Mercurial > pylearn
view algorithms/logistic_regression.py @ 495:7560817a07e8
nnet_ops => nnet
author | Joseph Turian <turian@gmail.com> |
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date | Tue, 28 Oct 2008 12:09:39 -0400 |
parents | 180d125dc7e2 |
children | a272f4cbf004 |
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import theano from theano import tensor as T from theano.tensor import nnet from theano.compile import module from theano import printing, pprint from theano import compile import numpy as N class Module_Nclass(module.FancyModule): class InstanceType(module.FancyModuleInstance): def initialize(self, n_in, n_out, rng=N.random): #self.component is the LogisticRegressionTemplate instance that built this guy. self.w = N.zeros((n_in, n_out)) self.b = N.zeros(n_out) self.lr = 0.01 self.__hide__ = ['params'] def __init__(self, x=None, targ=None, w=None, b=None, lr=None, regularize=False): super(Module_Nclass, self).__init__() #boilerplate self.x = x if x is not None else T.matrix() self.targ = targ if targ is not None else T.lvector() self.w = w if w is not None else module.Member(T.dmatrix()) self.b = b if b is not None else module.Member(T.dvector()) self.lr = lr if lr is not None else module.Member(T.dscalar()) self.params = [p for p in [self.w, self.b] if p.owner is None] xent, y = nnet.crossentropy_softmax_1hot( T.dot(self.x, self.w) + self.b, self.targ) sum_xent = T.sum(xent) self.y = y self.sum_xent = sum_xent self.cost = sum_xent #define the apply method self.pred = T.argmax(T.dot(self.x, self.w) + self.b, axis=1) self.apply = module.Method([self.x], self.pred) if self.params: gparams = T.grad(sum_xent, self.params) self.update = module.Method([self.x, self.targ], sum_xent, updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams))) class Module(module.FancyModule): class InstanceType(module.FancyModuleInstance): def initialize(self, n_in): #self.component is the LogisticRegressionTemplate instance that built this guy. self.w = N.random.randn(n_in,1) self.b = N.random.randn(1) self.lr = 0.01 self.__hide__ = ['params'] def __init__(self, x=None, targ=None, w=None, b=None, lr=None, regularize=False): super(Module, self).__init__() #boilerplate self.x = x if x is not None else T.matrix() self.targ = targ if targ is not None else T.lcol() self.w = w if w is not None else module.Member(T.dmatrix()) self.b = b if b is not None else module.Member(T.dvector()) self.lr = lr if lr is not None else module.Member(T.dscalar()) self.params = [p for p in [self.w, self.b] if p.owner is None] y = nnet.sigmoid(T.dot(self.x, self.w)) xent = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y) sum_xent = T.sum(xent) self.y = y self.xent = xent self.sum_xent = sum_xent self.cost = sum_xent #define the apply method self.pred = (T.dot(self.x, self.w) + self.b) > 0.0 self.apply = module.Method([self.x], self.pred) #if this module has any internal parameters, define an update function for them if self.params: gparams = T.grad(sum_xent, self.params) self.update = module.Method([self.x, self.targ], sum_xent, updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams))) class Learner(object): """TODO: Encapsulate the algorithm for finding an optimal regularization coefficient""" pass