在 Tensorflow 中查找方法的实现

Question

我想更改一些最小化优化器，例如在 Tensorflow 中使用的 AdadeltaOptimizer。我拿到了license，但是lib里没有代码，只有参考，那我怎么找实现呢？这是 API 的 Adadelta 示例：

@tf_export("train.AdadeltaOptimizer") class 
AdadeltaOptimizer(optimizer.Optimizer) 
Optimizer that implements the Adadelta algorithm.
See [M. D. Zeiler](http://arxiv.org/abs/1212.5701) ([pdf]
(http://arxiv.org/pdf/1212.5701v1.pdf))

Answer 1

第一个入口点是 python/training/adadelta.py from tensorflow main repo. But you may notice it's a python wrapper, all ops are actually implemented in native C++ and loaded in python (this is the usual practice in tensorflow, see for instance this question: )。

例如，在core/kernels/training_ops.cc you can find CPU impelmentation of ApplyAdadelta op. GPU implementation of the same op is in core/kernels/training_ops_gpu.cu.cc中：

template <typename T>
struct ApplyAdadelta<GPUDevice, T> {
  void operator()(const GPUDevice& d, typename TTypes<T>::Flat var,
                  typename TTypes<T>::Flat accum,
                  typename TTypes<T>::Flat accum_update,
                  typename TTypes<T>::ConstScalar lr,
                  typename TTypes<T>::ConstScalar rho,
                  typename TTypes<T>::ConstScalar epsilon,
                  typename TTypes<T>::ConstFlat grad) {
    Eigen::array<typename TTypes<T>::Tensor::Index, 1> bcast;
    bcast[0] = grad.dimension(0);
    Eigen::Sizes<1> single;

    accum.device(d) = accum * rho.reshape(single).broadcast(bcast) +
                      grad.square() * (grad.constant(T(1)) -
                                       rho.reshape(single).broadcast(bcast));
    const auto update =
        (accum_update + epsilon.reshape(single).broadcast(bcast)).sqrt() *
        (accum + epsilon.reshape(single).broadcast(bcast)).rsqrt() * grad;
    var.device(d) -= update * lr.reshape(single).broadcast(bcast);
    accum_update.device(d) =
        accum_update * rho.reshape(single).broadcast(bcast) +
        update.square() *
            (grad.constant(T(1)) - rho.reshape(single).broadcast(bcast));
  }
};

如果您想修补 C++ 代码，则必须重建 .so 库。为了能够在 CPU 和 GPU 上运行你的新优化器，你必须接触并重建两者。

在 Tensorflow 中查找方法的实现

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