CNN卷积神经网络数字识别

所属分类:matlab编程
开发工具:matlab
文件大小:5909KB
下载次数:22
上传日期:2018-06-14 16:30:30
上 传 者ygc
说明:  用 GUI 界面展现图形,提供 RMSE MCR 情节,培训和黑 森计算进度条,通过随机梯度,黑森估计计算和运行批处理黑森计算,采用有监督的 CNN 网络完成对MNIST 数字的识别。
(Graphical presentations using the GUI interface, RMSE MCR plots, training, and progress scores for Hessian calculations. Through random gradients, Hessian estimating calculations and running batches of Hesse calculations, supervised CNN networks were used to complete MNIST number recognition.)

文件列表:
20122200064_方洲 (0, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别 (0, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn (0, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\adapt_dw.m (3820, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\calcMCR.m (679, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\calchx.m (5080, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\calcje.m (5911, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\check_finit_dif.m (2250, 2013-03-11)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\cnn.m (7147, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\cnn_size.m (942, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\cutrain.m (7120, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\init.m (3840, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\sim.m (2936, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\subsasgn.m (8998, 2013-03-11)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\subsref.m (1616, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\@cnn\train.m (6741, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\back_conv2.m (480, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\back_subsample.m (922, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\cnet.mat (570755, 2010-02-08)
20122200064_方洲\CNN—卷积神经网络数字识别\cnet_tool.m (20961, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\cnn2singlestruct.m (919, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\cnn_gui.fig (9952, 2010-01-05)
20122200064_方洲\CNN—卷积神经网络数字识别\cnn_gui.m (6651, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\cucalcMCR.m (509, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\cutrain_cnn.m (4018, 2014-12-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit (0, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test (0, 2014-12-28)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\441.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\442.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\443.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\444.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\445.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\446.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\447.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\448.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\449.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\450.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\451.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\452.bmp (174, 2013-03-25)
20122200064_方洲\CNN—卷积神经网络数字识别\digit\test\453.bmp (174, 2013-03-25)
... ...

一、运行cnet_tool。你会看到一个简单的GUI。它从cet.mat 加载pretrained cnet的卷积神经网络和识别图像的或从MNIST下载数据库。 二、如果你要培训,应该打开train_cnn.m, 设置下面的所有参数后开始学习运行。 1、创建cnn对象。 2、设置层数(层数、权量、训练参数,等等)。 3、调用init方法。 4、定义层的连接矩阵,如果必要的话。 5、负荷训练数据。 6、训练数据进行预处理。 7、开始训练。 8、测试神经网络。

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