def Evaluate(self, sess):
    pass
    "\n        with open(evaluate_file, 'rb') as f:\n           history, true_utt, labels = pickle.load(f)\n        self.all_candidate_scores = []\n        history, history_len = utils.multi_sequences_padding(history, self.max_sentence_len)\n        history, history_len = np.array(history), np.array(history_len)\n        true_utt_len = np.array(utils.get_sequences_length(true_utt, maxlen=self.max_sentence_len))\n        true_utt = np.array(pad_sequences(true_utt, padding='post', maxlen=self.max_sentence_len))\n        low = 0\n        while True:\n            feed_dict = {\n                self.utterance_ph: np.concatenate([history[low:low + 200]], axis=0),\n                self.all_utterance_len_ph: np.concatenate([history_len[low:low + 200]], axis=0),\n                self.response_ph: np.concatenate([true_utt[low:low + 200]], axis=0),\n                self.response_len: np.concatenate([true_utt_len[low:low + 200]], axis=0),\n            }\n            candidate_scores = sess.run(self.y_pred, feed_dict=feed_dict)\n            self.all_candidate_scores.append(candidate_scores[:, 1])\n            low = low + 200\n            if low >= history.shape[0]:\n                break\n        all_candidate_scores = np.concatenate(self.all_candidate_scores, axis=0)\n        evaluate.ComputeR10_1(all_candidate_scores,labels)\n        evaluate.ComputeR2_1(all_candidate_scores,labels)\n        "
def Evaluate(self, sess):
    pass
    "\n        with open(evaluate_file, 'rb') as f:\n           history, true_utt, labels = pickle.load(f)\n        self.all_candidate_scores = []\n        history, history_len = utils.multi_sequences_padding(history, self.max_sentence_len)\n        history, history_len = np.array(history), np.array(history_len)\n        true_utt_len = np.array(utils.get_sequences_length(true_utt, maxlen=self.max_sentence_len))\n        true_utt = np.array(pad_sequences(true_utt, padding='post', maxlen=self.max_sentence_len))\n        low = 0\n        while True:\n            feed_dict = {\n                self.utterance_ph: np.concatenate([history[low:low + 200]], axis=0),\n                self.all_utterance_len_ph: np.concatenate([history_len[low:low + 200]], axis=0),\n                self.response_ph: np.concatenate([true_utt[low:low + 200]], axis=0),\n                self.response_len: np.concatenate([true_utt_len[low:low + 200]], axis=0),\n            }\n            candidate_scores = sess.run(self.y_pred, feed_dict=feed_dict)\n            self.all_candidate_scores.append(candidate_scores[:, 1])\n            low = low + 200\n            if low >= history.shape[0]:\n                break\n        all_candidate_scores = np.concatenate(self.all_candidate_scores, axis=0)\n        evaluate.ComputeR10_1(all_candidate_scores,labels)\n        evaluate.ComputeR2_1(all_candidate_scores,labels)\n        "