【AI大模型】如何让大模型变得更聪明？基于时代背景的思考

import random
from nltk.corpus import wordnet

def synonym_replacement(sentence, n):
    """
    使用同义词替换句子中的单词来进行数据增强。

    参数:
    sentence (str): 输入的句子。
    n (int): 要替换的单词数量。

    返回:
    str: 经过同义词替换后的句子。
    """
    words = sentence.split()  # 将句子拆分成单词列表
    new_words = words.copy()  # 复制一份新词列表
    # 选择句子中有同义词的单词，并随机打乱顺序
    random_word_list = list(set([word for word in words if wordnet.synsets(word)]))
    random.shuffle(random_word_list)
    num_replaced = 0  # 初始化替换计数

    for random_word in random_word_list:
        synonyms = wordnet.synsets(random_word)  # 获取该单词的所有同义词
        if len(synonyms) > 0:
            synonym = random.choice(synonyms).lemmas()[0].name()  # 随机选择一个同义词
            # 用选择的同义词替换句子中的该单词
            new_words = [synonym if word == random_word else word for word in new_words]
            num_replaced += 1  # 更新替换计数
        if num_replaced >= n:  # 如果达到替换数量，停止替换
            break
            
    return ' '.join(new_words)  # 返回替换后的句子

sentence = "The quick brown fox jumps over the lazy dog"
augmented_sentence = synonym_replacement(sentence, 2)  # 进行同义词替换
print(augmented_sentence)  # 输出替换后的句子

from transformers import BertTokenizer, BertModel
import torch

# 加载BERT的分词器和模型
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# 对输入句子进行编码
input_ids = tokenizer("The quick brown fox jumps over the lazy dog", return_tensors="pt")["input_ids"]
# 创建掩码标签
labels = tokenizer("The quick brown fox [MASK] over the lazy dog", return_tensors="pt")["input_ids"]

# 前向传播，计算损失和logits
outputs = model(input_ids, labels=labels)
loss = outputs.loss
logits = outputs.logits

print(f"Loss: {loss.item()}")  # 输出损失值

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.data import Data

# 定义图卷积网络模型
class GCN(torch.nn.Module):
    def __init__(self, num_node_features, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(num_node_features, 16)  # 第一层图卷积
        self.conv2 = GCNConv(16, num_classes)  # 第二层图卷积

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)  # 进行第一次卷积
        x = F.relu(x)  # 应用ReLU激活函数
        x = self.conv2(x, edge_index)  # 进行第二次卷积
        return F.log_softmax(x, dim=1)  # 输出分类结果

# 生成示例数据
num_nodes = 100
num_node_features = 3
num_classes = 2
x = torch.randn((num_nodes, num_node_features))  # 随机生成节点特征
edge_index = torch.randint(0, num_nodes, (2, num_nodes*2))  # 随机生成边索引
y = torch.randint(0, num_classes, (num_nodes,))  # 随机生成节点标签

data = Data(x=x, edge_index=edge_index, y=y)  # 构建图数据对象
model = GCN(num_node_features, num_classes)  # 初始化GCN模型
output = model(data)  # 前向传播，获得输出
print(output)  # 打印输出结果

import gym

# 创建CartPole环境
env = gym.make('CartPole-v1')
observation = env.reset()

for _ in range(1000):
    env.render()  # 渲染环境
    action = env.action_space.sample()  # 随机选择一个动作
    observation, reward, done, info = env.step(action)  # 执行动作
    if done:
        observation = env.reset()  # 重置环境

env.close()

from py2neo import Graph

# 连接到Neo4j数据库
# "bolt://localhost:7687" 是Neo4j数据库的Bolt协议URL
# auth=("neo4j", "password") 是Neo4j数据库的认证信息，用户名是 "neo4j"，密码是 "password"
graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))

# 定义一个Cypher查询
# 这个查询匹配具有Person标签的节点之间的KNOWS关系
# 并返回这些Person节点的名称
query = """
MATCH (n:Person)-[r:KNOWS]->(m:Person)
RETURN n.name AS name1, m.name AS name2
"""

# 执行查询并获得结果
results = graph.run(query)

# 遍历查询结果，逐行打印每对名字
# record["name1"] 和 record["name2"] 分别表示KNOWS关系的两端节点的名称
for record in results:
    print(record["name1"], "knows", record["name2"])

import torch
from torch.quantization import quantize_dynamic

model = torch.nn.Linear(5, 10)
quantized_model = quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)