Additional fixes of PEP8 errors

theano/compile/tests/test_builders.py

@@ -108,7 +108,7 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
         fn = function([x, y, z], f)
         assert numpy.allclose(15.0 + s.get_value(),
                               fn(xv, yv, zv))
- 
+
     def test_connection_pattern(self):
         # Basic case
         x, y, z = T.matrices('xyz')

theano/compile/tests/test_debugmode.py

@@ -525,7 +525,7 @@ class Test_ViewMap(unittest.TestCase):
         try:
             f([1, 2, 3, 4], [5, 6, 7, 8])
             assert False  # DebugMode should have caught the error
-        except debugmode.BadViewMap as e:
+        except debugmode.BadViewMap:
             # print e
             pass
 

theano/compile/tests/test_function_module.py

@@ -351,10 +351,10 @@ class T_function(unittest.TestCase):
         A special testcase for logistic_sgd.py in Deep Learning Tutorial
         This test assert that SharedVariable in different function have same storage
         """
-        train_x = theano.shared(value=numpy.random.rand(10, 10).astype(config.floatX))
+        # train_x = theano.shared(value=numpy.random.rand(10, 10).astype(config.floatX))
         test_x = theano.shared(value=numpy.random.rand(10, 10).astype(config.floatX))
 
-        train_y = theano.shared(value=numpy.random.rand(10, 1).astype(config.floatX))
+        # train_y = theano.shared(value=numpy.random.rand(10, 1).astype(config.floatX))
         test_y = theano.shared(value=numpy.random.rand(10, 1).astype(config.floatX))
 
         i = T.iscalar('index')
@@ -363,9 +363,9 @@ class T_function(unittest.TestCase):
         # this formular has no sense but for a test
         out = (T.sum(x) - y) ** 2
         out2 = (T.sum(x) + y) ** 2
-        train = theano.function([i], out,
-                                givens={x: train_x[i], y: train_y[i]},
-                                updates={train_x: train_x + 0.1})
+        # train = theano.function([i], out,
+        #                        givens={x: train_x[i], y: train_y[i]},
+        #                        updates={train_x: train_x + 0.1})
 
         test_def = theano.function([i], out, givens={x: test_x[i], y: test_y[i]})
         test_def2 = theano.function([i], out2, givens={x: test_x[i], y: test_y[i]})