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comparison pylearn/algorithms/logistic_regression.py @ 537:b054271b2504

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new file structure layout, factories, etc.
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 12 Nov 2008 21:57:54 -0500
parents algorithms/logistic_regression.py@c7ce66b4e8f4
children 85d3300c9a9c
comparison
equal deleted inserted replaced
518:4aa7f74ea93f 537:b054271b2504
1 import sys, copy
2 import theano
3 from theano import tensor as T
4 from theano.tensor import nnet
5 from theano.compile import module
6 from theano import printing, pprint
7 from theano import compile
8
9 import numpy as N
10
11 from ..datasets import make_dataset
12 from .minimizer import make_minimizer
13 from .stopper import make_stopper
14
15 class LogRegN(module.FancyModule):
16
17 def __init__(self,
18 n_in=None, n_out=None,
19 input=None, target=None,
20 w=None, b=None,
21 l2=None, l1=None):
22 super(LogRegN, self).__init__() #boilerplate
23
24 self.n_in = n_in
25 self.n_out = n_out
26
27 self.input = input if input is not None else T.matrix()
28 self.target = target if target is not None else T.lvector()
29
30 self.w = w if w is not None else module.Member(T.dmatrix())
31 self.b = b if b is not None else module.Member(T.dvector())
32
33 #the params of the model are the ones we fit to the data
34 self.params = [p for p in [self.w, self.b] if p.owner is None]
35
36 #the hyper-parameters of the model are not fit to the data
37 self.l2 = l2 if l2 is not None else module.Member(T.dscalar())
38 self.l1 = l1 if l1 is not None else module.Member(T.dscalar())
39
40 #here we actually build the model
41 self.linear_output = T.dot(self.input, self.w) + self.b
42 if 0:
43 self.softmax = nnet.softmax(self.linear_output)
44
45 self._max_pr, self.argmax = T.max_and_argmax(self.linear_output)
46 self._xent = self.target * T.log(self.softmax)
47 else:
48 (self._xent, self.softmax, self._max_pr, self.argmax) =\
49 nnet.crossentropy_softmax_max_and_argmax_1hot(
50 self.linear_output, self.target)
51
52 self.unregularized_cost = T.sum(self._xent)
53 self.l1_cost = self.l1 * T.sum(abs(self.w))
54 self.l2_cost = self.l2 * T.sum(self.w**2)
55 self.regularized_cost = self.unregularized_cost + self.l1_cost + self.l2_cost
56 self._loss_zero_one = T.mean(T.neq(self.argmax, self.target))
57
58 # METHODS
59 if 0: #TODO: PENDING THE BETTER IMPLEMENTATION ABOVE
60 self.predict = module.Method([self.input], self.argmax)
61 self.label_probs = module.Method([self.input], self.softmax)
62 self.validate = module.Method([self.input, self.target],
63 [self._loss_zero_one, self.regularized_cost, self.unregularized_cost])
64
65 def _instance_initialize(self, obj):
66 obj.w = N.zeros((self.n_in, self.n_out))
67 obj.b = N.zeros(self.n_out)
68 obj.__pp_hide__ = ['params']
69
70 def logistic_regression(n_in, n_out, l1, l2, minimizer=None):
71 if n_out == 2:
72 raise NotImplementedError()
73 else:
74 rval = LogRegN(n_in=n_in, n_out=n_out, l1=l1, l2=l2)
75 rval.minimizer = minimizer([rval.input, rval.target], rval.regularized_cost,
76 rval.params)
77 return rval.make()
78
79 #TODO: grouping parameters by prefix does not play well with providing defaults. Think...
80 class _fit_logreg_defaults(object):
81 minimizer_algo = 'dummy'
82 #minimizer_lr = 0.001
83 dataset = 'MNIST_1k'
84 l1 = 0.0
85 l2 = 0.0
86 batchsize = 8
87 verbose = 1
88
89 from ..datasets import MNIST
90 import sgd #TODO: necessary to add it to factory list
91 # consider pre-importing each file in algorithms, datasets (possibly with try/catch around each
92 # import so that this import failure is ignored)
93
94 def fit_logistic_regression_online(state, channel=lambda *args, **kwargs:None):
95 #use stochastic gradient descent
96 state.use_defaults(_fit_logreg_defaults)
97
98 dataset = make_dataset(**state.subdict(prefix='dataset_'))
99 train = dataset.train
100 valid = dataset.valid
101 test = dataset.test
102
103 logreg = logistic_regression(
104 n_in=train.x.shape[1],
105 n_out=dataset.n_classes,
106 l2=state.l2,
107 l1=state.l1,
108 minimizer=make_minimizer(**state.subdict(prefix='minimizer_')))
109
110 batchsize = state.batchsize
111 verbose = state.verbose
112 iter = [0]
113
114 def step():
115 # step by making a pass through the training set
116 for j in xrange(0,len(train.x)-batchsize+1,batchsize):
117 cost_j = logreg.minimizer.step_cost(train.x[j:j+batchsize], train.y[j:j+batchsize])
118 if verbose > 1:
119 print 'estimated train cost', cost_j
120 #TODO: consult iter[0] for periodic saving to cwd (model, minimizer, and stopper)
121
122 def check():
123 validate = logreg.validate(valid.x, valid.y)
124 if verbose > 0:
125 print 'iter', iter[0], 'validate', validate
126 sys.stdout.flush()
127 iter[0] += 1
128 return validate[0]
129
130 def save():
131 return copy.deepcopy(logreg)
132
133 stopper = make_stopper(**state.subdict(prefix='stopper_'))
134 stopper.find_min(step, check, save)
135
136 state.train_01, state.train_rcost, state.train_cost = logreg.validate(train.x, train.y)
137 state.valid_01, state.valid_rcost, state.valid_cost = logreg.validate(valid.x, valid.y)
138 state.test_01, state.test_rcost, state.test_cost = logreg.validate(test.x, test.y)
139
140 state.n_train = len(train.y)
141 state.n_valid = len(valid.y)
142 state.n_test = len(test.y)
143
144 class LogReg2(module.FancyModule):
145 def __init__(self, input=None, targ=None, w=None, b=None, lr=None, regularize=False):
146 super(LogReg2, self).__init__() #boilerplate
147
148 self.input = input if input is not None else T.matrix('input')
149 self.targ = targ if targ is not None else T.lcol()
150
151 self.w = w if w is not None else module.Member(T.dmatrix())
152 self.b = b if b is not None else module.Member(T.dvector())
153 self.lr = lr if lr is not None else module.Member(T.dscalar())
154
155 self.params = [p for p in [self.w, self.b] if p.owner is None]
156
157 output = nnet.sigmoid(T.dot(self.x, self.w) + self.b)
158 xent = -self.targ * T.log(output) - (1.0 - self.targ) * T.log(1.0 - output)
159 sum_xent = T.sum(xent)
160
161 self.output = output
162 self.xent = xent
163 self.sum_xent = sum_xent
164 self.cost = sum_xent
165
166 #define the apply method
167 self.pred = (T.dot(self.input, self.w) + self.b) > 0.0
168 self.apply = module.Method([self.input], self.pred)
169
170 #if this module has any internal parameters, define an update function for them
171 if self.params:
172 gparams = T.grad(sum_xent, self.params)
173 self.update = module.Method([self.input, self.targ], sum_xent,
174 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
175
176