文件名称:LightNet-master
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2017-03-21
- 文件大小：
- 2.4mb
- 下载次数：
- 0次
- 提 供 者：
- L***
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
目前最轻量级别的深度学习源码，采用CNN实现高精度识别，与目前流行的Caffe相比，具有实现灵活，可迁移程度高等优势，值得深入学习！-Currently the lightest level of deep learning source, using CNN to achieve high-precision identification, compared with the current popular Caffe, with the realization of flexible, high degree of migration advantages, it is worth learning!
(系统自动生成,下载前可以参看下载内容)
下载文件列表
LightNet-master
...............\CNN
...............\...\Main_CIFAR_CNN_slow_SGD.m
...............\...\Main_CNN_ImageNet_minimal.m
...............\...\PrepareData_CIFAR_CNN.m
...............\...\getCifarImdb.m
...............\...\net_init_cifar_slow.m
...............\...\test_im.JPG
...............\CoreModules
...............\...........\Main_Template.m
...............\...........\SwitchProcessor.m
...............\...........\TrainingScript.m
...............\...........\adagrad.m
...............\...........\adam.m
...............\...........\average_gradients_in_frames.m
...............\...........\bnorm.m
...............\...........\dropout.m
...............\...........\error_multiclass.m
...............\...........\fast_conv_layer.m
...............\...........\fast_mlp_layer.m
...............\...........\flipall.m
...............\...........\generate_output_filename.m
...............\...........\im2col_ln.m
...............\...........\lrn.m
...............\...........\maxpool.m
...............\...........\net_bp.m
...............\...........\net_ff.m
...............\...........\pad_data.m
...............\...........\relu.m
...............\...........\rmsprop.m
...............\...........\select_learning_rate.m
...............\...........\selective_sgd.m
...............\...........\sgd.m
...............\...........\sigmoid_ln.m
...............\...........\softmax.m
...............\...........\softmaxlogloss.m
...............\...........\tanh_ln.m
...............\...........\test_net.m
...............\...........\train_net.m
...............\ImageNetPreTrain.png
...............\License.txt
...............\LightNet.png
...............\Log.txt
...............\MLP
...............\...\Main_MNIST_MLP_Dropout.m
...............\...\Main_MNIST_MLP_RMSPROP.m
...............\...\PrepareData_MNIST_MLP.m
...............\...\get_mnist.m
...............\...\net_init_mlp_mnist.m
...............\...\net_init_mlp_mnist_dropout.m
...............\README.md
...............\RNN
...............\...\Main_Char_RNN.m
...............\...\gru_bp.m
...............\...\gru_ff.m
...............\...\lm_data
...............\...\.......\PrepareData_Char_RNN.m
...............\...\.......\dict.txt
...............\...\.......\test_x.txt
...............\...\.......\test_y.txt
...............\...\.......\train_x.txt
...............\...\.......\train_y.txt
...............\...\lstm_bp.m
...............\...\lstm_ff.m
...............\...\net_init_char_gru.m
...............\...\net_init_char_lstm.m
...............\...\net_init_char_rnn.m
...............\...\rnn_bp.m
...............\...\rnn_ff.m
...............\...\test_rnn.m
...............\...\train_rnn.m
...............\ReinforcementLearning
...............\.....................\Cart_Pole.m
...............\.....................\Main_Cart_Pole_Q_Network.m
...............\.....................\Random_Pole_Cart.m
...............\.....................\is_valid_state.m
...............\.....................\net_init_pole.m
...............\.....................\plot_Cart_Pole.m
...............\.....................\plotcircle.m
...............\.....................\prob_push_right.m
...............\RunAll.m
...............\coco.png
...............\lightnet-intro.pdf
...............\lightnet-supplementary-materials.pdf