small doc/comment changes.

doc/library/tensor/nnet/conv.txt

@@ -28,11 +28,11 @@
     changed. Here is the algo:
 
     - If we can use `cuDNN <https://developer.nvidia.com/cuDNN>`_, use it.
-    - If not, use gemm version (slower then cuDNN, use more memory).
+    - If not, use gemm version (slower then cuDNN, uses more memory).
 
-    If the user don't want the extra memory of the gemm version, they
-    can enable the legacy code that is even slower, but don't use
-    extra memory. For this, use the Theano flag
+    If the users don't want the extra memory usage of the gemm
+    version, they can enable the legacy code that is even slower, but
+    does not use extra memory. For this, use the Theano flag
     ``optimizer_excluding=conv_gemm``.
 
     There is no reason to use the legacy code or the gemm version if
@@ -41,11 +41,11 @@
     2 other options:
 
     - There is also the fft version that is the fastest in some cases,
-      but use even more memory. It don't support striding to remove
-      computation and have some shapes restriction.
+      but uses even more memory. It does not support striding to remove
+      computation and has some shapes restriction.
 
-    - There is also the cuda_convnet convolution in Pylearn2. It use a
-      different memory layout, have shapes restriction, but don't use
+    - There is also the cuda_convnet convolution in Pylearn2. It uses a
+      different memory layout, has shapes restrictions, but does not use
       extra memory and is faster then the legacy convolution.
 
 

theano/sandbox/cuda/opt.py

@@ -1109,14 +1109,11 @@ def local_gpu_softmax_with_bias(node):
 from theano.tensor.nnet import conv
 
 
-# Need to be registered before local_gpu_conv_legacy. Otherwise, it
+# Needs to be registered before local_gpu_conv_legacy. Otherwise, it
 # will have priority over this optimization.  We want, if cudnn is
-# available and the GPU support it, use it.  Otherwise, the gemm
-# version should be used.  If the user want the legacy convolution,
-# they should use the Theano flag:
-# optimizer_excluding=local_conv_gemm.
-# If cudnn is available, this flag should be added:
-# optimizer_excluding=local_gpu_conv
+# available and the GPU supports it, to use it.  Otherwise, the gemm
+# version should be used.  If the users want the legacy convolution,
+# they should use the Theano flag to disable the dnn and/or gemm version.
 @register_opt("dnn")
 @local_optimizer([gpu_from_host, conv.ConvOp])
 def local_gpu_conv(node):

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -588,7 +588,7 @@ def test_dnn_valid():
 
 def test_default_conv():
     """Just test that we introduce the right GPU convolution
-    versoin.
+    version.
 
     """
     img = theano.tensor.ftensor4()