Mercurial > pylearn

comparison pylearn/sandbox/scan_inputs_groups.py @ 694:69947f4e9c0e

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added a Mask creation Op and fixed some bugs
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Thu, 14 May 2009 19:34:21 -0400
parents 0457dfa6fcad
children 33e13d8ca7d3
comparison
equal deleted inserted replaced
693:ee7026de9681 694:69947f4e9c0e
103 raise NotImplementedError('size of index different of inputs list size',idx_list) 103 raise NotImplementedError('size of index different of inputs list size',idx_list)
104 if max(idx_list) >= (len(args)-2)+1 : 104 if max(idx_list) >= (len(args)-2)+1 :
105 raise NotImplementedError('index superior to weight list length',idx_list) 105 raise NotImplementedError('index superior to weight list length',idx_list)
106 for i in range(len(args[1])): 106 for i in range(len(args[1])):
107 if (args[1][i].shape)[0] != batchsize: 107 if (args[1][i].shape)[0] != batchsize:
108 raise NotImplementedError('different batchsize in the inputs list',args[1][i]) 108 raise NotImplementedError('different batchsize in the inputs list',args[1][i].shape)
109 for i in range(len(args)-2): 109 for i in range(len(args)-2):
110 if (args[2+i].shape)[1] != n_hid: 110 if (args[2+i].shape)[1] != n_hid:
111 raise NotImplementedError('different length of hidden in the weights list',args[2+i]) 111 raise NotImplementedError('different length of hidden in the weights list',args[2+i].shape)
112 112
113 for i in range(len(idx_list)): 113 for i in range(len(idx_list)):
114 if idx_list[i]>0: 114 if idx_list[i]>0:
115 if hidcalc: 115 if hidcalc:
116 self.m.dotin(args[1][i],args[2+int(idx_list[i]-1)]) 116 self.m.dotin(args[1][i],args[2+int(idx_list[i]-1)])
166 raise NotImplementedError('size of index different of inputs list size',idx_list) 166 raise NotImplementedError('size of index different of inputs list size',idx_list)
167 if max(idx_list) >= (len(args)-3)+1 : 167 if max(idx_list) >= (len(args)-3)+1 :
168 raise NotImplementedError('index superior to weight list length',idx_list) 168 raise NotImplementedError('index superior to weight list length',idx_list)
169 for i in range(len(args[1])): 169 for i in range(len(args[1])):
170 if (args[1][i].shape)[0] != batchsize: 170 if (args[1][i].shape)[0] != batchsize:
171 raise NotImplementedError('different batchsize in the inputs list',args[1][i]) 171 raise NotImplementedError('different batchsize in the inputs list',args[1][i].shape)
172 for i in range(len(args)-3): 172 for i in range(len(args)-3):
173 if (args[2+i].shape)[1] != n_hid: 173 if (args[2+i].shape)[1] != n_hid:
174 raise NotImplementedError('different length of hidden in the weights list',args[2+i]) 174 raise NotImplementedError('different length of hidden in the weights list',args[2+i].shape)
175 175
176 zcalc = [False for i in range(len(args)-3)] 176 zcalc = [False for i in range(len(args)-3)]
177 177
178 for i in range(len(idx_list)): 178 for i in range(len(idx_list)):
179 if idx_list[i]>0: 179 if idx_list[i]>0:
232 raise NotImplementedError('index superior to weight list length',idx_list) 232 raise NotImplementedError('index superior to weight list length',idx_list)
233 if len(idx_list) != len(args[1]) : 233 if len(idx_list) != len(args[1]) :
234 raise NotImplementedError('size of index different of inputs list size',idx_list) 234 raise NotImplementedError('size of index different of inputs list size',idx_list)
235 for i in range(len(args)-3): 235 for i in range(len(args)-3):
236 if (args[3+i].shape)[0] != n_hid: 236 if (args[3+i].shape)[0] != n_hid:
237 raise NotImplementedError('different length of hidden in the weights list',args[3+i]) 237 raise NotImplementedError('different length of hidden in the weights list',args[3+i].shape)
238 238
239 zcalc = [False for i in idx_list] 239 zcalc = [False for i in idx_list]
240 z[0] = [None for i in idx_list] 240 z[0] = [None for i in idx_list]
241 241
242 for i in range(len(idx_list)): 242 for i in range(len(idx_list)):
302 raise NotImplementedError('index superior to weight list length',idx_list) 302 raise NotImplementedError('index superior to weight list length',idx_list)
303 if len(idx_list) != len(args[1]) : 303 if len(idx_list) != len(args[1]) :
304 raise NotImplementedError('size of index different of inputs list size',idx_list) 304 raise NotImplementedError('size of index different of inputs list size',idx_list)
305 for i in range(len(args)-4): 305 for i in range(len(args)-4):
306 if (args[3+i].shape)[0] != n_hid: 306 if (args[3+i].shape)[0] != n_hid:
307 raise NotImplementedError('different length of hidden in the weights list',args[3+i]) 307 raise NotImplementedError('different length of hidden in the weights list',args[3+i].shape)
308 308
309 zidx=numpy.zeros((len(idx_list)+1)) 309 zidx=numpy.zeros((len(idx_list)+1))
310 310
311 for i in range(len(idx_list)): 311 for i in range(len(idx_list)):
312 if idx_list[i] == 0: 312 if idx_list[i] == 0:
375 def perform(self, node, (idx_list,inputs_list,noise_level_bit,noise_level_group), (y,z)): 375 def perform(self, node, (idx_list,inputs_list,noise_level_bit,noise_level_group), (y,z)):
376 376
377 if len(idx_list) != len(inputs_list) : 377 if len(idx_list) != len(inputs_list) :
378 raise NotImplementedError('size of index different of inputs list size',idx_list) 378 raise NotImplementedError('size of index different of inputs list size',idx_list)
379 379
380 y[0] = [-i if (i>0 and not(self.R.noisify_group_bool(noise_level_group))) else i for i in idx_list] 380 y[0] = numpy.asarray([-i if (i>0 and not(self.R.noisify_group_bool(noise_level_group))) else i for i in idx_list])
381 z[0] = [(self.R.noisify_bit(inputs_list[i],noise_level_bit) if y[0][i]>0 else numpy.zeros((inputs_list[i].shape)))\ 381 z[0] = [(self.R.noisify_bit(inputs_list[i],noise_level_bit) if y[0][i]>0 else numpy.zeros((inputs_list[i].shape)))\
382 for i in range(len(inputs_list))] 382 for i in range(len(inputs_list))]
383 383
384 def grad(self,args,gz): 384 def grad(self,args,gz):
385 return [None,None,None,None] 385 return [None,None,None,None]
386 386
387 387
388 def __hash__(self): 388 def __hash__(self):
389 return hash(ScanNoise)^hash(self.seed)^hash(self.R)^12254 389 return hash(ScanNoise)^hash(self.seed)^hash(self.R.rand)^12254
390 390
391 def __str__(self): 391 def __str__(self):
392 return "ScanNoise" 392 return "ScanNoise"
393 393
394 scannoise=ScanNoise() 394 scannoise=ScanNoise()
498 def __hash__(self): 498 def __hash__(self):
499 return hash(ScanBiasDecGrad)^41256 499 return hash(ScanBiasDecGrad)^41256
500 500
501 def __str__(self): 501 def __str__(self):
502 return "ScanBiasDecGrad" 502 return "ScanBiasDecGrad"
503
504 # Mask construction------------------------------------
505 class ScanMask(Op):
506 """This Op takes an index list (as tensor.ivector) and a list of weigths.
507 It will construct a list of T.iscalar representing the Mask
508 to do the correct regularisation on the weigths"""
509 def __init__(self,encbool=True):
510 self.encbool = encbool
511
512 def make_node(self, idx_list, weights_list):
513 idx_list = Checkidx_list(idx_list)
514 weights_list = Checkweights_list(weights_list)
515 return Apply(self, [idx_list] + weights_list, [T.iscalar() for i in range(len(weights_list))])
516
517 def perform(self, node, args, z):
518 if self.encbool:
519 idx_list = args[0][args[0]>0]
520 dim = 1
521 else:
522 idx_list = abs(args[0][args[0] != 0])
523 dim = 0
524 n_hid = args[1].shape[dim]
525
526 if max(idx_list) >= (len(args)-1)+1 :
527 raise NotImplementedError('index superior to weights list length',idx_listdec)
528 for i in range(len(args)-1):
529 if args[1+i].shape[dim] != n_hid:
530 raise NotImplementedError('different length of hidden in the encoding weights list',args[1+i].shape)
531
532 for i in range(len(args[1:])):
533 z[i][0] = numpy.asarray((idx_list == i+1).sum(),dtype='int32')
534
535 def __hash__(self):
536 return hash(ScanMask)^hash(self.encbool)^11447
537
538 def grad(self,args,gz):
539 return [None] * len(args)
540
541 def __str__(self):
542 if self.encbool:
543 string = "Enc"
544 else:
545 string = "Dec"
546 return "ScanMask" + string
547
548 scanmaskenc=ScanMask(True)
549 scanmaskdec=ScanMask(False)