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Matlab深度学习工具箱.zip 大约有1302个文件
  1. Matlab深度学习工具箱/
  2. __MACOSX/._Matlab深度学习工具箱 212B
  3. Matlab深度学习工具箱/.DS_Store 6KB
  4. __MACOSX/Matlab深度学习工具箱/._.DS_Store 120B
  5. Matlab深度学习工具箱/matconvnet-1.0-beta23/
  6. __MACOSX/Matlab深度学习工具箱/._matconvnet-1.0-beta23 212B
  7. Matlab深度学习工具箱/matconvnet-1.0-beta23.tar.gz 1.4MB
  8. __MACOSX/Matlab深度学习工具箱/._matconvnet-1.0-beta23.tar.gz 212B
  9. Matlab深度学习工具箱/libsvm-3.22.zip 857.56KB
  10. __MACOSX/Matlab深度学习工具箱/._libsvm-3.22.zip 212B
  11. Matlab深度学习工具箱/说明.txt 40B
  12. __MACOSX/Matlab深度学习工具箱/._说明.txt 474B
  13. Matlab深度学习工具箱/DeepLearnToolbox-master.zip 14.09MB
  14. __MACOSX/Matlab深度学习工具箱/._DeepLearnToolbox-master.zip 212B
  15. Matlab深度学习工具箱/libsvm-3.22/
  16. __MACOSX/Matlab深度学习工具箱/._libsvm-3.22 212B
  17. Matlab深度学习工具箱/DeepLearnToolbox-master/
  18. __MACOSX/Matlab深度学习工具箱/._DeepLearnToolbox-master 212B
  19. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/
  20. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/._matconvnet-1.0-beta23 212B
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  27. Matlab深度学习工具箱/libsvm-3.22/python/
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  35. Matlab深度学习工具箱/libsvm-3.22/java/
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  39. Matlab深度学习工具箱/libsvm-3.22/svm-train.c 8.78KB
  40. __MACOSX/Matlab深度学习工具箱/libsvm-3.22/._svm-train.c 212B
  41. Matlab深度学习工具箱/libsvm-3.22/svm-scale.c 8.34KB
  42. __MACOSX/Matlab深度学习工具箱/libsvm-3.22/._svm-scale.c 212B
  43. Matlab深度学习工具箱/libsvm-3.22/matlab/
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  45. Matlab深度学习工具箱/libsvm-3.22/svm-toy/
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  49. Matlab深度学习工具箱/libsvm-3.22/windows/
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  57. Matlab深度学习工具箱/libsvm-3.22/svm-predict.c 5.41KB
  58. __MACOSX/Matlab深度学习工具箱/libsvm-3.22/._svm-predict.c 212B
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  60. __MACOSX/Matlab深度学习工具箱/libsvm-3.22/._svm.cpp 212B
  61. Matlab深度学习工具箱/DeepLearnToolbox-master/REFS.md 950B
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  71. Matlab深度学习工具箱/DeepLearnToolbox-master/util/
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  73. Matlab深度学习工具箱/DeepLearnToolbox-master/DBN/
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  77. Matlab深度学习工具箱/DeepLearnToolbox-master/create_readme.sh 744B
  78. __MACOSX/Matlab深度学习工具箱/DeepLearnToolbox-master/._create_readme.sh 212B
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  109. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/
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  691. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/vl_nnpool.mexw64 38KB
  692. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/._vl_nnpool.mexw64 212B
  693. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/vl_nnbilinearsampler.mexw64 37.5KB
  694. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/._vl_nnbilinearsampler.mexw64 212B
  695. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/vl_nnroipool.mexw64 43.5KB
  696. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/._vl_nnroipool.mexw64 212B
  697. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/vl_nnconvt.mexw64 48.5KB
  698. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/mex/._vl_nnconvt.mexw64 212B
  699. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnconvt.cu 13.25KB
  700. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnconvt.cu 212B
  701. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_cudatool.cu 4.01KB
  702. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_cudatool.cu 212B
  703. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnpool.cpp 111B
  704. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnpool.cpp 212B
  705. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnconv.cpp 111B
  706. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnconv.cpp 212B
  707. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/config/
  708. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._config 212B
  709. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnpool.cu 9.32KB
  710. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnpool.cu 212B
  711. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_taccummex.cpp 114B
  712. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_taccummex.cpp 212B
  713. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_imreadjpeg.cpp 115B
  714. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_imreadjpeg.cpp 212B
  715. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_imreadjpeg_old.cu 13.28KB
  716. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_imreadjpeg_old.cu 212B
  717. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_tmove.cu 75.72KB
  718. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_tmove.cu 212B
  719. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnconv.cu 17.25KB
  720. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnconv.cu 212B
  721. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnroipool.cpp 114B
  722. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnroipool.cpp 212B
  723. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_taccummex.cu 5.37KB
  724. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_taccummex.cu 212B
  725. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnbnorm.cpp 112B
  726. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnbnorm.cpp 212B
  727. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnbilinearsampler.cpp 122B
  728. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnbilinearsampler.cpp 212B
  729. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_cudatool.cpp 113B
  730. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_cudatool.cpp 212B
  731. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_tmove.cpp 110B
  732. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_tmove.cpp 212B
  733. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/bits/
  734. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._bits 212B
  735. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnnormalize.cu 5.16KB
  736. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnnormalize.cu 212B
  737. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnroipool.cu 8.29KB
  738. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnroipool.cu 212B
  739. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_imreadjpeg_old.cpp 115B
  740. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_imreadjpeg_old.cpp 212B
  741. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_imreadjpeg.cu 43.02KB
  742. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_imreadjpeg.cu 212B
  743. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnbilinearsampler.cu 6.53KB
  744. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnbilinearsampler.cu 212B
  745. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnconvt.cpp 112B
  746. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnconvt.cpp 212B
  747. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnnormalize.cpp 116B
  748. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnnormalize.cpp 212B
  749. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/vl_nnbnorm.cu 9.63KB
  750. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/._vl_nnbnorm.cu 212B
  751. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/mnist/cnn_mnist.m 4.5KB
  752. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/mnist/._cnn_mnist.m 212B
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  755. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/mnist/cnn_mnist_init.m 3.04KB
  756. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/mnist/._cnn_mnist_init.m 212B
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  758. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_googlenet.m 212B
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  760. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_init_inception.m 212B
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  762. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_evaluate.m 212B
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  764. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_minimal.m 212B
  765. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/getImageStats.m 1.36KB
  766. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._getImageStats.m 212B
  767. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/cnn_imagenet_init_resnet.m 6.56KB
  768. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_init_resnet.m 212B
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  770. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet.m 212B
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  772. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._getImageBatch.m 212B
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  776. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_deploy.m 212B
  777. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/cnn_imagenet_camdemo.m 1.66KB
  778. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_camdemo.m 212B
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  780. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_sync_labels.m 212B
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  782. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/imagenet/._cnn_imagenet_init.m 212B
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  784. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/vggfaces/._cnn_vgg_faces.m 212B
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  786. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/._fast_rcnn_demo.m 212B
  787. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/datasets/
  788. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/._datasets 212B
  789. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/fast_rcnn_evaluate.m 6.78KB
  790. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/._fast_rcnn_evaluate.m 212B
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  795. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/fast_rcnn/README.md 986B
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  815. Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/examples/spatial_transformer/cnn_stn_cluttered_mnist.m 3.78KB
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  1258. __MACOSX/Matlab深度学习工具箱/matconvnet-1.0-beta23/matconvnet-1.0-beta23/matlab/src/bits/impl/._roipooling_gpu.cu 212B
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资源介绍:

Matlab深度学习工具箱
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 com Directories 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 working For references on each library check REFS.md Setup ----- 1. Download. 2. addpath(genpath('DeepLearnToolbox')); Everything is work in progress ------------------------------ Example: Deep Belief Network --------------------- ```matlab function test_example_DBN load 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 weights rng(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 NN rng(0); %train dbn dbn.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 nn nn = dbnunfoldtonn(dbn, 10); nn.activation_function = 'sigm'; %train nn opts.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 --------------------- ```matlab function test_example_SAE load 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) rng(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 FFNN nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; nn.W{1} = sae.ae{1}.W{1}; % Train the FFNN opts.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'); %% ex2 train a 100-100 hidden unit SDAE and use it to initialize a FFNN % Setup and train a stacked denoising autoencoder (SDAE) rng(0); sae = saesetup([784 100 100]); sae.ae{1}.activation_function = 'sigm'; sae.ae{1}.learningRate = 1; sae.ae{1}.inputZeroMaskedFraction = 0.5; sae.ae{2}.activation_function = 'sigm'; sae.ae{2}.learningRate = 1; sae.ae{2}.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 FFNN nn = nnsetup([784 100 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; %add pretrained weights nn.W{1} = sae.ae{1}.W{1}; nn.W{2} = sae.ae{2}.W{1}; % Train the FFNN opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); ``` Example: Convolutional Neural Nets --------------------- ```matlab function test_example_CNN load 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% error rng(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 error figure; plot(cnn.rL); assert(er<0.12, 'Too big error'); ``` Example: Neural Networks --------------------- ```matlab function test_example_NN load 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 net rng(0); nn = nnsetup([784 100 10]); opts.numepochs = 1; % Number of full sweeps through data opts.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'); % Make an artificial one and verify that we can predict it x = zeros(1,28,28); x(:, 14:15, 6:22) = 1; x = reshape(x,1,28^2); figure; visualize(x'); predicted = nnpredict(nn,x)-1; assert(predicted == 1); %% ex2 neural net with L2 weight decay rng(0); nn = nnsetup([784 100 10]); nn.weightPenaltyL2 = 1e-4; % L2 weight decay opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = 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 dropout rng(0); nn = nnsetup([784 100 10]); nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = 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 function rng(0); nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; % Sigmoid activation function nn.learningRate = 1; % Sigm require a lower learning rate opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this
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