Flake8 + review fixes.

theano/gradient.py

@@ -1335,7 +1335,7 @@ def _float_ones_like(x):
     floating point dtype """
 
     dtype = x.type.dtype
-    if 'float' not in dtype:
+    if dtype not in tensor.float_dtypes:
         dtype = theano.config.floatX
 
     return tensor.ones_like(x, dtype=dtype)

theano/scalar/basic.py

@@ -288,7 +288,7 @@ class Scalar(Type):
         if 'complex' in self.dtype:
             raise NotImplementedError("No literal for complex values.")
         if self.dtype == 'bool':
-            return '1' if b else '0'
+            return '1' if data else '0'
         return str(data)
 
     def c_declare(self, name, sub, check_input=True):
@@ -683,11 +683,6 @@ complexs64 = _multi(complex64)
 complexs128 = _multi(complex128)
 
 
-# Using a class instead of a function makes it possible to deep-copy it.
-# Note that currently only a few functions use this mechanism, because
-# it is enough to make the test-suite pass. However, it may prove
-# necessary to use this same mechanism in other places as well in the
-# future.
 def upcast_out(*types):
     dtype = Scalar.upcast(*types)
     return get_scalar_type(dtype),
@@ -698,7 +693,8 @@ def upgrade_to_float(*types):
     Upgrade any int types to float32 or float64 to avoid losing precision.
 
     """
-    conv = {int8: float32,
+    conv = {bool: float32,
+            int8: float32,
             int16: float32,
             int32: float64,
             int64: float64,
@@ -1265,7 +1261,7 @@ switch = Switch()
 class UnaryBitOp(UnaryScalarOp):
     def output_types(self, *input_types):
         for i in input_types[0]:
-            if i not in ((bool,) + discrete_types):
+            if i not in discrete_types:
                 raise TypeError('input to a BitOp must have type (u)int8, '
                                 '(u)int16, (u)int32 or (u)int64 or bool not %s' % i)
         return upcast_out(*input_types[0])

theano/tensor/basic.py

@@ -3182,6 +3182,7 @@ def mean(input, axis=None, dtype=None, op=False, keepdims=False,
     for i in axis:
         s = true_div(s, shp[i])
 
+    # This can happen when axis is an empty list/tuple
     if s.dtype != shp.dtype and s.dtype in discrete_dtypes:
         s = cast(s, shp.dtype)
 

theano/tensor/elemwise.py

@@ -1716,7 +1716,7 @@ class All(CAReduce):
 
     def make_node(self, input):
         input = as_tensor_variable(input)
-        if input.dtype is not "bool":
+        if input.dtype != "bool":
             input = theano.tensor.neq(input, 0)
         ret = super(All, self).make_node(input)
         return ret
@@ -1746,7 +1746,7 @@ class Any(CAReduce):
 
     def make_node(self, input):
         input = as_tensor_variable(input)
-        if input.dtype is not "bool":
+        if input.dtype != "bool":
             input = theano.tensor.neq(input, 0)
         ret = super(Any, self).make_node(input)
         return ret