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...
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 ...
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
Sonnet is a library built on top of TensorFlow for building complex neural networks. Installation instructions To install Sonnet, you will need to compile the library using bazel against the TensorFlow header files. You should have installed TensorFlow by following the TensorFlow installation instructions...
First clone the Sonnet source code with TensorFlow as a submodule: $ git clone --recursive https://github.com/deepmind/sonnet and then call configure: $ cd sonnet/tensorflow $ ./configure $ cd ../ You can choose the suggested defaults during the TensorFlow configuration. Note: This will not...
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. B...
本文翻译自 imple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents, 作者是 Arthur Juliani, 原文链接 在Part1中,我已经介绍了如何构造一个简单的agent,该agent可以从两个不同的动作中选择回报更高的那一个。在这篇文章中,我将要基于这个agent,构造一个能够利用环境信息(observation),并且选...
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
It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, principled multi-class handling, and a number of regularization techniques to prevent overfitting.关键词...
本代码使用 Tensorflow 框架,搭建 ResNet50 模型,对花卉数据集 —— Oxford 102 Flowers 中的图片进行迁移学习,从而实现对花卉图片的分类任务。 1. 环境搭建 python==3.7 tensorflow==2.5.0 scipy==1.6.2 Pillow==6.2.0 joblib==1.0.1 本人使用的是 CPU 进行训练 ...