Merge pull request #1596 from delallea/minor

doc/acknowledgement.txt

@@ -19,7 +19,7 @@ Acknowledgements
   `theano/misc/cpucount.py` come from the project `pyprocessing
   <http://pyprocessing.berlios.de/>`_. It is available under the same license
   as Theano.
-* Our random number generator implementation on CPU and GPU use the MRG31k3p algorithm that is described in:
+* Our random number generator implementation on CPU and GPU uses the MRG31k3p algorithm that is described in:
 
     P. L'Ecuyer and R. Touzin, `Fast Combined Multiple Recursive Generators with Multipliers of the form a = +/- 2^d +/- 2^e <http://www.informs-sim.org/wsc00papers/090.PDF>`_, Proceedings of the 2000 Winter Simulation Conference, Dec. 2000, 683--689.
 

doc/library/gof/fgraph.txt

@@ -36,7 +36,7 @@ Reference
 
     ***TODO***
 
-    .. note:: FunctionGraph(inputs, outputs) clone the inputs by
-        default. To don't have this behavior, add the parameter
-        clone=False. This is needed as we don't want cached constant
+    .. note:: FunctionGraph(inputs, outputs) clones the inputs by
+        default. To avoid this behavior, add the parameter
+        clone=False. This is needed as we do not want cached constants
         in fgraph.

doc/library/tensor/nnet/conv.txt

@@ -29,17 +29,17 @@ TODO: Give examples for how to use these things! They are pretty complicated.
     - :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
     - :func:`conv3D <theano.tensor.nnet.Conv3D.conv3D>`.
     - :func:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>`
-      Another conv3d implementation that use the conv2d with data reshaping.
-      It is faster in some case then conv3d, specificaly on the GPU.
+      Another conv3d implementation that uses the conv2d with data reshaping.
+      It is faster in some cases than conv3d, specifically on the GPU.
     - `Faster conv2d <http://deeplearning.net/software/pylearn2/library/alex.html>`_
 
-      This is in Pylearn2, not very documented and use a different
-      memory layout for the input.  It is important to have the input
+      This is in Pylearn2, not very documented and uses a different
+      memory layout for the input. It is important to have the input
       in the native memory layout, and not use dimshuffle on the
-      inputs, otherwise you loose much of the speed up. So this is not
+      inputs, otherwise you lose most of the speed up. So this is not
       a drop in replacement of conv2d.
 
-      Normally those are called from the `linear transfrom
+      Normally those are called from the `linear transform
       <http://deeplearning.net/software/pylearn2/library/linear.html>`_
       implementation.
 

theano/gof/fg.py

@@ -86,7 +86,7 @@ class FunctionGraph(utils.object2):
         is added via the constructor.  How constructed is the FunctionGraph?
 
         :param clone: If true, we will clone the graph. This is
-        usefull to remove the constant cache problem.
+        useful to remove the constant cache problem.
 
         """
         if clone:
@@ -138,8 +138,8 @@ class FunctionGraph(utils.object2):
         if getattr(r, 'cached', False):
             raise CachedConstantError(
                 "You manually constructed a FunctionGraph, but you passed it a"
-                " graph that have cached constant. This should happen."
-                " Clone the graph before building the FunctionGraph")
+                " graph that has a cached constant. This should not happen."
+                " Clone the graph before building the FunctionGraph.")
         if (hasattr(r, 'fgraph') and
             r.fgraph is not None and
             r.fgraph is not self):

theano/tensor/utils.py

@@ -89,8 +89,8 @@ def shape_of_variables(fgraph, input_shapes):
     if any([i not in fgraph.inputs for i in input_shapes.keys()]):
         raise ValueError(
             "input_shapes keys aren't in the fgraph.inputs. FunctionGraph()"
-            " interface changed. Now by default, it clone the graph it receive."
-            " To have the old behavior, give him this new parameter `clone=False`.")
+            " interface changed. Now by default, it clones the graph it receives."
+            " To have the old behavior, give it this new parameter `clone=False`.")
 
     numeric_input_dims  = [dim for inp in fgraph.inputs
                                for dim in input_shapes[inp]]