Do not use .value in the Theano tutorials (and associated tests)

doc/tutorial/aliasing.txt

@@ -77,12 +77,13 @@ subsequently make to ``np_array`` have no effect on our shared variable.
 
     np_array += 1 # now it is an array of 2.0 s
 
-    s_default.value  # -> array([1.0, 1.0])
-    s_false.value    # -> array([1.0, 1.0])
-    s_true.value    # -> array([2.0, 2.0])
+    s_default.get_value()  # -> array([1.0, 1.0])
+    s_false.get_value()    # -> array([1.0, 1.0])
+    s_true.get_value()     # -> array([2.0, 2.0])
 
 If we are running this with the CPU as the device,
-then changes we make to np_array *right away* will show up in ``s_true.value``
+then changes we make to np_array *right away* will show up in
+``s_true.get_value()``
 because numpy arrays are mutable, and ``s_true`` is using the ``np_array``
 object as it's internal buffer.
 
@@ -101,8 +102,8 @@ will terminate the aliasing).
 
 It is safe practice (and a good idea) to use ``borrow=True`` in a shared
 variable constructor when the shared variable stands for a large object (in
-terms of memory footprint) and you do not want to create copies of it in memory
-.
+terms of memory footprint) and you do not want to create copies of it in
+memory.
 
 It is not a reliable technique to use ``borrow=True`` to modify shared variables
 by side-effect, because with some devices (e.g. GPU devices) this technique will

doc/tutorial/examples.txt

@@ -256,10 +256,11 @@ internal state, and returns the old state value.
 This code introduces a few new concepts.  The ``shared`` function constructs
 so-called :term:`shared variables`.  These are hybrid symbolic and non-symbolic
 variables.  Shared variables can be used in symbolic expressions just like
-the objects returned by ``dmatrices(...)`` but they also have a ``.value``
-property that defines the value taken by this symbolic variable in *all* the
+the objects returned by ``dmatrices(...)`` but they also have an internal
+value, that defines the value taken by this symbolic variable in *all* the
 functions that use it.  It is called a *shared* variable because its value is
-shared between many functions.  We will come back to this soon.
+shared between many functions.  The value can be accessed and modified by the
+``.get_value()`` and ``.set_value()`` methods. We will come back to this soon.
 
 The other new thing in this code is the ``updates`` parameter of function.
 The updates is a list of pairs of the form (shared-variable, new expression).
@@ -274,23 +275,23 @@ Anyway, let's try it out!
 .. If you modify this code, also change :
 .. theano/tests/test_tutorial.py:T_examples.test_examples_8
 
->>> state.value
+>>> state.get_value()
 array(0)
 >>> accumulator(1)
 array(0)
->>> state.value
+>>> state.get_value()
 array(1)
 >>> accumulator(300)
 array(1)
->>> state.value
+>>> state.get_value()
 array(301)
 
-It is possible to reset the state. Just assign to the ``.value`` property:
+It is possible to reset the state. Just use the ``.set_value()`` method:
 
->>> state.value = -1
+>>> state.set_value(-1)
 >>> accumulator(3)
 array(-1)
->>> state.value
+>>> state.get_value()
 array(2)
 
 As we mentioned above, you can define more than one function to use the same
@@ -302,7 +303,7 @@ shared variable.  These functions can both update the value.
 >>> decrementor = function([inc], state, updates=[(state, state-inc)])
 >>> decrementor(2)
 array(2)
->>> state.value
+>>> state.get_value()
 array(0)
 
 You might be wondering why the updates mechanism exists.  You can always
@@ -329,7 +330,7 @@ for the purpose of one particular function.
         givens=[(state, foo)])
 >>> skip_shared(1, 3)  # we're using 3 for the state, not state.value
 array(7)
->>> state.value        # old state still there, but we didn't use it
+>>> state.get_value()  # old state still there, but we didn't use it
 array(0)
 
 The givens parameter can be used to replace any symbolic variable, not just a
@@ -411,9 +412,11 @@ Seedings Streams
 Random variables can be seeded individually or collectively.
 
 You can seed just one random variable by seeding or assigning to the
-``.rng.value`` attribute.
+``.rng`` attribute, using ``.rng.set_value()``.
 
->>> rv_u.rng.value.seed(89234)  # seeds the generator for rv_u
+>>> rng_val = rv_u.rng.get_value(borrow=True)   # Get the rng for rv_u
+>>> rng_val.seed(89234)                         # seeds the generator
+>>> rv_u.rng.set_value(rng_val, borrow=True)    # Assign back seeded rng
 
 You can also seed *all* of the random variables allocated by a :class:`RandomStreams`
 object by that object's ``seed`` method.  This seed will be used to seed a
@@ -431,10 +434,12 @@ update the state of the generators used in function ``f`` above.
 
 For example:
 
->>> state_after_v0 = rv_u.rng.value.get_state()
+>>> state_after_v0 = rv_u.rng.get_value().get_state()
 >>> nearly_zeros()       # this affects rv_u's generator
 >>> v1 = f()
->>> rv_u.rng.value.set_state(state_after_v0)
+>>> rng = rng.get_value(borrow=True)
+>>> rng.set_state(state_after_v0)
+>>> rv_u.rng.set_value(rng, borrow=True)
 >>> v2 = f()             # v2 != v1
 
 

theano/tests/test_tutorial.py

@@ -595,19 +595,19 @@ class T_examples(unittest.TestCase):
         inc = T.iscalar('inc')
         accumulator = function([inc], state, updates=[(state, state+inc)])
 
-        assert state.value             == array(0)
+        assert state.get_value()       == array(0)
         assert accumulator(1)          == array(0)
-        assert state.value             == array(1)
+        assert state.get_value()       == array(1)
         assert accumulator(300)        == array(1)
-        assert state.value             == array(301)
+        assert state.get_value()       == array(301)
 
-        state.value = -1
+        state.set_value(-1)
         assert accumulator(3)          == array(-1)
-        assert state.value             == array(2)
+        assert state.get_value()       == array(2)
 
         decrementor = function([inc], state, updates=[(state, state-inc)])
         assert decrementor(2)          == array(2)
-        assert state.value             == array(0)
+        assert state.get_value()       == array(0)
 
         fn_of_state = state * 2 + inc
         foo = T.lscalar()    # the type (lscalar) must match the shared variable we
@@ -615,7 +615,7 @@ class T_examples(unittest.TestCase):
         skip_shared = function([inc, foo], fn_of_state,
                                                 givens=[(state, foo)])
         assert skip_shared(1, 3)       == array(7)
-        assert state.value             == array(0)
+        assert state.get_value()       == array(0)
 
 
     def test_examples_9(self):
@@ -643,13 +643,17 @@ class T_examples(unittest.TestCase):
         nearly_zeros = function([], rv_u + rv_u - 2 * rv_u)
         assert numpy.allclose(nearly_zeros(), [[0.,0.],[0.,0.]])
 
-        rv_u.rng.value.seed(89234)  # seeds the generator for rv_u
+        rng_val = rv_u.rng.get_value(borrow=True)   # Get the rng for rv_u
+        rng_val.seed(89234)                         # seeds the generator
+        rv_u.rng.set_value(rng_val, borrow=True)    # Assign back seeded rng
 
         srng.seed(902340)  # seeds rv_u and rv_n with different seeds each
-        state_after_v0 = rv_u.rng.value.get_state()
+        state_after_v0 = rv_u.rng.get_value().get_state()
         nearly_zeros()       # this affects rv_u's generator
         v1 = f()
-        rv_u.rng.value.set_state(state_after_v0)
+        rng = rv_u.rng.get_value(borrow=True)
+        rng.set_state(state_after_v0)
+        rv_u.rng.set_value(rng, borrow=True)
         v2 = f()             # v2 != v1
         assert numpy.all(v1 != v2)
 
@@ -671,9 +675,9 @@ class T_aliasing(unittest.TestCase):
 
         np_array += 1 # now it is an array of 2.0 s
 
-        assert numpy.all(s_default.value == array([1.0, 1.0]))
-        assert numpy.all(s_false.value   == array([1.0, 1.0]))
-        assert numpy.all(s_true.value    == array([2.0, 2.0]))
+        assert numpy.all(s_default.get_value() == array([1.0, 1.0]))
+        assert numpy.all(s_false.get_value()   == array([1.0, 1.0]))
+        assert numpy.all(s_true.get_value()    == array([2.0, 2.0]))
 
 
     def test_aliasing_2(self):