Replaced softmax with either softmax_op or softmax_graph

theano/tensor/nnet/nnet.py

@@ -413,7 +413,7 @@ class Softmax(gof.Op):
     def grad(self, inp, grads):
         x, = inp
         g_sm, = grads
-        sm = softmax(x)
+        sm = softmax_op(x)
         return [softmax_grad(g_sm, sm)]
 
     def R_op(self, inputs, eval_points):
@@ -578,7 +578,7 @@ def softmax_graph(c):
 def local_softmax_with_bias(node):
     """Try to turn softmax(sum_of_stuff) -> softmax_w_bias(matrix, bias)
     """
-    if node.op == softmax:
+    if node.op == softmax_op:
         x, = node.inputs
         if x.owner and x.owner.op == tensor.add:
             vectors = []
@@ -1406,7 +1406,7 @@ def crossentropy_to_crossentropy_with_softmax(fgraph):
             if node.op == crossentropy_categorical_1hot:
                 nll, = node.outputs
                 sm, one_of_n = node.inputs
-                if sm.owner and sm.owner.op == softmax:
+                if sm.owner and sm.owner.op == softmax_op:
                     x, = sm.owner.inputs
                     new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(x,
                             tensor.zeros_like(x[0]), one_of_n)
@@ -1556,7 +1556,7 @@ def local_advanced_indexing_crossentropy_onehot(node):
             except Exception:
                 pass
 
-    if sm is not None and sm.owner and sm.owner.op in (softmax,
+    if sm is not None and sm.owner and sm.owner.op in (softmax_op,
                                                        softmax_with_bias):
         sm_w_bias = local_softmax_with_bias.transform(sm.owner)
         if sm_w_bias:
@@ -1586,7 +1586,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
     except Exception:
         return
 
-    if (sm is not None) and sm.owner and (sm.owner.op in (softmax,
+    if (sm is not None) and sm.owner and (sm.owner.op in (softmax_op,
                                                           softmax_with_bias)):
         sm_w_bias = local_softmax_with_bias.transform(sm.owner)
         if sm_w_bias:
@@ -2056,7 +2056,7 @@ def make_out_pattern(X):
     return out_var
 
 
-local_log_softmax = gof.PatternSub(in_pattern=(tensor.log, (softmax, 'x')),
+local_log_softmax = gof.PatternSub(in_pattern=(tensor.log, (softmax_op, 'x')),
                                    out_pattern=(make_out_pattern, 'x'),
                                    allow_multiple_clients=True)