Method test_log_barrier

caffe2/python/regularizer_test.py:58–83

(self, X)

class TestRegularizer(LayersTestCase):
    @given(X=hu.arrays(dims=[2, 5], elements=hu.floats(min_value=-1.0, max_value=1.0)))
    def test_log_barrier(self, X):
        param = core.BlobReference("X")
        workspace.FeedBlob(param, X)
        train_init_net, train_net = self.get_training_nets()
        reg = regularizer.LogBarrier(1.0)
        output = reg(train_net, train_init_net, param, by=RegularizationBy.ON_LOSS)
        reg(
            train_net,
            train_init_net,
            param,
            grad=None,
            by=RegularizationBy.AFTER_OPTIMIZER,
        )
        workspace.RunNetOnce(train_init_net)
        workspace.RunNetOnce(train_net)

        def ref(X):
            return (
                np.array(np.sum(-np.log(np.clip(X, 1e-9, None))) * 0.5).astype(
                    np.float32
                ),
                np.clip(X, 1e-9, None),
            )

        for x, y in zip(workspace.FetchBlobs([output, param]), ref(X)):
            npt.assert_allclose(x, y, rtol=1e-3)

    @given(
        X=hu.arrays(dims=[2, 5], elements=hu.floats(min_value=-1.0, max_value=1.0)),