How to set initial weights in MLPClassifier?

Question

I cannot find a way to set the initial weights of the neural network, could someone tell me how please? I am using python package sklearn.neural_network.MLPClassifier.

Here is the code for reference:

from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier(solver="sgd")
classifier.fit(X_train, y_train)

Answer 1

The docs show you the attributes in use.

Attributes:
...

coefs_ : list, length n_layers - 1 The ith element in the list represents the weight matrix corresponding to > layer i.

intercepts_ : list, length n_layers - 1 The ith element in the list represents the bias vector corresponding to layer > i + 1.

Just build your classifier clf=MLPClassifier(solver="sgd") and set coefs_ and intercepts_ before calling clf.fit().

The only remaining question is: does sklearn overwrite your inits?

The code looks like:

    if not hasattr(self, 'coefs_') or (not self.warm_start and not
                                       incremental):
        # First time training the model
        self._initialize(y, layer_units)

This looks to me like it won't replace your given coefs_ (you might check biases too).

The packing and unpacking functions further indicates that this should be possible. These are probably used for serialization through pickle internally.