Small fixes to the svd and lstsq ops

theano/tensor/nlinalg.py

@@ -657,7 +657,7 @@ class SVD(Op):
         x = as_tensor_variable(x)
         assert x.ndim == 2, "The input of svd function should be a matrix."
         w = theano.tensor.matrix(dtype=x.dtype)
-        u = theano.tensor.matrix(dtype=x.dtype)
+        u = theano.tensor.vector(dtype=x.dtype)
         v = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [w, u, v])
 
@@ -718,16 +718,18 @@ class lstsq(Op):
         x = theano.tensor.as_tensor_variable(x)
         y = theano.tensor.as_tensor_variable(y)
         rcond = theano.tensor.as_tensor_variable(rcond)
-        return theano.Apply(self, [x, y, rcond], [y.type(), theano.tensor.dvector(), theano.tensor.lscalar(), theano.tensor.dvector()])
+        return theano.Apply(self, [x, y, rcond],
+                            [theano.tensor.matrix(), theano.tensor.dvector(),
+                             theano.tensor.lscalar(), theano.tensor.dvector()])
 
     def perform(self, node, inputs, outputs):
         x = inputs[0]
         y = inputs[1]
         rcond = inputs[2]
-        zz = numpy.linalg.lstsq(inputs[0], inputs[1], inputs[2])            
+        zz = numpy.linalg.lstsq(inputs[0], inputs[1], inputs[2])
         outputs[0][0] = zz[0]
         outputs[1][0] = zz[1]
-        outputs[2][0] = zz[2]
+        outputs[2][0] = numpy.array(zz[2])
         outputs[3][0] = zz[3]