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annotate pylearn/algorithms/rbm.py @ 544:de6de7c2c54b

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merged and changed state to dictionary
author James Bergstra <bergstrj@iro.umontreal.ca>
date Mon, 17 Nov 2008 20:05:31 -0500
parents ee5324c21e60 5b4ccbf022c8
children 40cae12a9bb8
rev   line source
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1 import sys, copy
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2 import theano
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3 from theano import tensor as T
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4 from theano.tensor.nnet import sigmoid
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5 from theano.compile import module
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6 from theano import printing, pprint
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7 from theano import compile
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8
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9 import numpy as N
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10
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11 from ..datasets import make_dataset
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12 from .minimizer import make_minimizer
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13 from .stopper import make_stopper
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14 from ..dbdict.experiment import subdict
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15
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16 class RBM(T.RModule):
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17
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18 # is it really necessary to pass ALL of these ? - GD
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19 def __init__(self,
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20 nvis=None, nhid=None,
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21 input=None,
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22 w=None, hidb=None, visb=None,
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23 seed=0, lr=0.1):
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24
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25 super(RBM, self).__init__()
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26 self.nhid, self.nvis = nhid, nvis
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27 self.lr = lr
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28
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29 # symbolic theano stuff
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30 # what about multidimensional inputs/outputs ? do they have to be
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31 # flattened or should we used tensors instead ?
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32 self.w = w if w is not None else module.Member(T.dmatrix())
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33 self.visb = visb if visb is not None else module.Member(T.dvector())
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34 self.hidb = hidb if hidb is not None else module.Member(T.dvector())
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35 self.seed = seed;
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36
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37 # 1-step Markov chain
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38 vis = T.dmatrix()
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39 hid = sigmoid(T.dot(vis, self.w) + self.hidb)
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40 hid_sample = self.random.binomial(T.shape(hid), 1, hid)
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41 neg_vis = sigmoid(T.dot(hid_sample, self.w.T) + self.visb)
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42 neg_vis_sample = self.random.binomial(T.shape(neg_vis), 1, neg_vis)
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43 neg_hid = sigmoid(T.dot(neg_vis_sample, self.w) + self.hidb)
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44
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45 # function which execute 1-step Markov chain (with and without cd updates)
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46 self.updownup = module.Method([vis], [hid, neg_vis_sample, neg_hid])
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47
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48 # function to perform manual cd update given 2 visible and 2 hidden values
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49 vistemp = T.dmatrix()
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50 hidtemp = T.dmatrix()
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51 nvistemp = T.dmatrix()
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52 nhidtemp = T.dmatrix()
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53 self.cd_update = module.Method([vistemp, hidtemp, nvistemp, nhidtemp],
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54 [],
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55 updates = {self.w: self.w + self.lr *
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56 (T.dot(vistemp.T, hidtemp) -
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57 T.dot(nvistemp.T, nhidtemp)),
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58 self.visb: self.visb + self.lr *
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59 (T.sum(vistemp - nvistemp,axis=0)),
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60 self.hidb: self.hidb + self.lr *
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61 (T.sum(hidtemp - nhidtemp,axis=0))});
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62
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63 # TODO: add parameter for weigth initialization
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64 def _instance_initialize(self, obj):
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65 obj.w = N.random.standard_normal((self.nvis,self.nhid))
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66 obj.visb = N.zeros(self.nvis)
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67 obj.hidb = N.zeros(self.nhid)
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68 obj.seed(self.seed);
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69
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70 def _instance_cd1(self, obj, input, k=1):
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71 poshid, negvissample, neghid = obj.updownup(input)
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72 for i in xrange(k-1):
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73 ahid, negvissample, neghid = obj.updownup(negvissample)
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74 # CD-k update
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75 obj.cd_update(input, poshid, negvissample, neghid)
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76
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77
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78 def train_rbm(state, channel=lambda *args, **kwargs:None):
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79 dataset = make_dataset(**subdict_copy(state, prefix='dataset_'))
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80 train = dataset.train
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81
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82 rbm_module = RBM(
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83 nvis=train.x.shape[1],
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84 nhid=state['nhid'])
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85 rbm = rbm_module.make()
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86
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87 batchsize = state.get('batchsize', 1)
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88 verbose = state.get('verbose', 1)
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89 iter = [0]
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90
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91 while iter[0] != state['max_iters']:
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92 for j in xrange(0,len(train.x)-batchsize+1,batchsize):
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93 rbm.cd1(train.x[j:j+batchsize])
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94 if verbose > 1:
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95 print 'estimated train cost...'
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96 if iter[0] == state['max_iters']:
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97 break
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98 else:
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99 iter[0] += 1
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100