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DeepLearnToolbox-master

于 2021-03-21 发布
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下载积分: 1 下载次数: 8

代码说明:

说明:  该工具包提供了一个用于通过算法、预训练模型和应用程序来设计和实现深度神经网络的框架。您可以使用卷积神经网络(ConvNet、CNN)和长短期记忆 (LSTM) 网络对图像、时序和文本数据执行分类和回归。应用程序和绘图可帮助您可视化激活值、编辑网络架构和监控训练进度。(The toolbox provides a framework for designing and implementing deep neural networks through algorithms, pre training models and applications. You can use convolutional neural networks (convnet, CNN) and long and short term memory (LSTM) networks to perform classification and regression on image, temporal, and text data. Applications and graphics help you visualize activation values, edit network architecture, and monitor training progress.)

文件列表:

DeepLearnToolbox-master, 0 , 2021-03-06
DeepLearnToolbox-master\.travis.yml, 249 , 2015-12-01
DeepLearnToolbox-master\CAE, 0 , 2021-03-06
DeepLearnToolbox-master\CAE\caeapplygrads.m, 1219 , 2015-12-01
DeepLearnToolbox-master\CAE\caebbp.m, 917 , 2015-12-01
DeepLearnToolbox-master\CAE\caebp.m, 1011 , 2015-12-01
DeepLearnToolbox-master\CAE\caedown.m, 259 , 2015-12-01
DeepLearnToolbox-master\CAE\caeexamples.m, 754 , 2015-12-01
DeepLearnToolbox-master\CAE\caenumgradcheck.m, 3618 , 2015-12-01
DeepLearnToolbox-master\CAE\caesdlm.m, 845 , 2015-12-01
DeepLearnToolbox-master\CAE\caetrain.m, 1148 , 2015-12-01
DeepLearnToolbox-master\CAE\caeup.m, 489 , 2015-12-01
DeepLearnToolbox-master\CAE\max3d.m, 173 , 2015-12-01
DeepLearnToolbox-master\CAE\scaesetup.m, 1937 , 2015-12-01
DeepLearnToolbox-master\CAE\scaetrain.m, 270 , 2015-12-01
DeepLearnToolbox-master\CNN, 0 , 2021-03-06
DeepLearnToolbox-master\CNN\cnnapplygrads.m, 575 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnbp.m, 2141 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnff.m, 1774 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnnumgradcheck.m, 3430 , 2015-12-01
DeepLearnToolbox-master\CNN\cnnsetup.m, 2020 , 2015-12-01
DeepLearnToolbox-master\CNN\cnntest.m, 193 , 2015-12-01
DeepLearnToolbox-master\CNN\cnntrain.m, 845 , 2015-12-01
DeepLearnToolbox-master\CNN\test_example_CNN.m, 981 , 2015-12-01
DeepLearnToolbox-master\CONTRIBUTING.md, 544 , 2015-12-01
DeepLearnToolbox-master\DBN, 0 , 2021-03-06
DeepLearnToolbox-master\DBN\dbnsetup.m, 557 , 2015-12-01
DeepLearnToolbox-master\DBN\dbntrain.m, 232 , 2015-12-01
DeepLearnToolbox-master\DBN\dbnunfoldtonn.m, 425 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmdown.m, 90 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmtrain.m, 1401 , 2015-12-01
DeepLearnToolbox-master\DBN\rbmup.m, 89 , 2015-12-01
DeepLearnToolbox-master\LICENSE, 1313 , 2015-12-01
DeepLearnToolbox-master\NN, 0 , 2021-03-06
DeepLearnToolbox-master\NN\nnapplygrads.m, 628 , 2015-12-01
DeepLearnToolbox-master\NN\nnbp.m, 1638 , 2015-12-01
DeepLearnToolbox-master\NN\nnchecknumgrad.m, 704 , 2015-12-01
DeepLearnToolbox-master\NN\nneval.m, 811 , 2015-12-01
DeepLearnToolbox-master\NN\nnff.m, 1849 , 2015-12-01
DeepLearnToolbox-master\NN\nnpredict.m, 192 , 2015-12-01
DeepLearnToolbox-master\NN\nnsetup.m, 1844 , 2015-12-01
DeepLearnToolbox-master\NN\nntest.m, 184 , 2015-12-01
DeepLearnToolbox-master\NN\nntrain.m, 2414 , 2015-12-01
DeepLearnToolbox-master\NN\nnupdatefigures.m, 1858 , 2015-12-01
DeepLearnToolbox-master\README.md, 8861 , 2015-12-01
DeepLearnToolbox-master\README_header.md, 2244 , 2015-12-01
DeepLearnToolbox-master\REFS.md, 950 , 2015-12-01
DeepLearnToolbox-master\SAE, 0 , 2021-03-06
DeepLearnToolbox-master\SAE\saesetup.m, 132 , 2015-12-01
DeepLearnToolbox-master\SAE\saetrain.m, 308 , 2015-12-01
DeepLearnToolbox-master\create_readme.sh, 744 , 2015-12-01
DeepLearnToolbox-master\data, 0 , 2021-03-06
DeepLearnToolbox-master\data\mnist_uint8.mat, 14735220 , 2015-12-01
DeepLearnToolbox-master\tests, 0 , 2021-03-06
DeepLearnToolbox-master\tests\runalltests.m, 165 , 2015-12-01
DeepLearnToolbox-master\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_CNN.m, 981 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_DBN.m, 1031 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_NN.m, 3247 , 2015-12-01
DeepLearnToolbox-master\tests\test_example_SAE.m, 934 , 2015-12-01
DeepLearnToolbox-master\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2015-12-01
DeepLearnToolbox-master\util, 0 , 2021-03-06
DeepLearnToolbox-master\util\allcomb.m, 2618 , 2015-12-01
DeepLearnToolbox-master\util\expand.m, 1958 , 2015-12-01
DeepLearnToolbox-master\util\flicker.m, 208 , 2015-12-01
DeepLearnToolbox-master\util\flipall.m, 80 , 2015-12-01
DeepLearnToolbox-master\util\fliplrf.m, 543 , 2015-12-01
DeepLearnToolbox-master\util\flipudf.m, 576 , 2015-12-01
DeepLearnToolbox-master\util\im2patches.m, 313 , 2015-12-01
DeepLearnToolbox-master\util\isOctave.m, 108 , 2015-12-01
DeepLearnToolbox-master\util\makeLMfilters.m, 1895 , 2015-12-01
DeepLearnToolbox-master\util\myOctaveVersion.m, 169 , 2015-12-01
DeepLearnToolbox-master\util\normalize.m, 97 , 2015-12-01
DeepLearnToolbox-master\util\patches2im.m, 242 , 2015-12-01
DeepLearnToolbox-master\util\randcorr.m, 283 , 2015-12-01
DeepLearnToolbox-master\util\randp.m, 2083 , 2015-12-01
DeepLearnToolbox-master\util\rnd.m, 49 , 2015-12-01
DeepLearnToolbox-master\util\sigm.m, 48 , 2015-12-01
DeepLearnToolbox-master\util\sigmrnd.m, 126 , 2015-12-01
DeepLearnToolbox-master\util\softmax.m, 256 , 2015-12-01
DeepLearnToolbox-master\util\tanh_opt.m, 54 , 2015-12-01
DeepLearnToolbox-master\util\visualize.m, 1072 , 2015-12-01
DeepLearnToolbox-master\util\whiten.m, 183 , 2015-12-01
DeepLearnToolbox-master\util\zscore.m, 137 , 2015-12-01

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发表评论

0 个回复

  • short-circuit
    这个说明倒不是说明函数的功能,只是声明一下函数中出现的问题。 shortest()函数没有问题,在secshortest中我希望只得到一条次短路(或几条roadcost()值相同的次短路),结果却出来好几条,我仔细检查了一下程序,应该没有什么问题。 因此在使用时,要得到次短路,只能用roadcost()函数分别求一下各条路的权重和,找到最小的。 有些奇怪 (Note that this is not the function of function, but a statement about the problems in function. shortest () function there is no problem, I hope that in secshortest only a short time (or several roadcost () times the value of the same short-circuit), the result is several out, I carefully examined the process, it should be no problem. Therefore, in use, to be at a short circuit can only be used roadcost () function, respectively, for some of the weight of the road and find the smallest. Some strange)
    2009-05-21 21:11:51下载
    积分:1
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    2010-01-25 12:49:11下载
    积分:1
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    瑞利衰落信道的MATLAB程序,适合对这个算法模型的理解(Rayleigh fading channel MATLAB, the algorithm for the model of understanding )
    2012-04-25 15:18:41下载
    积分:1
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    2013-04-09 01:40:53下载
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    分形插值曲面的MATLAB程序 这时pdf程序资料(MATLAB program of fractal interpolation of time pdf program information)
    2013-12-16 09:59:59下载
    积分:1
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    image processing KLT with samples
    2009-12-13 00:29:19下载
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    说明:  SHP格式的飓风数据 SHP格式的飓风数据 SHP格式的飓风数据 SHP格式的飓风数据(SHP SHP format data format of Hurricane Katrina data in SHP format Hurricane Katrina data in SHP format data)
    2010-04-03 14:16:09下载
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    鲍威尔算法的matlab实现,鲍威尔算法是直接利用函数值来构造共轭方向的一种方法(Powell algorithm matlab realize)
    2013-08-20 11:33:13下载
    积分:1
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    基于循环前缀的同步链路,包括调制解调等流程(Cyclic prefix based synchronization link, including the modulation and demodulation process)
    2013-12-18 14:43:27下载
    积分:1
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