removed useless code for removing previous redundant sum and dimshuffle

theano/tensor/nnet/nnet.py

@@ -1728,21 +1728,6 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
     else:
         return
 
-    # If the arg to softmax is a broadcasted vector, d_sm has the form:
-    #   DimShuffle{x,0}(Sum{0}(...))
-    # we consider what's inside of the sum instead
-    vector_softmax = False
-    if d_sm.owner and isinstance(d_sm.owner.op, tensor.DimShuffle):
-        ds_op = d_sm.owner.op
-        if ds_op.input_broadcastable == (False,) and ds_op.new_order == ('x', 0):
-            maybe_sum = d_sm.owner.inputs[0]
-            if maybe_sum.owner and isinstance(maybe_sum.owner.op, tensor.Sum):
-                if sm.broadcastable == (True, False)\
-                        and maybe_sum.owner.op.axis == (0,)\
-                        and len(maybe_sum.owner.inputs) == 1:
-                    vector_softmax = True
-                    d_sm = maybe_sum.owner.inputs[0]
-
     # Two cases are supported:
     # 1. AdvancedIncSubtensor(
     #           zeros_like(softmax(x)),
@@ -1885,8 +1870,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
             # Check z is zeros_like(log(sm))
             if not _is_const(z, 0):
                 return
-            if z.broadcastable != (False, False):
-                if not (vector_softmax and z.broadcastable == (True, False)):
+            if z.broadcastable not in [(False, False), (True, False)]:
                     return
             # here we know that we are incrementing a matrix of zeros
             # (or a broadcasted vector).