test_ifelse.py 文件源码

def test_grad_lazy_if(self):
        # Tests that we can compute the gradients through lazy if
        x = tensor.vector('x', dtype=self.dtype)
        y = tensor.vector('y', dtype=self.dtype)
        c = tensor.iscalar('c')
        z = ifelse(c, x, y)
        gx, gy = tensor.grad(z.sum(), [x, y])

        f = theano.function([c, x, y], [self.cast_output(gx),
                                        self.cast_output(gy)],
                            mode=self.mode)
        # There is only 2 of the 3 ifelse that are moved on the GPU.
        # The one that stay on the CPU is for the shape.
        self.assertFunctionContains(f, self.get_ifelse(1), min=2, max=3)
        rng = numpy.random.RandomState(utt.fetch_seed())

        xlen = rng.randint(200)
        ylen = rng.randint(200)

        vx = numpy.asarray(rng.uniform(size=(xlen,)), self.dtype)
        vy = numpy.asarray(rng.uniform(size=(ylen,)), self.dtype)
        gx0, gy0 = f(1, vx, vy)
        assert numpy.allclose(gx0.shape, vx.shape)
        assert numpy.allclose(gy0.shape, vy.shape)
        assert numpy.all(numpy.asarray(gx0) == 1.)
        assert numpy.all(numpy.asarray(gy0) == 0.)

        gx0, gy0 = f(0, vx, vy)
        assert numpy.allclose(gx0.shape, vx.shape)
        assert numpy.allclose(gy0.shape, vy.shape)
        assert numpy.all(numpy.asarray(gx0) == 0.)
        assert numpy.all(numpy.asarray(gy0) == 1.)