Merge pull request #310 from delallea/minor

NEWS.txt

@@ -123,8 +123,8 @@ Known bugs:
  * CAReduce with nan in inputs don't return the good output (`Ticket <https://www.assembla.com/spaces/theano/tickets/763>`_).
      * This is used in tensor.{max,mean,prod,sum} and in the grad of PermuteRowElements.
  * If you take the grad of a grad of scan, now we raise an error during the construction of the graph. In the past, you could have wrong results in some cases or an error at run time.
- * Scan can raise an IncSubtensor error at run time (no wrong result possible). The current work around is to disable an optimization with this Theano flags: "optimizer_excluding=scanOp_save_mem".
-   * If you have more then 1 optimization to disable, you must separate them with ":".
+ * Scan can raise an IncSubtensor error at run time (no wrong result possible). The current workaround is to disable an optimization with this Theano flag: "optimizer_excluding=scanOp_save_mem".
+   * If you have multiple optimizations to disable, you must separate them with ":".
 
 
 Sandbox:

doc/NEWS.txt

@@ -123,8 +123,8 @@ Known bugs:
  * CAReduce with nan in inputs don't return the good output (`Ticket <https://www.assembla.com/spaces/theano/tickets/763>`_).
      * This is used in tensor.{max,mean,prod,sum} and in the grad of PermuteRowElements.
  * If you take the grad of a grad of scan, now we raise an error during the construction of the graph. In the past, you could have wrong results in some cases or an error at run time.
- * Scan can raise an IncSubtensor error at run time (no wrong result possible). The current work around is to disable an optimization with this Theano flags: "optimizer_excluding=scanOp_save_mem".
-   * If you have more then 1 optimization to disable, you must separate them with ":".
+ * Scan can raise an IncSubtensor error at run time (no wrong result possible). The current workaround is to disable an optimization with this Theano flag: "optimizer_excluding=scanOp_save_mem".
+   * If you have multiple optimizations to disable, you must separate them with ":".
 
 
 Sandbox:

doc/tutorial/gpu_data_convert.txt

@@ -35,8 +35,8 @@ Compiling with PyCUDA
 You can use PyCUDA to compile some CUDA function that work directly on
 CudaNdarray. There is an example in the function `test_pycuda_theano`
 in the file `theano/misc/tests/test_pycuda_theano_simple.py`. Also,
-there is an example that show how to make an op that call a pycuda
-function :ref:`here <pyCUDA_theano>`
+there is an example that shows how to make an op that calls a pycuda
+function :ref:`here <pyCUDA_theano>`.
 
 Theano op using PyCUDA function
 -------------------------------

theano/misc/tests/test_pycuda_theano_simple.py

@@ -16,7 +16,7 @@ import theano.misc.pycuda_init
 if not theano.misc.pycuda_init.pycuda_available:
     from nose.plugins.skip import SkipTest
     raise SkipTest("Pycuda not installed."
-                   " We skip test of theano op with pycuda code.")
+                   " We skip tests of Theano Ops with pycuda code.")
 
 if cuda_ndarray.cuda_available == False:
     from nose.plugins.skip import SkipTest
@@ -28,7 +28,7 @@ import pycuda.gpuarray
 
 
 def test_pycuda_only():
-    """Run pycuda only example to test that pycuda work."""
+    """Run pycuda only example to test that pycuda works."""
     from pycuda.compiler import SourceModule
     mod = SourceModule("""
 __global__ void multiply_them(float *dest, float *a, float *b)

theano/sandbox/cuda/type.py

@@ -51,8 +51,9 @@ class CudaNdarrayType(Type):
 
     def __init__(self, broadcastable, name=None, dtype=None):
         if dtype != None and dtype != 'float32':
-            raise TypeError(self.__class__.__name__+' only support dtype float32 for now.'\
-                    'Tried using dtype %s for variable %s' % (dtype, name))
+            raise TypeError('%s only supports dtype float32 for now. Tried '
+                            'using dtype %s for variable %s' %
+                            (self.__class__.__name__, dtype, name))
         self.broadcastable = tuple(broadcastable)
         self.name = name
         self.dtype_specs() # error checking is done there