last_state = hidden_states[-1]

这是一个在神经网络中处理序列数据时常用的代码片段。其中，hidden_states是一个保存了序列数据中每个时刻的隐藏状态的列表，最后一个元素hidden_states[-1]代表了序列数据的最后一个时刻的隐藏状态。在一些需要处理序列数据的任务中，比如自然语言处理中的情感分析、语言模型等任务中，可以使用最后一个时刻的隐藏状态来表示整个序列的特征，进而将其用于分类、预测等任务。

相关问题

上述代码报错了，报错信息：Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 327, in <module> similarities = cosine_similarity(vectors, np.array([tokenizer.encode(word, return_tensors='pt')[0].numpy() for word in seed_words])) ValueError: Incompatible dimension for X and Y matrices: X.shape[1] == 768 while Y.shape[1] == 4 怎么修改，给出修改代码

根据报错信息来看，似乎是因为种子词的编码长度不同导致的维度不匹配错误。你可以尝试使用`tokenizer.encode_plus()`方法来获取种子词的编码和相应的attention mask，然后将编码和attention mask传递给Bert模型，获取词向量。同时，由于种子词的数量较少，所以维度不对的问题可以通过扩展向量维度解决。

以下是修改后的代码：

from transformers import BertTokenizer, BertModel
import torch
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# 人工选取的隐私词列表
seed_words = ['个人信息', '身份证', '手机号码', '银行卡', '密码', '账号', '地址', '社保', '职业', '学历', '收入', '家庭情况']

# 加载Bert tokenizer和模型
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertModel.from_pretrained('bert-base-chinese')
model.eval()

# 读取微博博文文本语料
with open('weibo_corpus.txt', 'r', encoding='utf-8') as f:
    corpus = f.readlines()

# 对每个词汇获取词向量
vectors = []
for text in corpus:
    # 对文本进行tokenize和padding
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    # 获取Bert模型的输出
    with torch.no_grad():
        outputs = model(**inputs)
    # 获取隐层输出（即词向量）
    hidden_states = outputs[0]
    # 对所有隐层输出取平均得到文本向量
    text_vector = torch.mean(hidden_states, dim=1)
    # 将文本向量转换为numpy数组
    text_vector = text_vector.numpy()[0]
    # 将文本向量添加到向量列表中
    vectors.append(text_vector)

# 将种子词的词向量与所有词汇的词向量计算余弦相似度
similarities = cosine_similarity(vectors, np.array([model(tokenizer.encode_plus(word, return_tensors='pt')['input_ids'], attention_mask=tokenizer.encode_plus(word, return_tensors='pt')['attention_mask']).last_hidden_state[0].numpy() for word in seed_words]))

# 获取每个种子词的关联词汇
related_words = {}
for i, word in enumerate(seed_words):
    # 获取与该种子词余弦相似度大于阈值的词汇
    sim_words = [corpus[j] for j in range(len(corpus)) if similarities[j][i] > q]
    # 合并、去重
    related_words[word] = list(set(sim_words))

# 人工筛选
privacy_words = []
for word in seed_words:
    # 将种子词添加到结果列表中
    privacy_words.append(word)
    # 将关联词汇添加到结果列表中
    for related_word in related_words[word]:
        # 如果关联词汇与已添加的词汇重复，则不加入结果列表
        if related_word not in privacy_words:
            privacy_words.append(related_word)

# 对结果列表进行去重
privacy_words = list(set(privacy_words))

在修改后的代码中，我们使用`model()`方法获取种子词的词向量，同时使用`last_hidden_state`属性获取隐层输出，然后将隐层输出转换为numpy数组。此外，我们还将向量的维度从4扩展到768，以解决维度不匹配问题。

给我TRPO解决BipedalWalkerHardcore_v3的代码

TRPO（Trust Region Policy Optimization）是一种用于强化学习的优化算法，用于更新策略参数。下面是使用TRPO解决BipedalWalkerHardcore_v3的Python代码示例：

import gym
import numpy as np
import tensorflow as tf
from scipy import optimize

env = gym.make('BipedalWalkerHardcore-v3')

# 策略网络
class PolicyNet:
    def __init__(self, state_dim, action_dim, hidden_size):
        self.state = tf.placeholder(tf.float32, [None, state_dim])
        l1 = tf.layers.dense(self.state, hidden_size, tf.nn.relu)
        l2 = tf.layers.dense(l1, hidden_size, tf.nn.relu)
        self.action_mean = tf.layers.dense(l2, action_dim, tf.nn.tanh)
        self.action_std = tf.Variable(1.0, trainable=True)

        self.action = tf.placeholder(tf.float32, [None, action_dim])
        self.advantage = tf.placeholder(tf.float32, [None])

        normal_dist = tf.distributions.Normal(self.action_mean, self.action_std)
        log_prob = normal_dist.log_prob(self.action)
        loss = -tf.reduce_mean(log_prob * self.advantage)
        kl = tf.distributions.kl_divergence(normal_dist, normal_dist)
        self.kl_mean = tf.reduce_mean(kl)

        self.train_op = self._create_train_op(loss)

    def _create_train_op(self, loss):
        optimizer = tf.train.AdamOptimizer()
        grads_and_vars = optimizer.compute_gradients(loss)
        flat_grads = tf.concat([tf.reshape(g, [-1]) for g, _ in grads_and_vars], axis=0)
        var_shapes = [tf.reshape(v, [-1]).shape for _, v in grads_and_vars]
        var_sizes = [np.prod(s) for s in var_shapes]
        cum_sizes = np.cumsum([0] + var_sizes)
        flat_params = tf.concat([tf.reshape(v, [-1]) for _, v in grads_and_vars], axis=0)
        kl_grads = tf.gradients(self.kl_mean, grads_and_vars)
        kl_grads = [tf.reshape(g, [-1]) / tf.cast(tf.reduce_prod(s), tf.float32) for g, (s, _) in zip(kl_grads, var_shapes)]
        kl_grad = tf.concat(kl_grads, axis=0)
        grad_kl_grad = tf.reduce_sum(flat_grads * kl_grad)
        hessian_vector_product = tf.gradients(grad_kl_grad, flat_params)
        hessian_vector_product = tf.concat(hessian_vector_product, axis=0)
        grads_and_hvp = list(zip(hessian_vector_product, flat_params))
        flat_grad_hvp = tf.concat([tf.reshape(g, [-1]) for g, _ in grads_and_hvp], axis=0)
        fisher_vector_product = flat_grad_hvp + 0.1 * flat_params
        gradient = tf.stop_gradient(fisher_vector_product)
        learning_rate = tf.sqrt(0.01 / tf.norm(gradient))
        clipped_gradient = tf.clip_by_norm(gradient, 0.5)
        train_op = tf.assign_sub(flat_params, learning_rate * clipped_gradient)
        train_op = tf.group(*[tf.assign(v, p) for (v, _), p in zip(grads_and_vars, tf.split(flat_params, cum_sizes[1:-1]))])
        return train_op

    def get_action(self, state):
        return self.action_mean.eval(feed_dict={self.state: state.reshape(1, -1)})[0]

    def get_kl(self, state, action):
        return self.kl_mean.eval(feed_dict={self.state: state, self.action: action})

    def train(self, state, action, advantage):
        feed_dict = {self.state: state, self.action: action, self.advantage: advantage}
        self.train_op.run(feed_dict=feed_dict)

# 值网络
class ValueNet:
    def __init__(self, state_dim, hidden_size):
        self.state = tf.placeholder(tf.float32, [None, state_dim])
        l1 = tf.layers.dense(self.state, hidden_size, tf.nn.relu)
        l2 = tf.layers.dense(l1, hidden_size, tf.nn.relu)
        self.value = tf.layers.dense(l2, 1)

        self.target_value = tf.placeholder(tf.float32, [None])
        loss = tf.reduce_mean(tf.square(self.value - self.target_value))
        self.train_op = tf.train.AdamOptimizer().minimize(loss)

    def get_value(self, state):
        return self.value.eval(feed_dict={self.state: state.reshape(1, -1)})[0, 0]

    def train(self, state, target_value):
        feed_dict = {self.state: state, self.target_value: target_value}
        self.train_op.run(feed_dict=feed_dict)

# 训练
def train():
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    hidden_size = 64

    policy_net = PolicyNet(state_dim, action_dim, hidden_size)
    value_net = ValueNet(state_dim, hidden_size)

    gamma = 0.99
    lam = 0.95
    batch_size = 2048
    max_step = 1000000
    render = False

    state = env.reset()
    for step in range(max_step):
        states = []
        actions = []
        rewards = []
        values = []

        for _ in range(batch_size):
            action = policy_net.get_action(state)
            next_state, reward, done, _ = env.step(action)
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            if done:
                values.append(0)
                state = env.reset()
            else:
                values.append(value_net.get_value(next_state))
                state = next_state
            if render:
                env.render()
        values = np.array(values)

        returns = np.zeros_like(rewards)
        advantages = np.zeros_like(rewards)
        last_return = 0
        last_value = 0
        last_advantage = 0
        for t in reversed(range(batch_size)):
            returns[t] = rewards[t] + gamma * last_return
            delta = rewards[t] + gamma * last_value - values[t]
            advantages[t] = delta + gamma * lam * last_advantage
            last_return = returns[t]
            last_value = values[t]
            last_advantage = advantages[t]

        advantages = (advantages - np.mean(advantages)) / np.std(advantages)
        policy_net.train(np.array(states), np.array(actions), advantages)
        value_net.train(np.array(states), returns)

        if step % 100 == 0:
            print('step=%d, reward=%f' % (step, np.mean(rewards)))
        if np.mean(rewards) > 300:
            render = True

train()

这段代码使用TensorFlow实现了一个策略网络和一个值网络，使用TRPO算法更新策略参数和值函数参数。在训练过程中，首先采集一定数量的数据，然后计算每个状态的回报和优势，并使用这些数据来更新策略网络和值网络。在每一步训练之后，打印出当前的平均回报。当平均回报超过300时，开始渲染环境。