Merged

theano/tensor/opt.py

@@ -346,7 +346,7 @@ def local_IncSubtensor_serialize(node):
 
     #
     #  add(x, incsubtensor(b, c), incsubtensor(b, d))
-    #  -> incsubtensor(incsubtensor(add(x,b), c), d)
+    #  -> incsubtensor(incsubtensor(add(x,b,b), c), d)
     
     """
     def movable(i):
@@ -354,7 +354,8 @@ def local_IncSubtensor_serialize(node):
         return i.owner \
                 and isinstance(i.owner.op, T.IncSubtensor) \
                 and i.type == o_type \
-                and len(i.clients) == 1
+                and len(i.clients) == 1 \
+                and not i.owner.op.set_instead_of_inc
 
     if node.op == T.add:
         o_type = node.outputs[0].type
@@ -383,7 +384,8 @@ def local_IncSubtensor_serialize(node):
 @gof.local_optimizer([None])
 def local_inplace_setsubtensor(node):
     if isinstance(node.op, T.IncSubtensor) and not node.op.inplace:
-        new_op = T.IncSubtensor(node.op.idx_list, inplace=True)
+        new_op = T.IncSubtensor(node.op.idx_list, inplace=True, \
+                        set_instead_of_inc=node.op.set_instead_of_inc)
         new_node = new_op(*node.inputs)
         return [new_node]
     return False

theano/tensor/raw_random.py

@@ -309,7 +309,9 @@ def permutation_helper(random_state, n, shape):
     """
     # n should be a 0-dimension array
     assert n.shape == ()
-    n = n.item()
+    # Note that it is important to convert `n` into an integer, because if it
+    # is a long, the numpy permutation function will crash on Windows.
+    n = int(n.item())
 
     out_shape = list(shape)
     out_shape.append(n)

theano/tensor/sharedvar.py

@@ -35,7 +35,7 @@ class ScalarSharedVariable(SharedVariable, _tensor_py_operators):
 
 @shared_constructor
 def scalar_constructor(value, name=None, strict=False, dtype=None):
-    """SharedVariable constructor for scalar values. Defaults to int64 or float64. 
+    """SharedVariable constructor for scalar values. Default: int64 or float64. 
 
     :note: We implement this using 0-d tensors for now.
     
@@ -50,12 +50,14 @@ def scalar_constructor(value, name=None, strict=False, dtype=None):
         else:
             dtype = type(value).__name__
 
-    type = TensorType(dtype=dtype, broadcastable=[])
+    tensor_type = TensorType(dtype=dtype, broadcastable=[])
 
     try:
-        # don't pass the dtype to asarray because we want this to fail if strict is True and the
-        # types do not match
-        rval = ScalarSharedVariable(type=type, value=numpy.asarray(value), name=name, strict=strict)
+        # Do not pass the dtype to asarray because we want this to fail if
+        # strict is True and the types do not match.
+        rval = ScalarSharedVariable(type=tensor_type,
+                value=numpy.asarray(value),
+                name=name, strict=strict)
         return rval
     except:
         traceback.print_exc()

theano/tensor/tests/test_randomstreams.py

@@ -277,12 +277,12 @@ class T_RandomStreams(unittest.TestCase):
         assert numpy.all(fn_val1 == numpy_val1)
 
     def test_shuffle_row_elements(self):
-        """Test that RandomStreams.shuffle_row_elements generates the right results"""
+        """Ensure RandomStreams.shuffle_row_elements generates right results"""
         # Check over two calls to see if the random state is correctly updated.
-
-        # On matrices, for each row, the elements of that row should be shuffled.
-        # Note that this differs from numpy.random.shuffle, where all the elements
-        # of the matrix are shuffled.
+        # On matrices, for each row, the elements of that row should be
+        # shuffled.
+        # Note that this differs from numpy.random.shuffle, where all the
+        # elements of the matrix are shuffled.
         mm = Module()
         mm.random = RandomStreams(234)
         m_input = tensor.dmatrix()