def _pred(sentence, temperature=1): if len(sentence) < max_len: print('in def _pred,length error ') return sentence = sentence[-max_len:] x_pred = np.zeros((1, max_len, len(words))) for t, char in enumerate(sentence): x_pred[0, t, word2numF(char)] = 1. preds = model.predict(x_pred, verbose=0)[0] preds = np.asarray(preds).astype('float64') exp_preds = np.power(preds, temperature) # 计算所有备选输出文字概率`preds`的`temperature`次方 preds = exp_preds / np.sum(exp_preds) # 重新统计概率分布 pro = np.random.choice(range(len(preds)), 1, p=preds) # 根据新概率随机选择候选文字 next_index = int(pro.squeeze()) next_char = num2word[next_index] return next_char

这段代码是一个用于生成文本的函数 `_pred`，它接受一个字符串 `sentence` 和一个温度参数 `temperature` 作为输入，返回一个字符作为输出。这个函数的实现基于一个训练好的模型，并利用该模型对给定的 `sentence` 进行预测，得到下一个字符的概率分布。然后，使用温度参数 `temperature` 对概率分布进行调节，加入一定的随机性，最后根据新的概率分布随机选择一个候选字符作为输出，并将其返回。

相关问题

import ast from dataclasses import dataclass from typing import List import pandas as pd import json ["text", "六十一岁还能办什么保险"] @dataclass class FAQ: title: str sim_questions: List[str] answer: str faq_id: int ori_data = pd.read_csv('baoxianzhidao_filter.csv') data = [] exist_titles = set() for index, row in enumerate(ori_data.iterrows()): row_dict = row[1] title = row_dict['title'] if title not in exist_titles: data.append(FAQ(title=title, answer=row_dict['reply'], sim_questions=[title], faq_id=index)) exist_titles.add(title) from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks pipeline_ins = pipeline(Tasks.faq_question_answering, 'damo/nlp_mgimn_faq-question-answering_chinese-base') bsz = 32 all_sentence_vecs = [] batch = [] sentence_list = [faq.title for faq in data] for i,sent in enumerate(sentence_list): batch.append(sent) if len(batch) == bsz or (i == len(sentence_list)-1 and len(batch)>0): # if i == len(sentence_list)-1 and len(batch)>0: sentence_vecs = pipeline_ins.get_sentence_embedding(batch) all_sentence_vecs.extend(sentence_vecs) batch.clear() import faiss import numpy as np hidden_size = pipeline_ins.model.network.bert.config.hidden_size # hidden_size = pipeline_ins.model.bert.config.hidden_size index = faiss.IndexFlatIP(hidden_size) vecs = np.asarray(all_sentence_vecs, dtype='float32') index.add(vecs) from modelscope.outputs import OutputKeys def ask_faq(input, history=[]): # step1: get sentence vector of query query_vec = pipeline_ins.get_sentence_embedding([input])[0] query_vec = np.asarray(query_vec, dtype='float32').reshape([1, -1]) # step2: faq dense retrieval _, indices = index.search(query_vec, k=30) # step3: build support set support_set = [] for i in indices.tolist()[0]: faq = data[i] support_set.append({"text": faq.title, "label": faq.faq_id, "index": i}) # step4: faq ranking rst = pipeline_ins(input={"query_set": input, "support_set": support_set}) rst = rst[OutputKeys.OUTPUT][0][0] pred_label = rst['label'] pred_score = rst['score'] # get answer by faq_id pred_answer = "" pred_title = "" for faq in data: if faq.faq_id == pred_label: pred_answer = faq.answer pred_title = faq.title break history.append((f'{pred_answer}|(pred_title:{pred_title},pred_score:{pred_score:.3f})')) return history优化这段代码

这段代码是一个Python脚本，用于读取CSV文件中的保险相关问题和答案，构建一个FAQ对象（包含问题、答案、相似问题和FAQ ID），并使用modelscope库中的pipeline进行常见问题解答。其中用到了ast、dataclass、List、pandas和json等Python模块。具体实现过程请参考上述代码。

解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'

在 TensorFlow 2.0 中，`tf.nn.rnn_cell` 模块已经被移除了，因此 `tf.nn.rnn_cell.seq2seq` 也不再可用。相反，您可以使用 `tf.keras.layers` 中的相应函数来构建您的模型。下面是一个使用 `tf.keras` 实现 seq2seq 模型的示例：

import tensorflow as tf

# 定义编码器
class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
        super(Encoder, self).__init__()
        self.batch_sz = batch_sz
        self.enc_units = enc_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')

    def call(self, x, hidden):
        x = self.embedding(x)
        output, state = self.gru(x, initial_state = hidden)
        return output, state

    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.enc_units))

# 定义注意力层
class BahdanauAttention(tf.keras.layers.Layer):
    def __init__(self, units):
        super(BahdanauAttention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, query, values):
        # query: 上一时间步的隐藏状态，shape=(batch_size, hidden_size)
        # values: 编码器的输出，shape=(batch_size, max_length, hidden_size)
        hidden_with_time_axis = tf.expand_dims(query, 1)

        score = self.V(tf.nn.tanh(
            self.W1(values) + self.W2(hidden_with_time_axis)))

        # attention_weights shape == (batch_size, max_length, 1)
        attention_weights = tf.nn.softmax(score, axis=1)

        # context_vector shape after sum == (batch_size, hidden_size)
        context_vector = attention_weights * values
        context_vector = tf.reduce_sum(context_vector, axis=1)

        return context_vector, attention_weights

# 定义解码器
class Decoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
        super(Decoder, self).__init__()
        self.batch_sz = batch_sz
        self.dec_units = dec_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
        self.fc = tf.keras.layers.Dense(vocab_size)

        # 用于注意力
        self.attention = BahdanauAttention(self.dec_units)

    def call(self, x, hidden, enc_output):
        # enc_output shape == (batch_size, max_length, hidden_size)
        context_vector, attention_weights = self.attention(hidden, enc_output)

        # x shape after passing through embedding == (batch_size, 1, embedding_dim)
        x = self.embedding(x)

        # 将上一时间步的隐藏状态和注意力向量拼接起来作为输入传给 GRU
        x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)

        # 将拼接后的向量传给 GRU
        output, state = self.gru(x)

        # output shape == (batch_size * 1, hidden_size)
        output = tf.reshape(output, (-1, output.shape[2]))

        # output shape == (batch_size, vocab)
        x = self.fc(output)

        return x, state, attention_weights

# 定义损失函数和优化器
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')

def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)

    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask

    return tf.reduce_mean(loss_)

# 定义训练步骤
@tf.function
def train_step(inp, targ, enc_hidden):
    loss = 0

    with tf.GradientTape() as tape:
        enc_output, enc_hidden = encoder(inp, enc_hidden)

        dec_hidden = enc_hidden

        dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * BATCH_SIZE, 1)

        # teacher forcing - 将目标词作为下一个输入传给解码器
        for t in range(1, targ.shape[1]):
            # 将编码器的输出和上一时间步的隐藏状态传给解码器
            predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)

            loss += loss_function(targ[:, t], predictions)

            # 使用 teacher forcing
            dec_input = tf.expand_dims(targ[:, t], 1)

    batch_loss = (loss / int(targ.shape[1]))

    variables = encoder.trainable_variables + decoder.trainable_variables

    gradients = tape.gradient(loss, variables)

    optimizer.apply_gradients(zip(gradients, variables))

    return batch_loss

# 定义预测函数
def evaluate(sentence):
    attention_plot = np.zeros((max_length_targ, max_length_inp))

    sentence = preprocess_sentence(sentence)

    inputs = [tokenizer.word_index[i] for i in sentence.split(' ')]
    inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')
    inputs = tf.convert_to_tensor(inputs)

    result = ''

    hidden = [tf.zeros((1, units))]
    enc_out, enc_hidden = encoder(inputs, hidden)

    dec_hidden = enc_hidden
    dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)

    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)

        # 存储注意力权重以便后面制图
        attention_weights = tf.reshape(attention_weights, (-1, ))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        result += tokenizer.index_word[predicted_id] + ' '

        if tokenizer.index_word[predicted_id] == '<end>':
            return result, sentence, attention_plot

        # 将预测的 ID 作为下一个解码器输入的 ID
        dec_input = tf.expand_dims([predicted_id], 0)

    return result, sentence, attention_plot

在上面的代码中，我们使用了 `tf.keras.layers` 中的 `Embedding`、`GRU` 和 `Dense` 层来构建编码器和解码器，使用 `tf.keras.optimizers.Adam` 作为优化器，使用 `tf.keras.losses.SparseCategoricalCrossentropy` 作为损失函数。同时，我们还定义了一个 `BahdanauAttention` 层来实现注意力机制。