Remove remaining uses of .value

Author: lamblin

code/dA.py

@@ -239,7 +239,7 @@ def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='../data/mnist.
     train_set_x, train_set_y = datasets[0]
 
     # compute number of minibatches for training, validation and testing
-    n_train_batches = train_set_x.value.shape[0] / batch_size
+    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
 
     # allocate symbolic variables for the data
     index = T.lscalar()    # index to a [mini]batch 
@@ -286,7 +286,7 @@ def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='../data/mnist.
     training_time = (end_time - start_time)
 
     print >> sys.stderr, ('The no corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % ((training_time)/60.))
-    image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
+    image = PIL.Image.fromarray(tile_raster_images(X = da.W.get_value(borrow=True).T,
                  img_shape = (28,28),tile_shape = (10,10), 
                  tile_spacing=(1,1)))
     image.save('filters_corruption_0.png') 
@@ -329,7 +329,7 @@ def test_dA( learning_rate = 0.1, training_epochs = 15, dataset ='../data/mnist.
 
     print >> sys.stderr, ('The 30% corruption code for file '+os.path.split(__file__)[1]+' ran for %.2fm' % (training_time/60.))
 
-    image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
+    image = PIL.Image.fromarray(tile_raster_images(X = da.W.get_value(borrow=True).T,
                  img_shape = (28,28),tile_shape = (10,10), 
                  tile_spacing=(1,1)))
     image.save('filters_corruption_30.png') 

doc/dA.txt

@@ -588,7 +588,7 @@ save the filters as an image :
 
 .. code-block:: python 
 
-    image = PIL.Image.fromarray(tile_raster_images( X = da.W.value.T,
+    image = PIL.Image.fromarray(tile_raster_images(X = da.W.get_value(borrow=True).T,
                  img_shape = (28,28),tile_shape = (10,10), 
                  tile_spacing=(1,1)))
     image.save('filters_corruption_30.png') 

doc/gettingstarted.txt

@@ -682,19 +682,19 @@ like:
 
     >>> import cPickle
     >>> save_file = open('path', 'wb')  # this will overwrite current contents
-    >>> cPickle.dump(w.value, save_file, -1) # the -1 is for HIGHEST_PROTOCOL
-    >>> cPickle.dump(v.value, save_file, -1) # .. and it triggers much more efficient
-    >>> cPickle.dump(u.value, save_file, -1) # .. storage than numpy's default
+    >>> cPickle.dump(w.get_value(borrow=True), save_file, -1) # the -1 is for HIGHEST_PROTOCOL
+    >>> cPickle.dump(v.get_value(borrow=True), save_file, -1) # .. and it triggers much more efficient
+    >>> cPickle.dump(u.get_value(borrow=True), save_file, -1) # .. storage than numpy's default
     >>> save_file.close()
 
 Then later, you can load your data back like this:
 
 .. code-block:: python
 
     >>> save_file = open('path')
-    >>> w.value = cPickle.load(save_file) 
-    >>> v.value = cPickle.load(save_file)
-    >>> u.value = cPickle.load(save_file)
+    >>> w.set_value(cPickle.load(save_file), borrow=True)
+    >>> v.set_value(cPickle.load(save_file), borrow=True)
+    >>> u.set_value(cPickle.load(save_file), borrow=True)
 
 This technique is a bit verbose, but it is tried and true.  You will be able
 to load your data and render it in matplotlib without trouble, years after