--- theano/compile/mode.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/compile/mode.py	2019-08-22 13:25:25.024334947 -0600
@@ -261,7 +261,7 @@ class Mode(object):
     def __init__(self, linker=None, optimizer='default'):
         if linker is None:
             linker = config.linker
-        if optimizer is 'default':
+        if optimizer == 'default':
             optimizer = config.optimizer
         Mode.__setstate__(self, (linker, optimizer))
 
--- theano/gof/opt.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/gof/opt.py	2019-08-22 14:06:43.820896086 -0600
@@ -1284,7 +1284,7 @@ def local_optimizer(tracks, inplace=Fals
 
         """
         if tracks is not None:
-            if len(tracks) is 0:
+            if len(tracks) == 0:
                 raise ValueError("Use None instead of an empty list to apply to all nodes.", f.__module__, f.__name__)
             for t in tracks:
                 if not (isinstance(t, op.Op) or issubclass(t, op.PureOp)):
--- theano/gof/tests/test_link.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/gof/tests/test_link.py	2019-08-22 16:29:02.294513027 -0600
@@ -113,7 +113,7 @@ class TestPerformLinker(unittest.TestCas
     def test_input_output_same(self):
         x, y, z = inputs()
         fn = perform_linker(FunctionGraph([x], [x])).make_function()
-        assert 1.0 is fn(1.0)
+        assert 1.0 == fn(1.0)
 
     def test_input_dependency0(self):
         x, y, z = inputs()
--- theano/tensor/nnet/bn.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/tensor/nnet/bn.py	2019-08-22 13:35:49.109305914 -0600
@@ -642,7 +642,7 @@ class AbstractBatchNormTrainGrad(Op):
         # some inputs should be disconnected
         results = [g_wrt_x, g_wrt_dy, g_wrt_scale, g_wrt_x_mean, g_wrt_x_invstd,
                    theano.gradient.DisconnectedType()()]
-        return [theano.gradient.DisconnectedType()() if r is 0 else r
+        return [theano.gradient.DisconnectedType()() if r == 0 else r
                 for r in results]
 
     def connection_pattern(self, node):
--- theano/tensor/nnet/tests/test_conv.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/tensor/nnet/tests/test_conv.py	2019-08-22 16:29:51.149656121 -0600
@@ -95,7 +95,7 @@ class TestConv2D(utt.InferShapeTester):
         # REFERENCE IMPLEMENTATION
         s = 1.
         orig_image_data = image_data
-        if border_mode is not 'full':
+        if border_mode != 'full':
             s = -1.
         out_shape2d = np.array(N_image_shape[-2:]) +\
             s * np.array(N_filter_shape[-2:]) - s
--- theano/tests/test_determinism.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/tests/test_determinism.py	2019-08-22 16:31:03.119393791 -0600
@@ -57,7 +57,7 @@ def test_determinism_1():
             updates.append((s, val))
 
         for var in theano.gof.graph.ancestors(update for _, update in updates):
-            if var.name is not None and var.name is not 'b':
+            if var.name is not None and var.name != 'b':
                 if var.name[0] != 's' or len(var.name) != 2:
                     var.name = None