墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
70平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
循环神经网络（Recurrent Neural Network，RNN）是一种强大的深度学习算法，专门用于处理序列数据。它在自然语言处理、语音识别、时间序列分析等领域中被广泛应用。本文将介绍如何使用TensorFlow构建和训练循环神经网络，并提供相应的源代码。
首先，我们需要安装TensorFlow库。可以使用以下命令安装最新版本的TensorFlow：
pip install tensorflow
安装完成后，我们可以开始构建循环神经网络模型。TensorFlow提供了tf.keras作为高级API，可以方便地构建神经网络模型。下面是一个简单的循环神经网络模型的示例：
import tensorflow as tf
from tensorflow.keras.layers import Dense, SimpleRNN
# 定义循环神经网络模型
model =《神经网络与深度学习》教材学习与实践指南
前馈神经网络（Feedforward Neural Network），亦称为多层感知器（MLP），是最基本的神经网络结构之一。它由至少三层神经元组成：输入层、隐藏层（可能有一层或多层）和输出层。每层由若干神经元构成，而层与层之间是全连接的，即前一层的每个神经元都与后一层的每个神经元相连。在前馈神经网络中，信息单向流动，从输入层经过隐藏层，最终传递至输出层。网络通过“前向传播”进行信息处理，然后通过“反向传播”算法调整权重，以最小化输出误差。这是深度学习中实现监督学习的一种核心机制。
《神经网络与深度学习》中英双语精讲教程
在神经网络中，损失函数（Loss Function）用于衡量模型输出与真实标签之间的差异。它是训练过程中优化目标的数学表达。常见的损失函数包括：均方误差（MSE）：适用于回归任务，公式为：$$$$其中 $ y_i $ 是真实值，$ \hat{y}_i $ 是模型预测值，$ N $ 是样本数量。交叉熵损失（Cross-Entropy Loss）：适用于分类任务，分为二分类和多分类两种形式：二分类交叉熵：$$$$多分类交叉熵：$$
告别调试烦恼：用Lattice Diamond手把手教你JTAG烧录FPGA（附SRAM/Flash模式详解）
本文详细介绍了使用Lattice Diamond软件进行JTAG烧录FPGA的全流程，包括SRAM临时调试和Flash固化的核心区别。从环境准备、硬件连接到文件准备、烧录模式选择，再到典型问题排查和高级技巧，手把手教你快速掌握FPGA烧录技术，提升开发效率。
XCKU060光纤卡学习资料保存：基于kintex UltraScale XCKU060的双路QSFP+光纤PCIe 卡
基于kintex UltraScale XCKU060的双路QSFP+光纤PCIe 卡 一、板卡概述 本板卡系我司自主研发，基于XilinxUltraScaleKintex系列FPGAXCKU060-FFVA1156-2-I架构，支持PCIEGen3x8模式的高速信号处理板卡，搭配两路40GQSFP+接口，两组64-bitDDR4，每组容量8Gbyte，可稳定运行在2400MT/s。板卡具有自控上电顺序，BP...
XTDrone平台FAST-LIO2与ego-planner结合路径规划部署
之前做了XTDrone平台FAST-LIO2的部署现在将其结合ego-planner进行导航规划，之前查看教程是使用视觉进行定位导航的，现在重新学习。修改advanced_param_xtdrone.xml，这里有视觉信息，不修改会报错显示为输入视觉话题信息。怎么描述 ，感觉当时觉得很难的问题突然就给解决了，一顿复杂，最近勤更新吧，继续开始学习。还有一个想法就是两个rviz结合在一起启动，写了一个demo，融合的不太好感觉额。改为只使用雷达点云，这里仿真的话题是/scan。
立创EDA实战：基于NE555的夜间氛围呼吸灯电路设计与模式切换
本文详细介绍了使用立创EDA设计基于NE555芯片的夜间氛围呼吸灯电路。项目核心是利用NE555产生PWM信号，并通过RC充放电电路实现LED亮度的缓慢周期性变化，形成呼吸效果。文章手把手教学，涵盖从电路原理分析、元件参数计算，到在立创EDA中进行原理图绘制、PCB布局布线，直至焊接调试的完整硬件设计流程，是电子爱好者入门实战的经典项目。
Mac上SSH连接神器SecureCRT：从安装到高效使用的完整指南
本文详细介绍了Mac上专业SSH连接工具SecureCRT的安装与高效使用技巧。从核心优势、会话管理到快捷键方案，全面解析如何提升远程开发与服务器管理效率。特别适合需要处理多服务器环境和复杂终端控制的Mac用户，SecureCRT的企业级加密和跨平台同步功能使其成为SSH连接的神器。
本地 AI 自由！OpenClaw+Ollama 双系统部署教程（Windows/Linux），零成本离线跑大模型
本文详细介绍了如何在Windows和Linux双系统中部署OpenClaw AI助手，并通过Ollama对接本地大模型实现完全离线使用。教程包含环境准备、OpenClaw安装配置、Ollama本地模型部署、系统对接等完整流程，特别强调隐私安全和零Token消耗的优势。针对不同操作系统提供具体命令，并给出常见问题解决方案，即使是新手也能快速搭建专属本地AI助手。最终实现的方案无需联网、无使用成本，且能保障数据隐私安全。
订餐平台餐厅信息抓取：使用Python爬虫获取商家信息和用户评价
在这篇博客中，我们展示了如何使用Python爬虫抓取订餐平台上的餐厅信息和用户评价数据。通过使用requests和Selenium，我们能够有效地从静态和动态网页中提取数据，并使用pandas和matplotlib对数据进行存储和分析。未来，您可以根据需求进一步扩展功能，例如增加错误处理、增加抓取多个页面的能力，或者使用高级技术如分布式爬虫来抓取更大规模的数据。
Cadence 批量一次性修改title 页码标题
本文介绍如何在Cadence软件中对DSN文件进行标题栏的批量编辑操作，包括选择编辑对象、使用快捷键打开属性面板以及如何修改标题、页码等关键信息。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
1186
扫一扫
