Function TestComputeUpdatedDual

tensorflow/core/kernels/loss_test.cc:37–50

This test checks that the dual value after update is optimal. At the optimum the dual value should be the opposite of the primal gradient. This does not hold at a point where the primal is not differentiable.

// At the optimum the dual value should be the opposite of the primal gradient.
// This does not hold at a point where the primal is not differentiable.
void TestComputeUpdatedDual(const DualLossUpdater &loss_updater,
                            const int num_loss_partitions, const double label,
                            const double example_weight,
                            const double current_dual, const double wx,
                            const double weighted_example_norm) {
  double new_dual = loss_updater.ComputeUpdatedDual(
      num_loss_partitions, label, example_weight, current_dual, wx,
      weighted_example_norm);
  // The primal gradient needs to be computed after the weight update.
  double new_wx = wx + (new_dual - current_dual) * num_loss_partitions *
                           weighted_example_norm * example_weight;
  EXPECT_NEAR(new_dual, -loss_updater.PrimalLossDerivative(new_wx, label, 1.0),
              1e-5);
}

TEST(LogisticLoss, ComputePrimalLoss) {
  LogisticLossUpdater loss_updater;