Merge pull request #1253 from delallea/minor

theano/compat/__init__.py

@@ -10,10 +10,9 @@ if PY3:
 
     from operator import truediv as operator_div
 
-
     # In python 3.x, when an exception is reraised it saves original
     # exception in its args, therefore in order to find the actual
-    # message, we need to unpack arguments recurcively.
+    # message, we need to unpack arguments recursively.
     def exc_message(e):
         msg = e.args[0]
         if isinstance(msg, Exception):
@@ -21,7 +20,7 @@ if PY3:
         return msg
 
     def cmp(a, b):
-        """Return -1 if x<y, 0 if x==y, 1 if x > y."""
+        """Return -1 if x < y, 0 if x == y, 1 if x > y."""
         return (a > b) - (a < b)
 
     all = all
@@ -36,7 +35,6 @@ else:
 
     from operator import div as operator_div
 
-
     def exc_message(e):
         return e[0]
 

theano/compile/pfunc.py

@@ -293,7 +293,6 @@ class Param(object):
             `mutable` flag.
             False: do not permit any output to be aliased to the input
 
-        False: do not permit any output to be aliased to the input
         :param strict: False -> function arguments may be copied or cast to match the
             type required by the parameter `variable`.
             True -> function arguments must exactly match the type

theano/sandbox/rng_mrg.py

@@ -615,7 +615,7 @@ def guess_n_streams(size, warn=True):
         for s in size:
             r *= s
         if r > 6:
-            r = r//6 # chosen as fastest for rbm_benchmark
+            r = r // 6 # chosen as fastest for rbm_benchmark
 
         # The purpose of sampling from many streams is to be able to use
         # the GPU to its full capacity.  It just wastes RAM and stream-initialization time to
@@ -693,7 +693,7 @@ class MRG_RandomStreams(object):
         rval = numpy.zeros((n_streams,6), dtype='int32')
         rval[0] = self.rstate
         for i in xrange(1, n_streams):
-            rval[i] = ff_2p72(rval[i-1])
+            rval[i] = ff_2p72(rval[i - 1])
         if inc_rstate:
             self.inc_rstate()
         return rval
@@ -741,8 +741,8 @@ class MRG_RandomStreams(object):
 
         if isinstance(size, tuple):
             msg = "size must be a tuple of int or a Theano variable"
-            assert all([isinstance(i, (numpy.integer, int)) or isinstance(i,Variable)
-                for i in size]), msg
+            assert all([isinstance(i, (numpy.integer, int, Variable))
+                        for i in size]), msg
             if any([isinstance(i, (numpy.integer, int)) and i <= 0 for i in size]):
                 raise ValueError(
                     "The specified size contains a dimension with value <= 0",
@@ -750,12 +750,12 @@ class MRG_RandomStreams(object):
 
         else:
             msg = "size must be a tuple of int or a Theano variable"
-            assert isinstance(size, Variable) and size.ndim==1, msg
+            assert isinstance(size, Variable) and size.ndim == 1, msg
 
         if nstreams is None:
             nstreams = self.n_streams(size)
 
-        if self.use_cuda and dtype=='float32':
+        if self.use_cuda and dtype == 'float32':
             rstates = self.get_substream_rstates(nstreams)
             rstates = rstates.flatten()
             # HACK - we use fact that int32 and float32 have same size to
@@ -779,10 +779,12 @@ class MRG_RandomStreams(object):
             node_rstate = shared(self.get_substream_rstates(nstreams))
             u = self.pretty_return(node_rstate,
                     *mrg_uniform.new(node_rstate, ndim, dtype, size))
-        r = u * (high-low) + low
+        r = u * (high - low) + low
 
         if u.type.broadcastable != r.type.broadcastable:
-            raise NotImplementedError( 'Increase the size to match the broadcasting pattern of `low` and `high` arguments')
+            raise NotImplementedError(
+                    'Increase the size to match the broadcasting pattern of '
+                    '`low` and `high` arguments')
 
         assert r.dtype == dtype
         return  r
@@ -836,9 +838,9 @@ class MRG_RandomStreams(object):
                         "MRG_RandomStreams.multinomial, which does not use "
                         "the ndim argument.")
             ndim, size, bcast = raw_random._infer_ndim_bcast(
-                    ndim, size, pvals[:,0])
-            assert ndim==1
-            bcast = bcast+(pvals.type.broadcastable[-1],)
+                    ndim, size, pvals[:, 0])
+            assert ndim == 1
+            bcast = bcast + (pvals.type.broadcastable[-1],)
             unis = self.uniform(size=size, ndim=1, nstreams=nstreams)
             op = multinomial.MultinomialFromUniform(dtype)
             return op(pvals, unis)
@@ -882,7 +884,7 @@ class MRG_RandomStreams(object):
                 evened = True
         else:
             #if even, don't change, if odd, +1
-            n_samples = prod(size)+(prod(size)%2)
+            n_samples = prod(size) + (prod(size) % 2)
         flattened = self.uniform(size=(n_samples,), dtype=dtype,
                                  nstreams=nstreams)
 
@@ -902,7 +904,7 @@ class MRG_RandomStreams(object):
 
         # so trying this instead
         first_half = sqrt_ln_U1 * cos(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
-        second_half = sqrt_ln_U1 * sin(numpy.array(2.0 * numpy.pi, dtype=dtype)*U2)
+        second_half = sqrt_ln_U1 * sin(numpy.array(2.0 * numpy.pi, dtype=dtype) * U2)
         normal_samples = join(0, first_half, second_half)
 
         final_samples = None
@@ -921,6 +923,7 @@ class MRG_RandomStreams(object):
         assert final_samples.dtype == dtype
         return final_samples
 
+
 @local_optimizer([None])
 def mrg_random_make_inplace(node):
     op = node.op

theano/sparse/tests/test_basic.py

@@ -1773,20 +1773,13 @@ class Remove0Tester(utt.InferShapeTester):
             if theano.config.mode not in ['FAST_COMPILE']:
                 # list of apply nodes in the optimized graph.
                 nodes = f.maker.fgraph.toposort()
-                v = [True for node in nodes]
-                # In python 3, list comprehention variables do not leak
-                # in the outside scope, so we bind node varible below
-                # to make the code behave the same under all
-                # versions. However, the logic here does not look
-                # right: the length of v is always the same as that of
-                # nodes and the only result of the assert is to check
-                # that nodes is not empty.  The intent was probably to
-                # keep if clause inside the [] and check every node.
-                node = nodes[-1]
-                if isinstance(node.op, Remove0) and node.op.inplace:
-                    assert len(v), \
-                    'Inplacing optimization should have been applied.'
-
+                # Check there isn't any Remove0 instance not inplace.
+                assert not any([isinstance(node.op, Remove0) and
+                                not node.op.inplace for node in nodes]), (
+                       'Inplace optimization should have been applied')
+                # Check there is at least one Remove0 inplace.
+                assert any([isinstance(node.op, Remove0) and node.op.inplace
+                            for node in nodes])
             # checking
             # makes sense to change its name
             target = mat