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comparison algorithms/logistic_regression.py @ 495:7560817a07e8

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nnet_ops => nnet
author Joseph Turian <turian@gmail.com>
date Tue, 28 Oct 2008 12:09:39 -0400
parents 180d125dc7e2
children a272f4cbf004
comparison
equal deleted inserted replaced
494:02a331ba833b 495:7560817a07e8
1 import theano 1 import theano
2 from theano import tensor as T 2 from theano import tensor as T
3 from theano.tensor import nnet_ops 3 from theano.tensor import nnet
4 from theano.compile import module 4 from theano.compile import module
5 from theano import printing, pprint 5 from theano import printing, pprint
6 from theano import compile 6 from theano import compile
7 7
8 import numpy as N 8 import numpy as N
28 self.b = b if b is not None else module.Member(T.dvector()) 28 self.b = b if b is not None else module.Member(T.dvector())
29 self.lr = lr if lr is not None else module.Member(T.dscalar()) 29 self.lr = lr if lr is not None else module.Member(T.dscalar())
30 30
31 self.params = [p for p in [self.w, self.b] if p.owner is None] 31 self.params = [p for p in [self.w, self.b] if p.owner is None]
32 32
33 xent, y = nnet_ops.crossentropy_softmax_1hot( 33 xent, y = nnet.crossentropy_softmax_1hot(
34 T.dot(self.x, self.w) + self.b, self.targ) 34 T.dot(self.x, self.w) + self.b, self.targ)
35 sum_xent = T.sum(xent) 35 sum_xent = T.sum(xent)
36 36
37 self.y = y 37 self.y = y
38 self.sum_xent = sum_xent 38 self.sum_xent = sum_xent
68 self.b = b if b is not None else module.Member(T.dvector()) 68 self.b = b if b is not None else module.Member(T.dvector())
69 self.lr = lr if lr is not None else module.Member(T.dscalar()) 69 self.lr = lr if lr is not None else module.Member(T.dscalar())
70 70
71 self.params = [p for p in [self.w, self.b] if p.owner is None] 71 self.params = [p for p in [self.w, self.b] if p.owner is None]
72 72
73 y = nnet_ops.sigmoid(T.dot(self.x, self.w)) 73 y = nnet.sigmoid(T.dot(self.x, self.w))
74 xent = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y) 74 xent = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y)
75 sum_xent = T.sum(xent) 75 sum_xent = T.sum(xent)
76 76
77 self.y = y 77 self.y = y
78 self.xent = xent 78 self.xent = xent