8acacf7ecdddd031366f2c044bdc6ce5ba8e5a62,art/classifiers/mxnet.py,MXTextClassifier,predict_from_embedding,#MXTextClassifier#Any#Any#Any#,344

Before Change

        preds = np.zeros((x_emb.shape[0], self.nb_classes), dtype=np.float32)
        for b in range(int(np.ceil(x_emb.shape[0] / float(batch_size)))):
            begin, end = b * batch_size,  min((b + 1) * batch_size, x_emb.shape[0])
            preds[begin:end] = self._preds_from_embedding([x_emb[begin:end]])[0]

            if not logits:
                exp = np.exp(preds[begin:end] - np.max(preds[begin:end], axis=1, keepdims=True))
                preds[begin:end] = exp / np.sum(exp, axis=1, keepdims=True)

After Change

            if logits is False:
                preds = preds.softmax()

            results[begin:end] = preds.asnumpy()

        return results

    def to_embedding(self, x):

Instances