快速神经网络在R上的绑定-libfannRbinding.ppt
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1、快速神经网络在R上的绑定-libfannRbinding Still waters run deep.流静水深流静水深,人静心深人静心深 Where there is life,there is hope。有生命必有希望。有生命必有希望视神经传导分层Picture from Simon.ThorpeNerual NetworkActivation Functionlibfann 介绍主要特点:C开发、多层、易用、快速、多参数易调节 支持NN BP 多种方法(RPROP,Quickprop,Batch,Incremental)主要语言种类:C+、Java、Ada、PERL、PHP、Python、
2、Ruby、Visual Prolog 7计算相关的:Mathematica、Matlab、Octaverfann下载及安装获取软件包:https:/ 环境,gcc 开发工具环境安装命令R CMD INSTALL./fann.tgzrfann 之 R APIfann_createfann_SetOptfann_optsfann_Commitfann_trainfann_testfann_save fann_read fann_version_inforfann参数 1-2 network type LAYER|SHORTCUT Cant be set 3 network_type=FANN_NE
3、TTYPE_LAYER 5-6 ann type Simple|Cascade|Expand|Sparse For Create 9-Basic parameters-10 init_weight=Init 11 num_layers=2 12 13 num_input=2 14 num_output=1 15 num_neurons_hidden=3 16 desired_error=0.002000 17-Expand parameters-18 randomize_weight_high=0.350000 19 randomize_weight_low=0.350000 20 learn
4、ing_rate=0.700000 21 connection_rate=0.200000 22 bit_fail_limit=0.010000 23 max_epochs=10000 24 epochs_between_reports=1000 25 learning_momentum=0.700000 26-27 train algorithm:INCREMENTAL|BATCH|RPROP|QUICKPROP rfann参数 cont.30-31 error function:LINEAR|TANH 32 train_error_function=FANN_ERRORFUNC_TANH
5、33 34-35 stop function:MSE|BIT 36 train_stop_function=FANN_STOPFUNC_BIT 37 38-rprop parameters-39 quickprop_decay=-0.000100 40 quickprop_mu=1.750000 41 rprop_increase_factor=1.200000 42 rprop_decrease_factor=0.500000 43 rprop_delta_min=0.000000 44 rprop_delta_max=50.000000 45 rprop_delta_zero=0.0000
6、00 46 47 activation_steepness_hidden=2.000000 48 activation_steepness_output=4.000000 49-50 Options below LINEAR|THRESHOLD|THRESHOLD_SYMMETRIC 51 cont:SIGMOID|SIGMOID_STEPWISE|SIGMOID_SYMMETRIC|SIGMOID_SYMMETRIC_STEPWISE 52 cont:GAUSSIAN|GAUSSIAN_SYMMETRIC|GAUSSIAN_STEPWISE 53 cont:ELLIOT|ELLIOT_SYM
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