import tensorflow as tf
from tensorflow.keras import layers, Model
from tensorflow.keras.losses import BinaryCrossentropy

def create_base_network(input_shape):
    input = layers.Input(shape=input_shape)
    x = layers.Conv2D(64, (3, 3), activation='relu')(input)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = layers.Flatten()(x)
    x = layers.Dense(128, activation='relu')(x)
    return Model(input, x)

def create_siamese_network(input_shape):
    base_network = create_base_network(input_shape)
    
    input_a = layers.Input(shape=input_shape)
    input_b = layers.Input(shape=input_shape)
    
    processed_a = base_network(input_a)
    processed_b = base_network(input_b)
    
    distance = layers.Lambda(lambda tensors: tf.norm(tensors[0] - tensors[1], axis=1))([processed_a, processed_b])
    
    model = Model(inputs=[input_a, input_b], outputs=distance)
    return model

# 模型定义
input_shape = (64, 64, 3)
siamese_network = create_siamese_network(input_shape)
siamese_network.compile(loss=BinaryCrossentropy(from_logits=False), optimizer='adam')

# 训练示范
# siamese_network.fit([input_a, input_b], labels, epochs=50, batch_size=32)

import tensorflow as tf
from tensorflow.keras import layers, Model

def create_siamese_cnn(input_shape):
    input_a = layers.Input(shape=input_shape)
    input_b = layers.Input(shape=input_shape)

    base_cnn = tf.keras.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu'),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dense(128, activation='relu')
    ])

    encoded_a = base_cnn(input_a)
    encoded_b = base_cnn(input_b)

    # 计算欧几里得距离
    distance = layers.Lambda(lambda tensors: tf.sqrt(tf.reduce_sum(tf.square(tensors[0] - tensors[1]))))([encoded_a, encoded_b])
    
    model = Model(inputs=[input_a, input_b], outputs=distance)
    return model

siamese_cnn_model = create_siamese_cnn((28, 28, 1))
siamese_cnn_model.summary()

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, LSTM, Dense

def create_siamese_lstm(input_shape):
    input_a = Input(shape=input_shape)
    input_b = Input(shape=input_shape)

    lstm_layer = LSTM(64)

    encoded_a = lstm_layer(input_a)
    encoded_b = lstm_layer(input_b)

    distance = layers.Lambda(lambda tensors: tf.sqrt(tf.reduce_sum(tf.square(tensors[0] - tensors[1]))))([encoded_a, encoded_b])
    
    model = Model(inputs=[input_a, input_b], outputs=distance)
    return model

siamese_lstm_model = create_siamese_lstm((None, 100))  # 假设输入为变长的100维特征序列
siamese_lstm_model.summary()

from tensorflow.keras.layers import MultiHeadAttention

def create_siamese_transformer(input_shape):
    input_a = Input(shape=input_shape)
    input_b = Input(shape=input_shape)

    transformer_layer = MultiHeadAttention(num_heads=4, key_dim=64)

    encoded_a = transformer_layer(input_a, input_a)
    encoded_b = transformer_layer(input_b, input_b)

    distance = layers.Lambda(lambda tensors: tf.sqrt(tf.reduce_sum(tf.square(tensors[0] - tensors[1]))))([encoded_a, encoded_b])
    
    model = Model(inputs=[input_a, input_b], outputs=distance)
    return model

siamese_transformer_model = create_siamese_transformer((10, 64))  # 假设输入为10个时间步，每个特征64维
siamese_transformer_model.summary()

import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models import resnet50

# 使用预训练的 ResNeXt 作为特征提取器
def get_resnext_model():
    # 导入 ResNeXt 作为骨干网络
    backbone = torchvision.models.resnext50_32x4d(pretrained=True)
    # 去掉最后的全连接层，只保留特征提取部分
    backbone = torch.nn.Sequential(*list(backbone.children())[:-2])
  
    # 将 backbone 的输出通道求解
    out_channels = 2048  # ResNeXt50 输出通道数
    
    # 创建 Faster R-CNN 模型
    model = FasterRCNN(backbone, num_classes=91)  # COCO 数据集类别数为 91
    return model

# 初始化模型
model = get_resnext_model()
model.eval()  # 设置为评估模式

import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

# 数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

import torch
import torch.nn as nn
import torchvision.models as models

class ResNeXt(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNeXt, self).__init__()
        self.resnext = models.resnext50_32x4d(pretrained=True)  # 使用32组4个通道
        self.fc = nn.Linear(self.resnext.fc.in_features, num_classes)

    def forward(self, x):
        x = self.resnext(x)
        x = self.fc(x)
        return x

import torch.optim as optim

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNeXt().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练
for epoch in range(10):
    model.train()
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    
    print(f'Epoch [{epoch+1}/10], Loss: {loss.item():.4f}')

model.eval()
correct = 0
total = 0

with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy of the model on the test images: {100 * correct / total:.2f}%')

from keras.layers import Input, Conv2D, Conv2DTranspose, concatenate
from keras.models import Model

def build_generator(img_shape):
    input_img = Input(shape=img_shape)

    # 编码器
    down1 = Conv2D(64, (4, 4), strides=2, padding='same')(input_img)
    down2 = Conv2D(128, (4, 4), strides=2, padding='same')(down1)

    # 解码器
    up1 = Conv2DTranspose(64, (4, 4), strides=2, padding='same')(down2)
    merge1 = concatenate([up1, down1])
    up2 = Conv2DTranspose(3, (4, 4), strides=2, padding='same')(merge1)

    model = Model(input_img, up2)
    return model

generator = build_generator((256, 256, 3))
generator.summary()

for epoch in range(num_epochs):
    for step, (real_x, real_y) in enumerate(dataset):
        # 生成假图像
        fake_y = generator(real_x)

        # 训练判别器
        with tf.GradientTape() as tape:
            real_logits = discriminator(real_x, real_y)
            fake_logits = discriminator(real_x, fake_y)
            d_loss = discriminator_loss(real_logits, fake_logits)
        grads = tape.gradient(d_loss, discriminator.trainable_variables)
        optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))

        # 训练生成器
        with tf.GradientTape() as tape:
            fake_y = generator(real_x)
            fake_logits = discriminator(real_x, fake_y)
            g_loss = generator_loss(fake_logits)
        grads = tape.gradient(g_loss, generator.trainable_variables)
        optimizer.apply_gradients(zip(grads, generator.trainable_variables))

    print(f'Epoch: {epoch}, D Loss: {d_loss.numpy()}, G Loss: {g_loss.numpy()}')

import torch
from torchvision import transforms
from PIL import Image

# 加载模型
generator = torch.load('pix2pix_generator.pth')

# 加载和预处理输入图像
input_image = Image.open('damaged_photo.jpg')
input_tensor = transforms.ToTensor()(input_image).unsqueeze(0)  # 添加批次维度

# 生成修复图像
with torch.no_grad():
    output_tensor = generator(input_tensor)

# 保存生成的图像
output_image = transforms.ToPILImage()(output_tensor.squeeze())
output_image.save('repaired_photo.jpg')

# 加载草图图像
input_sketch = Image.open('sketch.jpg')
input_tensor = transforms.ToTensor()(input_sketch).unsqueeze(0)

# 生成效果图
with torch.no_grad():
    output_tensor = generator(input_tensor)

# 保存生成的效果图
output_image = transforms.ToPILImage()(output_tensor.squeeze())
output_image.save('output_rendering.jpg')

# 加载MRI图像
input_mri = Image.open('mri_with_tumor.jpg')
input_tensor = transforms.ToTensor()(input_mri).unsqueeze(0)

# 生成分割图
with torch.no_grad():
    output_tensor = generator(input_tensor)

# 保存分割结果
output_image = transforms.ToPILImage()(output_tensor.squeeze())
output_image.save('tumor_segmented.jpg')

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # 定义网络结构
        self.model = nn.Sequential(
            # 定义卷积层，激活函数等
        )
        
    def forward(self, x):
        return self.model(x)

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        # 定义网络结构
        self.model = nn.Sequential(
            # 定义卷积层，激活函数等
        )
        
    def forward(self, x):
        return self.model(x)

# 实例化生成器和判别器
G = Generator()
F = Generator()
D_X = Discriminator()
D_Y = Discriminator()

# 伪代码示例
def train_cyclegan(epochs):
    for epoch in range(1, epochs + 1):
        for real_x, real_y in data_loader:
            # 更新判别器
            ...
            # 更新生成器
            ...

import torch
from torchvision import datasets, transforms
from cycle_gan import CycleGAN  # 假设cycle_gan.py包含CycleGAN实现

# 数据加载和预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(256),
    transforms.ToTensor(),
])
train_dataset_X = datasets.ImageFolder(root='path/to/real/images', transform=transform)
train_dataset_Y = datasets.ImageFolder(root='path/to/oil/paintings', transform=transform)

# 使用DataLoader加载数据
train_loader_X = torch.utils.data.DataLoader(dataset=train_dataset_X, batch_size=1, shuffle=True)
train_loader_Y = torch.utils.data.DataLoader(dataset=train_dataset_Y, batch_size=1, shuffle=True)

# 初始化CycleGAN
cycle_gan = CycleGAN()
cycle_gan.train(train_loader_X, train_loader_Y, num_epochs=200)

# 加载训练后的模型
cycle_gan.load_model('path/to/saved/model')

# 生成油画风格图像
sample_image = transforms.ToTensor()(Image.open('path/to/sample/real/image.jpg')).unsqueeze(0)
generated_image = cycle_gan.generate(sample_image, style='oil_painting')

# 保存生成的图像
generated_image.save('path/to/generated/oil_painting.jpg')

from keras.models import Sequential
from keras.layers import Conv2D, DepthwiseConv2D, GlobalAveragePooling2D, Dense

def lightweight_cnn(input_shape):
    model = Sequential()
    
    # Depthwise Separable Convolution
    model.add(DepthwiseConv2D(kernel_size=3, padding='same', input_shape=input_shape))
    model.add(Conv2D(filters=32, kernel_size=1, padding='same', activation='relu'))
    
    # Global Average Pooling
    model.add(GlobalAveragePooling2D())
    model.add(Dense(10, activation='softmax'))  # for 10 classes
    
    return model

# Example usage
input_shape = (224, 224, 3)
model = lightweight_cnn(input_shape)
model.summary()

import tensorflow as tf
from tensorflow.keras.applications import MobileNet
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 使用 MobileNet 模型
model = MobileNet(weights='imagenet')

# 数据预处理
datagen = ImageDataGenerator(rescale=1./255)
train_generator = datagen.flow_from_directory(
    'data/train',
    target_size=(224, 224),
    class_mode='categorical'
)

# 模型训练
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_generator, steps_per_epoch=len(train_generator), epochs=5)

import cv2
import numpy as np

# 加载 YOLO 模型
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]

# 进行目标检测
def detect_objects(image):
    height, width, channels = image.shape
    blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)
    
    # 解析检测结果
    # (此处省略解析代码，包含信心度、边框和类别)

# 加载图像并检测
image = cv2.imread("image.jpg")
detect_objects(image)

from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model

def lightweight_unet(input_shape):
    inputs = Input(shape=input_shape)
    # 编码器部分
    conv1 = Conv2D(32, 3, activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(64, 3, activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    # 解码器部分
    up1 = UpSampling2D(size=(2, 2))(pool2)
    merge1 = concatenate([up1, conv1], axis=3)
    conv3 = Conv2D(32, 3, activation='relu', padding='same')(merge1)

    outputs = Conv2D(1, 1, activation='sigmoid')(conv3)
    
    return Model(inputs, outputs)

# 创建模型
model = lightweight_unet((128, 128, 1))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

import tensorflow as tf
from tensorflow.keras import layers, Model

def lightweight_stn(input_shape):
    inputs = layers.Input(shape=input_shape)

    # 定位网络，这里使用简单的卷积和全连接层
    x = layers.Conv2D(16, (3, 3), padding='same', activation='relu')(inputs)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Conv2D(32, (3, 3), padding='same', activation='relu')(x)
    x = layers.GlobalAveragePooling2D()(x)
    loc = layers.Dense(6, activation='sigmoid')(x)  # 输出变换矩阵的参数

    # 生成网格
    grid = layers.Lambda(lambda x: tf.contrib.image.transform(x[0], x[1]))([inputs, loc])

    # 采样器
    output = layers.Conv2D(3, (3, 3), activation='sigmoid')(grid)

    return Model(inputs, output)

# 构建轻量化空间变换网络
model = lightweight_stn((64, 64, 3))
model.summary()

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import datasets

# 假设有一个简单的STN模块
class STN(nn.Module):
    # 定义STN中的网络结构
    pass

def preprocess_images(images):
    stn = STN()
    transformed_images = stn(images)  # 将图像传入STN进行空间变换
    return transformed_images

class FasterRCNNWithSTN(nn.Module):
    def __init__(self):
        super(FasterRCNNWithSTN, self).__init__()
        self.stn = STN()
        self.rcnn = FasterRCNN()  # 引入Faster R-CNN模型

    def forward(self, x):
        x = self.stn(x)  # 使用STN对输入图像进行预处理
        return self.rcnn(x)  # 将预处理后的图像传入Faster R-CNN