moved comments, added gradient test for log2

_test_tensor.py

@@ -441,6 +441,9 @@ class T_div(unittest.TestCase):
         verify_grad(self, DivElemwise, [numpy.random.rand(3), numpy.ones(3)])
         verify_grad(self, DivElemwise, [numpy.random.rand(3,5), numpy.random.rand(3,5)+0.1])
 
+class T_log2(unittest.TestCase):
+    def test0(self):
+        verify_grad(self, Log2, [numpy.random.rand(3,1)+0.0001])
 
 class T_pow(unittest.TestCase):
     def setUp(self):

tensor.py

@@ -130,31 +130,6 @@ class _Op(BaseTensorOp):
     def input_wrapper(cls, obj):
         return _as_tensor(obj)
     
-#         def upcast(dtype, *dtypes):
-#             z = numpy.zeros((), dtype = dtype)
-#             for dtype in dtypes:
-#                 z = z + numpy.zeros((), dtype = dtype)
-#             return str(z.dtype)
-#         for dtype in i_dtypes:
-#             if dtype is None:
-#                 raise TypeError("Expected a Tensor.")
-#         upcasted = upcast(*i_dtypes)
-#         return [upcasted] * self.nout
-# #         try:
-# #             dmap = self.destroy_map()
-# #         except AttributeError:
-# #             dmap = {}
-# #         rval = []
-# #         for i in xrange(self.nout):
-# #             if i in dmap:
-# #                 destroyed = dmap[output]
-# #                 if len(destroyed) != 1:
-# #                     raise TypeError("Cannot infer dtype of output %s because it destroys more than one input." % output)
-# #                 rval.append(destroyed[0])
-# #             else:
-# #                 rval.append(upcasted)
-# #         return rval
-    
     def impl(self, *inputs):
         raise AbstractFunctionError()
     
@@ -811,3 +786,28 @@ if 0:
         return t
 
 
+#         def upcast(dtype, *dtypes):
+#             z = numpy.zeros((), dtype = dtype)
+#             for dtype in dtypes:
+#                 z = z + numpy.zeros((), dtype = dtype)
+#             return str(z.dtype)
+#         for dtype in i_dtypes:
+#             if dtype is None:
+#                 raise TypeError("Expected a Tensor.")
+#         upcasted = upcast(*i_dtypes)
+#         return [upcasted] * self.nout
+# #         try:
+# #             dmap = self.destroy_map()
+# #         except AttributeError:
+# #             dmap = {}
+# #         rval = []
+# #         for i in xrange(self.nout):
+# #             if i in dmap:
+# #                 destroyed = dmap[output]
+# #                 if len(destroyed) != 1:
+# #                     raise TypeError("Cannot infer dtype of output %s because it destroys more than one input." % output)
+# #                 rval.append(destroyed[0])
+# #             else:
+# #                 rval.append(upcasted)
+# #         return rval
+    

tensor_ops.py

@@ -62,98 +62,3 @@ class Dot(TensorOp):
 
 
 
-
-
-class Min:
-    pass
-
-class Max:
-    pass
-
-class Argmin:
-    pass
-
-class Argmax:
-    pass
-
-class MinMax:
-    pass
-#     nout = 2
-#     def impl(x):
-#         return x.min, x.max
-#     def specs(x):
-#         return [(numpy.ndarray, x[1], ())] * 2
-# #     def alloc((x, ), (_min, _max)):
-# #         _min.data = numpy.ndarray((), x.dtype)
-# #         _max.data = numpy.ndarray((), x.dtype)
-#     def c_init((x, ), (_min, _max)):
-#         raise NotImplementedError
-#         return """
-#         _x_dtype min = _x[0];
-#         _x_dtype max = _x[0];
-#         """
-#     def c_foreach((x, ), (_min, _max)):
-#         return """
-#         if (x < min) min = x;
-#         if (x > max) max = x;
-#         """
-#     def c_finalize((x, ), (_min, _max)):
-#         return """
-#         _min[0] = min;
-#         _max[0] = max;
-#         """
-
-
-
-
-# class Transpose(UnaryTensorOp):
-
-#     def propagate_broadcastable(self, x):
-#         x2 = copy(x)
-#         x2.reverse()
-#         return [x2]
-
-#     def impl(self, x):
-#         return x.T
-
-#     def c_impl(self, x, z):
-#         return """
-#         PyArrayObject* transposed = (PyArrayObject*)PyArray_Transpose(%(x)s, NULL);
-#         //if (PyArray_REFCOUNT(transposed) == 1) {
-#         //    printf("lala\\n");
-#         //}
-#         //if (%(z)s) {
-#         //    Py_XDECREF(%(z)s);
-#         //}
-#         %(z)s = transposed;
-#         Py_XINCREF(%(z)s);
-#         """
-
-
-
-
-
-
-
-# # class Transpose(UnaryTensorOp):
-
-# #     def propagate_broadcastable(self, x):
-# #         x2 = copy(x)
-# #         x2.reverse()
-# #         return [x2]
-
-# #     def impl(self, x):
-# #         return x.T
-
-# #     def c_impl(self, x, z):
-# #         return """
-# #         PyArrayObject* transposed = (PyArrayObject*)PyArray_Transpose(%(x)s, NULL);
-# #         //if (PyArray_REFCOUNT(transposed) == 1) {
-# #         //    printf("lala\\n");
-# #         //}
-# #         //if (%(z)s) {
-# #         //    Py_XDECREF(%(z)s);
-# #         //}
-# #         %(z)s = transposed;
-# #         Py_XINCREF(%(z)s);
-# #         """