9e49dce4a0b35b7daba7fa5d4ed529e36979a19a,src/classifiers/keras.py,KerasClassifier,predict,#KerasClassifier#Any#Any#,97

Before Change

        preds = self._preds([inputs])[0]
        if not logits:
            // TODO check axis and shapes
            preds = np.exp(preds) / np.sum(np.exp(preds), axis=0)
        return preds

    def fit(self, inputs, outputs, batch_size=128, nb_epochs=20):
        

After Change

        k.set_learning_phase(0)
        preds = self._preds([inputs])[0]
        if not logits:
            exp = np.exp(preds - np.max(preds, axis=1, keepdims=True))
            preds = exp / np.sum(exp, axis=1, keepdims=True)

        return preds

    def fit(self, inputs, outputs, batch_size=128, nb_epochs=20):

Instances