是否可以使用`sklearn`重新训练保存的神经网络

Question

I'm working on a project that looks to classify tweets and am using sklearn 's neural network model . Is it possible to retrain this using sklearn and if so please guide me in the right direction. Also, is it worth it to retrain a model or should I just adjust values when constructing a network.

Answer 1

You may try the following.

from sklearn.externals import joblib
##Suppose your trained model is named MyTrainedModel
##This is how you save it in a file called MyTrainedModelFile.txt.
joblib.dump(MyTrainedModel, 'MyTrainedModelFile.txt')
##Later you can recall the model and use it
Loaded_model = joblib.load('MyTrainedModelFile.txt')

The tutorial is here .

Please let me know if this is what you wanted.

Answer 2

You could very well do that using the partial_fit method that MLPClasifier offers. I have written a sample code for doing it. You could very well retrain your saved model if you get data in batches and training is a costy operation for you so can't afford to train on the entire dataset each and every time you get a new batch of data.

import pickle
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_classes=4, n_features=11,
                       n_informative=4, weights=[0.25,0.25,0.25,0.25],
                       random_state=0)

x_batch1 = X[0:500]
y_batch1 = y[0:500]

x_batch2 = X[500:999]
y_batch2 = y[500:999]

clf = MLPClassifier()
clf.partial_fit(x_batch1, y_batch1, classes = np.unique(y))  # you need to pass the classes when you fit for the first time


pickle.dump(clf, open("MLP_classifier", 'wb'))
restored_clf = pickle.load(open("MLP_classifier", 'rb'))

restored_clf.partial_fit(x_batch2, y_batch2)

Hope this helps!