fxsjy-gonn-bb82a6c
所属分类：模式识别(视觉/语音等)
开发工具：matlab
文件大小：11249KB
下载次数：4
上传日期：2018-05-28 17:09:04
上 传 者：
大螃蟹1996
说明： 通过matlab语言对图像进行数字识别，bp网络
(Digital recognition of images through MATLAB language, BP network)
文件列表:
fxsjy-gonn-bb82a6c (0, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn (0, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\iris2.data (2700, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\iris_proc.data (2700, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\plot_result.py (258, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\randomize.py (180, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\sin.out (3736, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\test_add.go (408, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\test_digit.go (2000, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\test_iris.go (1882, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\test_sin.go (601, 2016-01-29)
fxsjy-gonn-bb82a6c\example_bpnn\test_xor.go (452, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist (0, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\mymnist.go (3850, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\mymnist_rbf.go (3857, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\run_bpnn.bat (130, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\run_rbf.bat (134, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\t10k-images-idx3-ubyte (7840016, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\t10k-labels-idx1-ubyte (10008, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\train-images-idx3-ubyte (47040016, 2016-01-29)
fxsjy-gonn-bb82a6c\example_mnist\train-labels-idx1-ubyte (60008, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf (0, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\iris2.data (2700, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\iris_proc.data (2700, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\plot_result.py (258, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\randomize.py (180, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\test_rbf_add.go (411, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\test_rbf_iris.go (1753, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\test_rbf_sin.go (607, 2016-01-29)
fxsjy-gonn-bb82a6c\example_rbf\test_rbf_xor.go (443, 2016-01-29)
fxsjy-gonn-bb82a6c\gonn (0, 2016-01-29)
fxsjy-gonn-bb82a6c\gonn\gonn.go (9256, 2016-01-29)
fxsjy-gonn-bb82a6c\gonn\pcn.go (3715, 2016-01-29)
fxsjy-gonn-bb82a6c\gonn\rbf.go (4779, 2016-01-29)
fxsjy-gonn-bb82a6c\parallel_version (0, 2016-01-29)
fxsjy-gonn-bb82a6c\parallel_version\gonn (0, 2016-01-29)
fxsjy-gonn-bb82a6c\parallel_version\gonn\gonn.go (7705, 2016-01-29)
... ...
GoNN [](https://godoc.org/github.com/fxsjy/gonn/gonn)
========
Neural Network in GoLang
Feature
=======
* BackPropagation Network / RBF Network / Perceptron Network
* Parallel BackPropagation Network (each neural has its own go-routine)
Benchmark
=======
* Dataset: MNIST Acurrency Rate : ***.2% (800 hidden nodes)
* Actually, you can get 96.9% using 100 hidden nodes in just three minutes of training
TODO
=======
* currently, the parallel version is much slower than the tranditional one, maybe caused by the cost of context switch of threads
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