Mercurial > pylearn
changeset 811:ecae21a7262e
Added a rescalwsaturation instance method for DAA inputs groups
| author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
|---|---|
| date | Thu, 13 Aug 2009 11:39:27 -0400 |
| parents | 3a4bc4a0dbf4 |
| children | 6f6724dafcf7 |
| files | pylearn/algorithms/sandbox/DAA_inputs_groups.py |
| diffstat | 1 files changed, 19 insertions(+), 1 deletions(-) [+] |
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--- a/pylearn/algorithms/sandbox/DAA_inputs_groups.py Wed Aug 12 19:19:24 2009 -0400 +++ b/pylearn/algorithms/sandbox/DAA_inputs_groups.py Thu Aug 13 11:39:27 2009 -0400 @@ -540,7 +540,7 @@ # facultative methods if self.debugmethod: - self.activation = [None] * (self.depth) + self.activation = [None] * (self.depth+1) self.representation = [None] * (self.depth) self.recactivation = [None] * (self.depth) self.reconstruction = [None] * (self.depth) @@ -706,6 +706,7 @@ self.NLL = theano.Method(self.inputs[-1],self.daaig[-1]._xent) if self.debugmethod: + self.activation[-1] = theano.Method(self.inputs[-2],self.daaig[-1].linear_output) self.compute_localcost[-1] = theano.Method(self.inputs[-1],self.localcost[-1]) self.compute_localgradients[-1] = theano.Method(self.inputs[-1],self.localgradients[-1]) self.compute_globalcost[-1] = theano.Method(self.inputs[-1],self.globalcost[-1]) @@ -827,6 +828,23 @@ def _instance_nll(self,inst,inputs,target): return numpy.sum(inst.NLL(*(inputs+[target]))) / float(len(target)) + #try-------------------------------------------------------------------- + def _instance_rescalwsaturation(self,inst,inputs): + sat = [None]*(self.depth+1) + for i in range(self.depth+1): + sat[i] = inst.hidsaturation(i,inputs[min(i,self.depth)]) + + for i in range(self.depth-1): + if sat[i+1] > max(sat[:i+1]): + inst.daaig[i+1].wenc = inst.daaig[i+1].wenc/sat[i+1]*max(sat[:i+1]) + inst.daaig[i+1].benc = inst.daaig[i+1].benc/sat[i+1]*max(sat[:i+1]) + sat[i+1] = max(sat[:i+1]) + if sat[-1]>max(sat[:-1]): + inst.daaig[-1].w = inst.daaig[i+1].w/sat[-1]*max(sat[:-1]) + inst.daaig[-1].b = inst.daaig[i+1].b/sat[-1]*max(sat[:-1]) + + #----------------------------------------------------------------------- + def _instance_unsupgrad(self,inst,inputs,layer,param_name): inst.noiseseed(0) gradin = inst.compute_localgradients_in[layer](*inputs)