如何在MATLAB中設置newff（神經網絡工具箱功能）？

Question

我運行我的簡單代碼後出現錯誤。由於

這裏是我的代碼

input_training_data = HandWritingData(random_list,:)'; %size = [256X788] 
target_training_data = Category(random_list,:)'; %size = [10x788] 
net = newff(minmax(input_training_data), [15 10], {'logsig','logsig'} , 'hardlim'); 



Error in ==> Neural_Network_code at 19 
net = newff(minmax(input_training_data), [15 10], 
{'logsig','logsig'} , 'hardlim'); %create a network 

Answer 1

有許多原因，爲什麼你會不想與newff工作，但RTFM：

newff創建前饋傳播網絡。

已在R2010b NNET 7.0中廢棄。最後在R2010a NNET 6.0.4中使用。 推薦的功能是feedforwardnet。

語法

net = newff(P,T,S) 
net = newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) 

說明

newff(P,T,S) takes, 
    P - RxQ1 matrix of Q1 representative R-element input vectors. 
    T - SNxQ2 matrix of Q2 representative SN-element target vectors. 
    Si - Sizes of N-1 hidden layers, S1 to S(N-1), default = []. 
     (Output layer size SN is determined from T.) 
and returns an N layer feed-forward backprop network. 

newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) takes optional inputs, 
    TFi - Transfer function of ith layer. Default is 'tansig' for 
     hidden layers, and 'purelin' for output layer. 
    BTF - Backprop network training function, default = 'trainlm'. 
    BLF - Backprop weight/bias learning function, default = 'learngdm'. 
    PF - Performance function, default = 'mse'. 
    IPF - Row cell array of input processing functions. 
     Default is {'fixunknowns','remconstantrows','mapminmax'}. 
    OPF - Row cell array of output processing functions. 
     Default is {'remconstantrows','mapminmax'}. 
    DDF - Data division function, default = 'dividerand'; 
and returns an N layer feed-forward backprop network. 

hardlim不是一個傳遞函數。使用trainbg的trainlm。

net = newff(minmax(input_training_data), [15 10], {'logsig','logsig'} , 'trainlm');