文件名称:Deep Neural Network

  • 所属分类:
  • matlab例程
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  • 上传时间:
  • 2019-08-19
  • 文件大小:
  • 20.22mb
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  • 0次
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  • 春风***
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深度神经网络训练过程中:首先是进行初始化,根据需求设置神经网络的基本结构;然后进行前向传递(feedforward),层与层之间进行传递,求得误差;然后进行反向传播(back propogation),根据误差最小化原则,使用随机梯度下降法,对各个参数进行求导,确定下降方向,对各个参数进行更新(In the training process of deep neural network, firstly, initialization is carried out, and the basic structure of the neural network is set up according to the demand; secondly, feedforward is carried out, and errors are obtained by transferring between layers; then back propogation is carried out, and random gradient is used according to the principle of error minimization. The descent method is used to derive the parameters, determine the descent direction and update the parameters.)
相关搜索: 深度神经网络

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下载文件列表

文件名大小更新时间
DeepNeuralNetwork20160805 0 2018-04-22
DeepNeuralNetwork20160805\DeepNeuralNetwork 0 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\CalcErrorRate.m 1030 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\CalcRmse.m 1138 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\DeepNeuralNetwork20160805.mltbx 4813902 2018-04-20
DeepNeuralNetwork20160805\DeepNeuralNetwork\GetDroppedDBN.m 1935 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\GetOnInd.m 1357 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\h2v.m 1794 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\linearMapping.m 1128 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist 0 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\evaMNIST.m 1124 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\mnistbbdbn.mat 4770186 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\mnistread.m 1884 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\readme.txt 240 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\t10k-images-idx3-ubyte 1648877 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\t10k-labels-idx1-ubyte 4542 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\testMNIST.m 22 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\train-images-idx3-ubyte 9912422 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\train-labels-idx1-ubyte 28881 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\mnist\trainMNIST.m 1502 2018-04-23
DeepNeuralNetwork20160805\DeepNeuralNetwork\ObjectFunc.m 673 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\pretrainDBN.m 2876 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\pretrainRBM.m 6644 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\randDBN.m 1611 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\randRBM.m 1355 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\SetLinearMapping.m 1690 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\sigmoid.m 618 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\testDNN.m 542 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\trainDBN.m 12217 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\v2h.m 1795 2016-08-04
DeepNeuralNetwork20160805\DeepNeuralNetwork\v2hall.m 1262 2016-08-04
DeepNeuralNetwork20160805\license.txt 1315 2016-08-04

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