《tensorflow》专题

Code: tensorflow/examples/tutorials/mnist/ The goal of this tutorial is to show how to use TensorFlow to train and evaluate a simple feed-forward neural network for handwritten digit classification us

本指南让你开始进行 TensorFlow 编程。开始之前，安装 TensorFlow， 为了充分利用本指南，您应该了解以下内容： Python 如何编程。 至少对数组了解一点。 最好了解一些机器学习。然而，如果你对机器学习了解很少或不了解，你仍然应该首先阅读本指南。 TensorFlow 提供了多种API。最底层的 API--TensorFlow Core-- 可以让你完全控制自己的程序。 我们推

Most users shouldn't need to care about the internal details of how TensorFlow stores data on disk, but you might if you're a tool developer. For example, you may want to analyze models, or convert ba

Background This document is intended as a guide for those interested in the creation or development of TensorFlow functionality in other programming languages. It describes the features of TensorFlow

We designed TensorFlow for large-scale distributed training and inference, but it is also flexible enough to support experimentation with new machine learning models and system-level optimizations. Th

Introduction TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorit

This document describes how to run TensorFlow on Hadoop. It will be expanded to describe running on various cluster managers, but only describes running on HDFS at the moment. HDFS We assume that you

This document shows how to create a cluster of TensorFlow servers, and how to distribute a computation graph across that cluster. We assume that you are familiar with the @{$get_started$basic concepts

Kubeflow 是 Google 发布的用于在 Kubernetes 集群中部署和管理 tensorflow 任务的框架。主要功能包括 用于管理 Jupyter 的 JupyterHub 服务 用于管理训练任务的 Tensorflow Training Controller 用于模型服务的 TF Serving 容器 部署 部署之前需要确保 一套部署好的 Kubernetes 集群或者 Mini

代码：tensorflow/g3doc/tutorials/mnist/ 本篇教程的目的，是向大家展示如何利用TensorFlow使用（经典）MNIST数据集训练并评估一个用于识别手写数字的简易前馈神经网络（feed-forward neural network）。我们的目标读者，是有兴趣使用TensorFlow的资深机器学习人士。 因此，撰写该系列教程并不是为了教大家机器学习领域的基础知识。 在

Code: tensorflow/g3doc/tutorials-mnist/ The goal of this tutorial is to show how to use TensorFlow to train and evaluate a simple feed-forward neural network for handwritten digit classification using

Building Graphs: add_to_collection as_dtype control_dependencies convert_to_tensor device Dimension DType get_collection get_default_graph get_seed Graph GraphKeys import_graph_def name_scope NoGradie

Options to configure a Thread . Note that the options are all hints, and the underlying implementation may choose to ignore it. Member Summary size_t tensorflow::ThreadOptions::stack_size Thread stack

Member Summary int tensorflow::TensorShapeDim::size tensorflow::TensorShapeDim::TensorShapeDim(int64 s) Member Details int tensorflow::TensorShapeDim::size tensorflow::TensorShapeDim::TensorShapeDim(i

Member Summary tensorflow::error::Code tensorflow::Status::State::code string tensorflow::Status::State::msg Member Details tensorflow::error::Code tensorflow::Status::State::code string tensorflow::S