最近在学习BP神经网络，想要自定义神经网络内部，但是网上找了很久都没有找到源代码，于是自己去搬运了一波matlab官方的源代码。
不多说，直接上代码，未编辑过的源码：
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- function out1 = newff(varargin)
- %NEWFF Create a feed-forward backpropagation network.
- %
- % Obsoleted in R2010b NNET 7.0. Last used in R2010a NNET 6.0.4.
- % The recommended function is <a href="matlab:doc feedforwardnet">feedforwardnet</a>.
- %
- % Syntax
- %
- % net = newff(P,T,S)
- % net = newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF)
- %
- % Description
- %
- % 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.
- %
- % The transfer functions TF{i} can be any differentiable transfer
- % function such as TANSIG, LOGSIG, or PURELIN.
- %
- % The training function BTF can be any of the backprop training
- % functions such as TRAINLM, TRAINBFG, TRAINRP, TRAINGD, etc.
- %
- % *WARNING*: TRAINLM is the default training function because it
- % is very fast, but it requires a lot of memory to run. If you get
- % an "out-of-memory" error when training try doing one of these:
- %
- % (1) Slow TRAINLM training, but reduce memory requirements, by
- % setting NET.<a href="matlab:doc nnproperty.net_efficiency">efficiency</a>.<a href="matlab:doc nnproperty.net_efficiency_memoryReduction">memoryReduction</a> to 2 or more. (See HELP TRAINLM.)
- % (2) Use TRAINBFG, which is slower but more memory efficient than TRAINLM.
- % (3) Use TRAINRP which is slower but more memory efficient than TRAINBFG.
- %
- % The learning function BLF can be either of the backpropagation
- % learning functions such as LEARNGD, or LEARNGDM.
- %
- % The performance function can be any of the differentiable performance
- % functions such as MSE or MSEREG.
- %
- % Examples
- %
- % [inputs,targets] = simplefitdata;
- % net = newff(inputs,targets,20);
- % net = train(net,inputs,targets);
- % outputs = net(inputs);
- % errors = outputs - targets;
- % perf = perform(net,outputs,targets)
- %
- % Algorithm
- %
- % Feed-forward networks consist of Nl layers using the DOTPROD
- % weight function, NETSUM net input function, and the specified
- % transfer functions.
- %
- % The first layer has weights coming from the input. Each subsequent
- % layer has a weight coming from the previous layer. All layers
- % have biases. The last layer is the network output.
- %
- % Each layer's weights and biases are initialized with INITNW.
- %
- % Adaption is done with TRAINS which updates weights with the
- % specified learning function. Training is done with the specified
- % training function. Performance is measured according to the specified
- % performance function.
- %
- % See also NEWCF, NEWELM, SIM, INIT, ADAPT, TRAIN, TRAINS
- % Mark Beale, 11-31-97
- % Copyright 1992-2010 The MathWorks, Inc.
- %disp('NEWFF is no longer recommended. FEEDFORWARD is simpler and more efficient.');
- % TODO - Recommendation function NNRECOMMEND
- %% Boilerplate Code - Same for all Network Functions
- persistent INFO;
- if (nargin < 1), error(message('nnet:Args:NotEnough')); end
- in1 = varargin{1};
- if ischar(in1)
- switch in1
- case 'info',
- if isempty(INFO), INFO = get_info; end
- out1 = INFO;
- end
- else
- out1 = create_network(varargin{:});
- end
- %% Boilerplate Code - Same for all Network Functions
- %%
- function info = get_info
- info.function = mfilename;
- info.name = 'Feed-Forward';
- info.description = nnfcn.get_mhelp_title(mfilename);
- info.type = 'nntype.network_fcn';
- info.version = 6.0;
- %%
- function net = create_network(varargin)
- if nargin < 2, error(message('nnet:Args:NotEnough')), end
- v1 = varargin{1};
- if isa(v1,'cell'), v1 = cell2mat(v1); end
- v2 = varargin{2};
- if nargin > 2, v3 = varargin{3}; end
- if (nargin<= 6) && (size(v1,2)==2) && (~iscell(v2)) && (size(v2,1)==1) && ((nargin<3)||iscell(v3))
- nnerr.obs_use(mfilename,['See help for ' upper(mfilename) ' to update calls to the new argument list.']);
- net = new_5p0(varargin{:});
- else
- net = new_5p1(varargin{:});
- end
- %=============================================================
- function net = new_5p1(p,t,s,tf,btf,blf,pf,ipf,tpf,ddf)
- if nargin < 2, error(message('nnet:Args:NotEnough')), end
- % Defaults
- if (nargin < 3), s = []; end
- if (nargin < 4), tf = {}; end
- if (nargin < 5), btf = 'trainlm'; end
- if (nargin < 6), blf = 'learngdm'; end
- if (nargin < 7), pf = 'mse'; end
- if (nargin < 8), ipf = {'fixunknowns','removeconstantrows','mapminmax'}; end
- if (nargin < 9), tpf = {'removeconstantrows','mapminmax'}; end
- if (nargin < 10), ddf = 'dividerand'; end
- % Format
- if isa(p,'cell'), p = cell2mat(p); end
- if isa(t,'cell'), t = cell2mat(t); end
- % Error checking
- if ~(isa(p,'double') || isreal(p) || islogical(t))
- error(message('nnet:NNet:XNotLegal'))
- end
- if ~(isa(t,'double') || isreal(t) || islogical(t))
- error(message('nnet:NNet:TNotLegal'))
- end
- if isa(s,'cell')
- if (size(s,1) ~= 1)
- error(message('nnet:NNet:LayerSizes'))
- end
- for i=1:length(s)
- si = s{i};
- if ~isa(si,'double') || ~isreal(si) || any(size(si) ~= 1) || any(si<1) || any(round(si) ~= si)
- error(message('nnet:NNet:LayerSizes'))
- end
- end
- s = cell2mat(s);
- end
- if (~isa(s,'double')) || ~isreal(s) || (size(s,1) > 1) || any(s<1) || any(round(s) ~= s)
- error(message('nnet:NNet:LayerSizes'))
- end
- % Architecture
- Nl = length(s)+1;
- net = network;
- net.numInputs = 1;
- net.numLayers = Nl;
- net.biasConnect = ones(Nl,1);
- net.inputConnect(1,1) = 1;
- [j,i] = meshgrid(1:Nl,1:Nl);
- net.layerConnect = (j == (i-1));
- net.outputConnect(Nl) = 1;
- % Simulation
- net.inputs{1}.processFcns = ipf;
- for i=1:Nl
- if (i < Nl)
- net.layers{i}.size = s(i);
- if (Nl == 2)
- net.layers{i}.name = 'Hidden Layer';
- else
- net.layers{i}.name = ['Hidden Layer ' num2str(i)];
- end
- else
- net.layers{i}.name = 'Output Layer';
- end
- if (length(tf) < i) || all(isnan(tf{i}))
- if (i<Nl)
- net.layers{i}.transferFcn = 'tansig';
- else
- net.layers{i}.transferFcn = 'purelin';
- end
- else
- net.layers{i}.transferFcn = tf{i};
- end
- end
- net.outputs{Nl}.processFcns = tpf;
- % Adaption
- net.adaptfcn = 'adaptwb';
- net.inputWeights{1,1}.learnFcn = blf;
- for i=1:Nl
- net.biases{i}.learnFcn = blf;
- net.layerWeights{i,:}.learnFcn = blf;
- end
- % Training
- net.trainfcn = btf;
- net.dividefcn = ddf;
- net.performFcn = pf;
- % Initialization
- net.initFcn = 'initlay';
- for i=1:Nl
- net.layers{i}.initFcn = 'initnw';
- end
- % Configuration
- % Warning: Use of these properties is no longer recommended
- net.inputs{1}.exampleInput = p;
- net.outputs{Nl}.exampleOutput = t;
- % Initialize
- net = init(net);
- % Plots
- net.plotFcns = {'plotperform','plottrainstate','plotregression'};
- %================================================================
- function net = new_5p0(p,s,tf,btf,blf,pf)
- % Backward compatible to NNT 5.0
- if nargin < 2, error(message('nnet:Args:NotEnough')), end
- % Defaults
- Nl = length(s);
- if nargin < 3, tf = {'tansig'}; tf = tf(ones(1,Nl)); end
- if nargin < 4, btf = 'trainlm'; end
- if nargin < 5, blf = 'learngdm'; end
- if nargin < 6, pf = 'mse'; end
- % Error checking
- if isa(p,'cell') && all(size(p)==[1 1]), p = p{1,1}; end
- if (~isa(p,'double')) || ~isreal(p)
- error(message('nnet:NNData:XNotMatorCell1Mat'))
- end
- if isa(s,'cell')
- if (size(s,1) ~= 1)
- error(message('nnet:NNet:LayerSizes'))
- end
- for i=1:length(s)
- si = s{i};
- if ~isa(si,'double') || ~isreal(si) || any(size(si) ~= 1) || any(si<1) || any(round(si) ~= si)
- error(message('nnet:NNet:LayerSizes'))
- end
- end
- s = cell2mat(s);
- end
- if (~isa(s,'double')) || ~isreal(s) || (size(s,1) ~= 1) || any(s<1) || any(round(s) ~= s)
- error(message('nnet:NNet:LayerSizes'))
- end
- % Architecture
- net = network(1,Nl);
- net.biasConnect = ones(Nl,1);
- net.inputConnect(1,1) = 1;
- [j,i] = meshgrid(1:Nl,1:Nl);
- net.layerConnect = (j == (i-1));
- net.outputConnect(Nl) = 1;
- % Simulation
- for i=1:Nl
- net.layers{i}.size = s(i);
- net.layers{i}.transferFcn = tf{i};
- end
- % Performance
- net.performFcn = pf;
- % Adaption
- net.adaptfcn = 'adaptwb';
- net.inputWeights{1,1}.learnFcn = blf;
- for i=1:Nl
- net.biases{i}.learnFcn = blf;
- net.layerWeights{i,:}.learnFcn = blf;
- end
- % Training
- net.trainfcn = btf;
- % Initialization
- net.initFcn = 'initlay';
- for i=1:Nl
- net.layers{i}.initFcn = 'initnw';
- end
- % Warning: this property is no longer recommended for use
- net.inputs{1}.exampleInput = p;
- net = init(net);
- % Plots
- net.plotFcns = {'plotperform','plottrainstate','plotregression'};
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