Neural Network Input Size and Data Issue - HELP
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Hi,
I am trying to code a feed forward neural network to predict future significant wave heights given known previous wave height values.
I have two inputs to my network. These are the wave heights over a year for two weather buoys. One input for my network is the wave heights over a year for one weather buoy (8744 data values), and the other input is the wave height values over one year for the second buoy (again 8744 data values).
I am coming up with the error 'Number of inputs does not match net.numInputs.' Can someone help spot my mistake please?
I am not sure what value I should be using in 'net.inputs{1}.size'.
My code is as follows:
%Data Selection
X = xlsread('2014_comp.xlsx', 'F5:F8748');
Y = xlsread('2014_comp.xlsx', 'L5:L8748');
%Network Architecture
net = feedforwardnet; % create feed forward network
net.numInputs = 2; % set number of inputs
net.inputs{1}.size = 1; % set size of input 1
net.inputs{2}.size = 1; % set size of input 2
net.numLayers = 1; % add 1 layer to network
net.layers{1}.size = 10; % assign number of neurons in layer
net.inputConnect(1) = 1; % connect input 1 to layer 1
net.inputConnect(2) = 1; % connect input 2 to layer 1
net.biasConnect(1) = 1; % connect bias to layer 1
net.biases{1}.learnFcn = 'learnp'; % set bias learning function
net.biases{1}.initFcn = 'initzero'; % set bias init function
net.outputConnect(1) = 1; %connect layer 1 to output
net.layers{1}.transferFcn = 'tansig'; % set layer transfer function to hyperbolic tangent sigmoid
net.inputWeights{1}.initFcn = 'initzero'; % set input wieghts init function
net.inputWeights{1}.learnFcn = 'learnp'; % set input weight learning function
net.initFcn = 'initlay'; % set network init function
net.trainFcn = 'trainrp'; % set network training function
net.performFcn = 'mse'; % set network performance evaluation function
%Training/Testing/Validation Ratio
net.divideParam.trainRatio = 0.66; % set proportion of data for training
net.divideParam.valRatio = 0.17; % set proportion of data for validation
net.divideParam.testRatio = 0.17; % set proportion of data for testing
view(net)
%Network Training
net = train(net,X); %Train the network
Thanks, James
采纳的回答
Greg Heath
2016-2-11
You are making this harder than it is. For the 1st time through all you have to specify is
1. Type of network
a fitnet for regression on curve-fitting
b. patternnet for classification or pattern-recognition
c. timedelaynet, narnet or narxnet for timeseries prediction
(fitnet and patternnet are special cases of feedforwardnet which never
has to be called explicitly)
2. input % size(input) = [ I N } for N I-dimensional Inputs
3. target % size(target) = [ O N ] for N O-dimensional Output targets
4. Ntrials: The number of candidate designs with different random data
divisions and random initial weights.
Then modify the code in the help and/or doc documentation. For example, use the commands
help fitnet
and
doc fitnet
to obtain code for fitnet. Put this code in a loop for ntrial = 1: Ntrials for different random data divisions and random initial weights.
This is the simplest code that I would use:
[ x t ] = simplefit_dataset;
vart = var(t,1)% Reference MSE
net = fitnet;
rng('default') % Needed for design duplication
for i = 1:Ntrials
net = configure(net,x,t);
[ net tr y e ] = train(net,x,t);
NMSE(i,1) = mse(e)/ vart; % Normalized MSE
end
output = NMSE
All 10 designs will be terrific because this is a simple data set. For other data I usually try to find the minimum number of hidden nodes (H, fitnet(H)) that will yield NMSE <= 0.01 ( i.e., the net successfully models 99% of the target variance)
Hope this helps.
Thank you for formally accepting my answer
Greg
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