Allow optimization to work on xent(softmax(vector))

theano/tensor/nnet/nnet.py

@@ -1002,6 +1002,21 @@ 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)),
@@ -1140,9 +1155,11 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
             if not _is_const(z, 0):
                 return
             if z.type not in (dmatrix, fmatrix):
-                return
+                if not (vector_softmax and z.broadcastable == (True, False)):
+                    return
             # here we know that we are incrementing a matrix of zeros
-            # Since out_grad and sm are the inputs of softmax_grad,
+            # (or a broadcasted vector).
+            # Since d_sm and sm are the inputs of softmax_grad,
             # if the graph is valid, they have the same shape, so we
             # also know that z has the right shape.