
user    0m0.072s
sys     0m0.016s

Rejoyce! Feel free to play around with the library and report any bug, feature request and whatnot to us on our github repository https://github.com/alpmestan/hnn/issues using the appropriate tags. Also, you can see the simple program we studied here with pretty colors at https://github.com/alpmestan/hnn/blob/master/examples/ff/xor.hs and other ones at https://github.com/alpmestan/hnn/tree/master/examples/ff.

Synopsis

# Types

newtype Network a Source #

Our feed-forward neural network type. Note the Binary instance, which means you can use encode and decode in case you need to serialize your neural nets somewhere else than in a file (e.g over the network)

Constructors

 Network Fieldsmatrices :: Vector (Matrix a)the weight matrices

Instances

 (Element a, Show a) => Show (Network a) Source # MethodsshowsPrec :: Int -> Network a -> ShowS #show :: Network a -> String #showList :: [Network a] -> ShowS # (Element a, Binary a) => Binary (Network a) Source # Methodsput :: Network a -> Put #get :: Get (Network a) #putList :: [Network a] -> Put #

type ActivationFunction a = a -> a Source #

The type of an activation function, mostly used for clarity in signatures

type ActivationFunctionDerivative a = a -> a Source #

The type of an activation function's derivative, mostly used for clarity in signatures

type Sample a = (Vector a, Vector a) Source #

Input vector and expected output vector

type Samples a = [Sample a] Source #

List of Samples

(-->) :: Vector a -> Vector a -> Sample a Source #

Handy operator to describe your learning set, avoiding unnecessary parentheses. It's just a synonym for '(,)'. Generally you'll load your learning set from a file, a database or something like that, but it can be nice for quickly playing with hnn or for simple problems where you manually specify your learning set. That is, instead of writing:

samples :: Samples Double
samples = [ (fromList [0, 0], fromList [0])
, (fromList [0, 1], fromList [1])
, (fromList [1, 0], fromList [1])
, (fromList [1, 1], fromList [0])
]

You can write:

samples :: Samples Double
samples = [ fromList [0, 0] --> fromList [0]
, fromList [0, 1] --> fromList [1]
, fromList [1, 0] --> fromList [1]
, fromList [1, 1] --> fromList [0]
]

# Creating a neural network

createNetwork :: (Variate a, Storable a) => Int -> [Int] -> Int -> IO (Network a) Source #

The following creates a neural network with n inputs and if l is [n1, n2, ...] the net will have n1 neurons on the first layer, n2 neurons on the second, and so on ending with k neurons on the output layer, with random weight matrices as a courtesy of uniformVector.

createNetwork n l k

fromWeightMatrices :: Storable a => Vector (Matrix a) -> Network a Source #

Creates a neural network with exactly the weight matrices given as input here. We don't check that the numbers of rows/columns are compatible, etc.

# Computing a neural network's output

output :: (Floating (Vector a), Numeric a, Storable a, Num (Vector a)) => Network a -> ActivationFunction a -> Vector a -> Vector a Source #

Computes the output of the network on the given input vector with the given activation function

tanh :: Floating a => a -> a #

tanh' :: Floating a => a -> a Source #

Derivative of the tanh function from the Prelude.

sigmoid :: Floating a => a -> a Source #

The sigmoid function: 1 / (1 + exp (-x))

sigmoid' :: Floating a => a -> a Source #

Derivative of the sigmoid function: sigmoid x * (1 - sigmoid x)

# Training a neural network

trainUntil :: (Floating (Vector a), Floating a, Numeric a, Num (Vector a), Container Vector a) => (Int -> Network a -> Samples a -> Bool) -> a -> ActivationFunction a -> ActivationFunctionDerivative a -> Network a -> Samples a -> Network a Source #

Generic training function.

The first argument is a predicate that will tell the backpropagation algorithm when to stop. The first argument to the predicate is the epoch, i.e the number of times the backprop has been executed on the samples. The second argument is the current network, and the third is the list of samples. You can thus combine these arguments to create your own criterion.

For example, if you want to stop learning either when the network's quadratic error on the samples, using the tanh function, is below 0.01, or after 1000 epochs, whichever comes first, you could use the following predicate:

pred epochs net samples = if epochs == 1000 then True else quadError tanh net samples < 0.01

You could even use trace to print the error, to see how the error evolves while it's learning, or redirect this to a file from your shell in order to generate a pretty graphics and what not.

The second argument (after the predicate) is the learning rate. Then come the activation function you want, its derivative, the initial neural network, and your training set. Note that we provide trainNTimes and trainUntilErrorBelow for common use cases.

trainNTimes :: (Floating (Vector a), Floating a, Numeric a, Num (Vector a), Container Vector a) => Int -> a -> ActivationFunction a -> ActivationFunctionDerivative a -> Network a -> Samples a -> Network a Source #

Trains the neural network with backpropagation the number of times specified by the Int argument, using the given learning rate (second argument).

trainUntilErrorBelow :: (Floating (Vector a), Floating a, Numeric a, Normed (Vector a), Ord a, Container Vector a, Num (RealOf a), a ~ RealOf a, Show a) => a -> a -> ActivationFunction a -> ActivationFunctionDerivative a -> Network a -> Samples a -> Network a Source #

Trains the neural network until the quadratic error (quadError) comes below the given value (first argument), using the given learning rate (second argument).

Note: this can loop pretty much forever when you're using a bad architecture for the problem, or inappropriate activation functions.

quadError :: (Floating (Vector a), Floating a, Fractional (RealOf a), Normed (Vector a), Numeric a) => ActivationFunction a -> Network a -> Samples a -> RealOf a Source #

Quadratic error on the given training set using the given activation function. Useful to create your own predicates for trainUntil.