登录
首页 » matlab » yechengxi-LightNet-6ada9dd

yechengxi-LightNet-6ada9dd

于 2020-01-20 发布
0 198
下载积分: 1 下载次数: 11

代码说明:

说明:  一个matlab神经网络工具箱,其中包含RNN,CNN等(Matlab neural network toolbox)

文件列表:

yechengxi-LightNet-6ada9dd, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\Main_CIFAR_CNN_SGD.m, 674 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\Main_CNN_ImageNet_minimal.m, 1194 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\PrepareData_CIFAR_CNN.m, 413 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\getCifarImdb.m, 2122 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\net_init_cifar_cnn.m, 1849 , 2017-10-21
yechengxi-LightNet-6ada9dd\CNN\test_im.JPG, 113805 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations\leaky_relu.m, 245 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations\modu.m, 310 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations\relu.m, 143 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations\sigmoid_ln.m, 149 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\activations\tanh_ln.m, 152 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\bnorm.m, 3375 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\conv_layer_1d.m, 5449 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\conv_layer_2d.m, 5367 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\dropout.m, 277 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\linear_layer.m, 2436 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\lrn.m, 2430 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\maxpool.m, 3523 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\maxpool_1d.m, 2936 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\rmsnorm.m, 2099 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\layers\softmax.m, 254 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\loss, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\loss\softmaxlogloss.m, 551 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\Main_Template.m, 3119 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\TrainingScript.m, 3614 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\net_bp.m, 5983 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\net_ff.m, 6106 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\test_net.m, 3978 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\net\train_net.m, 4654 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\adagrad.m, 2055 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\adam.m, 3192 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\gradient_decorrelation.m, 3624 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\rmsprop.m, 2991 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\select_learning_rate.m, 2321 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\selective_sgd.m, 867 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\sgd.m, 2378 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\optim\sgd2.m, 5401 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\SwitchProcessor.m, 565 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\average_gradients_in_frames.m, 942 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\error_multiclass.m, 689 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\flipall.m, 80 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\generate_output_filename.m, 947 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\im2col_ln.m, 1267 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\pad_data.m, 866 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\pad_data_1d.m, 686 , 2017-10-21
yechengxi-LightNet-6ada9dd\CoreModules\util\unroll_ln.m, 858 , 2017-10-21
yechengxi-LightNet-6ada9dd\Documentations, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\Documentations\LightNet Tutorial.pptx, 1285467 , 2017-10-21
yechengxi-LightNet-6ada9dd\Documentations\lightnet-supplementary-materials.pdf, 172375 , 2017-10-21
yechengxi-LightNet-6ada9dd\Documentations\lightnet-versatile-standalone.pdf, 373087 , 2017-10-21
yechengxi-LightNet-6ada9dd\ImageNetPreTrain.png, 312780 , 2017-10-21
yechengxi-LightNet-6ada9dd\Init.png, 51249 , 2017-10-21
yechengxi-LightNet-6ada9dd\License.txt, 736 , 2017-10-21
yechengxi-LightNet-6ada9dd\LightNet.png, 84805 , 2017-10-21
yechengxi-LightNet-6ada9dd\Log.txt, 2348 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\Main_MNIST_MLP_Dropout.m, 923 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\Main_MNIST_MLP_RMSPROP.m, 918 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\PrepareData_MNIST_MLP.m, 665 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\get_mnist.m, 1620 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\net_init_mlp_mnist.m, 967 , 2017-10-21
yechengxi-LightNet-6ada9dd\MLP\net_init_mlp_mnist_dropout.m, 1668 , 2017-10-21
yechengxi-LightNet-6ada9dd\README.md, 5613 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\Main_Char_RNN.m, 4440 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\gru_bp.m, 1780 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\gru_ff.m, 2349 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\PrepareData_Char_RNN.m, 561 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\dict.txt, 147 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\test_x.txt, 1118891 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\test_y.txt, 1118891 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\train_x.txt, 1997710 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lm_data\train_y.txt, 1997710 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lstm_bp.m, 1947 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\lstm_ff.m, 2582 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\net_init_char_gru.m, 1228 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\net_init_char_lstm.m, 1351 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\net_init_char_qrnn.m, 1197 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\net_init_char_rnn.m, 877 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\qrnn_bp.m, 1350 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\qrnn_ff.m, 1883 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\rnn_bp.m, 1138 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\rnn_ff.m, 2129 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\test_rnn.m, 1209 , 2017-10-21
yechengxi-LightNet-6ada9dd\RNN\train_rnn.m, 4898 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning, 0 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning\Cart_Pole.m, 1138 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning\Main_Cart_Pole_Policy_Network.m, 4506 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning\Main_Cart_Pole_Q_Network.m, 4754 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning\is_valid_state.m, 273 , 2017-10-21
yechengxi-LightNet-6ada9dd\ReinforcementLearning\net_init_pole.m, 509 , 2017-10-21

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 91823d98aa5c
    说明:  该函数用来计算时间序列的最大Lyapunov 指数--Wolf 方法(This function is used to calculate the maximum Lyapunov index time series- Wolf method)
    2011-04-14 09:21:27下载
    积分:1
  • researchoncontrolsystemformultistageinvertedpendul
    这是一篇大连理工大学的硕士学位论文,题为基于模糊逻辑的多级倒立摆控制系统研究,是在李洪兴教授的指导下完成的。该论文的亮点在于详细的介绍变论域自适应模糊控制思想,并以二级倒立摆为实验对象给出了相应的方针结果。(This is a master' s degree thesis, Dalian University of Technology, entitled Multi-level Fuzzy Logic Inverted Pendulum Control System, is under the guidance of Professor Li Hongxing completed. The paper highlights the detailed description of variable universe adaptive fuzzy control ideas, and to double inverted pendulum as experimental results of the corresponding policy.)
    2010-05-05 17:16:04下载
    积分:1
  • SpeechDenoising
    MATLAB实现语音去噪处理,测试已经通过了(Speech Denoising MATLAB realize treatment, testing has been adopted)
    2009-02-17 19:37:49下载
    积分:1
  • schlpolymethod
    Its is Assump.m used in antennae theory and applications. Can be run matlab 2007 or higher.
    2011-01-04 23:33:50下载
    积分:1
  • matlab
    个人所得税分配方案设计 使用 mlatlab进行仿真(matlab geren suodsui)
    2011-12-02 20:33:34下载
    积分:1
  • Matlab-time-frequency-toolbox
    matlab时频分析工具包,挺好用的 ,用于信号时频处理 (matlab time-freq analysis toolbox)
    2015-03-03 22:02:44下载
    积分:1
  • PCNN-quantization-revision
    PCNN量化,利用人眼视觉特性对图像进行量化,保持细节信息。(PCNN quantified using the human visual system to quantify the image, keeping details.)
    2014-02-18 14:50:11下载
    积分:1
  • Even_and_O1945021112005
    this is a fairly simple code that determines whether the integer you will enter is odd or even. Based on your choices by the questions asked by the program, the ouput background and foreground will change. and one thing: please don t enter such numbers as 0987 or .983 as they are not integers!
    2009-11-08 13:59:31下载
    积分:1
  • EKFcstr
    非线性扩展卡尔曼滤波,在catr模型中的应用。(extending kalman filtering,cstr)
    2013-09-01 16:04:55下载
    积分:1
  • License-plate-recognition
    基于神经网络的车牌识别matlab源码。好东西(License plate recognition based on neural network matlab source)
    2013-11-18 22:16:24下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载