Therefore, in this paper, a novel TensorFlow-based semantic technique is designed and implemented to significantly port the applications between high-end servers. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. Extensive experiments reveal that the ...
今天的这篇"TFX: A TensorFlow-Based Production-Scala Machine Learning Platform"同样出自于Google。在这篇发表于2017 KDD的论文中,作者们提出了一个可以用于一般用途(general-purpose)、端到端(end-to-end)的机器学习平台:TFX(TensorFlow Extended)。目前,TFX是完全基于TensorFlow的。这篇论文着重讲解了TFX的各个组件...
TensorFlow-based neural network library. Contribute to google-deepmind/sonnet development by creating an account on GitHub.
To install Sonnet, you will need to compile the library using bazel against the TensorFlow header files. You should have installed TensorFlow by following theTensorFlow installation instructions. This installation is compatible with Linux/Mac OS X and Python 2.7. The version of TensorFlow installed mu...
在基于循环神经网络的语言模型的介绍与TensorFlow实现(3):PTB数据集batching中我们介绍了如何对PTB数据集进行连接、切割成多个batch,作为神经网络语言模型的输入。本文将介绍如何采用TensorFlow实现RNN-based NNLM。 我们将要实现的NNLM如下图1所示,其中包括Embedding层、循环神经网络层、Softmax层,完整代码请见TensorFlowExamp...
AI engine: TensorFlow 1.8; Environment: Python 2.7. This template is used to import a TensorFlow-based image classification model saved in SavedModel format. This templat
This code sample shows how to deploy Caffe-based Faster RCNN object detection model. Caffe used prototxt file and all layers are defined in the prototxt file. Layer names like "bbox_name", "proposal_name" and "prob_name" are defaulted to those used in Caffe. Bu...
serverless Devsを使用したTensorFlowのサーバーレスAI推論コードのデプロイ,Function Compute:Serverless Devsを使用すると、AIモデルに基づいてオンデマンドおよび自動拡張推論を実装するインフラストラクチャを管理することなく、AI推論アプリケーションをFunction Compu
How can I use Pytorch/Tensorflow based custom... Learn more about ground truth labelling, automation, computer vision, annotation, algorithms
TF Boosted Trees (TFBT) is a new open-sourced framework for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosti