太强了！深度学习的Top10模型！

import tensorflow as tffrom tensorflow.keras.datasets import irisfrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense
# 加载鸢尾花数据集(x_train, y_train), (x_test, y_test) = iris.load_data()
# 对数据进行预处理y_train = tf.keras.utils.to_categorical(y_train)  # 将标签转换为one-hot编码y_test = tf.keras.utils.to_categorical(y_test)
# 创建神经网络模型model = Sequential([    Dense(64, activation='relu', input_shape=(4,)),  # 输入层，有4个输入节点    Dense(32, activation='relu'),  # 隐藏层，有32个节点    Dense(3, activation='softmax')  # 输出层，有3个节点（对应3种鸢尾花）])
# 编译模型model.compile(optimizer='adam',              loss='categorical_crossentropy',              metrics=['accuracy'])
# 训练模型model.fit(x_train, y_train, epochs=10, batch_size=32)
# 测试模型test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)print('Test accuracy:', test_acc)

import tensorflow as tffrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# 设置超参数input_shape = (28, 28, 1)  # 假设输入图像是28x28像素的灰度图像num_classes = 10  # 假设有10个类别
# 创建CNN模型model = Sequential()
# 添加卷积层，32个3x3的卷积核，使用ReLU激活函数model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
# 添加卷积层，64个3x3的卷积核，使用ReLU激活函数model.add(Conv2D(64, (3, 3), activation='relu'))
# 添加最大池化层，池化窗口为2x2model.add(MaxPooling2D(pool_size=(2, 2)))
# 将多维输入展平为一维，以便输入全连接层model.add(Flatten())
# 添加全连接层，128个神经元，使用ReLU激活函数model.add(Dense(128, activation='relu'))
# 添加输出层，10个神经元，使用softmax激活函数进行多分类model.add(Dense(num_classes, activation='softmax'))
# 编译模型，使用交叉熵作为损失函数，使用Adam优化器model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 打印模型结构model.summary()

from keras.models import Sequentialfrom 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 Add()([x, input])  # Add shortcut connection
# 构建ResNet模型model = Sequential()# 添加输入层和其他必要的层# ...# 添加残差块model.add(residual_block(previous_layer, filters=64))# 继续添加更多的残差块和其他层# ...# 添加输出层# ...
# 编译和训练模型# model.compile(...)# model.fit(...)

from keras.models import Sequentialfrom 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, activation='softmax'))  # 添加一个全连接层    return model

from gensim.models import Word2Vecfrom nltk.tokenize import word_tokenizeimport nltk
# 下载punkt分词模型nltk.download('punkt')
# 假设我们有一些文本数据sentences = [    "我喜欢吃苹果",    "苹果是我的最爱",    "我不喜欢吃香蕉",    "香蕉太甜了",    "我喜欢读书",    "读书让我快乐"]
# 对文本数据进行分词处理sentences = [word_tokenize(sentence) for sentence in sentences]
# 创建 Word2Vec 模型# 这里的参数可以根据需要进行调整model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
# 训练模型model.train(sentences, total_examples=model.corpus_count, epochs=10)
# 获取词向量vector = model.wv['苹果']
# 找出与“苹果”最相似的词similar_words = model.wv.most_similar('苹果')
print("苹果的词向量:", vector)print("与苹果最相似的词:", similar_words)

import torchimport torch.nn as nnimport torch.optim as optim#该示例仅用于说明Transformer的基本结构和原理。实际的Transformer模型（如GPT或BERT）要复杂得多，并且需要更多的预处理步骤，如分词、填充、掩码等。class Transformer(nn.Module):    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward=2048):        super(Transformer, self).__init__()        self.model_type = 'Transformer'        # encoder layers        self.src_mask = None        self.pos_encoder = PositionalEncoding(d_model, max_len=5000)        encoder_layers = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward)        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_encoder_layers)        # decoder layers        decoder_layers = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward)        self.transformer_decoder = nn.TransformerDecoder(decoder_layers, num_decoder_layers)        # decoder        self.decoder = nn.Linear(d_model, d_model)        self.init_weights()    def init_weights(self):        initrange = 0.1        self.decoder.weight.data.uniform_(-initrange, initrange)    def forward(self, src, tgt, teacher_forcing_ratio=0.5):        batch_size = tgt.size(0)        tgt_len = tgt.size(1)        tgt_vocab_size = self.decoder.out_features        # forward pass through encoder        src = self.pos_encoder(src)        output = self.transformer_encoder(src)        # prepare decoder input with teacher forcing        target_input = tgt[:, :-1].contiguous()        target_input = target_input.view(batch_size * tgt_len, -1)        target_input = torch.autograd.Variable(target_input)        # forward pass through decoder        output2 = self.transformer_decoder(target_input, output)        output2 = output2.view(batch_size, tgt_len, -1)        # generate predictions        prediction = self.decoder(output2)        prediction = prediction.view(batch_size * tgt_len, tgt_vocab_size)        return prediction[:, -1], predictionclass PositionalEncoding(nn.Module):    def __init__(self, d_model, max_len=5000):        super(PositionalEncoding, self).__init__()        # Compute the positional encodings once in log space.        pe = torch.zeros(max_len, d_model)        position = torch.arange(0, max_len).unsqueeze(1).float()        div_term = torch.exp(torch.arange(0, d_model, 2).float() *                             -(torch.log(torch.tensor(10000.0)) / d_model))        pe[:, 0::2] = torch.sin(position * div_term)        pe[:, 1::2] = torch.cos(position * div_term)        pe = pe.unsqueeze(0)        self.register_buffer('pe', pe)    def forward(self, x):        x = x + self.pe[:, :x.size(1)]        return x# 超参数d_model = 512nhead = 8num_encoder_layers = 6num_decoder_layers = 6dim_feedforward = 2048# 实例化模型model = Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward)# 随机生成数据src = torch.randn(10, 32, 512)tgt = torch.randn(10, 32, 512)# 前向传播prediction, predictions = model(src, tgt)
print(prediction)

import torchimport torch.nn as nnimport torch.optim as optimimport 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 torchimport torch.nn as nnimport torch.optim as optim
# 定义U-Net模型class UNet(nn.Module):    # ...省略模型定义...
# 定义Diffusion Modelclass DiffusionModel(nn.Module):    def __init__(self, unet):        super(DiffusionModel, self).__init__()        self.unet = unet
    def forward(self, x_t, t):        # x_t为当前时刻的数据，t为噪声水平        # 使用U-Net预测噪声水平        noise_pred = self.unet(x_t, t)        # 根据噪声水平生成数据        x_t_minus_1 = x_t - noise_pred * torch.sqrt(1 - torch.exp(-2 * t))        return x_t_minus_1
# 初始化模型和优化器unet = UNet()model = DiffusionModel(unet)optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练过程for epoch in range(num_epochs):    for x_real in dataloader:  # 从数据加载器中获取真实数据        # 前向过程        x_t = x_real  # 从真实数据开始        for t in torch.linspace(0, 1, num_steps):            # 添加噪声            noise = torch.randn_like(x_t) * torch.sqrt(1 - torch.exp(-2 * t))            x_t = x_t + noise * torch.sqrt(torch.exp(-2 * t))
            # 计算预测噪声            noise_pred = model(x_t, t)
            # 计算损失            loss = nn.MSELoss()(noise_pred, noise)
            # 反向传播和优化            optimizer.zero_grad()            loss.backward()            optimizer.step()

import torchfrom torch_geometric.nn import GCNConvfrom torch_geometric.data import Data
# 定义一个简单的图结构edge_index = torch.tensor([[0, 1, 1, 2],                           [1, 0, 2, 1]], dtype=torch.long)x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
data = Data(x=x, edge_index=edge_index)
# 定义一个简单的两层图卷积网络class Net(torch.nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = GCNConv(dataset.num_features, 16)        self.conv2 = GCNConv(16, dataset.num_classes)
    def forward(self, data):        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)        x = torch.relu(x)        x = torch.dropout(x, training=self.training)        x = self.conv2(x, edge_index)
        return torch.log_softmax(x, dim=1)
# 实例化模型、损失函数和优化器model = Net()criterion = torch.nn.NLLLoss()optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
# 训练模型model.train()for epoch in range(200):    optimizer.zero_grad()    out = model(data)    loss = criterion(out[data.train_mask], data.y[data.train_mask])    loss.backward()    optimizer.step()
# 在测试集上评估模型model.eval()_, pred = model(data).max(dim=1)correct = int((pred == data.y).sum().item())acc = correct / int(data.y.sum().item())print('Accuracy: {:.4f}'.format(acc))

import tensorflow as tfimport numpy as npimport randomimport gymfrom collections import deque
# 设置超参数BUFFER_SIZE = int(1e5)  # 经验回放存储的大小BATCH_SIZE = 64        # 每次从经验回放中抽取的样本数量GAMMA = 0.99           # 折扣因子TAU = 1e-3             # 目标网络更新的步长LR = 1e-3              # 学习率UPDATE_RATE = 10       # 每多少步更新一次目标网络
# 定义经验回放存储class ReplayBuffer:    def __init__(self, capacity):        self.buffer = deque(maxlen=capacity)
    def push(self, state, action, reward, next_state, done):        self.buffer.append((state, action, reward, next_state, done))
    def sample(self, batch_size):        return random.sample(self.buffer, batch_size)
# 定义DQN模型class DQN:    def __init__(self, state_size, action_size):        self.state_size = state_size        self.action_size = action_size        self.model = self._build_model()
    def _build_model(self):        model = tf.keras.Sequential()        model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))        model.add(tf.keras.layers.Dense(24, activation='relu'))        model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=LR))        return model
    def remember(self, state, action, reward, next_state, done):        self.replay_buffer.push((state, action, reward, next_state, done))
    def act(self, state):        if np.random.rand() <= 0.01:            return random.randrange(self.action_size)        act_values = self.model.predict(state)        return np.argmax(act_values[0])
    def replay(self, batch_size):        minibatch = self.replay_buffer.sample(batch_size)        for state, action, reward, next_state, done in minibatch:            target = self.model.predict(state)            if done:                target[0][action] = reward            else:                Q_future = max(self.target_model.predict(next_state)[0])                target[0][action] = reward + GAMMA * Q_future            self.model.fit(state, target, epochs=1, verbose=0)        if self.step % UPDATE_RATE == 0:            self.target_model.set_weights(self.model.get_weights())
    def load(self, name):        self.model.load_weights(name)
    def save(self, name):        self.model.save_weights(name)
# 创建环境env = gym.make('CartPole-v1')state_size = env.observation_space.shape[0]action_size = env.action_space.n
# 初始化DQN和回放存储dqn = DQN(state_size, action_size)replay_buffer = ReplayBuffer(BUFFER_SIZE)
# 训练过程total_steps = 10000for step in range(total_steps):    state = env.reset()    state = np.reshape(state, [1, state_size])    for episode in range(100):        action = dqn.act(state)        next_state, reward, done, _ = env.step(action)        next_state = np.reshape(next_state, [1, state_size])        replay_buffer.remember(state, action, reward, next_state, done)        state = next_state        if done:            break    if replay_buffer.buffer.__