Typo fixes

doc/library/tensor/nnet/nnet.txt

@@ -15,8 +15,8 @@
     :Parameters: *x* - symbolic Tensor (or compatible)
     :Return type: same as x
     :Returns: element-wise sigmoid: :math:`sigmoid(x) = \frac{1}{1 + \exp(-x)}`.
-    :note: see :func:`ultra_fast_sigmoid` or :func:`hard_sigmoid` for faster version.
-        Speed comparison for 100M float64 element on a Core2 Duo @ 3.16 GHz.
+    :note: see :func:`ultra_fast_sigmoid` or :func:`hard_sigmoid` for faster versions.
+        Speed comparison for 100M float64 elements on a Core2 Duo @ 3.16 GHz:
 
           - hard_sigmoid: 1.0s
           - ultra_fast_sigmoid: 1.3s
@@ -44,15 +44,15 @@
     :Parameters: *x* - symbolic Tensor (or compatible)
     :Return type: same as x
     :Returns: approximated element-wise sigmoid: :math:`sigmoid(x) = \frac{1}{1 + \exp(-x)}`.
-    :note: To automatically change all :func:`sigmoid` op to this version, use
+    :note: To automatically change all :func:`sigmoid` ops to this version, use
       the Theano optimization ``local_ultra_fast_sigmoid``. This can be done
       with the Theano flag ``optimizer_including=local_ultra_fast_sigmoid``.
-      This optimization is done late, so it shouldn't affect
+      This optimization is done late, so it should not affect
       stabilization optimization.
 
    .. note:: The underlying code will return 0.00247262315663 as the
        minimum value and 0.997527376843 as the maximum value. So it
-       never return 0 or 1.
+       never returns 0 or 1.
 
 
 
@@ -63,10 +63,10 @@
     :Parameters: *x* - symbolic Tensor (or compatible)
     :Return type: same as x
     :Returns: approximated element-wise sigmoid: :math:`sigmoid(x) = \frac{1}{1 + \exp(-x)}`.
-    :note: To automatically change all :func:`sigmoid` op to this version, use
+    :note: To automatically change all :func:`sigmoid` ops to this version, use
       the Theano optimization ``local_hard_sigmoid``. This can be done
       with the Theano flag ``optimizer_including=local_hard_sigmoid``.
-      This optimization is done late, so it shouldn't affect
+      This optimization is done late, so it should not affect
       stabilization optimization.
 
    .. note:: The underlying code will return an exact 0 or 1 if an

theano/tensor/nnet/sigm.py

@@ -98,7 +98,7 @@ for i in xrange(750):
         // We block to keep the data in l1
         // normal l1 size = 32k: 32k/2(input + output)/8(nb bytes of double)=2k
         // We stay bellow the 2k limit to let space for
-        // This is faster then the not blocking version
+        // This is faster than the not blocking version
         for(int i=0;i<n;i+=2048){
             npy_intp nb = (n-i<2048)?n-i:2048;
             for(int j=0;j<nb;j++){
@@ -261,16 +261,16 @@ theano.compile.optdb['uncanonicalize'].register("local_ultra_fast_sigmoid",
 def hard_sigmoid(x):
     """An approximation of sigmoid.
 
-    More approximate and faster then ultra_fast_sigmoid.
+    More approximate and faster than ultra_fast_sigmoid.
 
     Approx in 3 parts: 0, scaled linear, 1
 
-    Removing the slop and shift don't make it faster.
+    Removing the slope and shift does not make it faster.
 
     """
-    slop = 0.2
+    slope = 0.2
     shift = 0.5
-    x = (x * 0.2) + shift
+    x = (x * slope) + shift
     x = tensor.clip(x, 0, 1)
     return x