test_pfunc.py 文件源码

def test_input_aliasing_affecting_inplace_operations(self):

        # Note: to trigger this bug with theano rev 4586:2bc6fc7f218b,
        #        you need to make in inputs mutable (so that inplace
        #        operations are used) and to break the elemwise composition
        #        with some non-elemwise op (here dot)
        x = theano.tensor.dvector()
        y = theano.tensor.dvector()
        m1 = theano.tensor.dmatrix()
        m2 = theano.tensor.dmatrix()
        f = theano.function([theano.In(x, mutable=True),
                             theano.In(y, mutable=True),
                             theano.In(m1, mutable=True),
                             theano.In(m2, mutable=True)],
                            theano.dot((x * 2), m1) + theano.dot((y * 3), m2))
        # Test 1. If the same variable is given twice

        # Compute bogus values
        v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
        m = numpy.asarray([[1, 0, 0, 0, 0],
                           [0, 1, 0, 0, 0],
                           [0, 0, 1, 0, 0],
                           [0, 0, 0, 1, 0],
                           [0, 0, 0, 0, 1]], dtype='float64')
        bogus_vals = f(v, v, m, m)
        # Since we used inplace operation v and m may be corrupted
        # so we need to recreate them

        v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
        m = numpy.asarray([[1, 0, 0, 0, 0],
                           [0, 1, 0, 0, 0],
                           [0, 0, 1, 0, 0],
                           [0, 0, 0, 1, 0],
                           [0, 0, 0, 0, 1]], dtype='float64')
        m_copy = m.copy()
        v_copy = v.copy()
        vals = f(v, v_copy, m, m_copy)

        assert numpy.allclose(vals, bogus_vals)