I have defined a regressor as follows:
nn1 = Regressor(
layers=[
Layer("Rectifier", units=150),
Layer("Rectifier", units=100),
Layer("Linear")],
regularize="L2",
# dropout_rate=0.25,
learning_rate=0.01,
valid_size=0.1,
learning_rule="adagrad",
verbose=False,
weight_decay=0.00030,
n_stable=10,
f_stable=0.00010,
n_iter=200)
I am using this regressor in a k-fold cross-validation. In order for cross-validation to work properly and not learn from the previous folds, it's necessary that the regressor to be reset after each fold.
How can I reset the regressor object?
sklearn.base.clone should achieve what you're looking to achieve