Python vSGDfd Exemples

Exemple #1

Fichier : test_xor.py Projet : yindlib/py-optim

def testNewTraining2(hidden=15, n=None):
    d = XORDataSet()
    if n is None:
        n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
    provider = ModuleWrapper(d, n, shuffling=False)
    algo = vSGDfd(provider, n.params.copy(), callback=printy)
    algo.run(1000)

Exemple #2

Fichier : test_xor.py Projet : bitfort/py-optim

def testNewTraining2(hidden=15, n=None):
    d = XORDataSet()
    if n is None:
        n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
    provider = ModuleWrapper(d, n, shuffling=False)
    algo = vSGDfd(provider, n.params.copy(), callback=printy)
    algo.run(1000)

Exemple #3

Fichier : test_xor.py Projet : Andres-Hernandez/py-optim

def testCE(hidden=15):
    """ Test when doing the classification using the cross-entropy loss."""
    
    d = XORDataSet(True)
    n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
    p0 = n.params.copy()
    
    provider = ClassificationModuleWrapper(d, n, shuffling=False)
    algo = SGD(provider, n.params.copy(), callback=printy, learning_rate=0.01, momentum=0.99)
    print '\n' * 2
    print 'SGD-CE'
    algo.run(1000)
    
    n._setParameters(p0)
    provider = ClassificationModuleWrapper(d, n, shuffling=False)
    algo = vSGDfd(provider, n.params.copy(), callback=printy)
    print '\n' * 2
    print 'vSGD-CE'
    algo.run(1000)

Exemple #4

Fichier : test_corn.py Projet : Andres-Hernandez/py-optim

def testTrain():
    from algorithms.vsgd import vSGDfd
    from core.datainterface import ModuleWrapper
    D, net = prepare()
    print 'init', rmse(D,net), net.params
    print
    p0 = net.params.copy()
    for bs in range(1, 11):
        net._setParameters(p0.copy())
        provider = ModuleWrapper(D, net, shuffling=True)
        algo = vSGDfd(provider, net.params.copy(),batch_size=bs,
                      #callback=printy,
                      #verbose=True,
                      )
        # 1000 epochs
        algo.run(1000 * 
                 len(D)/bs)
        #print 
        net._setParameters(algo.bestParameters)
        print 'batch size',bs, 'RMSE', rmse(D, net), 'weights', net.params
        print

Exemple #5

Fichier : test_xor.py Projet : yindlib/py-optim

def testCE(hidden=15):
    """ Test when doing the classification using the cross-entropy loss."""

    d = XORDataSet(True)
    n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
    p0 = n.params.copy()

    provider = ClassificationModuleWrapper(d, n, shuffling=False)
    algo = SGD(provider,
               n.params.copy(),
               callback=printy,
               learning_rate=0.01,
               momentum=0.99)
    print '\n' * 2
    print 'SGD-CE'
    algo.run(1000)

    n._setParameters(p0)
    provider = ClassificationModuleWrapper(d, n, shuffling=False)
    algo = vSGDfd(provider, n.params.copy(), callback=printy)
    print '\n' * 2
    print 'vSGD-CE'
    algo.run(1000)

Exemple #6

Fichier : test_corn.py Projet : yindlib/py-optim

def testTrain():
    from algorithms.vsgd import vSGDfd
    from core.datainterface import ModuleWrapper
    D, net = prepare()
    print 'init', rmse(D, net), net.params
    print
    p0 = net.params.copy()
    for bs in range(1, 11):
        net._setParameters(p0.copy())
        provider = ModuleWrapper(D, net, shuffling=True)
        algo = vSGDfd(
            provider,
            net.params.copy(),
            batch_size=bs,
            #callback=printy,
            #verbose=True,
        )
        # 1000 epochs
        algo.run(1000 * len(D) / bs)
        #print
        net._setParameters(algo.bestParameters)
        print 'batch size', bs, 'RMSE', rmse(D, net), 'weights', net.params
        print