Kalchbrenner et al. (2014) report much worse results with a CNN that has essentially the same architecture as our single channel model. Forexample, theirMax-TDNN(Time Delay Neural Network) with randomly initialized words obtains 37.4% on the SST-1 dataset, compared to 45.0% for our model. ...
所有代码均在 GPL 许可下发布,并通过 GitHub (GitHub - XiongfengYan/gcnn: A graph convolutional neural network for classification of building patterns using spatial vector data) 分发。 本节描述了在大型建筑数据集上进行的一系列实验,以测试所提出方法的有效性、鲁棒性和泛化能力。 4.1 实验设置与数据预...
6. CNN 全流程总结 pooling 对于 rerformance 会带来一点伤害.如果运算资源足够,现今很多 network 的架构的设计往往就不做 pooling,改为全 convolution.(因为现在很多实验环境算力都够用,池化层的目的显得越来越没必要,所以在设计每一模块的时候都要考虑当前任务及实验环境,根据这些因素去设计最符合的网络) 7...
We trained a large, deep convolutional neural network to classify the1.2 million high-resolution images in the ImageNet LSVRC-2010 contestinto the 1000 different classes. On the test data, we achieved top-1 and top5 error rates of 37.5% and 17.0% which is considerably better than theprevious...
卷积神经网络(Convolutional Neural Networks, CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(Feedforward Neural Networks),是深度学习(deep learning)的代表算法之一。卷积神经网络具有表征学习(representation learning)能力,能够按其阶层结构对输入信息进行平移不变分类(shift-invariant classification),因此也被称为“...
《Convolutional Neural Networks for Sentence Classification》阅读笔记 任务:句子级别的分类 用CNN Word vectors,wherein words are projected from a sparse,1-of-V encoding (here V is the vocabulary size) onto a lower dimensional vector space via a hidden layer, are essentially feature extractors that ...
Convolutional Neural Networks for Sentence Classification(卷积神经网络用于句子分类),程序员大本营,技术文章内容聚合第一站。
深度学习论文阅读图像分类篇(一):AlexNet《ImageNet Classification with Deep Convolutional Neural Networks》 Abstract 摘要 1.Introduction 引言 2.The Dataset 数据集 3.The Architecture 架构 3.1 非线性ReLU 函数 3.2在多 GPU 上训练 3.3局部响应归一化 ...
《Convolutional Neural Networks for Sentence Classification》 概述:使用基于word2vec的单层CNN,对多个文本分类任务进行了评测。 结论: 1.word2vec是个好东西。使用pre-training 训练的资源成为nlp不可或缺的一部分。 2.Dropout可以提高2%-4%的相对性能。 3.多通道的CNN相对于Stati... 查看原文 word2vec doc2...
An activation function is used in the final layer depending on the type of problem. Asigmoid activationis used for binary classification, while asoftmax activationfunction is used for multi-class image classification. Fully connected convolutional neural network ...