Remove unused implementation start

theano/tensor/nnet/nnet.py

@@ -432,48 +432,6 @@ def softmax_simplifier(numerators, denominators):
 opt.local_mul_canonizer.add_simplifier(softmax_simplifier, 'softmax_simplifier')
 
 if 0:
-    def softmax_grad_simplifier(numerators, denominators):
-        print "mul simplify numerators"
-        printing.debugprint(numerators)
-        print "mul simplify denominators"
-        printing.debugprint(denominators)
-        for numerator in list(numerators):
-            #TODO: a single softmax'd vector??
-            if not numerator.type.dtype.startswith('float'):
-                continue
-            if not numerator.type.broadcastable == (False, False):
-                continue
-            if numerator.owner and numerator.owner.op == tensor.exp:
-                x = numerator.owner.inputs[0]
-            else:
-                continue
-            print "A", denominators
-
-            matching_denom = None
-
-            for denominator in denominators:
-                if denominator.owner and denominator.owner.op == tensor.add:
-                    if len(denominator.owner.inputs)==2:
-                        dl,dr = denominator.owner.inputs
-                        # check to see if either dl or dr is softmax(x)
-                        # if yes, we are probably dealing with the gradient of softmax
-                        
-                        other=None
-                        if dl.owner and dl.owner.op == softmax and dl.owner.inputs[0]==x:
-                            other=dr
-                        if dr.owner and dr.owner.op == softmax and dr.owner.inputs[0]==x:
-                            other=dl
-
-                        if other:
-                            print "OTHER", other
-
-            if matching_denom:
-                numerators.remove(numerator)
-                denominators.remove(matching_denom)
-                numerators.append(softmax(x))
-        return numerators, denominators
-    #opt.local_mul_canonizer.add_simplifier(softmax_grad_simplifier, 'softmax_grad_simplifier')
-
     @opt.register_specialize
     @gof.local_optimizer([])
     def local_softmax_grad(node):