merged

.hgignore

@@ -7,6 +7,7 @@ cutils_ext.cpp
 html
 *.o
 pdf
+pull.sh
 *.pyc
 *.so
 *.sw?

_test_tensor.py

@@ -463,6 +463,16 @@ class T_log2(unittest.TestCase):
     def test0(self):
         verify_grad(self, Log2, [numpy.random.rand(3,1)+0.0001])
 
+class T_log(unittest.TestCase):
+    def test0(self):
+        verify_grad(self, Log, [numpy.random.rand(3,1)+0.0001])
+    def test1(self):
+        a = astensor(numpy.ones(2))
+        b = astensor(numpy.ones(2))
+        aa = numpy.asarray([0.5, 4.0])
+        bb = numpy.asarray([0.5, 2.0])
+        check_eq2(self, [a], log(a), [aa], numpy.log(numpy.asarray(aa)))
+
 class T_pow(unittest.TestCase):
     def setUp(self):
         numpy.random.seed(9999)
@@ -493,10 +503,7 @@ class _testCase_matinv(unittest.TestCase):
         # Sum of squared errors
         ssdiff = sum((diff**2.0))
 
-        # May be able to abbreviate this by assuming default parameter
-        # TODO: Test that default works
-        g_b = gradient.grad(ssdiff, b, astensor(numpy.ones(1),name='g_cost'))
-        #g_b = gradient.grad(ssdiff, b)     # This should be the abbreviated version
+        g_b = gradient.grad(ssdiff, b)
 
         # compilation to function
         # [a,b] are the inputs, [ssdiff,g_b] are the outputs

gradient.py

@@ -81,7 +81,10 @@ def grad_sources_inputs(sources, graph_inputs):
         op_grad = op.grad(input_arg, output_arg)
         if op_grad is None:
             raise ValueError(_msg_retNone, op.__class__)
+        if isinstance(op_grad, float):
+            raise TypeError('wtf!!!!!!!!', op)
         g_inputs = _pack_result(op_grad)
+        assert isinstance(g_inputs, (list, tuple))
         if len(g_inputs) != len(op.inputs):
             raise ValueError(_msg_badlen, 
                     op.__class__, 

tensor.py

@@ -107,7 +107,7 @@ def _assert_same_shapes(x, *rest):
     shape = x.shape
     for other in rest:
         if other.shape != shape:
-            raise ValueError(_assert_same_shapes.E_shape)
+            raise ValueError(_assert_same_shapes.E_shape, shape, other.shape)
 _assert_same_shapes.E_shape = "The dimensions of the inputs do not match."
 
 def _assert_tensor_scalar(x, a):
@@ -470,7 +470,11 @@ subtensor = _constructor(Subtensor)
 # Elemwise #
 class AddElemwise(_Elemwise):
     def impl(self, x, y):
-        _assert_same_shapes(x, y)
+        try:
+            _assert_same_shapes(x, y)
+        except Exception, e:
+            print '------ ERROR HERE'
+            raise
         return x + y
     def grad(self, (x, y), gz):
         return gz, gz