https://github.com/cgarciae/cybrain

Neural Networks in Cython, inspired by PyBrain.
https://github.com/cgarciae/cybrain

Last synced: 7 days ago
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Neural Networks in Cython, inspired by PyBrain.

• Host: GitHub
• URL: https://github.com/cgarciae/cybrain
• Owner: cgarciae
• Created: 2014-01-25T00:42:36.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2016-01-25T20:56:59.000Z (almost 9 years ago)
• Last Synced: 2024-05-02T01:24:35.791Z (7 months ago)
• Language: HTML
• Size: 21 MB
• Stars: 58
• Watchers: 9
• Forks: 11
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
CyBrain

=======

Neural Networks in Cython, inspired by PyBrain, but focused on speed.

Check examples/logistic_XOR_example.py for a first approach.

XOR Example

===========

    import cybrain as cb

    import numpy as np

    from time import time

    #TRUTH TABLE (DATA)

    X =     [[0.0,0.0]];     Y = [[0.0]]

    X.append([1.0,0.0]); Y.append([1.0])

    X.append([0.0,1.0]); Y.append([1.0])

    X.append([1.0,1.0]); Y.append([0.0])

    #CONVERT DATA TO NUMPY ARRAY

    X, Y = np.array(X), np.array(Y)

    #CREATE NETWORK

    nnet = cb.Network()

    #CREATE LAYERS

    Lin = cb.LinearLayer(2)

    Lhidden = cb.LogisticLayer(2)

    Lout = cb.LogisticLayer(1)

    bias = cb.BiasUnit()

    #ADD LAYERS TO NETWORK

    nnet.inputLayers = [Lin]

    nnet.hiddenLayers = [Lhidden]

    nnet.outputLayers = [Lout]

    nnet.autoInputLayers = [bias]

    #CONNECT LAYERS

    Lin.fullConnectTo(Lhidden)

    Lhidden.fullConnectTo(Lout)

    bias.fullConnectTo(Lhidden)

    bias.fullConnectTo(Lout)

    #CREATE BATCH TRAINER

    rate = 0.1

    nnet.setup()

    batch = cb.FullBatchTrainer(nnet, X, Y, rate)

    #TRAIN

    t1 = time()

    batch.epochs(2000)

    print "Time CyBrain {}".format(time()-t1)

    #PRINT RESULTS

    for i in range(len(X)):

        print "{} ==> {}".format(X[i], nnet.activate(X[i:i+1,:]))

Same Example in PyBrain

========================

    import cybrain as cb

    import numpy as np

    from time import time

    #TRUTH TABLE (DATA)

    X =     [[0.0,0.0]];     Y = [[0.0]]

    X.append([1.0,0.0]); Y.append([1.0])

    X.append([0.0,1.0]); Y.append([1.0])

    X.append([1.0,1.0]); Y.append([0.0])

    #CONVERT DATA TO NUMPY ARRAY

    X, Y = np.array(X), np.array(Y)

    #CREATE NETWORK

    nnet = cb.Network()

    #CREATE LAYERS

    Lin = cb.LinearLayer(2)

    Lhidden = cb.LogisticLayer(2)

    Lout = cb.LogisticLayer(1)

    bias = cb.BiasUnit()

    #ADD LAYERS TO NETWORK

    nnet.inputLayers = [Lin]

    nnet.hiddenLayers = [Lhidden]

    nnet.outputLayers = [Lout]

    nnet.autoInputLayers = [bias]

    #CONNECT LAYERS

    Lin.fullConnectTo(Lhidden)

    Lhidden.fullConnectTo(Lout)

    bias.fullConnectTo(Lhidden)

    bias.fullConnectTo(Lout)

    #CREATE BATCH TRAINER

    rate = 0.1

    nnet.setup()

    batch = cb.FullBatchTrainer(nnet, X, Y, rate)

    #TRAIN

    t1 = time()

    batch.epochs(2000)

    print "Time CyBrain {}".format(time()-t1)

    #PRINT RESULTS

    for i in range(len(X)):

        print "{} ==> {}".format(X[i], np.array(nnet.activate(X[i:i+1,:])))

Outputs

=======

    Time CyBrain 0.211654996872

    [ 0.  0.] ==> [ '0.0365560102866' ]

    [ 1.  0.] ==> [ '0.951081842587'  ]

    [ 0.  1.] ==> [ '0.951928021684'  ]

    [ 1.  1.] ==> [ '0.0332036251855' ]

    

    Time PyBrain 7.03572702408

    [ 0.  0.] ==> [  1.67662906e-08]

    [ 1.  0.] ==> [ 0.99999998]

    [ 0.  1.] ==> [ 0.99999998]

    [ 1.  1.] ==> [  7.30255101e-09]

Roadmap

========

* Implement other common Layers: Tanh, Softmax, etc [High].

* Refactor variable and methods according to python style guide [Medium].

* Cython memoryview operations optimization [High]

* Auto-encoders and CNNs [Requires research]

* Try to organize the folders [Cython research]

* Solutions to Andrew Ng's ML class with CyBrain [Soon]

About the Author

================

My name is Cristian Garcia, I am a Mathematical Engineer and Software developer, so I claim to be a Mathematical Developer.

I live in Colombia but plan to travel abroad as soon as I get my collage degree to do a Master in Artificial Intelligence

or get a job as a Mathematical Developer.

If you happen to need a Mathematical Developer at your company and find my code reasonable, contact me.