fix for tests with outputgaurd

theano/tensor/nnet/tests/test_nnet.py

@@ -579,8 +579,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         theano.compile.mode.optdb.query(
             theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
 
-        assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
-        assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+        assert (fgraph.outputs[0].owner.op ==
                 crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_vector(self):
@@ -594,8 +593,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         theano.compile.mode.optdb.query(
             theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
-        assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
-        assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+        assert (fgraph.outputs[0].owner.op ==
                 crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias(self):
@@ -624,10 +622,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         #    print node.op
         # print printing.pprint(node.outputs[0])
         # print '===='
-        assert len(fgraph.toposort()) == 2
-
-        assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
-        assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+        assert len(fgraph.toposort()) == 1
+        assert (fgraph.outputs[0].owner.op ==
                 crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias2(self):
@@ -654,10 +650,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         # for node in fgraph.toposort():
         #    print node.op
         # print '===='
-        assert len(fgraph.toposort()) == 3
+        assert len(fgraph.toposort()) == 2
 
-        assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
-        assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+        assert (fgraph.outputs[0].owner.op ==
                 crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias_vector(self):
@@ -681,9 +676,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         # for node in fgraph.toposort():
         #    print node.op
         # print '===='
-        assert len(fgraph.toposort()) == 3
-        assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
-        assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+        assert len(fgraph.toposort()) == 2
+        assert (fgraph.outputs[0].owner.op ==
                 crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_grad_optimizations(self):
@@ -1338,9 +1332,8 @@ def test_argmax_pushdown():
         # print 'AFTER'
         # for node in fgraph.toposort():
         # print node.op
-        assert len(fgraph.toposort()) == 2  # an output_guard is second
+        assert len(fgraph.toposort()) == 1  # an output_guard is second
         assert fgraph.toposort()[0].op == tensor.basic._argmax
-        assert str(fgraph.toposort()[1].op) == 'OutputGuard'
         assert check_stack_trace(
             fgraph, ops_to_check=tensor.basic._argmax)
         x = tensor.matrix()
@@ -1364,12 +1357,11 @@ def test_argmax_pushdown():
         # print 'AFTER'
         # for node in fgraph.toposort():
             # print node.op
-        assert len(fgraph.toposort()) == 4  # an output_guard is second
+        assert len(fgraph.toposort()) == 3  # an output_guard is second
         assert isinstance(fgraph.toposort()[0].op, tensor.Elemwise)
         assert isinstance(fgraph.toposort()[1].op, Softmax)
         assert isinstance(fgraph.toposort()[2].op, tensor.CAReduce)
         assert isinstance(fgraph.toposort()[2].op.scalar_op, theano.scalar.Maximum)
-        assert str(fgraph.toposort()[3].op) == 'OutputGuard'
 
 
 def test_argmax_pushdown_bias():
@@ -1388,10 +1380,9 @@ def test_argmax_pushdown_bias():
     # for node in fgraph.toposort():
     #    print node.op
     types_to_check = (tensor.DimShuffle, tensor.Elemwise, tensor.Argmax)
-    assert len(fgraph.toposort()) == 4
+    assert len(fgraph.toposort()) == 3
     for i, type in enumerate(types_to_check):
         assert isinstance(fgraph.toposort()[i].op, type)
-    assert str(fgraph.toposort()[3].op) == 'OutputGuard'
     assert check_stack_trace(fgraph, ops_to_check=types_to_check)
 
     x = tensor.matrix()
@@ -1412,11 +1403,10 @@ def test_argmax_pushdown_bias():
     # print 'AFTER'
     # for node in fgraph.toposort():
     #    print node.op
-    assert len(fgraph.toposort()) == 3
+    assert len(fgraph.toposort()) == 2
     assert isinstance(fgraph.toposort()[0].op, SoftmaxWithBias)
     assert isinstance(fgraph.toposort()[1].op, tensor.CAReduce)
     assert isinstance(fgraph.toposort()[1].op.scalar_op, theano.scalar.Maximum)
-    assert str(fgraph.toposort()[2].op) == 'OutputGuard'
     assert check_stack_trace(
         fgraph, ops_to_check=(SoftmaxWithBias, tensor.CAReduce))