Fix E261: at least two spaces before inline comment

theano/compile/debugmode.py

@@ -326,7 +326,7 @@ class BadDestroyMap(DebugModeError):
     wasn't in the destroy_map"""
     def __init__(self, node, idx, old_val, new_val, perform):
         #super(BadDestroyMap, self).__init__()
-        DebugModeError.__init__(self)#to be compatible with python2.4
+        DebugModeError.__init__(self)  # to be compatible with python2.4
         self.node = node
         self.idx = idx
         self.old_val = old_val
@@ -794,7 +794,7 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
                         # bad: there should only be one active node that destroys any variable
                         raise Exception('failure in topological ordering')
                     if clobber_dr_vals:
-                        dr_vals[r] = (storage_map[r][0], node) #no copy, this is the last use of this variable
+                        dr_vals[r] = (storage_map[r][0], node)  # no copy, this is the last use of this variable
                     # make sure that dr_vals[r] doens't get used again
                     storage_map[r][0] = data_destroyed
             else:
@@ -1999,7 +1999,7 @@ class _Linker(gof.link.LocalLinker):
                                 raise InvalidValueError(r, storage_map[r][0], hint='c output')
 
                             if thunk_py:
-                                assert r in r_vals #because we put it in during the thunk_py branch
+                                assert r in r_vals  # because we put it in during the thunk_py branch
                                 # check for stride correctness (may raise exception)
                                 _check_strides_match(r_vals[r],
                                     storage_map[r][0],
@@ -2032,7 +2032,7 @@ class _Linker(gof.link.LocalLinker):
                                 #print >> sys.stderr, i, "DEBUGMODE storing reference output %x" % id(storage_map[r][0])
                                 # retrieve each output from the storage_map
                                 r_vals[r] = storage_map[r][0]
-                            storage_map[r][0] = None #clear the storage_map for the thunk_c
+                            storage_map[r][0] = None  # clear the storage_map for the thunk_c
 
                         if self.maker.mode.check_preallocated_output:
                             prealloc_modes = \

theano/compile/profilemode.py

@@ -402,7 +402,7 @@ class ProfileMode(Mode):
         sop_time={}
         sop_call={}
         sop_op = {}
-        sop_cimpl={} #map each op class to Bool. True iff all applies were done in c.
+        sop_cimpl={}  # map each op class to Bool. True iff all applies were done in c.
         for a, t in op_time.items():
             typ = type(a)
             sop_time.setdefault(typ, 0)

theano/compile/tests/test_function_module.py

@@ -36,7 +36,7 @@ def checkfor(testcase, fn, E):
 
 class T_function(unittest.TestCase):
     def test_none(self):
-        fn = function([], None) #ok
+        fn = function([], None)  # ok
         rval = fn()
         if rval == []:
             raise KnownFailureTest('See #254: Using None as function output leads to [] return value')
@@ -44,7 +44,7 @@ class T_function(unittest.TestCase):
             assert rval is None
 
     def test_empty(self):
-        fn = function([], []) #ok
+        fn = function([], [])  # ok
         self.assertTrue(fn() == [])
 
     def test_extra_inputs(self):
@@ -122,9 +122,9 @@ class T_function(unittest.TestCase):
         self.assertTrue(f(s=2, x=1) == 0.5)
         self.assertTrue(f(x=2, s=1) == 2.0)
         self.assertTrue(f(2, s=1) == 2.0)
-        checkfor(self, lambda : f(2, x=2.0), TypeError) #got multiple values for keyword argument 'x'
-        checkfor(self, lambda : f(x=1), TypeError) #takes exactly 2 non-keyword arguments (1 given)
-        checkfor(self, lambda : f(s=1), TypeError) #takes exactly 2 non-keyword arguments (0 given)
+        checkfor(self, lambda : f(2, x=2.0), TypeError)  # got multiple values for keyword argument 'x'
+        checkfor(self, lambda : f(x=1), TypeError)  # takes exactly 2 non-keyword arguments (1 given)
+        checkfor(self, lambda : f(s=1), TypeError)  # takes exactly 2 non-keyword arguments (0 given)
 
     def test_naming_rule1(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -133,8 +133,8 @@ class T_function(unittest.TestCase):
         self.assertTrue(f(1, 2) == 0.5)
         self.assertTrue(f(2, 1) == 2.0)
         self.assertTrue(f(2, s=1) == 2.0)
-        checkfor(self, lambda: f(q=2, s=1), TypeError) #got unexpected keyword argument 'q'
-        checkfor(self, lambda: f(a=2, s=1), TypeError) #got unexpected keyword argument 'a'
+        checkfor(self, lambda: f(q=2, s=1), TypeError)  # got unexpected keyword argument 'q'
+        checkfor(self, lambda: f(a=2, s=1), TypeError)  # got unexpected keyword argument 'a'
 
     def test_naming_rule2(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -146,8 +146,8 @@ class T_function(unittest.TestCase):
         self.assertTrue(f(9, 1, 2) == 0.5)
         self.assertTrue(f(9, 2, 1) == 2.0)
         self.assertTrue(f(9, 2, s=1) == 2.0)
-        checkfor(self, lambda: f(x=9, a=2, s=1), TypeError) #got unexpected keyword argument 'x'
-        checkfor(self, lambda: f(5.0, x=9), TypeError) #got unexpected keyword argument 'x'
+        checkfor(self, lambda: f(x=9, a=2, s=1), TypeError)  # got unexpected keyword argument 'x'
+        checkfor(self, lambda: f(5.0, x=9), TypeError)  # got unexpected keyword argument 'x'
 
     def test_naming_rule3(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -155,13 +155,13 @@ class T_function(unittest.TestCase):
 
         # x's name is not ignored (as in test_naming_rule2) because a has a default value.
         f = function([x, In(a, value=1.0), s], a/s+x)
-        self.assertTrue(f(9, 2, 4) == 9.5) #can specify all args in order
+        self.assertTrue(f(9, 2, 4) == 9.5)  # can specify all args in order
         self.assertTrue(f(9, 2, s=4) == 9.5)  # can give s as kwarg
         self.assertTrue(f(9, s=4) == 9.25)  # can give s as kwarg, get default a
         self.assertTrue(f(x=9, s=4) == 9.25)  # can give s as kwarg, omit a, x as kw
-        checkfor(self, lambda: f(x=9, a=2, s=4), TypeError) #got unexpected keyword argument 'a'
-        checkfor(self, lambda: f(), TypeError) #takes exactly 3 non-keyword arguments (0 given)
-        checkfor(self, lambda: f(x=9), TypeError) #takes exactly 3 non-keyword arguments (1 given)
+        checkfor(self, lambda: f(x=9, a=2, s=4), TypeError)  # got unexpected keyword argument 'a'
+        checkfor(self, lambda: f(), TypeError)  # takes exactly 3 non-keyword arguments (0 given)
+        checkfor(self, lambda: f(x=9), TypeError)  # takes exactly 3 non-keyword arguments (1 given)
 
     def test_naming_rule4(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -169,14 +169,14 @@ class T_function(unittest.TestCase):
 
         f = function([x, In(a, value=1.0, name='a'), s], a/s+x)
 
-        self.assertTrue(f(9, 2, 4) == 9.5) #can specify all args in order
+        self.assertTrue(f(9, 2, 4) == 9.5)  # can specify all args in order
         self.assertTrue(f(9, 2, s=4) == 9.5)  # can give s as kwarg
         self.assertTrue(f(9, s=4) == 9.25)  # can give s as kwarg, get default a
         self.assertTrue(f(9, a=2, s=4) == 9.5)  # can give s as kwarg, a as kwarg
         self.assertTrue(f(x=9, a=2, s=4) == 9.5)  # can give all kwargs
         self.assertTrue(f(x=9, s=4) == 9.25)  # can give all kwargs
-        checkfor(self, lambda: f(), TypeError) #takes exactly 3 non-keyword arguments (0 given)
-        checkfor(self, lambda: f(5.0, x=9), TypeError) #got multiple values for keyword argument 'x'
+        checkfor(self, lambda: f(), TypeError)  # takes exactly 3 non-keyword arguments (0 given)
+        checkfor(self, lambda: f(5.0, x=9), TypeError)  # got multiple values for keyword argument 'x'
 
     def test_state_access(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -188,14 +188,14 @@ class T_function(unittest.TestCase):
         self.assertTrue(f[s] == 0.0)
 
         self.assertTrue(f(3.0) == 3.0)
-        self.assertTrue(f(3.0, a=2.0) == 9.0) #3.0 + 2*3.0
+        self.assertTrue(f(3.0, a=2.0) == 9.0)  # 3.0 + 2*3.0
 
-        self.assertTrue(f[a] == 1.0) #state hasn't changed permanently, we just overrode it last line
+        self.assertTrue(f[a] == 1.0)  # state hasn't changed permanently, we just overrode it last line
         self.assertTrue(f[s] == 9.0)
 
         f[a] = 5.0
         self.assertTrue(f[a] == 5.0)
-        self.assertTrue(f(3.0) == 24.0) #9 + 3*5
+        self.assertTrue(f(3.0) == 24.0)  # 9 + 3*5
         self.assertTrue(f[s] == 24.0)
 
     def test_same_names(self):
@@ -233,10 +233,10 @@ class T_function(unittest.TestCase):
         self.assertFalse(g.value[s] is f.value[s])  # should have been copied because it is mutable.
         self.assertFalse((g.value[s] != f.value[s]).any())  # its contents should be identical
 
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
         f(1, 2)  # put them out of sync
-        self.assertFalse(f(1, 2) == g(1, 2)) #they should not be equal anymore.
+        self.assertFalse(f(1, 2) == g(1, 2))  # they should not be equal anymore.
 
     def test_shared_state0(self):
         a = T.scalar()  # the a is for 'anonymous' (un-named).
@@ -357,20 +357,20 @@ class T_function(unittest.TestCase):
         a = T.dmatrix()
         f = function([a], Out(a, borrow=False))
         o = N.ones((3, 3))
-        assert o is not f(o) #function no longer permits aliasing outputs to inputs
+        assert o is not f(o)  # function no longer permits aliasing outputs to inputs
 
         f = function([a], Out(a*4, borrow=False))
         o = N.ones((3, 3))
         four = f(o)
         assert numpy.all(four==4)
-        f(o+.1) #should not clobber the memory used to store four
+        f(o+.1)  # should not clobber the memory used to store four
         assert numpy.all(four==4)
 
         f = function([a], Out(a*4, borrow=True), mode=theano.Mode('c|py_nogc', 'fast_run'))
         o = N.ones((3, 3))
         four = f(o)
         assert numpy.all(four==4)
-        f(o+.1) #should clobber the memory used to store four
+        f(o+.1)  # should clobber the memory used to store four
         if theano.config.cxx:
             assert not numpy.all(four==4)
         else:
@@ -456,10 +456,10 @@ class T_picklefunction(unittest.TestCase):
         self.assertFalse(g.value[2] is f.value[2])  # should have been copied because it is mutable.
         self.assertFalse((g.value[2] != f.value[2]).any())  # its contents should be identical
 
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
         f(1, 2)  # put them out of sync
-        self.assertFalse(f(1, 2) == g(1, 2)) #they should not be equal anymore.
+        self.assertFalse(f(1, 2) == g(1, 2))  # they should not be equal anymore.
         g(1, 2)  # put them back in sync
         self.assertTrue(f(3) == g(3))  # They should be in sync again.
 
@@ -517,10 +517,10 @@ class T_picklefunction(unittest.TestCase):
         self.assertFalse(g.value[2] is f.value[2])  # should have been copied because it is mutable.
         self.assertFalse((g.value[2] != f.value[2]).any())  # its contents should be identical
 
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
-        self.assertTrue(f(2, 1) == g(2)) #they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
+        self.assertTrue(f(2, 1) == g(2))  # they should be in sync, default value should be copied.
         f(1, 2)  # put them out of sync
-        self.assertFalse(f(1, 2) == g(1, 2)) #they should not be equal anymore.
+        self.assertFalse(f(1, 2) == g(1, 2))  # they should not be equal anymore.
 
     def test_optimizations_preserved(self):
         a = T.dvector()  # the a is for 'anonymous' (un-named).

theano/gof/callcache.py

@@ -7,7 +7,7 @@ class CallCache(object):
         self.filename = filename
         try:
             if filename is None:
-                raise IOError('bad filename') #just goes to except
+                raise IOError('bad filename')  # just goes to except
             f = open(filename, 'r')
             self.cache = cPickle.load(f)
             f.close()

theano/gof/destroyhandler.py

@@ -325,7 +325,7 @@ if 0:
             fgraph.destroy_handler = self
 
             self.fgraph = fgraph
-            self.destroyers = OrderedSet() #set of Apply instances with non-null destroy_map
+            self.destroyers = OrderedSet()  # set of Apply instances with non-null destroy_map
             self.view_i = {}  # variable -> variable used in calculation
             self.view_o = {}  # variable -> set of variables that use this one as a direct input
             # clients: how many times does an apply use a given variable
@@ -725,7 +725,7 @@ class DestroyHandler(toolbox.Bookkeeper):
         fgraph.destroy_handler = self
 
         self.fgraph = fgraph
-        self.destroyers = OrderedSet() #set of Apply instances with non-null destroy_map
+        self.destroyers = OrderedSet()  # set of Apply instances with non-null destroy_map
         self.view_i = OrderedDict()  # variable -> variable used in calculation
         self.view_o = OrderedDict()  # variable -> set of variables that use this one as a direct input
         # clients: how many times does an apply use a given variable

theano/gof/fg.py

@@ -396,7 +396,7 @@ class FunctionGraph(utils.object2):
         # then __prune__ is a no-op.
         for output in node.outputs:
             # Cannot prune an op which is an output or used somewhere
-            if self.clients(output) or output in self.outputs: #output in self.outputs or self.clients(output):
+            if self.clients(output) or output in self.outputs:  # output in self.outputs or self.clients(output):
                 return
         self.apply_nodes.remove(node)
         self.variables.difference_update(node.outputs)

theano/gof/link.py

@@ -685,7 +685,7 @@ class PerformLinker(LocalLinker):
         f = streamline(fgraph, thunks, order, post_thunk_old_storage,
                        no_recycling=no_recycling)
 
-        f.allow_gc = self.allow_gc #HACK: this is a way of passing an arg to Function.__call__
+        f.allow_gc = self.allow_gc  # HACK: this is a way of passing an arg to Function.__call__
         add_clear_storage(f, computed, storage_map)
         f.storage_map = storage_map
 

theano/gof/type.py

@@ -226,8 +226,8 @@ class PureType(object):
 
     """
 
-    Variable = graph.Variable #the type that will be created by call to make_variable.
-    Constant = graph.Constant #the type that will be created by call to make_constant
+    Variable = graph.Variable  # the type that will be created by call to make_variable.
+    Constant = graph.Constant  # the type that will be created by call to make_constant
 
     def filter(self, data, strict=False, allow_downcast=None):
         """Required: Return data or an appropriately wrapped/converted data.

theano/misc/check_duplicate_key.py

@@ -55,7 +55,7 @@ for val in keys.values():
     nbs_keys[val]+=1
 
 nbs_mod = {}  # nb seen -> how many key
-nbs_mod_to_key = {} #nb seen -> keys
+nbs_mod_to_key = {}  # nb seen -> keys
 more_than_one = 0
 for mod, kk in mods.iteritems():
     val = len(kk)

theano/sandbox/cuda/basic_ops.py

@@ -3415,7 +3415,7 @@ class GpuAlloc(GpuAllocEmpty):
                 # If the output is a constant, it will have to be deepcopied
                 # each time the function is called.  So we do not fold.
                 return False
-            elif (#The following ops work inplace of their input id 0.
+            elif (  # The following ops work inplace of their input id 0.
                   client[1] == 0 and
                   isinstance(client[0].op, (
                     # Ops that will work inplace on the Alloc. So if they

theano/sandbox/cuda/elemwise.py

@@ -260,7 +260,7 @@ class NaiveAlgo(object):
                     , nodename + '_scalar_'
                     , get_str_list_logical_scalar(node, value_str='value0[%i]')
                     , ['ii_o%i_data[0]'%ipos for ipos, i in enumerate(node.outputs)]
-                    , sub=dict(fail='return;')) #TODO: set a failure code somehow!!!
+                    , sub=dict(fail='return;'))  # TODO: set a failure code somehow!!!
             print >> sio, "       ", task_code
 
             print >> sio, "    }" * nd
@@ -398,7 +398,7 @@ class NaiveAlgo(object):
                 , nodename + '_scalar_'
                 , ['i%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.inputs)]
                 , ['o%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.outputs)]
-                , sub=dict(fail='return;')) #TODO: set a failure code somehow!!!
+                , sub=dict(fail='return;'))  # TODO: set a failure code somehow!!!
 
         if nd == 4:
             decl_shared_stride(n_in, n_out, nd)
@@ -473,7 +473,7 @@ class NaiveAlgo(object):
                 #, ['i%i_data[i]'%ipos for ipos, i in enumerate(node.inputs)]
                 , get_str_list_logical_scalar(node, data_str='i%i_data[i]')
                 , ['o%i_data[i]'%ipos for ipos, i in enumerate(node.outputs)]
-                , sub=dict(fail='return;')) #TODO: set a failure code somehow!!!
+                , sub=dict(fail='return;'))  # TODO: set a failure code somehow!!!
         print >> sio, "       ", task_code
         print >> sio, "    }"
         print >> sio, "}"
@@ -844,9 +844,9 @@ nd_collapse_[i]=0;
             launch_General(nodename, scalar_op, i, self.sync)
             print >> sio, "        } break;"
 
-        print >> sio, "}"#end case
+        print >> sio, "}"  # end case
         print >> sio, "return -2;"  # should not get to this point
-        print >> sio, "}"#end fct
+        print >> sio, "}"  # end fct
 
         # N.B. cudaGetLastError is called by c_code
         return sio.getvalue()

theano/sandbox/cuda/tests/test_bench_loopfusion.py

@@ -171,7 +171,7 @@ class Kouh2008(object):
         rval = cls.new_expbounds(rng, x_list, n_out, dtype=dtype, params=f_list + b_list,
                 exponent_range=exponent_range)
         rval.f_list = f_list
-        rval.input = input #add the input to the returned object
+        rval.input = input  # add the input to the returned object
         rval.filter_l1 = sum(abs(fi).sum() for fi in f_list)
         rval.filter_l2_sqr = sum((fi**2).sum() for fi in f_list)
         return rval
@@ -259,7 +259,7 @@ class Config(object):
 
     ft_batchsize = 30
     ft_epoch_len = 50000
-    ft_status_interval = 50 #property( lambda s:s.ft_epoch_len/s.ft_batchsize)
+    ft_status_interval = 50  # property( lambda s:s.ft_epoch_len/s.ft_batchsize)
     ft_validation_interval = property( lambda s: s.ft_epoch_len/s.ft_batchsize)
     ft_ntrain_limit = 0
     ft_test_lag1 = True

theano/sandbox/cuda/var.py

@@ -89,7 +89,7 @@ class CudaNdarraySharedVariable(_operators, SharedVariable):
                 return self.container.value
             else:
                 return copy.deepcopy(self.container.value)
-        else: #return an ndarray
+        else:  # return an ndarray
             return numpy.asarray(self.container.value)
 
     def set_value(self, value, borrow=False):

theano/sandbox/gpuarray/basic_ops.py

@@ -696,7 +696,7 @@ class GpuAlloc(HideC, Alloc):
                 # If the output is a constant, it will have to be deepcopied
                 # each time the function is called.  So we do not fold.
                 return False
-            elif (#The following ops work inplace of their input id 0.
+            elif (  # The following ops work inplace of their input id 0.
                   client[1] == 0 and
                   isinstance(client[0].op, (
                     # Ops that will work inplace on the Alloc. So if they

theano/sandbox/gpuarray/tests/test_elemwise.py

@@ -124,39 +124,39 @@ class test_GpuCAReduceCuda(test_GpuCAReduceCPY):
              ((1, 2), (1,)),
              ((100, 3, 1300), [1]),
              ((0,), [0]), ((5,), [0]),
-             ((0, 0), [0, 1]), ((1, 0), [0, 1]), ((5, 4), [0, 1]), ((33, 31), [0, 1]), ((5, 4), [1]), ((5, 4), [0]),#need something bigger then 32 for some opt test.
+             ((0, 0), [0, 1]), ((1, 0), [0, 1]), ((5, 4), [0, 1]), ((33, 31), [0, 1]), ((5, 4), [1]), ((5, 4), [0]),  # need something bigger then 32 for some opt test.
              ((5, 4, 3), [0]), ((5, 4, 3), [1]), ((5, 4, 3), [0, 1]), ((5, 4, 3), [2]), ((5, 4, 3), [1, 2]), ((5, 4, 3), [0, 1, 2]),
              ((0, 0, 0, 0), [0, 1, 2, 3]),
              ((5, 4, 3, 20), [2, 3]), ((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]), ((5, 4, 3, 2), [1, 2, 3]),
 
                                # test shape bigger then 4096 on each dimension to make sure that we work correctly when we don't have enough thread/block in each dimensions
-             ((4100, 3), [0]), ((3, 4101), [0]),#10
-             ((1024, 33), [0]), ((33, 1024), [0]),#10
-             ((1025, 33), [0]), ((33, 1025), [0]),#10
-
-             ((4100, 3), [1]), ((3, 4101), [1]),#01
-             ((1024, 33), [1]), ((33, 1024), [1]),#01
-             ((1025, 33), [1]), ((33, 1025), [1]),#01
-
-             ((4100, 3), [0, 1]), ((3, 4101), [0, 1]),#11
-             ((1024, 33), [0, 1]), ((33, 1024), [0, 1]),#01
-             ((1025, 33), [0, 1]), ((33, 1025), [0, 1]),#01
-
-             ((4100, 4, 3), [0]), ((5, 4100, 3), [0]), ((5, 4, 4100), [0]), ((3, 65536, 1), [0]),#100
-             ((4100, 4, 3), [1]), ((5, 4100, 3), [1]), ((5, 4, 4100), [1]),#010
-             ((4100, 4, 3), [2]), ((5, 4100, 3), [2]), ((5, 4, 4100), [2]),#001
-             ((4100, 4, 3), [0, 1]), ((5, 4100, 3), [0, 1]), ((5, 4, 4100), [0, 1]),#110
-             ((4100, 4, 3), [1, 2]), ((5, 4100, 3), [1, 2]), ((5, 4, 4100), [1, 2]),#011
+             ((4100, 3), [0]), ((3, 4101), [0]),  # 10
+             ((1024, 33), [0]), ((33, 1024), [0]),  # 10
+             ((1025, 33), [0]), ((33, 1025), [0]),  # 10
+
+             ((4100, 3), [1]), ((3, 4101), [1]),  # 01
+             ((1024, 33), [1]), ((33, 1024), [1]),  # 01
+             ((1025, 33), [1]), ((33, 1025), [1]),  # 01
+
+             ((4100, 3), [0, 1]), ((3, 4101), [0, 1]),  # 11
+             ((1024, 33), [0, 1]), ((33, 1024), [0, 1]),  # 01
+             ((1025, 33), [0, 1]), ((33, 1025), [0, 1]),  # 01
+
+             ((4100, 4, 3), [0]), ((5, 4100, 3), [0]), ((5, 4, 4100), [0]), ((3, 65536, 1), [0]),  # 100
+             ((4100, 4, 3), [1]), ((5, 4100, 3), [1]), ((5, 4, 4100), [1]),  # 010
+             ((4100, 4, 3), [2]), ((5, 4100, 3), [2]), ((5, 4, 4100), [2]),  # 001
+             ((4100, 4, 3), [0, 1]), ((5, 4100, 3), [0, 1]), ((5, 4, 4100), [0, 1]),  # 110
+             ((4100, 4, 3), [1, 2]), ((5, 4100, 3), [1, 2]), ((5, 4, 4100), [1, 2]),  # 011
              #((4100,4,3),[0,2]),((5,4100,3),[0,2]),((5,4,4100),[0,2]),#101 ##not implemented
-             ((4100, 4, 3), [0, 1, 2]), ((5, 4100, 3), [0, 1, 2]), ((5, 4, 4100), [0, 1, 2]),#111
-             ((65, 4, 3), [0, 1, 2]), ((5, 65, 3), [0, 1, 2]), ((5, 4, 65), [0, 1, 2]),#111
-
-             ((4100, 4, 3, 2), [2, 3]), ((4, 4100, 3, 2), [2, 3]), ((4, 3, 4100, 2), [2, 3]), ((4, 3, 2, 4100), [2, 3]),#0011
-             ((4100, 4, 3, 2), [1, 3]), ((4, 4100, 3, 2), [1, 3]), ((4, 3, 4100, 2), [1, 3]), ((4, 3, 2, 4100), [1, 3]),#0101
-             ((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]),#((4,3,2,4100),[0,2,3]),#1011
-             ((4100, 4, 3, 2), [1, 2, 3]), ((4, 4100, 3, 2), [1, 2, 3]), ((4, 3, 4100, 2), [1, 2, 3]), ((4, 3, 2, 4100), [1, 2, 3]),#0111
-             ((65, 4, 3, 2), [1, 2, 3]), ((4, 65, 3, 2), [1, 2, 3]), ((4, 3, 65, 2), [1, 2, 3]), ((4, 3, 2, 65), [1, 2, 3]),#0111
-             ((4100, 2, 3, 4), [0, 1, 2, 3]), ((2, 4100, 3, 4), [0, 1, 2, 3]), ((2, 3, 4100, 4), [0, 1, 2, 3]), ((2, 3, 4, 4100), [0, 1, 2, 3]), ((128, 1, 2, 3), [0, 1, 2, 3]),#1111
+             ((4100, 4, 3), [0, 1, 2]), ((5, 4100, 3), [0, 1, 2]), ((5, 4, 4100), [0, 1, 2]),  # 111
+             ((65, 4, 3), [0, 1, 2]), ((5, 65, 3), [0, 1, 2]), ((5, 4, 65), [0, 1, 2]),  # 111
+
+             ((4100, 4, 3, 2), [2, 3]), ((4, 4100, 3, 2), [2, 3]), ((4, 3, 4100, 2), [2, 3]), ((4, 3, 2, 4100), [2, 3]),  # 0011
+             ((4100, 4, 3, 2), [1, 3]), ((4, 4100, 3, 2), [1, 3]), ((4, 3, 4100, 2), [1, 3]), ((4, 3, 2, 4100), [1, 3]),  # 0101
+             ((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]),  # ((4,3,2,4100),[0,2,3]),#1011
+             ((4100, 4, 3, 2), [1, 2, 3]), ((4, 4100, 3, 2), [1, 2, 3]), ((4, 3, 4100, 2), [1, 2, 3]), ((4, 3, 2, 4100), [1, 2, 3]),  # 0111
+             ((65, 4, 3, 2), [1, 2, 3]), ((4, 65, 3, 2), [1, 2, 3]), ((4, 3, 65, 2), [1, 2, 3]), ((4, 3, 2, 65), [1, 2, 3]),  # 0111
+             ((4100, 2, 3, 4), [0, 1, 2, 3]), ((2, 4100, 3, 4), [0, 1, 2, 3]), ((2, 3, 4100, 4), [0, 1, 2, 3]), ((2, 3, 4, 4100), [0, 1, 2, 3]), ((128, 1, 2, 3), [0, 1, 2, 3]),  # 1111
 
              # test pattern implemented by reshape
              # Skip them as this test the op directly, not the optimization with reshape

theano/sandbox/symbolic_module.py

@@ -184,7 +184,7 @@ def compile(smod, initial_values=None):
                     print p
                     reflected[thing] = p
                 else:
-                    reflected[thing] = None #TODO: how to reflect derived resuls?
+                    reflected[thing] = None  # TODO: how to reflect derived resuls?
             elif issymbolicmethod(thing):
                 reflected[thing] = compiled_functions[thing]
             else :
@@ -332,7 +332,7 @@ if 0:
             x=T.dmatrix(),    #our points, one point per row
             w=T.dmatrix(),    #first layer weights
             b=T.dvector(),    #first layer bias
-            **kwargs #other things from logistic_regression
+            **kwargs  # other things from logistic_regression
             ):
         hid = T.tanh(T.dot(x, w) + b)
         if top_part:
@@ -353,7 +353,7 @@ if 0:
 
 if 0:
     class SymbolicModule(object):
-        name = "__no_name__" #name of this module
+        name = "__no_name__"  # name of this module
 
         variable_table = {}  #map strings (names) to Variables
         method_table = {}  #map strings to compilable functions
@@ -385,8 +385,8 @@ if 0:
             b=T.dvector(),    #first layer bias
             v=T.dmatrix(),    #second layer weights
             c=T.dvector(),    #second layer bias
-            step=T.dscalar(), #step size for gradient descent
-            l2_coef=T.dscalar() #l2 regularization amount
+            step=T.dscalar(),  # step size for gradient descent
+            l2_coef=T.dscalar()  # l2 regularization amount
             ):
         """Idea A:
         """

theano/sandbox/test_theano_object.py

@@ -19,7 +19,7 @@ class MyModule(TheanoObject):
         super(MyModule, self).__init__()
         self.a = self.symbolic_member(2)
         self.b = self.symbolic_member(3)
-        self.c = 100 #a constant
+        self.c = 100  # a constant
         self.d = [self.symbolic_member(5), self.symbolic_member(6)]
         self.e = ['a', self.symbolic_member(6)]
 
@@ -46,11 +46,11 @@ def test_outputs():
     assert MM.add(5) == 12
     assert MM.b.get() == 4
     MM.sub(3)
-    assert MM.b.get() == 1 #test get()
-    assert MM.add(5) == 9 #test that b's container is shared between add and sub
-    MM.b.set(2) #test set
-    assert MM.b.get() == 2 #test get()
-    assert MM.add(5) == 10 #test that b's container is shared between add and sub
+    assert MM.b.get() == 1  # test get()
+    assert MM.add(5) == 9  # test that b's container is shared between add and sub
+    MM.b.set(2)  # test set
+    assert MM.b.get() == 2  # test get()
+    assert MM.add(5) == 10  # test that b's container is shared between add and sub
 
 @run(True)
 def test_submodule():
@@ -59,7 +59,7 @@ def test_submodule():
     assert MM.add(5) == 8
     MM.submodule.sub(7)
     assert MM.submodule.b.get() == -3
-    assert MM.use_submodule(0) == -2 #self.a is 1 + self.submodule.b is -3
+    assert MM.use_submodule(0) == -2  # self.a is 1 + self.submodule.b is -3
 
 
 @run(False)

theano/sparse/sandbox/test_sp.py

@@ -265,7 +265,7 @@ class TestSP(unittest.TestCase):
         kshp = ((3, 3), (2, 2))
         nkerns = (3, 6)  # per output pixel
         ssizes = (((1, 1), (2, 2)),)
-        convmodes = ('full',)#'valid',)
+        convmodes = ('full',)  # 'valid',)
 
         # symbolic stuff
         kerns = [tensor.dmatrix(), tensor.dmatrix()]

theano/tensor/nnet/tests/speed_test_conv.py

@@ -111,14 +111,14 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
             time1 = time.time()
             for i in range(repeat):
                 hidval2_ = propup2(imgval, w_flip)
-            hidval2 = hidval2_#[:,:,0::ss[0],0::ss[1]]
+            hidval2 = hidval2_  # [:,:,0::ss[0],0::ss[1]]
             tctot += time.time() - time1
 
             if conv_op_py:
                 time1 = time.time()
                 for i in range(repeat):
                     hidval3_ = propup3(imgval, w_flip)
-                hidval3 = hidval3_#[:,:,0::ss[0],0::ss[1]]
+                hidval3 = hidval3_  # [:,:,0::ss[0],0::ss[1]]
                 tpytot += time.time() - time1
                 assert (N.abs(hidval2-hidval3)<1e-5).all()
             else:
@@ -183,7 +183,7 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
             time1 = time.time()
             for i in range(repeat):
                 hidval2_ = propup2(imgval, w_flip)
-            hidval2 = hidval2_#[:,:,0::ss[0],0::ss[1]]
+            hidval2 = hidval2_  # [:,:,0::ss[0],0::ss[1]]
             tctot += time.time() - time1
 
             imshp = tuple(outshp)
@@ -197,11 +197,11 @@ def speed_multilayer_conv():
         # calculate the speed up of different combination of unroll
         # put the paramter to the same you will try. 
         
-        validate=False# we don't validate the result to have it much faster!
+        validate=False  # we don't validate the result to have it much faster!
         repeat = 3
         verbose=1
-        unroll_batch = [1, 2, 3, 4, 5, 6, 10]#15, 30, 60 always much slower
-        unroll_kern = [1, 2, 3, 4, 5, 6, 10]#15, 30, 60 always much slower
+        unroll_batch = [1, 2, 3, 4, 5, 6, 10]  # 15, 30, 60 always much slower
+        unroll_kern = [1, 2, 3, 4, 5, 6, 10]  # 15, 30, 60 always much slower
         #unroll_batch = [1,4,5]
         #unroll_kern = [1,4,5]
         #unroll_batch = [1,4]
@@ -209,10 +209,10 @@ def speed_multilayer_conv():
         unroll_patch = [True, False]
         
         bsize = 60  # batch size
-        imshp_start = (1, 48, 48)#un square shape to test more corner case.
-        kshps = ([11, 12],)#un square shape to test more corner case.
+        imshp_start = (1, 48, 48)  # un square shape to test more corner case.
+        kshps = ([11, 12],)  # un square shape to test more corner case.
         nkerns = [60]  # per output pixel
-        ssizes = [(1, 1), ]#(1,1)]#(2,2) bugged
+        ssizes = [(1, 1), ]  # (1,1)]#(2,2) bugged
         convmodes = ['valid', 'full']
         do_convolve2=False
         a=T.dmatrix()
@@ -246,9 +246,9 @@ def speed_multilayer_conv():
                     if unroll_b==1 and unroll_k==1:
                         # print "unroll 1/1",tctot
                         worst=tctot
-                    timing[n_b, n_k]=[tctot, tpytot, ntot]#[sum(tctot), sum(tpytot), sum(ntot)]
+                    timing[n_b, n_k]=[tctot, tpytot, ntot]  # [sum(tctot), sum(tpytot), sum(ntot)]
         if not t_:
-            t=timing[:, :, 0, :]#We select only the c timing.
+            t=timing[:, :, 0, :]  # We select only the c timing.
         else:
             t=t_
         t=N.asarray(t)

theano/tensor/tests/test_blas_scipy.py

@@ -43,19 +43,19 @@ class TestScipyGer(TestCase, TestOptimizationMixin):
         f = self.function([self.A, self.x, self.y],
                 self.A + tensor.outer(self.x, self.y))
         self.assertFunctionContains(f, ScipyGer(destructive=False))
-        self.run_f(f) #DebugMode tests correctness
+        self.run_f(f)  # DebugMode tests correctness
 
     def test_A_plus_scaled_outer(self):
         f = self.function([self.A, self.x, self.y],
                 self.A + 0.1 * tensor.outer(self.x, self.y))
         self.assertFunctionContains(f, ScipyGer(destructive=False))
-        self.run_f(f) #DebugMode tests correctness
+        self.run_f(f)  # DebugMode tests correctness
 
     def test_scaled_A_plus_scaled_outer(self):
         f = self.function([self.A, self.x, self.y],
                 0.2 * self.A + 0.1 * tensor.outer(self.x, self.y))
         self.assertFunctionContains(f, gemm_no_inplace)
-        self.run_f(f) #DebugMode tests correctness
+        self.run_f(f)  # DebugMode tests correctness
 
 class TestBlasStridesScipy(TestBlasStrides):
     mode = theano.compile.get_default_mode()

theano/tensor/tests/test_opt.py

@@ -983,12 +983,12 @@ class test_fusion(unittest.TestCase):
             (fx-theano.tensor.true_div(fy, fz), (fx, fy, fz), (fxv,
                 fyv, fzv), 1, fxv-(fyv/fzv), 'float32'),
             (fx-theano.tensor.int_div(ix*100, iy*1000), (fx, ix,
-                iy), (fxv, ixv, iyv), 1, fxv-((ixv*100)//(iyv*1000)), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}), #40
+                iy), (fxv, ixv, iyv), 1, fxv-((ixv*100)//(iyv*1000)), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}),  # 40
             (fx-(fy/2), (fx, fy), (fxv, fyv), 1, fxv-(fyv/2), 'float32'),
             (fx-(fy%fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv%fzv), 'float32'),
             (fx-(fy>fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv>fzv), 'float32'),
             (fx-(fy>=fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv>=fzv), 'float32'),
-            (fx-(fy<fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv<fzv), 'float32'),#45
+            (fx-(fy<fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv<fzv), 'float32'),  # 45
             (fx-(fy<=fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(fyv<=fzv), 'float32'),
             (fx-T.eq(fy, fz), (fx, fy, fz), (fxv, fyv, fzv), 1, fxv-(
                 fyv==fzv), 'float32'),
@@ -997,7 +997,7 @@ class test_fusion(unittest.TestCase):
             (fx-fy+tensor.tan(fz), (fx, fy, fz), (fxv, fyv, fzv), 1,
                 fxv-fyv+numpy.tan(fzv), 'float32'),
             (fx-fy+tensor.tanh(fz), (fx, fy, fz), (fxv, fyv, fzv), 1,
-                fxv-fyv+numpy.tanh(fzv), 'float32'),#50
+                fxv-fyv+numpy.tanh(fzv), 'float32'),  # 50
             (fx-fy+tensor.sin(fz), (fx, fy, fz), (fxv, fyv, fzv), 1,
                 fxv-fyv+numpy.sin(fzv), 'float32'),
             (fx-fy+tensor.sinh(fz), (fx, fy, fz), (fxv, fyv, fzv), 1,
@@ -1007,7 +1007,7 @@ class test_fusion(unittest.TestCase):
             (fx-fy+theano.tensor.sqrt(fz), (fx, fy, fz), (fxv, fyv,
                 fzv), 1, fxv-fyv+numpy.sqrt(fzv), 'float32'),
             (fx-fy+theano.tensor.inv(fz), (fx, fy, fz), (fxv, fyv,
-                fzv), 1, fxv-fyv+(1/fzv), 'float32'),#55
+                fzv), 1, fxv-fyv+(1/fzv), 'float32'),  # 55
             (fx-fy+theano.tensor.neg(fz), (fx, fy, fz), (fxv, fyv,
                 fzv), 1, fxv-fyv+(-fzv), 'float32'),
             (fx-fy+theano.tensor.round(fz), (fx, fy, fz), (fxv, fyv,
@@ -1018,7 +1018,7 @@ class test_fusion(unittest.TestCase):
             (fx-theano.tensor.or_(iy, iz), (fx, iy, iz), (fxv, iyv,
                 izv), 1, fxv-(iyv|izv), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}),
             (fx-theano.tensor.xor(iy, iz), (fx, iy, iz), (fxv, iyv,
-                izv), 1, fxv-(iyv^izv), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}),#60
+                izv), 1, fxv-(iyv^izv), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}),  # 60
             (fx-theano.tensor.and_(iy, iz), (fx, iy, iz), (fxv, iyv,
                 izv), 1, fxv-(iyv&izv), {'custom': 'float64', 'numpy+floatX': config.floatX, 'numpy': 'float64'}),
             (fx-theano.tensor.invert(iy), (fx, iy), (fxv, iyv), 1,
@@ -1029,9 +1029,9 @@ class test_fusion(unittest.TestCase):
             (theano.tensor.pow(fx*fy+fz, fx*fy), (fx, fy, fz), (fxv,
                 fyv, fzv), 1, numpy.power(fxv*fyv+fzv, fxv*fyv), 'float32'),
             (fv+fy**fz, (fv, fy, fz), (fvv, fyv, fzv), 2, fvv+fyv**fzv,
-                'float32'),#fused with a dimshuffle #65
+                'float32'),  # fused with a dimshuffle #65
             (fv-fy+tensor.tanh(fz), (fv, fy, fz), (fvv, fyv, fzv), 2,
-                fvv-fyv+numpy.tanh(fzv), 'float32'),#fused with a dimshuffle
+                fvv-fyv+numpy.tanh(fzv), 'float32'),  # fused with a dimshuffle
 
             # Cases where the same input is reused many times.
             (theano.tensor.mul(fx, fx, fx, fx), (fx,), (fxv,), 1, fxv*

theano/tensor/tests/test_shared_randomstreams.py

@@ -43,7 +43,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         gn_val0 = gn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
 
         # print fn_val0
         numpy_val0 = rng.uniform(size=(2, 2))
@@ -86,7 +86,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         random.seed(utt.fetch_seed())
 
         rng = numpy.random.RandomState()
-        rng.set_state(random[out.rng].get_state()) #tests getitem
+        rng.set_state(random[out.rng].get_state())  # tests getitem
 
         fn_val0 = fn()
         fn_val1 = fn()
@@ -143,7 +143,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.uniform(-1, 1, size=(2, 2))
         numpy_val1 = rng.uniform(-1, 1, size=(2, 2))
 
@@ -160,7 +160,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.normal(-1, 2, size=(2, 2))
         numpy_val1 = rng.normal(-1, 2, size=(2, 2))
 
@@ -176,7 +176,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.random_integers(-5, 5, size=(20, 20))
         numpy_val1 = rng.random_integers(-5, 5, size=(20, 20))
 
@@ -198,7 +198,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.choice(10, (11, 8), True, None)
         numpy_val1 = rng.choice(10, (11, 8), True, None)
 
@@ -215,7 +215,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.poisson(lam=5, size=(11, 8))
         numpy_val1 = rng.poisson(lam=5, size=(11, 8))
 
@@ -232,7 +232,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
 
         # rng.permutation outputs one vector at a time, so we iterate.
         numpy_val0 = numpy.asarray([rng.permutation(10) for i in range(20)])
@@ -251,7 +251,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         fn_val1 = fn()
 
         rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
         numpy_val0 = rng.multinomial(1, [0.1]*10, size=(4, 4))
         numpy_val1 = rng.multinomial(1, [0.1]*10, size=(4, 4))
 

theano/tensor/tests/test_sharedvar.py

@@ -97,7 +97,7 @@ def makeSharedTester(shared_constructor_,
 
             values_to_div = .5
             if self.op_by_matrix:
-                values_to_div = self.internal_type(numpy.ones(x.shape, dtype=dtype)/2)#supported for cudandarray, but not ndarray.
+                values_to_div = self.internal_type(numpy.ones(x.shape, dtype=dtype)/2)  # supported for cudandarray, but not ndarray.
                 assert self.test_internal_type(values_to_div)
             x /= values_to_div
             total_val_2 = total_func()
@@ -193,7 +193,7 @@ def makeSharedTester(shared_constructor_,
                 # supported for cudandarray, but not ndarray.
                 values_to_div = self.internal_type(
                     numpy.ones(x.shape, dtype=dtype)/2)
-            x /= values_to_div#supported by ndarray and CudaNdarray
+            x /= values_to_div  # supported by ndarray and CudaNdarray
 
             # this is not required by the contract but it is a feature we can
             # implement for some type of SharedVariable.
@@ -202,7 +202,7 @@ def makeSharedTester(shared_constructor_,
             x = x_shared.get_value(borrow=False, return_internal_type=True)
             assert self.test_internal_type(x)
             assert x is not x_shared.container.value
-            x /= values_to_div#supported by ndarray and CudaNdarray
+            x /= values_to_div  # supported by ndarray and CudaNdarray
 
             # this is required by the contract
             assert not numpy.allclose(self.ref_fct(x), total_func())
@@ -251,7 +251,7 @@ def makeSharedTester(shared_constructor_,
 
             # test if that theano shared variable optimize set_value(borrow=True)
             get_x = x_shared.get_value(borrow=True)
-            assert get_x is not x_orig#borrow=False to shared_constructor
+            assert get_x is not x_orig  # borrow=False to shared_constructor
             get_x /= values_to_div
             x_shared.set_value(get_x, borrow=True)
             x = x_shared.get_value(borrow=True)
@@ -263,10 +263,10 @@ def makeSharedTester(shared_constructor_,
 
             # test optimized get set value on the gpu(don't pass data to the cpu)
             get_x = x_shared.get_value(borrow=True, return_internal_type=True)
-            assert get_x is not x_orig#borrow=False to shared_constructor
+            assert get_x is not x_orig  # borrow=False to shared_constructor
             assert self.test_internal_type(get_x)
 
-            get_x /= values_to_div#supported by ndarray and CudaNdarray
+            get_x /= values_to_div  # supported by ndarray and CudaNdarray
             assert self.test_internal_type(get_x)
             x_shared.set_value(get_x, borrow=True)
             x = x_shared.get_value(borrow=True, return_internal_type=True)
@@ -316,7 +316,7 @@ def makeSharedTester(shared_constructor_,
             if dtype is None:
                 dtype = theano.config.floatX
 
-            shp = (100/4, 1024)#100KB
+            shp = (100/4, 1024)  # 100KB
 
             x = numpy.zeros(shp, dtype=dtype)
             x = self.cast_value(x)

theano/tests/test_tutorial.py

@@ -776,7 +776,7 @@ class T_examples(unittest.TestCase):
         p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))   # Probability that target = 1
         prediction = p_1 > 0.5                    # The prediction thresholded
         xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1)  # Cross-entropy loss function
-        cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
+        cost = xent.mean() + 0.01 * (w ** 2).sum()  # The cost to minimize
         gw, gb = T.grad(cost, [w, b])             # Compute the gradient of the cost
                                                   # (we shall return to this in a
                                                   # following section of this tutorial)