A transfer learning Neural Network that uses features learnt from past training.
This is implementation of Layered Neural Network (LNN) first proposed in this MS Thesis
The difference between normal classifier and this one is this is not reset after every training.
Here every training task has a label (like class name).
Once a mapping from input to output is learnt, this is used as a feature for the subsequent task.
- Fit(X,Y, label) : Trains the model using data X for class named "label". Y is binary indicating presence/absence of class. X is a numpy matrix of size (samples, features), Y is a numpy array of integers with values 0,1. label is a string.
- Predict(X, label) : Predicts presence or absence of class "label" in X.
- Identify(X) : Guesses the best class for X
- Normalize input
- Create notebooks
- str
- plot classifier like scikit-learn
- naming nodes incrementally