I am trying to create an MPLClassifier with predefined weights and biases so that I can save them to a file and then
If I train the.network like this:
import numpy as np
from sklearn.neural_network import MLPClassifier
data = np.load("data.npy")
labels = np.load("labels.npy")
clf = MLPClassifier()
clf.fit(data, labels)
np.save("weights.npy", clf.coefs_)
np.save("biases.npy", clf.intercepts_)
and then access the weights an biases like this:
import numpy as np
from sklearn.neural_network import MLPClassifier
weights = np.load("weights.npy")
biases = np.load("biases.npy")
I want to be able to create a new.network like:
clf = MLPClassifier(weights=weights, biases=biases)
As @Plagon commented, you can't create an MLPClassifier from weights and biases. Instead, you should import pickle and use it like:
with open("network.pkl", "wb") as network:
pickle.dump(clf, network)
and access it like:
with open("network.pkl", "wb") as network:
clf = pickle.load(network)
For more information on pickle you can go its documentation at https://docs.python.org/3/library/pickle.html .
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