Train multiple neural networks in one Analysis?
The title basically says it all. I want to try different hyperparameters for my Neural Network (or algorithms in general). For some, like random forest, I can specify a list - e.g., max_depth. What I need is a queue of Neural Networks with different hyperparameters, so that I can start them in the evening and come back to the results in the morning.
How to do this?
Best Answer
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Hello,
It is not possible at the moment on the visual interface.
Instead, for hyperparameter optimization on neural networks, we invite you to code your own custom Python model (in the Analysis > Design > Algorithms section). For instance, for a neural network from scikit-learn (MLP), you can use this:
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
parameters={
'alpha': [1,10,0.1],
'activation': ["logistic", "relu"]
}
mlp = MLPClassifier()
clf = GridSearchCV(
estimator=mlp,
param_grid=parameters,
n_jobs=-1,
verbose=2,
cv=5
)Note that we are looking to integrate neural networks more deeply
into our product. We will keep you posted!Cheers,
Alex
Answers
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Thanks! I will try the custom version as soon as the current NN classifier is finished.
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Unfortunately, I get the following error:
NameError: name 'gridSearchCV' is not defined
--> Fixed by using GridSearchCV instead.
After fixing this, I now get:
get_params() must be called with MLPClassifier instance as first argument.
and I'm not really sure what to do about that. -
Yes, you first need to declare an instance of MLPClassifier: `mlp=MLPClassifier()` before it can be passed to GridSearchCV.