Uncommenting softmax optimization

theano/tensor/nnet/nnet.py

@@ -397,41 +397,41 @@ def local_softmax_with_bias(node):
                     return
                 return [sm_bias]
 
-if 0:
-    def softmax_simplifier(numerators, 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
-
-            matching_denom = None
-
-            for denominator in denominators:
-                if denominator.owner and isinstance(denominator.owner.op, tensor.DimShuffle):
-                    if denominator.owner.op.new_order == (0,'x'):
-                        z = denominator.owner.inputs[0] # thing getting dimshuffled
-                        if z.owner and isinstance(z.owner.op, tensor.Sum):
-                            #print 'ASDF', denominator.owner.op.new_order
-                            #print z.owner.op.axis
-                            if z.owner.op.axis == (1,):
-                                #print "almost there.. softmax", x, z.owner.inputs[0]
-                                if z.owner.inputs[0] is numerator:
-                                    matching_denom = denominator
-                                    break
-            if matching_denom:
-                numerators.remove(numerator)
-                denominators.remove(matching_denom)
-                numerators.append(softmax(x))
-        return numerators, denominators
-    opt.local_mul_canonizer.add_simplifier(softmax_simplifier, 'softmax_simplifier')
+def softmax_simplifier(numerators, 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
+
+        matching_denom = None
+
+        for denominator in denominators:
+            if denominator.owner and isinstance(denominator.owner.op, tensor.DimShuffle):
+                if denominator.owner.op.new_order == (0,'x'):
+                    z = denominator.owner.inputs[0] # thing getting dimshuffled
+                    if z.owner and isinstance(z.owner.op, tensor.Sum):
+                        #print 'ASDF', denominator.owner.op.new_order
+                        #print z.owner.op.axis
+                        if z.owner.op.axis == (1,):
+                            #print "almost there.. softmax", x, z.owner.inputs[0]
+                            if z.owner.inputs[0] is numerator:
+                                matching_denom = denominator
+                                break
+        if matching_denom:
+            numerators.remove(numerator)
+            denominators.remove(matching_denom)
+            numerators.append(softmax(x))
+    return 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)