auto forward(const T& inputs) const {
        auto out = (weights_ * inputs) + biases_;
        auto out_activated = activation_.f(out);

        using ResultType = typename Eigen::internal::remove_all<decltype(out)>::type;
        using Traits = typename Eigen::internal::traits<ResultType>;

        return FwdOutput<Eigen::Matrix<fp_t, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime>> { out, out_activated };
    }

    template <typename Error, typename SelfOutUnactivated, typename LayerInputs>
    auto backProp(const Error& e, const SelfOutUnactivated& selfOut, const LayerInputs& inputs, fp_t learningRate) {
        auto delta = activation_.f_grad(selfOut).cwiseProduct(e);
        auto oldWeights = weights_;
        auto weightAdj = learningRate * delta * inputs.transpose();
        auto biasAdj = learningRate * delta;
        weights_ -= weightAdj;
        biases_ -= biasAdj;

        return std::make_tuple(delta, oldWeights.transpose());