7130245fb45db86263963fe237b40ce1f5b06c73,model/sentiment_classifier.py,MultiLayerBinaryClassifier,forward,#MultiLayerBinaryClassifier#Any#,106

Before Change

        print("init MultiLayerBinaryClassifier with layers %s and dropout %s" % (self.layer_sizes, self.dropout))

    def forward(self, X, **kwargs):
        return (self.model(X).float())

    // HACK -- parameter to measure *variation* between last layer of the MLP.
    // Why? To support multihead -- for the same category, where we want multiple heads to predict with different functions
    // (similar to training a mixture of models) -- useful for uncertainty sampling

After Change

        clf_out = self.model(X).float()
        if self.heads_per_class <= 1:
            return clf_out
        clf_out = clf_out.view(clf_out.size(0), -1, self.heads_per_class)
        probs = clf_out
        if self.softmax:
            probs = F.softmax(probs, 1)
        clf_mean = probs.mean(dim=2)
        clf_std = probs.std(dim=2)
        return clf_out, clf_mean, clf_std

    // HACK -- parameter to measure *variation* between last layer of the MLP.
    // Why? To support multihead -- for the same category, where we want multiple heads to predict with different functions
    // (similar to training a mixture of models) -- useful for uncertainty sampling

Instances