sklearn: How to reset a Regressor or classifier object in sknn

Question

I have defined a regressor as follows:

nn1 = Regressor(
layers=[
    Layer(\"Rectifier\", units=150),
    Layer(\"Rectifier\", units=100),
    Layer(\"Linear\")]         


        

Answer 1

sklearn.base.clone should achieve what you're looking to achieve

Answer 2

The pattern that I use for cross validation instantiates a new classifier for each training/test pair:

from sklearn.cross_validation import KFold

kf = KFold(len(labels),n_folds=5, shuffle=True)
for train, test in kf:
    clf = YourClassifierClass()
    clf.fit(data[train],labels[train])
    # Do evaluation with data[test] and labels[test]

You can save your current best classifier in a separate variable and access its parameters after cross validation (this is also useful if you want to try different parameters).