import numpy as np
from keras.models import Sequential
from keras.layers import Dense
# 假设有10个输入特征和3个输出类别  
input_dim = 10
num_classes = 3
# 创建DNN模型  
model = Sequential()
model.add(Dense(64, activatinotallow='relu', input_shape=(input_dim,)))
model.add(Dense(32, activatinotallow='relu'))
model.add(Dense(num_classes, activatinotallow='softmax'))
# 编译模型，选择优化器和损失函数  
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 假设有100个样本的训练数据和标签  
X_train = np.random.rand(100, input_dim)
y_train = np.random.randint(0, 2, size=(100, num_classes))
# 训练模型  
model.fit(X_train, y_train, epochs=10)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# 假设输入图像的形状是64x64像素，有3个颜色通道  
input_shape = (64, 64, 3)
# 创建CNN模型  
model = Sequential()
model.add(Conv2D(32, (3, 3), activatinotallow='relu', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activatinotallow='relu'))
model.add(Flatten())
model.add(Dense(128, activatinotallow='relu'))
model.add(Dense(num_classes, activatinotallow='softmax'))
# 编译模型，选择优化器和损失函数  
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 假设有100个样本的训练数据和标签  
X_train = np.random.rand(100, *input_shape)
y_train = np.random.randint(0, 2, size=(100, num_classes))
# 训练模型  
model.fit(X_train, y_train, epochs=10)

from keras.models import Sequential
from keras.layers import Conv2D, Add, Activation, BatchNormalization, Shortcut
def residual_block(input, filters):
x = Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x

from keras.models import Sequential
from keras.layers import LSTM, Dense
def lstm_model(input_shape, num_classes):
model = Sequential()
model.add(LSTM(units=128, input_shape=input_shape))  # 添加一个LSTM层  
model.add(Dense(units=num_classes, activatinotallow='softmax'))  # 添加一个全连接层  
return model

from gensim.models import Word2Vec  
from nltk.tokenize import word_tokenize  
from nltk.corpus import abc  
import nltk  
  
# 下载和加载abc语料库  
nltk.download('abc')  
corpus = abc.sents()  
  
# 将语料库分词并转换为小写  
sentences = [[word.lower() for word in word_tokenize(text)] for text in corpus]  
  
# 训练Word2Vec模型  
model = Word2Vec(sentences, vector_size=100, window=5, min_count=5, workers=4)  
  
# 查找词"the"的向量表示  
vector = model.wv['the']  
  
# 计算与其他词的相似度  
similarity = model.wv.similarity('the', 'of')  
  
# 打印相似度值  
print(similarity)

import torch  
import torch.nn as nn  
import torch.nn.functional as F  
  
class TransformerModel(nn.Module):  
    def __init__(self, vocab_size, embedding_dim, num_heads, num_layers, dropout_rate=0.5):  
        super(TransformerModel, self).__init__()  
        self.embedding = nn.Embedding(vocab_size, embedding_dim)  
        self.transformer = nn.Transformer(d_model=embedding_dim, nhead=num_heads, num_encoder_layers=num_layers, num_decoder_layers=num_layers, dropout=dropout_rate)  
        self.fc = nn.Linear(embedding_dim, vocab_size)  
      
    def forward(self, src, tgt):  
        embedded = self.embedding(src)  
        output = self.transformer(embedded)  
        output = self.fc(output)  
        return output

 pip install transformers

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
# 定义生成器和判别器网络结构  
class Generator(nn.Module):
def __init__(self, input_dim, output_dim):
super(Generator, self).__init__()
self.model = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Linear(128, output_dim),
nn.Sigmoid()
)
def forward(self, x):
return self.model(x)
class Discriminator(nn.Module):
def __init__(self, input_dim):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Linear(128, 1),
nn.Sigmoid()
)
def forward(self, x):
return self.model(x)
# 实例化生成器和判别器对象  
input_dim = 100  # 输入维度可根据实际需求调整  
output_dim = 784  # 对于MNIST数据集，输出维度为28*28=784  
gen = Generator(input_dim, output_dim)
disc = Discriminator(output_dim)
# 定义损失函数和优化器  
criterion = nn.BCELoss()  # 二分类交叉熵损失函数适用于GAN的判别器部分和生成器的logistic损失部分。但是，通常更常见的选择是采用二元交叉熵损失函数（binary cross

import torch  
import torch.nn as nn  
import torch.optim as optim  
  
# 假设我们有一个简单的Diffusion模型  
class DiffusionModel(nn.Module):  
    def __init__(self, input_dim, hidden_dim, num_timesteps):  
        super(DiffusionModel, self).__init__()  
        self.num_timesteps = num_timesteps  
        self.noises = nn.ModuleList([  
            nn.Linear(input_dim, hidden_dim),  
            nn.ReLU(),  
            nn.Linear(hidden_dim, input_dim)  
        ] for _ in range(num_timesteps))  
  
    def forward(self, x, t):  
        noise_prediction = self.noises[t](x)  
        return noise_prediction  
  
# 设置模型参数  
input_dim = 784  # 假设输入是28x28的灰度图像  
hidden_dim = 128  
num_timesteps = 1000  
  
# 初始化模型  
model = DiffusionModel(input_dim, hidden_dim, num_timesteps)  
  
# 定义损失函数和优化器  
criterion = nn.MSELoss()  
optimizer = optim.Adam(model.parameters(), lr=1e-3)

import torch  
from torch_geometric.datasets import Planetoid  
from torch_geometric.nn import GCNConv  
from torch_geometric.data import DataLoader  
import time  

# 加载Cora数据集  
dataset = Planetoid(root='/tmp/Cora', name='Cora')  

# 定义GNN模型  
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
        super(GNN, self).__init__()  
        self.conv1 = GCNConv(in_channels, hidden_channels)  
        self.conv2 = GCNConv(hidden_channels, out_channels)  

def forward(self, data):
        x, edge_index = data.x, data.edge_index  
        x = self.conv1(x, edge_index)  
        x = F.relu(x)  
        x = F.dropout(x, training=self.training)  
        x = self.conv2(x, edge_index)  
return F.log_softmax(x, dim=1)  

# 定义超参数和模型训练过程  
num_epochs = 1000
lr = 0.01
hidden_channels = 16
out_channels = dataset.num_classes  
data = dataset[0]  # 使用数据集中的第一个数据作为示例数据  
model = GNN(dataset.num_features, hidden_channels, out_channels)  
optimizer = torch.optim.Adam(model.parameters(), lr=lr)  
data = DataLoader([data], batch_size=1)  # 将数据集转换为DataLoader对象，以支持批量训练和评估  
model.train()  # 设置模型为训练模式  
for epoch in range(num_epochs):  
for data in data:  # 在每个epoch中遍历整个数据集一次  
        optimizer.zero_grad()  # 清零梯度  
        out = model(data)  # 前向传播，计算输出和损失函数值  
        loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])  # 计算损失函数值，这里使用负对数似然损失函数作为示例损失函数  
        loss.backward()  # 反向传播，计算梯度  
        optimizer.step()  # 更新权重参数

python
import numpy as np  
import tensorflow as tf  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import Dense, Dropout  
class DQN:  
def __init__(self, state_size, action_size):  
self.state_size = state_size  
self.action_size = action_size  
self.memory = np.zeros((MEM_CAPACITY, state_size * 2 + 2))  
self.gamma = 0.95
self.epsilon = 1.0
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
self.learning_rate = 0.005
self.model = self.create_model()  
def create_model(self):  
        model = Sequential()  
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))  
        model.add(Dense(24, activation='relu'))  
        model.add(Dense(self.action_size, activation='linear'))  
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))  
return model  
def remember(self, state, action, reward, next_state, done):  
self.memory[self.memory_counter % MEM_CAPACITY, :] = [state, action, reward, next_state, done]  
self.memory_counter += 1
def act(self, state):  
if np.random.rand() <= self.epsilon:
return np.random.randint(self.action_size)  
        act_values = self.model.predict(state)  
return np.argmax(act_values[0])  
def replay(self):  
        batch_size = 32
        start = np.random.randint(0, self.memory_counter - batch_size, batch_size)  
        sample = self.memory[start:start + batch_size]  
        states = np.array([s[0] for s in sample])  
        actions = np.array([s[1] for s in sample])  
        rewards = np.array([s[2] for s in sample])  
        next_states = np.array([s[3] for s in sample])  
        done = np.array([s[4] for s in sample])  
        target = self.model.predict(next_states)  
        target_q = rewards + (1 - done) * self.gamma * np.max(target, axis=1)  
        target_q = np.asarray([target_q[i] for i in range(batch_size)])  
        target = self.model.predict(states)  
        indices = np.arange(batch_size)  
for i in range(batch_size):  
if done[i]: continue  # no GAE calc for terminal states (if you want to include terminal states see line 84)  
            target[indices[i]] = rewards[i] + self.gamma * target_q[indices[i]]  # GAE formula line 84 (https://arxiv.org/pdf/1506.02438v5) instead of line 85 (https://arxiv.org/pdf/1506.02438v5) (if you want to include terminal states see line 84)  
            indices[i] += batch_size  # resets the indices for the next iteration (https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c.py#L173) (if you want to include terminal states see line 84)  
            target[indices[i]] = target[indices[i]]  # resets the indices for the next iteration (https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c.py#L173) (if you want to include terminal states see line 84) (https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c.py#L173)