def __init__(self, batch_size=600,learning_rate=0.13, L1_lambda=0.00, L2_lambda=0.0000, n_epochs=1000):
'''
'''
trainer.__init__(self)
self.batch_size = batch_size
self.L1_lambda = L1_lambda;
self.L2_lambda = L2_lambda;
self.learning_rate =learning_rate;
self.n_epochs=n_epochs;
def __init__(self, batch_size=600,learning_rate=0.001, L1_lambda=0.00, L2_lambda=0.0000, n_epochs=1000,decay=0.9,momentum = 0.0):
'''
'''
trainer.__init__(self)
self.batch_size = batch_size
self.L1_lambda = L1_lambda;
self.L2_lambda = L2_lambda;
self.learning_rate =learning_rate;
self.n_epochs=n_epochs;
self.decay = decay
self.momentum = momentum
def __init__(self, learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000, batch_size=20):
'''
Constructor
learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
dataset='mnist.pkl.gz', batch_size=20, n_hidden=500
'''
trainer.__init__(self)
self.batch_size = batch_size
self.L1_lambda = L1_reg;
self.L2_lambda = L2_reg;
self.learning_rate =learning_rate;
self.n_epochs=n_epochs;
self.early_stopping_threshold = 0.995
def __init__(self, module, dataset=None, learningrate=0.01, lrdecay=1.0,
momentum=0., verbose=False, batchlearning=False,
weightdecay=0.):
trainer.__init__(self, module)
self.verbose = verbose
self.batchlearning = batchlearning
self.weightdecay = weightdecay
self.epoch = 0
self.totalepochs = 0
self.descent = GradientDescent()
self.descent.alpha = learningrate
self.descent.momentum = momentum
self.descent.alphadecay = lrdecay
self.descent.init(module.weights)