原文链接:https://arxiv.org/abs/1901.10444 发表在:ICLR 2019 -------------------------------------------------------------------------------------------------------------------------------------------------------------------- 介绍了3中sentence嵌入的结构(RANDOM SENTENC…

论文  < Convolutional Neural Networks for Sentence Classification>通过CNN实现了文本分类. 论文地址: 666666 模型图: 模型解释可以看论文,给出code and comment: # -*- coding: utf-8 -*- # @time : 2019/11/9 13:55 import numpy as np import torch import torch.nn as nn import torch.optim…

This example shows how to use Neural Network Toolbox™ to train a deep neural network to classify images of digits. Neural networks with multiple hidden layers can be useful for solving classification problems with complex data, such as images. Each l…

1. Overview 本文将CNN用于句子分类任务 (1) 使用静态vector + CNN即可取得很好的效果:=> 这表明预训练的vector是universal的特征提取器,可以被用于多种分类任务中. (2) 根据特定任务进行fine-tuning 的vector + CNN 取得了更好的效果. (3) 改进模型架构,使得可以使用 task-specific 和 static 的vector. (4) 在7项任务中的4项取得了SOTA的效果. 思考:卷积神经网络的核心思想是捕获局部特征.在…

文本分类任务中可以利用CNN来提取句子中类似 n-gram 的关键信息. TextCNN的详细过程原理图见下: keras 代码: def convs_block(data, convs=[3, 3, 4, 5, 5, 7, 7], f=256): pools = [] for c in convs: conv = Activation(activation="relu")(BatchNormalization()( Conv1D(filters=f, kernel_size=c, p…

读了一篇文章,用到卷积神经网络的方法来进行文本分类,故写下一点自己的学习笔记: 本文在事先进行单词向量的学习的基础上,利用卷积神经网络(CNN)进行句子分类,然后通过微调学习任务特定的向量,提高性能. 在从无监督神经语言模型中获得单词向量(Tomas Mikolov等人做过相关工作,即谷歌的word2vector完成,将原始的1/V模型变化为分布式低维表示)后利用一层卷积层的CNN进行学习. 模型结构: 首先输入具有两个通道,分别对应static和non-static的方式,其中static方式…

numpy.random模块中常用函数解析 numpy.random模块官方文档 1. numpy.random.rand(d0, d1, ..., dn)Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)按照给定形状产生一个多维数组,每个元素在0到1之间注意: 这里定义数组形状时,不能采用tuple import numpy…

Awesome-Pytorch-list 2018-08-10 09:25:16 This blog is copied from: https://github.com/Epsilon-Lee/Awesome-pytorch-list Pytorch & related libraries pytorch : Tensors and Dynamic neural networks in Python with strong GPU acceleration. pytorch extras :…

About this Course AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: The meaning behind com…

Attention Is All You Need Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. 显性序列转换模型基于复杂的递归或卷积神经网络,包括编码器和解码器. The best performing models also conn…