import torch
import torch.nn as nn

class FeatureFusion(nn.Module):
    def __init__(self):
        super(FeatureFusion, self).__init__()
        self.conv1 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)  # 第一层
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) # 第二层
    
    def forward(self, x):
        x1 = self.conv1(x)  # 从第一层提取特征
        x2 = self.conv2(x)  # 从第二层提取特征
        fused = torch.cat((x1, x2), dim=1)  # 特征拼接
        return fused

model = FeatureFusion()
input_tensor = torch.randn(1, 32, 224, 224)
output = model(input_tensor)
print(f"输出特征图形状: {output.shape}")

class WeightedSumFusion(nn.Module):
    def __init__(self):
        super(WeightedSumFusion, self).__init__()
        self.conv1 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.alpha = 0.5  # 权重因子
        
    def forward(self, x):
        x1 = self.conv1(x)  
        x2 = self.conv2(x)  
        fused = self.alpha * x1 + (1 - self.alpha) * x2  # 加权求和
        return fused

model = WeightedSumFusion()
output = model(input_tensor)
print(f"输出特征图形状: {output.shape}")

class AttentionFusion(nn.Module):
    def __init__(self):
        super(AttentionFusion, self).__init__()
        self.conv1 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(64 * 2, 2)  # 压缩特征
        
    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(x)
        attention_weights = torch.softmax(self.fc(torch.flatten(torch.cat((x1, x2), dim=1), start_dim=1)), dim=1)  # 计算注意力权重
        fused = attention_weights[:, 0].view(-1, 64, 1, 1) * x1 + attention_weights[:, 1].view(-1, 64, 1, 1) * x2
        return fused

model = AttentionFusion()
output = model(input_tensor)
print(f"输出特征图形状: {output.shape}")

import cv2
import numpy as np
from keras.models import load_model

# 加载MobileNet预训练模型
model = load_model('mobilenet_face_recognition.h5')

# 初始化视频捕捉
video_capture = cv2.VideoCapture(0)

while True:
    # 捕获每帧
    ret, frame = video_capture.read()
    
    # 对图像预处理
    input_frame = cv2.resize(frame, (224, 224))
    input_frame = np.expand_dims(input_frame, axis=0) / 255.0

    # 预测
    predictions = model.predict(input_frame)
    # 这里假设predictions为1表示识别到人脸
    if predictions[0][1] > 0.5:
        print("人脸检测到!")

    # 显示结果
    cv2.imshow('Video', frame)
    
    # 按'q'键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
cv2.destroyAllWindows()

import torch
import torchvision.models as models

# 加载预训练的DenseNet模型
model = models.densenet121(pretrained=True)

# 将模型设置为评估模式
model.eval()

from torchvision.models.detection import fasterrcnn_resnet50_fpn

# 创建以DenseNet作为特征提取器的Faster R-CNN
class DenseNetFasterRCNN(torch.nn.Module):
    def __init__(self):
        super(DenseNetFasterRCNN, self).__init__()
        self.densenet = models.densenet121(pretrained=True)
        self.detector = fasterrcnn_resnet50_fpn(pretrained=True)

    def forward(self, images):
        features = self.densenet(images)
        detections = self.detector(features)
        return detections

# 实例化模型
model = DenseNetFasterRCNN()

import cv2

# 打开视频流
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 对视频帧进行处理
    image_tensor = preprocess(frame)  # 图像预处理函数
    detections = model(image_tensor)

    # 可视化检测结果
    visualize_detections(frame, detections)  # 可视化函数

    cv2.imshow('Real-time Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import tensorflow as tf
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据预处理
datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_directory(
    'data/medical_images/',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='training'
)

validation_generator = datagen.flow_from_directory(
    'data/medical_images/',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='validation'
)

# 使用DenseNet进行模型构建
model = tf.keras.Sequential([
    DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3)),
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(len(train_generator.class_indices), activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_generator, validation_data=validation_generator, epochs=10)

from keras.datasets import cifar10
from keras.utils import to_categorical

# 导入数据
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 构建DenseNet模型
from keras.applications import DenseNet121

model = DenseNet121(input_shape=(32, 32, 3), include_top=True, weights=None, classes=10)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 安装 YOLOv5
!git clone https://github.com/ultralytics/yolov5  # 克隆 YOLOv5 代码库
%cd yolov5
!pip install -r requirements.txt  # 安装依赖

import torch

# 使用 YOLOv5 的分割模型
segmentation_model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# 加载测试图像
img = 'test.jpg'  # 输入待分割的图像

# 进行分割推理
results = segmentation_model(img)

# 展示结果
results.show()
# 另存结果
results.save()

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

def build_segnet(input_shape):
    inputs = layers.Input(shape=input_shape)
    
    # Encoder
    encoder = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    encoder = layers.MaxPooling2D((2, 2))(encoder)
    encoder = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(encoder)
    encoder = layers.MaxPooling2D((2, 2))(encoder)
    
    # Bottleneck
    bottleneck = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(encoder)
    
    # Decoder
    decoder = layers.Conv2DTranspose(128, (3, 3), activation='relu', padding='same')(bottleneck)
    decoder = layers.UpSampling2D((2, 2))(decoder)
    decoder = layers.Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(decoder)
    decoder = layers.UpSampling2D((2, 2))(decoder)
    
    outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder)  # Assume binary segmentation
    
    model = models.Model(inputs, outputs)
    return model

# Example usage
model = build_segnet((128, 128, 3))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()

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

def build_segnet(input_shape):
    inputs = Input(shape=input_shape)
    # Encoder
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    # Bottleneck
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)
    
    # Decoder
    up1 = UpSampling2D(size=(2, 2))(conv3)
    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(up1)

    up2 = UpSampling2D(size=(2, 2))(conv4)
    conv5 = Conv2D(64, (3, 3), activation='relu', padding='same')(up2)

    outputs = Conv2D(1, (1, 1), activation='sigmoid')(conv5)
    
    model = Model(inputs=inputs, outputs=outputs)
    return model

segnet_model = build_segnet((256, 256, 3))
segnet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 条件变分自编码器的简单实现示例
import torch
import torch.nn as nn

class ConditionalVAE(nn.Module):
    def __init__(self, input_dim, latent_dim, num_classes):
        super(ConditionalVAE, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_dim + num_classes, 128),
            nn.ReLU(),
            nn.Linear(128, 2 * latent_dim)  # 输出均值和方差
        )
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim + num_classes, 128),
            nn.ReLU(),
            nn.Linear(128, input_dim),
            nn.Sigmoid()
        )

    def encode(self, x, c):
        h = torch.cat((x, c), dim=1)
        z_params = self.encoder(h)
        mu, logvar = z_params.chunk(2, dim=1)  # 将均值和方差分开
        return mu, logvar

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def decode(self, z, c):
        h = torch.cat((z, c), dim=1)
        return self.decoder(h)

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

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

cifar10_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
data_loader = DataLoader(cifar10_dataset, batch_size=64, shuffle=True)

import torch.optim as optim

def loss_function(recon_x, x, mu, logvar):
    BCE = nn.functional.binary_cross_entropy(recon_x, x, reduction='sum')
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
    return BCE + KLD

# 初始化模型和优化器
model = ConditionalVAE(input_dim=3072, latent_dim=32, num_classes=10).to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# 训练循环
for epoch in range(num_epochs):
    model.train()
    for data, labels in data_loader:
        optimizer.zero_grad()
        mu, logvar = model.encode(data.view(-1, 3072).to(device), labels.to(device))
        z = model.reparameterize(mu, logvar)
        recon_batch = model.decode(z, labels.to(device))
        loss = loss_function(recon_batch, data.view(-1, 3072).to(device), mu, logvar)
        loss.backward()
        optimizer.step()

import numpy as np
from keras.preprocessing.image import ImageDataGenerator

# 使用数据增强
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

# 适配生成器
datagen.fit(train_images)

from keras.losses import MeanSquaredError
import keras.backend as K

def vae_loss(x, x_decoded_mean):
    recon_loss = MeanSquaredError()(x, x_decoded_mean)
    kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return K.mean(recon_loss + kl_loss)

from keras.layers import BatchNormalization, Dense, Input
from keras.models import Model

inputs = Input(shape=(original_dim,))
x = Dense(intermediate_dim, activation='relu')(inputs)
x = BatchNormalization()(x)  # 批归一化
z_mean = Dense(latent_dim)(x)
z_log_var = Dense(latent_dim)(x)

from keras.callbacks import EarlyStopping, ModelCheckpoint

early_stopping = EarlyStopping(monitor='val_loss', patience=5)
model_checkpoint = ModelCheckpoint('vae_best_model.h5', save_best_only=True)

model.fit(train_data, 
          epochs=100, 
          batch_size=32, 
          validation_data=(val_data),
          callbacks=[early_stopping, model_checkpoint])

# 生成表格和可视化效果
import matplotlib.pyplot as plt

decoded_images = vae.predict(test_data)
n = 10  # 显示10个手写数字
plt.figure(figsize=(20, 4))
for i in range(n):
    # 显示原始图像
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(test_data[i].reshape(28, 28))
    plt.gray()
    ax.axis('off')

    # 显示重构图像
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(decoded_images[i].reshape(28, 28))
    plt.gray()
    ax.axis('off')
plt.show()

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

# 加载Xception模型
model = Xception(weights='imagenet', include_top=False, input_shape=(299, 299, 3))

# 数据预处理
datagen = ImageDataGenerator(rescale=1.0/255.0, validation_split=0.2)
train_generator = datagen.flow_from_directory('path_to_data', target_size=(299, 299), subset='training')
validation_generator = datagen.flow_from_directory('path_to_data', target_size=(299, 299), subset='validation')

# 添加自定义的分类层
x = model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)

# 完成模型
model = tf.keras.models.Model(inputs=model.input, outputs=predictions)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_generator, validation_data=validation_generator, epochs=10)

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import cifar10

# 加载数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# 建立Xception模型
model = tf.keras.Sequential([
    tf.keras.applications.Xception(input_shape=(32, 32, 3), weights=None, classes=10)
])

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))

import tensorflow_hub as hub

# 定义Faster R-CNN并使用Xception作为特征提取器
detector = hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inference/1')

# 执行目标检测
def detect_objects(image):
    result = detector(image)
    return result

# 加载并处理图像
import cv2
image = cv2.imread('image.jpg')
detections = detect_objects(image)

import tensorflow as tf
from tensorflow.keras import layers

def build_unet_with_xception(input_size):
    inputs = layers.Input(input_size)
    x = tf.keras.applications.Xception(input_shape=input_size, include_top=False)(inputs)
    # 这里可以添加更多的层以构建U-Net结构
    return tf.keras.Model(inputs, x)

# 使用定义的U-Net模型
unet_model = build_unet_with_xception((128, 128, 3))
unet_model.summary()