Problem: feed-forward neural network - the connection between the hidden layer and output layer is removed.
if true load fisheriris feedforwardNetwork = feedforwardnet(10); feedforwardNetwork.divideFcn = 'dividetrain'; feedforwardNetwork.trainFcn = 'traingd'; feedforwardNetwork.trainParam.epochs = 10; feedforwardNetwork = train(feedforwardNetwork, meas'); end
John Michell answered . 2024-04-19 19:53:15
Your problem is that you did not do the following:
1. Identify the problem as one of the following
a. regression/curvefitting
b. classification/patternrecognition
c. clustering
d. time-series
2. Search both NEWSGROUP and ANSWERS using
a. classification
b. pattern-recognition
to identify
a. classification/pattern-recognition functions
(e.g., patternnet)
b. example classification/pattern-recognition code
and data examples
3. Practice using one or more of the MATLAB classification/...
pattern-recognition example data obtained from
help nndata
doc nndata
No. Create an input and target sets for 2 classes.
% As regard the default settings, I have just modified the % algorithm in order to have the classic gradient descent.
No. Always begin using as many defaults as possible. Then consider (one-at-a-time) replacing default settins.
% P.s. Little question: when there is no target data defined, % isn't the problem an unsupervised machine learning problem? % Theorically, I could do it (given that my data set is not a % one-class data set).
Your best bet is to create a 2-class dataset.
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