Mercurial > pylearn
changeset 814:90b50bd79bc0
independant Tie weigths auxiliary bug fix DAA inputs groups
| author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
|---|---|
| date | Fri, 14 Aug 2009 19:19:30 -0400 |
| parents | 928f12f9c6fe |
| children | 4a52878d1ddc |
| files | pylearn/algorithms/sandbox/DAA_inputs_groups.py |
| diffstat | 1 files changed, 3 insertions(+), 3 deletions(-) [+] |
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--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py Thu Aug 13 12:11:51 2009 -0400 +++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py Fri Aug 14 19:19:30 2009 -0400 @@ -219,7 +219,7 @@ if self.auxinput is not None: self.wauxenc = [T.dmatrix('wauxenc%s'%i) for i in range(len(auxin_size))] - self.wauxdec =[ self.wauxenc[i].T if tie_weights_aux else T.dmatrix('wauxdec%s'%i) for i in\ + self.wauxdec =[ self.wauxenc[i].T if self.tie_weights_aux else T.dmatrix('wauxdec%s'%i) for i in\ range(len(auxin_size))] self.bauxdec = [T.dvector('bauxdec%s'%i) for i in range(len(auxin_size))] @@ -823,7 +823,7 @@ return numpy.mean(numpy.median(abs(inst.recactivation[layer](*inputs)),1)) def _instance_error(self,inst,inputs,target): - return numpy.sum(inst.classify(*inputs) != target) / float(len(target)) *100.0 + return numpy.sum(inst.classify(*inputs) != target) / float(len(target))*100.0 def _instance_nll(self,inst,inputs,target): return numpy.sum(inst.NLL(*(inputs+[target]))) / float(len(target)) @@ -842,7 +842,7 @@ if sat[-1]>max(sat[:-1]): inst.daaig[-1].w = inst.daaig[-1].w/sat[-1]*max(sat[:-1]) inst.daaig[-1].b = inst.daaig[-1].b/sat[-1]*max(sat[:-1]) - + #----------------------------------------------------------------------- def _instance_unsupgrad(self,inst,inputs,layer,param_name):