cnn

所属分类:人工智能/神经网络/深度学习
开发工具:matlab
文件大小:7540KB
下载次数:188
上传日期:2015-08-29 21:40:00
上 传 者nancy_hx
说明:  卷积神经网络(cnn)的练习程序,包含多个exercise
(convolutional nerual network)

文件列表:
decodeCharacters.m (766, 2015-01-07)
exercise1.m (2049, 2015-01-07)
exercise2.m (1896, 2015-01-07)
exercise3.m (3785, 2015-06-07)
exercise4.m (4884, 2015-01-21)
exercise5.m (1032, 2015-01-07)
extractBlackBlobs.m (358, 2015-01-07)
initializeCharacterCNN.m (1675, 2015-01-07)
mnistclassify_error.mat (8412, 2015-06-07)
mnistclassify_weights.mat (3847153, 2015-06-07)
mnisthp2classify.mat (156253, 2015-06-07)
mnisthpclassify.mat (926558, 2015-06-07)
mnistvhclassify.mat (2770999, 2015-06-07)
setup.m (676, 2015-01-21)
tinycnn.m (1158, 2015-06-07)

Convolutional neural network practical ====================================== A computer vision practical by the Oxford Visual Geometry group, authored by Andrea Vedaldi and Andrew Zisserman. Start from `doc/instructions.html`. > Note that this practical requires compiling the (included) > MatConvNet library. This should happen automatically (see the > `setup.m` script), but make sure that the compilation succeeds on > the laboratory computers. Package contents ---------------- The practical consists of four exercises, organized in the following files: * `exercise1.m` -- Part 1: CNN fundamentals * `exercise2.m` -- Part 2: Derivatives and backpropagation * `exercise3.m` -- Part 3: Learning a tiny CNN * `exercise4.m` -- Part 4: Learning a CNN to recognize characters * `exercise5.m` -- Part 5: Using a pretrained CNN The practical runs in MATLAB and uses [MatConvNet](http://www.vlfeat.org/matconvnet) and [VLFeat](http://www.vlfeat.org). This package contains the following MATLAB functions: * `extractBlackBlobs.m`: extract black blobs from an image. * `tinycnn.m`: implements a very simple CNN. * `initializeCharacterCNN.m`: initialize a CNN to recognize characters. * `decodeCharacters.m`: visualize the output of the character CNN. * `setup.m`: setup MATLAB environment. Appendix: Installing from scratch --------------------------------- The practical requires both VLFeat and MatConvNet. VLFeat comes with pre-built binaries, but MatConvNet does not. 0. Set the current directory to the practical base directory. 1. From Bash: 1. Run `./extras/download.sh`. This will download the `imagenet-vgg-verydeep-16.mat` model as well as a binary copy of the VLFeat library and a copy of MatConvNet. 2. Run `./extra/genfonts.sh`. This will download the Google Fonts and extract them as PNG files. 3. Run `./extra/genstring.sh`. This will create `data/sentence-lato.png`. 2. From MATLAB run `addpath extra ; packFonts ;`. This will create `data/charsdb.mat`. 3. Test the practical: from MATLAB run all the exercises in order. Changes ------- * *2015a* - Initial edition License ------- Copyright (c) 2015 Andrea Vedaldi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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