merge

theano/gof/link.py

@@ -146,9 +146,9 @@ class Container(object):
         self.storage = storage
         self.readonly = readonly
         self.strict = strict
-    def __get(self):
+    def __get__(self):
         return self.storage[0]
-    def __set(self, value):
+    def __set__(self, value):
         if self.readonly:
             raise Exception("Cannot set readonly storage: %s" % self.name)
         try:
@@ -162,8 +162,8 @@ class Container(object):
         except Exception, e:
             e.args = e.args + (('Container name "%s"' % self.name),)
             raise
-    data = property(__get, __set)
-    value = property(__get, __set)
+    data = property(__get__, __set__)
+    value = property(__get__, __set__)
     def __str__(self):
         return "<" + str(self.storage[0]) + ">"
     def __repr__(self):

theano/tensor/basic.py

@@ -373,9 +373,11 @@ class TensorType(Type):
 
     def c_extract(self, name, sub):
         """Override `CLinkerOp.c_extract` """
+        # TODO: make the error message print out the dtype of the
+        # input received.
         return """
         %(name)s = NULL;
-        type_num_%(name)s = %(type_num)s;
+        type_num_%(name)s = ((PyArrayObject*)py_%(name)s)->descr->type_num; //we expect %(type_num)s
         if (py_%(name)s == Py_None) {
             // We can either fail here or set %(name)s to NULL and rely on Ops using
             // tensors to handle the NULL case, but if they fail to do so they'll end up
@@ -387,7 +389,7 @@ class TensorType(Type):
             PyErr_SetString(PyExc_ValueError, "expected an ndarray");
             %(fail)s
         }
-        else if (((PyArrayObject*)py_%(name)s)->descr->type_num != %(type_num)s) {
+        else if (type_num_%(name)s != %(type_num)s) {
             PyErr_SetString(PyExc_ValueError, "expected %(type_num)s");
             %(fail)s
         }
@@ -1358,6 +1360,15 @@ class Repeat(gof.Op):
 
 repeat = Repeat()
 
+class SetDefault(gof.Op):
+    view_map = {0: [1]}
+    def make_node(self, x, default):
+        assert x.type == default.type
+        return gof.Apply(self, [x, default], [default.type()])
+    def perform(self, node, (x, default), (out, )):
+        out[0] = default.copy() if x is None else x
+
+setdefault = SetDefault()
 
 
 ##########################
@@ -1852,7 +1863,6 @@ class Split(Op):
         return [join(axis, *g_outputs), None, None]
 
 
-
 class Rebroadcast(Op):
     """
     Change the input's broadcastable fields in
@@ -1892,6 +1902,7 @@ def unbroadcast(x, *axes):
     return Rebroadcast(*[(axis, False) for axis in axes])(x)
 
 
+
 class Join(Op):
     """
     Concatenate multiple `TensorVariable`s along some axis.
@@ -2523,6 +2534,7 @@ class Outer(Op):
         return "outer"
 outer = Outer()
 
+
 #########################
 # Gradient
 #########################

theano/tensor/nnet.py

@@ -394,10 +394,12 @@ def local_softmax_with_bias(node):
             vectors = []
             non_vectors = []
             for x_in in x.owner.inputs:
-                if list(x_in.type.broadcastable) == [True, False] \
-                        and isinstance(x_in.owner.op, tensor.DimShuffle):
-                    assert len(x_in.owner.inputs)==1
-                    vectors.append(x_in.owner.inputs[0])
+                if list(x_in.type.broadcastable) == [True, False]:
+                    if x_in.owner and isinstance(x_in.owner.op, tensor.DimShuffle):
+                        assert len(x_in.owner.inputs)==1
+                        vectors.append(x_in.owner.inputs[0])
+                    else:
+                        vectors.append(tensor.DimShuffle((True, False), (1,))(x_in))
                 else:
                     non_vectors.append(x_in)