merge

theano/gof/graph.py

@@ -364,6 +364,12 @@ class Constant(Value):
         if self.name is not None:
             return self.name
         return str(self.data) #+ "::" + str(self.type)
+    def clone(self):
+        """
+        We clone this object, but we don't clone the data to lower memory requirement
+        We suppose that the data will never change.
+        """
+        return self.__class__(self.type, self.data, self.name)
 
 
 

theano/tensor/basic.py

@@ -3712,6 +3712,11 @@ pprint.assign(dot, printing.OperatorPrinter(printing.special['middle_dot'], -1,
 #########################
 class TensorDotGrad(Op):
     def __init__(self, axes):
+        if isinstance(axes,list):
+            for i,a in enumerate(axes):
+                if isinstance(a,list):
+                    axes[i]=tuple(a)
+            axes=tuple(axes)
         self.axes = axes;
 
     def __eq__(self, other):
@@ -3759,6 +3764,11 @@ class TensorDot(Op):
     """
 
     def __init__(self, axes):
+        if isinstance(axes,list):
+            for i,a in enumerate(axes):
+                if isinstance(a,list):
+                    axes[i]=tuple(a)
+            axes=tuple(axes)
         self.axes = axes;
 
     def __eq__(self, other):

theano/tensor/tests/test_basic.py

@@ -2444,6 +2444,20 @@ class test_tensordot(unittest.TestCase):
                                   f6(bval,aval)))
         utt.verify_grad(TensorDot(axes), [bval,aval])
 
+    def test_list(self):
+        # test matrix-matrix
+        amat = dmatrix()
+        bmat = dmatrix()
+        axes = [[1,],[0,]]
+        c = tensordot(axes)(amat, bmat)
+        f3 = inplace_func([amat,bmat],c)
+        aval = numpy.random.rand(4,7);
+        bval = numpy.random.rand(7,9);
+        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+                                  f3(aval,bval)))
+        utt.verify_grad(TensorDot(axes), [aval,bval])
+
+
 def test_smallest_stack():
     sx, sy = dscalar(), dscalar()
 

theano/tensor/tests/test_opt.py

@@ -1015,15 +1015,15 @@ def test_log1p():
     # the first three ops are Shape_i, Shape_i, and Dimshuffle
     theano.printing.debugprint(f)
     assert [node.op for node in f.maker.env.toposort()][3:] \
-            == [inplace.log1p_inplace, alloc]
+            == [T.log1p, alloc]
     f = function([x,y], T.log(0+(x) + fill(y,1.0)), mode=m)
     theano.printing.debugprint(f)
     assert [node.op for node in f.maker.env.toposort()][3:] \
-            == [inplace.log1p_inplace, alloc]
+            == [T.log1p, alloc]
     f = function([x,y], T.log(2+(x) - fill(y,1.0)), mode=m)
     theano.printing.debugprint(f)
     assert [node.op for node in f.maker.env.toposort()][3:] \
-            == [inplace.log1p_inplace, alloc]
+            == [T.log1p, alloc]
 
     f([1e-7, 10], [[0, 0], [0, 0]]) #debugmode will verify values