Minor sharpening

Author: mnielsen

code/mnist_loader.py

@@ -21,14 +21,14 @@
 
 
 def load_data():
-    """ Return the MNIST data as a tuple containing the training data,
+    """Return the MNIST data as a tuple containing the training data,
     the validation data, and the test data.
 
     The ``training_data`` is returned as a tuple with two entries.
     The first entry contains the actual training images.  This is a
     numpy ndarray with 50,000 entries.  Each entry is, in turn, a
     numpy ndarray with 784 values, representing the 28 * 28 = 784
-    pixels.
+    pixels in a single MNIST image.
 
     The second entry in the ``training_data`` tuple is a numpy ndarray
     containing 50,000 entries.  Those entries are just the digit
@@ -38,10 +38,10 @@ def load_data():
     The ``validation_data`` and ``test_data`` are similar, except
     each contains only 10,000 images.
 
-    This is a nice and convenient data format, but for use in neural
-    networks it's actually helpful to modify the format of the
-    ``training_data`` a little.  That's done in the wrapper function
-    ``load_data_wrapper()``, see below.
+    This is a nice data format, but for use in neural networks it's
+    helpful to modify the format of the ``training_data`` a little.
+    That's done in the wrapper function ``load_data_wrapper()``, see
+    below.
     """
     f = open('../data/mnist.pkl', 'rb')
     training_data, validation_data, test_data = cPickle.load(f)
@@ -50,8 +50,8 @@ def load_data():
 
 def load_data_wrapper():
     """Return a tuple containing ``(training_data, validation_data,
-    test_data)``. Based on ``load_data``, but the format is a little more
-    convenient for use in neural networks.
+    test_data)``. Based on ``load_data``, but the format is more
+    convenient for use in our implementation of neural networks.
 
     In particular, ``training_data`` is a list containing 50,000
     2-tuples ``(x, y)``.  ``x`` is a 784-dimensional numpy.ndarray