How do I operate fitnet function of Matlab by Python?
조회 수: 7 (최근 30일)
이전 댓글 표시
Hi. I'm a Japanese university student.
I'm researching AI and will be doing neural network analysis using MATLAB and Python.
Previously, We had been manually analyzing hundreds of data one by one using MATLAB's Neural Net Fitting APP (nftool), but now I would like to automate the process of starting MATLAB and analyzing the data using Python.
I would like to execute the following code in Python.
net = fitnet(10,'trainlm');
net = train(net, input, target);
output = net(input);
R = corrcoef(output, target);
R = (1,2)
I've written this in Python like below
import matlab.engine
eng = matlab.engine.start_matlab('-desktop')
eng.workspace['net'] = eng.fitnet(10.)
eng.net = eng.train(eng.net,input,target)
eng.workspace['output'] = eng.net(input)
R = eng.corrcoef(eng.output,eng,target)
Necessary data are loaded at workspace in Matlab by using another Python code.
At Running it, this error occurred.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [78], in <cell line: 3>()
1 import matlab.engine
2 eng = matlab.engine.start_matlab('-desktop')
----> 3 eng.workspace['net'] = eng.fitnet(10.)
File ~\anaconda3\envs\python_ex\lib\site-packages\matlabengineforpython-r2021a-py3.8.egg\matlab\engine\matlabengine.py:70, in MatlabFunc.__call__(self, *args, **kwargs)
68 return FutureResult(self._engine(), future, nargs, _stdout, _stderr, feval=True)
69 else:
---> 70 return FutureResult(self._engine(), future, nargs, _stdout,
71 _stderr, feval=True).result()
File ~\anaconda3\envs\python_ex\lib\site-packages\matlabengineforpython-r2021a-py3.8.egg\matlab\engine\futureresult.py:67, in FutureResult.result(self, timeout)
64 if timeout < 0:
65 raise TypeError(pythonengine.getMessage('TimeoutCannotBeNegative'))
---> 67 return self.__future.result(timeout)
File ~\anaconda3\envs\python_ex\lib\site-packages\matlabengineforpython-r2021a-py3.8.egg\matlab\engine\fevalfuture.py:82, in FevalFuture.result(self, timeout)
79 if not result_ready:
80 raise TimeoutError(pythonengine.getMessage('MatlabFunctionTimeout'))
---> 82 self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)
83 self._retrieved = True
84 return self._result
ValueError: MATLAB can return only 1-by-N and N-by-1 cell arrays.
This's the similar situation as this questioner.
According to this thread, fitnet returns a variable that Python cannot read, so it cannot process it and is causing this error, and I think so.
My Matlab's varsion is R2021a.
How should I code it to make it work correctly?
댓글 수: 0
채택된 답변
David Willingham
2022년 6월 24일
Hi,
Here is code in python that will call the neural network training:
import matlab.engine
eng = matlab.engine.start_matlab('-desktop')
data = eng.simplefit_dataset(nargout=2)
x = data[0][0]
t = data[1][0]
eng.workspace["x"] = x
eng.workspace["t"] = t
eng.evalc("net = fitnet(10.0);")
eng.evalc("net = train(net,x,t);")
eng.evalc("out = net(x);")
out = eng.workspace["out"]
R = eng.corrcoef(out,t)
If you want to test it from MATLAB you can run (where the code above is saved as 'mypythonscript.py':
pyrunfile('mypythonscript.py')
댓글 수: 1
David Willingham
2022년 6월 24일
Another option is to create functions in MATLAB (attached) which will minimise calls to evalc and then use the following python code to call them:
import matlab.engine
eng = matlab.engine.start_matlab('-desktop')
data = eng.simplefit_dataset(nargout=2)
x = data[0][0]
t = data[1][0]
hiddenSizes = 10.0
modelFilename = eng.myNNTrain(hiddenSizes, x, t)
out = eng.myNNPredict(x)
R = eng.corrcoef(out,t)
추가 답변 (1개)
David Willingham
2022년 6월 22일
Try changing this line in python:
eng.workspace['net'] = eng.fitnet(10.)
to
a = matlab.double(10)
eng.workspace['net'] = eng.fitnet(a)
Does this solve your error?
참고 항목
카테고리
Help Center 및 File Exchange에서 Sequence and Numeric Feature Data Workflows에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!