如何将 2D Eigen::Tensor 更改为 Eigen::Matrix [英] how to change 2D Eigen::Tensor to Eigen::Matrix

问题描述

看起来很简单.我原以为可以进行某种类型的转换，但我似乎找不到任何相关文档.

Seems simple enough. I would have thought some kind of casting would be possible, but I can't seem to find any documentation for it.

虽然我在我的应用程序中找到了避免使用 Eigen::Matrix 类的方法，但 TensorFlow 仅适用于 Eigen::Tensor，而我使用的另一个库仅具有直接使用 Eigen::Matrix 的功能.如果我可以将张量转换为矩阵并使用它，那么代码可读性将非常棒.

While I have found ways in my application to avoid using the Eigen::Matrix class, TensorFlow only works with Eigen::Tensor, and another library I use only has functionality for working directly with Eigen::Matrix. It would be spectacular for code readability if I could cast a Tensor as a Matrix and work with that.

似乎 TensorFlow 确实有一个功能可以输出 Eigen::Matrix(仍在测试中).也许这让最初的问题变得不那么有趣(也许没有人需要将张量转换为矩阵.)但是我仍然认为这是一个有效的问题.所以我不会放下我的

编辑 2:在一些构建错误后查看 TF 文档，似乎 tensorflow 的 Tensor::matrix() 函数只是返回一个 2d Eigen::Tensor，因此实际上需要进行转换.

edit 2: going through the TF documentation after some build errors, it seems that tensorflow's Tensor::matrix() function simply returns a 2d Eigen::Tensor, so the conversion is in fact necessary.

推荐答案

这是 TensorFlow 线性代数运算的常见用例，可以在 tensorflow/core/kernels.comcode>al但是，该代码高度模板化，因此提供一个具体示例可能会很有用.

This is a common use case for TensorFlow's linear algebra ops, and an implementation can be found in tensorflow/core/kernels/linalg_ops_common.cc. However, that code is highly templatized, so it might be useful to have a concrete example.

假设您从一个名为 t 且元素类型为 float 的 tensorflow::Tensor 开始，您可以创建一个特征矩阵 m如下:

Assuming you start with a tensorflow::Tensor called t with element type float, you can make an Eigen matrix m as follows:

tensorflow::Tensor t = ...;

auto m = Eigen::Map<Eigen::Matrix<
             float,           /* scalar element type */
             Eigen::Dynamic,  /* num_rows is a run-time value */
             Eigen::Dynamic,  /* num_cols is a run-time value */
             Eigen::RowMajor  /* tensorflow::Tensor is always row-major */>>(
                 t.flat<float>().data(),  /* ptr to data */
                 t.dim_size(0),           /* num_rows */
                 t.dim_size(1)            /* num_cols */);

如果您的张量来自 tensorflow::OpKernel 的输入(例如在 Compute() 方法中)，您将使用稍微不同的类型和适当的const 限定:

If your tensor comes from the input of a tensorflow::OpKernel (e.g. in the Compute() method), you would use a slightly different type with the appropriate const qualification:

OpKernelContext* ctx = ...;
const tensorflow::Tensor t = ctx->input(...);

const auto m = Eigen::Map<const Eigen::Matrix<
                   float,           /* scalar element type */
                   Eigen::Dynamic,  /* num_rows is a run-time value */
                   Eigen::Dynamic,  /* num_cols is a run-time value */
                   Eigen::RowMajor  /* tensorflow::Tensor is always row-major */>>(
                       t.flat<float>().data(),  /* ptr to data */
                       t.dim_size(0),           /* num_rows */
                       t.dim_size(1)            /* num_cols */);

这篇关于如何将 2D Eigen::Tensor 更改为 Eigen::Matrix的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！