pep8

theano/tests/test_tutorial.py

@@ -1137,6 +1137,7 @@ class T_graphstructures(unittest.TestCase):
         assert e.owner.inputs[1].owner.inputs[0] is y
         assert e.owner.inputs[1].owner.inputs[1] is z
 
+
 class T_scan(unittest.TestCase):
     ## All tests here belong to
     ## http://deeplearning.net/software/theano/tutorial/loop.html
@@ -1144,7 +1145,6 @@ class T_scan(unittest.TestCase):
     ## Any change you do here also add it to the tutorial !
 
     def test_elemwise(self):
-        
           # defining the tensor variables
           X = T.matrix("X")
           W = T.matrix("W")
@@ -1167,7 +1167,6 @@ class T_scan(unittest.TestCase):
           print "Numpy results:", numpy.tanh(x.dot(w) + b)
 
     def test_sequence(self):
-        
         # define tensor variables
         X = T.vector("X")
         W = T.matrix("W")
@@ -1228,7 +1227,6 @@ class T_scan(unittest.TestCase):
                             numpy.sqrt((x**2).sum(0))
 
     def test_trace(self):
-        
         # define tensor variable
         X = T.matrix("X")
         results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j] + \
@@ -1250,7 +1248,6 @@ class T_scan(unittest.TestCase):
         print "Numpy results:", numpy.diagonal(x).sum()
 
     def test_taps(self):
-    
         # define tensor variables
         X = T.matrix("X")
         W = T.matrix("W")
@@ -1293,7 +1290,6 @@ class T_scan(unittest.TestCase):
         print "Numpy results:", x_res
 
     def test_jacobian(self):
-    
         # define tensor variables
         v = T.vector()
         A = T.matrix()
@@ -1318,7 +1314,7 @@ class T_scan(unittest.TestCase):
         n_sym = T.iscalar("n_sym")
 
         results, updates = theano.scan(lambda:{k:(k+1)}, n_steps=n_sym)
-          accumulator = theano.function([n_sym], [], updates=updates, \
+        accumulator = theano.function([n_sym], [], updates=updates,
                                       allow_input_downcast = True)
 
         print "Before 5 steps:", k.get_value()
@@ -1346,4 +1342,3 @@ class T_scan(unittest.TestCase):
         b = numpy.ones((2))
 
         print compute_with_bnoise(x, w, b)
-