python 3 compatibility

doc/cifarSC2011/advanced_theano.txt

@@ -41,14 +41,14 @@ Conditions
    n_times = 10
 
    tic = time.clock()
-   for i in xrange(n_times):
+   for i in range(n_times):
        f_switch(val1, val2, big_mat1, big_mat2)
-   print 'time spent evaluating both values %f sec'%(time.clock()-tic)
+   print('time spent evaluating both values %f sec' % (time.clock()-tic))
 
    tic = time.clock()
-   for i in xrange(n_times):
+   for i in range(n_times):
        f_lazyifelse(val1, val2, big_mat1, big_mat2)
-   print 'time spent evaluating one value %f sec'%(time.clock()-tic)
+   print('time spent evaluating one value %f sec' % (time.clock()-tic))
 
 .. testoutput::
    :hide:
@@ -142,7 +142,7 @@ Loops
                                        outputs=polynomial)
 
   test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
-  print calculate_polynomial(test_coeff, 3)
+  print(calculate_polynomial(test_coeff, 3))
 
 .. testoutput::
 

doc/install_windows.txt

@@ -420,9 +420,9 @@ Create a test file containing:
    t_start = time.time()
    tAB = mf(A,B)
    t_end = time.time()
-   print "NP time: %f[s], theano time: %f[s] (times should be close when run on CPU!)" %(
-                                              np_end-np_start, t_end-t_start)
-   print "Result difference: %f" % (np.abs(AB-tAB).max(), )
+   print("NP time: %f[s], theano time: %f[s] (times should be close when run on CPU!)" %(
+                                              np_end-np_start, t_end-t_start))
+   print("Result difference: %f" % (np.abs(AB-tAB).max(), ))
    
 .. testoutput::
    :hide:

doc/library/scan.txt

@@ -187,7 +187,7 @@ downcast** of the latter.
     # test
     some_num = 15
     print(triangular_sequence(some_num))
-    print([n * (n + 1) // 2 for n in xrange(some_num)])
+    print([n * (n + 1) // 2 for n in range(some_num)])
 
 .. testoutput::
 

doc/tutorial/examples.txt

@@ -206,15 +206,15 @@ Let's try it out!
 .. If you modify this code, also change :
 .. theano/tests/test_tutorial.py:T_examples.test_examples_8
 
->>> state.get_value()
+>>> print(state.get_value())
 0
 >>> accumulator(1)
 array(0)
->>> state.get_value()
+>>> print(state.get_value())
 1
 >>> accumulator(300)
 array(1)
->>> state.get_value()
+>>> print(state.get_value())
 301
 
 It is possible to reset the state. Just use the ``.set_value()`` method:
@@ -222,7 +222,7 @@ It is possible to reset the state. Just use the ``.set_value()`` method:
 >>> state.set_value(-1)
 >>> accumulator(3)
 array(-1)
->>> state.get_value()
+>>> print(state.get_value())
 2
 
 As we mentioned above, you can define more than one function to use the same
@@ -234,7 +234,7 @@ shared variable.  These functions can all update the value.
 >>> decrementor = function([inc], state, updates=[(state, state-inc)])
 >>> decrementor(2)
 array(2)
->>> state.get_value()
+>>> print(state.get_value())
 0
 
 You might be wondering why the updates mechanism exists.  You can always
@@ -261,7 +261,7 @@ for the purpose of one particular function.
 >>> skip_shared = function([inc, foo], fn_of_state, givens=[(state, foo)])
 >>> skip_shared(1, 3)  # we're using 3 for the state, not state.value
 array(7)
->>> state.get_value()  # old state still there, but we didn't use it
+>>> print(state.get_value())  # old state still there, but we didn't use it
 0
 
 The ``givens`` parameter can be used to replace any symbolic variable, not just a

doc/tutorial/loading_and_saving.txt

@@ -30,10 +30,7 @@ The Basics of Pickling
 The two modules ``pickle`` and ``cPickle`` have the same functionalities, but
 ``cPickle``, coded in C, is much faster.
 
-.. If you modify this code, also change :
-.. theano/tests/test_tutorial.py:T_loading_and_saving.test_loading_and_saving_3
-
->>> import cPickle
+>>> from six.moves import cPickle
 
 You can serialize (or *save*, or *pickle*) objects to a file with
 ``cPickle.dump``:
@@ -42,7 +39,7 @@ You can serialize (or *save*, or *pickle*) objects to a file with
 
    my_obj = object()
 
->>> f = file('obj.save', 'wb')
+>>> f = open('obj.save', 'wb')
 >>> cPickle.dump(my_obj, f, protocol=cPickle.HIGHEST_PROTOCOL)
 >>> f.close()
 
@@ -60,7 +57,7 @@ You can serialize (or *save*, or *pickle*) objects to a file with
 To de-serialize (or *load*, or *unpickle*) a pickled file, use
 ``cPickle.load``:
 
->>> f = file('obj.save', 'rb')
+>>> f = open('obj.save', 'rb')
 >>> loaded_obj = cPickle.load(f)
 >>> f.close()
 
@@ -74,14 +71,14 @@ same order):
    obj2 = object()
    obj3 = object()
 
->>> f = file('objects.save', 'wb')
+>>> f = open('objects.save', 'wb')
 >>> for obj in [obj1, obj2, obj3]:
 ...     cPickle.dump(obj, f, protocol=cPickle.HIGHEST_PROTOCOL)
 >>> f.close()
 
 Then:
 
->>> f = file('objects.save', 'rb')
+>>> f = open('objects.save', 'rb')
 >>> loaded_objects = []
 >>> for i in range(3):
 ...     loaded_objects.append(cPickle.load(f))
@@ -121,7 +118,7 @@ For instance, you can define functions along the lines of:
 
     def __setstate__(self, d):
         self.__dict__.update(d)
-        self.training_set = cPickle.load(file(self.training_set_file, 'rb'))
+        self.training_set = cPickle.load(open(self.training_set_file, 'rb'))
 
 
 Robust Serialization

doc/tutorial/printing_drawing.txt

@@ -66,7 +66,7 @@ Debug Print
 
 The pre-compilation graph:
 
->>> theano.printing.debugprint(prediction) # doctest: +NORMALIZE_WHITESPACE
+>>> theano.printing.debugprint(prediction) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
 Elemwise{gt,no_inplace} [id A] ''
  |Elemwise{true_div,no_inplace} [id B] ''
  | |DimShuffle{x} [id C] ''
@@ -87,9 +87,9 @@ Elemwise{gt,no_inplace} [id A] ''
 
 The post-compilation graph:
 
->>> theano.printing.debugprint(predict)  # doctest: +NORMALIZE_WHITESPACE
+>>> theano.printing.debugprint(predict)  # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
 Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}} [id A] ''   4
- |Gemv{inplace} [id B] ''   3
+ |...Gemv{inplace} [id B] ''   3
  | |AllocEmpty{dtype='float64'} [id C] ''   2
  | | |Shape_i{0} [id D] ''   1
  | |   |x [id E]

theano/compile/function.py

@@ -45,7 +45,8 @@ def function_dump(filename, inputs, outputs=None, mode=None, updates=None,
 
     To load such a dump and do the compilation:
 
-    >>> import cPickle, theano
+    >>> from six.moves import cPickle
+    >>> import theano
     >>> d = cPickle.load(open("func_dump.bin", "rb"))  # doctest: +SKIP
     >>> f = theano.function(**d)  # doctest: +SKIP
 

theano/misc/pkl_utils.py

@@ -327,13 +327,13 @@ def dump(obj, file_handler, protocol=DEFAULT_PROTOCOL,
     >>> import theano
     >>> foo_1 = theano.shared(0, name='foo')
     >>> foo_2 = theano.shared(1, name='foo')
-    >>> with open('model.zip', 'w') as f:
+    >>> with open('model.zip', 'wb') as f:
     ...     dump((foo_1, foo_2, numpy.array(2)), f)
     >>> numpy.load('model.zip').keys()
     ['foo', 'foo_2', 'array_0', 'pkl']
     >>> numpy.load('model.zip')['foo']
     array(0)
-    >>> with open('model.zip') as f:
+    >>> with open('model.zip', 'rb') as f:
     ...     foo_1, foo_2, array = load(f)
     >>> array
     array(2)