Typo fixes

doc/cifarSC2011/theano.txt

@@ -208,7 +208,7 @@ Exercise 2
 
 
     # Construct Theano expression graph
-    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probabily of having a one
+    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
     prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
     xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
     cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize

doc/hpcs2011_tutorial/logreg_example.py

@@ -20,7 +20,7 @@ y.tag.test_value = D[1]
 
 
 # Construct Theano expression graph
-p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probabily of having a one
+p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
 prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
 xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
 cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize

doc/tutorial/extending_theano.txt

@@ -9,7 +9,7 @@ Theano Graphs
 =============
 
 - Theano works with symbolic graphs.
-- Those graphs are bi-partite graphs (graph with 2 types of nodes).
+- Those graphs are bi-partite graphs (graphs with 2 types of nodes).
 - The two types of nodes are ``Apply`` and ``Variable`` nodes.
 - Each ``Apply`` node has a link to the op that it executes.
 
@@ -359,7 +359,7 @@ file containing a specific test of interest and run the file. In this example, t
        t.setUp()
        t.test_double_rop()
 
-We recommand that when we execute a file, we run all tests in that
+We recommend that when we execute a file, we run all tests in that
 file. This can be done by adding this at the end of your test files:
 
 .. code-block:: python

doc/tutorial/modes.txt

@@ -67,7 +67,7 @@ Consider the logistic regression:
     #print w.get_value(), b.get_value()
 
     # Construct Theano expression graph
-    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probabily of having a one
+    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
     prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
     xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
     cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
@@ -110,7 +110,7 @@ as it will be useful later on.
 
 .. Note::
 
-   * Apply the Theano flag ``floatX=float32`` through (``theano.config.floatX``) in your code.
+   * Apply the Theano flag ``floatX=float32`` (through ``theano.config.floatX``) in your code.
    * Cast inputs before storing them into a shared variable.
    * Circumvent the automatic cast of *int32* with *float32* to *float64*:
     

doc/tutorial/modes_solution_1.py

@@ -27,7 +27,7 @@ y.tag.test_value = D[1]
 #print w.get_value(), b.get_value()
 
 # Construct Theano expression graph
-p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probabily of having a one
+p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probability of having a one
 prediction = p_1 > 0.5  # The prediction that is done: 0 or 1
 xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1)  # Cross-entropy
 cost = tt.cast(xent.mean(), 'float32') + \

doc/tutorial/printing_drawing.txt

@@ -42,7 +42,7 @@ The following output depicts the pre- and post- compilation graphs.
 
 
     # Construct Theano expression graph
-    p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probabily of having a one
+    p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability of having a one
     prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
     xent = -y * T.log(p_1) - (1 - y) * T.log(1 - p_1) # Cross-entropy
     cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to optimize

doc/tutorial/using_gpu.txt

@@ -342,7 +342,7 @@ Consider again the logistic regression:
     #print w.get_value(), b.get_value()
 
     # Construct Theano expression graph
-    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probabily of having a one
+    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
     prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
     xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
     cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
@@ -400,7 +400,7 @@ What can be done to further increase the speed of the GPU version? Put your idea
    * Use the Theano flag ``device=gpu`` to require use of the GPU device.
    * Use ``device=gpu{0, 1, ...}`` to specify which GPU if you have more than one.
  
-   * Apply the Theano flag ``floatX=float32`` through (``theano.config.floatX``) in your code.
+   * Apply the Theano flag ``floatX=float32`` (through ``theano.config.floatX``) in your code.
    * ``Cast`` inputs before storing them into a ``shared`` variable.
    * Circumvent the automatic cast of *int32* with *float32* to *float64*:
     
@@ -623,5 +623,4 @@ only applicable to computations involving a single output. Hence, to gain
 efficiency over the basic solution that is asked here, the two operations would
 have to be jointly optimized explicitly in the code.)
 
-Modify and execute to support *stride* (i.e. so as not constrain the input to be *C-contiguous*).
-
+Modify and execute to support *stride* (i.e. to avoid constraining the input to be *C-contiguous*).

doc/tutorial/using_gpu_solution_1.py

@@ -38,7 +38,7 @@ y.tag.test_value = D[1]
 #print w.get_value(), b.get_value()
 
 # Construct Theano expression graph
-p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probabily of having a one
+p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probability of having a one
 prediction = p_1 > 0.5  # The prediction that is done: 0 or 1
 xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1)  # Cross-entropy
 cost = tt.cast(xent.mean(), 'float32') + \

theano/sandbox/scan_module/scan.py

@@ -132,7 +132,7 @@ def scan(fn,
         The list of ``non_sequences`` can also contain shared variables
         used in the function, though ``scan`` is able to figure those
         out on its own so they can be skipped. For the clarity of the
-        code we recommand though to provide them to scan. To some extend
+        code we recommend though to provide them to scan. To some extend
         ``scan`` can also figure out other ``non sequences`` (not shared)
         even if not passed to scan (but used by `fn`). A simple example of
         this would be :

theano/scan_module/scan.py

@@ -130,7 +130,7 @@ def scan(fn,
         The list of ``non_sequences`` can also contain shared variables
         used in the function, though ``scan`` is able to figure those
         out on its own so they can be skipped. For the clarity of the
-        code we recommand though to provide them to scan. To some extend
+        code we recommend though to provide them to scan. To some extend
         ``scan`` can also figure out other ``non sequences`` (not shared)
         even if not passed to scan (but used by `fn`). A simple example of
         this would be :