文件名称:mycnn
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- 上传时间:2015-05-20
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文件大小:2.5mb
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卷积神经网络识别字符的Matlab程序,包含所需的所有素材和自己改进的一部分代码-Convolutional neural network for handwriten digits recognition: training
and simulation.
This program implements the convolutional neural network for MNIST handwriten
digits recognition, created by Yann LeCun. CNN class allows to make your
own convolutional neural net, defining arbitrary structure and parameters.
and simulation.
This program implements the convolutional neural network for MNIST handwriten
digits recognition, created by Yann LeCun. CNN class allows to make your
own convolutional neural net, defining arbitrary structure and parameters.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
mycnn/
mycnn/license.txt
mycnn/mycnn/
mycnn/mycnn/CNN/
mycnn/mycnn/CNN/@cnn/
mycnn/mycnn/CNN/@cnn/adapt_dw.m
mycnn/mycnn/CNN/@cnn/calcMCR.m
mycnn/mycnn/CNN/@cnn/calchx.m
mycnn/mycnn/CNN/@cnn/calcje.m
mycnn/mycnn/CNN/@cnn/check_finit_dif.m
mycnn/mycnn/CNN/@cnn/cnn.m
mycnn/mycnn/CNN/@cnn/cnn_size.m
mycnn/mycnn/CNN/@cnn/cutrain.m
mycnn/mycnn/CNN/@cnn/init.m
mycnn/mycnn/CNN/@cnn/rbm.m
mycnn/mycnn/CNN/@cnn/sim.m
mycnn/mycnn/CNN/@cnn/subsasgn.m
mycnn/mycnn/CNN/@cnn/subsref.m
mycnn/mycnn/CNN/@cnn/train.m
mycnn/mycnn/CNN/back_conv2.m
mycnn/mycnn/CNN/back_subsample.m
mycnn/mycnn/CNN/changelog.txt
mycnn/mycnn/CNN/cnet.mat
mycnn/mycnn/CNN/cnet_tool.m
mycnn/mycnn/CNN/cnn2singlestruct.m
mycnn/mycnn/CNN/cnn_gui.fig
mycnn/mycnn/CNN/cnn_gui.m
mycnn/mycnn/CNN/cucalcMCR.m
mycnn/mycnn/CNN/cutrain_cnn.m
mycnn/mycnn/CNN/fastFilter2.m
mycnn/mycnn/CNN/license.txt
mycnn/mycnn/CNN/preproc_data.m
mycnn/mycnn/CNN/preproc_image.m
mycnn/mycnn/CNN/rand_std.m
mycnn/mycnn/CNN/readMNIST.m
mycnn/mycnn/CNN/readMNIST_image.m
mycnn/mycnn/CNN/readme.txt
mycnn/mycnn/CNN/rot180.m
mycnn/mycnn/CNN/singlestruct2cnn.m
mycnn/mycnn/CNN/subsample.m
mycnn/mycnn/CNN/tansig_mod.m
mycnn/mycnn/CNN/test_dgt.m
mycnn/mycnn/CNN/train_cnn.m
mycnn/mycnn/CNN/ver 0.8.zip
mycnn/mycnn/license.txt
mycnn/ver 0.8/
mycnn/ver 0.8/@cnn/
mycnn/ver 0.8/@cnn/adapt_dw.m
mycnn/ver 0.8/@cnn/calcMCR.m
mycnn/ver 0.8/@cnn/calchx.m
mycnn/ver 0.8/@cnn/calcje.m
mycnn/ver 0.8/@cnn/check_finit_dif.m
mycnn/ver 0.8/@cnn/cnn.m
mycnn/ver 0.8/@cnn/cnn_size.m
mycnn/ver 0.8/@cnn/cutrain.m
mycnn/ver 0.8/@cnn/init.m
mycnn/ver 0.8/@cnn/sim.m
mycnn/ver 0.8/@cnn/subsasgn.m
mycnn/ver 0.8/@cnn/subsref.m
mycnn/ver 0.8/@cnn/train.m
mycnn/ver 0.8/back_conv2.m
mycnn/ver 0.8/back_subsample.m
mycnn/ver 0.8/changelog.txt
mycnn/ver 0.8/cnet.mat
mycnn/ver 0.8/cnet_tool.m
mycnn/ver 0.8/cnn2singlestruct.m
mycnn/ver 0.8/cnn_gui.fig
mycnn/ver 0.8/cnn_gui.m
mycnn/ver 0.8/cucalcMCR.m
mycnn/ver 0.8/cutrain_cnn.m
mycnn/ver 0.8/fastFilter2.m
mycnn/ver 0.8/license.txt
mycnn/ver 0.8/preproc_data.m
mycnn/ver 0.8/preproc_image.m
mycnn/ver 0.8/rand_std.m
mycnn/ver 0.8/readMNIST.m
mycnn/ver 0.8/readMNIST_image.m
mycnn/ver 0.8/readme.txt
mycnn/ver 0.8/rot180.m
mycnn/ver 0.8/singlestruct2cnn.m
mycnn/ver 0.8/subsample.m
mycnn/ver 0.8/tansig_mod.m
mycnn/ver 0.8/test_dgt.m
mycnn/ver 0.8/train_cnn.m
mycnn/ver 0.8/ver 0.8.zip
mycnn/license.txt
mycnn/mycnn/
mycnn/mycnn/CNN/
mycnn/mycnn/CNN/@cnn/
mycnn/mycnn/CNN/@cnn/adapt_dw.m
mycnn/mycnn/CNN/@cnn/calcMCR.m
mycnn/mycnn/CNN/@cnn/calchx.m
mycnn/mycnn/CNN/@cnn/calcje.m
mycnn/mycnn/CNN/@cnn/check_finit_dif.m
mycnn/mycnn/CNN/@cnn/cnn.m
mycnn/mycnn/CNN/@cnn/cnn_size.m
mycnn/mycnn/CNN/@cnn/cutrain.m
mycnn/mycnn/CNN/@cnn/init.m
mycnn/mycnn/CNN/@cnn/rbm.m
mycnn/mycnn/CNN/@cnn/sim.m
mycnn/mycnn/CNN/@cnn/subsasgn.m
mycnn/mycnn/CNN/@cnn/subsref.m
mycnn/mycnn/CNN/@cnn/train.m
mycnn/mycnn/CNN/back_conv2.m
mycnn/mycnn/CNN/back_subsample.m
mycnn/mycnn/CNN/changelog.txt
mycnn/mycnn/CNN/cnet.mat
mycnn/mycnn/CNN/cnet_tool.m
mycnn/mycnn/CNN/cnn2singlestruct.m
mycnn/mycnn/CNN/cnn_gui.fig
mycnn/mycnn/CNN/cnn_gui.m
mycnn/mycnn/CNN/cucalcMCR.m
mycnn/mycnn/CNN/cutrain_cnn.m
mycnn/mycnn/CNN/fastFilter2.m
mycnn/mycnn/CNN/license.txt
mycnn/mycnn/CNN/preproc_data.m
mycnn/mycnn/CNN/preproc_image.m
mycnn/mycnn/CNN/rand_std.m
mycnn/mycnn/CNN/readMNIST.m
mycnn/mycnn/CNN/readMNIST_image.m
mycnn/mycnn/CNN/readme.txt
mycnn/mycnn/CNN/rot180.m
mycnn/mycnn/CNN/singlestruct2cnn.m
mycnn/mycnn/CNN/subsample.m
mycnn/mycnn/CNN/tansig_mod.m
mycnn/mycnn/CNN/test_dgt.m
mycnn/mycnn/CNN/train_cnn.m
mycnn/mycnn/CNN/ver 0.8.zip
mycnn/mycnn/license.txt
mycnn/ver 0.8/
mycnn/ver 0.8/@cnn/
mycnn/ver 0.8/@cnn/adapt_dw.m
mycnn/ver 0.8/@cnn/calcMCR.m
mycnn/ver 0.8/@cnn/calchx.m
mycnn/ver 0.8/@cnn/calcje.m
mycnn/ver 0.8/@cnn/check_finit_dif.m
mycnn/ver 0.8/@cnn/cnn.m
mycnn/ver 0.8/@cnn/cnn_size.m
mycnn/ver 0.8/@cnn/cutrain.m
mycnn/ver 0.8/@cnn/init.m
mycnn/ver 0.8/@cnn/sim.m
mycnn/ver 0.8/@cnn/subsasgn.m
mycnn/ver 0.8/@cnn/subsref.m
mycnn/ver 0.8/@cnn/train.m
mycnn/ver 0.8/back_conv2.m
mycnn/ver 0.8/back_subsample.m
mycnn/ver 0.8/changelog.txt
mycnn/ver 0.8/cnet.mat
mycnn/ver 0.8/cnet_tool.m
mycnn/ver 0.8/cnn2singlestruct.m
mycnn/ver 0.8/cnn_gui.fig
mycnn/ver 0.8/cnn_gui.m
mycnn/ver 0.8/cucalcMCR.m
mycnn/ver 0.8/cutrain_cnn.m
mycnn/ver 0.8/fastFilter2.m
mycnn/ver 0.8/license.txt
mycnn/ver 0.8/preproc_data.m
mycnn/ver 0.8/preproc_image.m
mycnn/ver 0.8/rand_std.m
mycnn/ver 0.8/readMNIST.m
mycnn/ver 0.8/readMNIST_image.m
mycnn/ver 0.8/readme.txt
mycnn/ver 0.8/rot180.m
mycnn/ver 0.8/singlestruct2cnn.m
mycnn/ver 0.8/subsample.m
mycnn/ver 0.8/tansig_mod.m
mycnn/ver 0.8/test_dgt.m
mycnn/ver 0.8/train_cnn.m
mycnn/ver 0.8/ver 0.8.zip
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