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

# 定义生成器模型
def build_generator():
    model = keras.Sequential()
    model.add(layers.Dense(128, activation='relu', input_dim=100))
    model.add(layers.Dense(784, activation='sigmoid'))
    model.add(layers.Reshape((28, 28, 1)))  # 形状为28x28的图像
    return model

# 定义鉴别器模型
def build_discriminator():
    model = keras.Sequential()
    model.add(layers.Flatten(input_shape=(28, 28, 1)))
    model.add(layers.Dense(128, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))  # 输出一个0到1之间的概率值
    return model

# 构建和编译模型
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(optimizer='adam', loss='binary_crossentropy')

# GAN模型
discriminator.trainable = False
gan_input = layers.Input(shape=(100,))
generated_image = generator(gan_input)
gan_output = discriminator(generated_image)
gan = keras.Model(gan_input, gan_output)
gan.compile(optimizer='adam', loss='binary_crossentropy')

import tensorflow as tf
from tensorflow.keras import layers

# 构造生成器
def build_generator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(256, input_dim=100, activation='relu'))
    model.add(layers.BatchNormalization(momentum=0.8))
    model.add(layers.Dense(512, activation='relu'))
    model.add(layers.BatchNormalization(momentum=0.8))
    model.add(layers.Dense(1024, activation='relu'))
    model.add(layers.BatchNormalization(momentum=0.8))
    model.add(layers.Dense(28 * 28 * 1, activation='tanh'))
    model.add(layers.Reshape((28, 28, 1)))
    return model

# 构造判别器
def build_discriminator():
    model = tf.keras.Sequential()
    model.add(layers.Flatten(input_shape=(28, 28, 1)))
    model.add(layers.Dense(512, activation='relu'))
    model.add(layers.Dense(256, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))
    return model

import dlib
import numpy as np
from PIL import Image
from stylegan2_pytorch import ModelLoader

# 加载预训练的模型
model_loader = ModelLoader(base_dir='path_to_pretrained_models')
model = model_loader.load('stylegan2')

# 生成图像
latents = np.random.randn(1, 512)  # latent vector
img = model.generate(latents)

# 将生成的图像保存
Image.fromarray(((img + 1) * 127.5).astype(np.uint8)).save('generated_face.png')

import torch
from torchvision import transforms
from PIL import Image
from srgan_model import SRGAN  # 假设有一个SRGAN模型的实现

# 加载模型
model = SRGAN()
model.load_state_dict(torch.load('path_to_srgan_weights.pth'))
model.eval()

# 加载低分辨率图像
lr_image = Image.open('low_res_image.jpg')
lr_image = transforms.ToTensor()(lr_image).unsqueeze(0)  # 变为张量

# 使用模型生成高分辨率图像
with torch.no_grad():
    sr_image = model(lr_image)

# 保存高分辨率图像
output_image = transforms.ToPILImage()(sr_image.squeeze(0))
output_image.save('high_res_image.png')

from cycle_gan_model import CycleGAN  # 假设有一个CycleGAN模型的实现

# 加载模型
model = CycleGAN()
model.load_state_dict(torch.load('path_to_cyclegan_weights.pth'))
model.eval()

# 加载输入图像
input_image = Image.open('input_image.jpg')
input_image = transforms.ToTensor()(input_image).unsqueeze(0)

# 进行风格转换
with torch.no_grad():
    output_image = model(input_image)

# 保存转换后的图像
output_image = transforms.ToPILImage()(output_image.squeeze(0))
output_image.save('output_painting_style.png')

import numpy as np

def generator(z):
    # 假设这是一个简单的生成器函数
    # 其输出是经过处理的图像
    return np.tanh(np.dot(z, weights_g) + bias_g)

def discriminator(x):
    # 假设这是一个简单的判别器函数
    # 输出为该样本是“真实”的概率
    return sigmoid(np.dot(x, weights_d) + bias_d)

import torch
import torch.nn as nn
import torch.optim as optim

# 定义生成器
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(100, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, 28*28),
            nn.Tanh()
        )

    def forward(self, z):
        return self.model(z).view(-1, 1, 28, 28)

# 定义判别器
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28*28, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )

    def forward(self, img):
        return self.model(img)

# 初始化网络
generator = Generator()
discriminator = Discriminator()

# 定义损失函数
criterion = nn.BCELoss()

# 定义优化器
optimizer_g = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 假设 z 是从标准正态分布中随机采样的噪声
# 真实样本的标签是真实标签 1，生成样本的标签是假标签 0
z = torch.randn(64, 100)
real_samples = torch.randint(0, 2, (64, 1)).float()  # 假设这是从真实数据集中提取的真实样本

# 判别器的损失
D_real = discriminator(real_samples)
D_fake = discriminator(generator(z))
loss_d = criterion(D_real, torch.ones_like(D_real)) + criterion(D_fake, torch.zeros_like(D_fake))

# 生成器的损失
loss_g = criterion(D_fake, torch.ones_like(D_fake))  # 生成器希望 D_fake 接近 1

# 更新判别器和生成器的参数
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()

optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

# 生成器
def build_generator(latent_dim):
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, activation='relu', input_dim=latent_dim))
    model.add(layers.Dense(784, activation='sigmoid'))  # 输出28*28的扁平化图像
    return model

# 判别器
def build_discriminator(input_shape):
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, activation='relu', input_shape=input_shape))
    model.add(layers.Dense(1, activation='sigmoid'))  # 二元分类
    return model

# GAN模型
latent_dim = 100
generator = build_generator(latent_dim)
discriminator = build_discriminator((784,))
discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# GAN组件
discriminator.trainable = False
gan_input = layers.Input(shape=(latent_dim,))
fake_img = generator(gan_input)
gan_output = discriminator(fake_img)
gan_model = tf.keras.Model(gan_input, gan_output)
gan_model.compile(loss='binary_crossentropy', optimizer='adam')

# 训练过程
def train_gan(epochs, batch_size):
    for epoch in range(epochs):
        # 生成假样本
        noise = np.random.normal(0, 1, size=[batch_size, latent_dim])
        generated_images = generator.predict(noise)

        # 真实样本样本
        real_images = ...  # 从真实数据集中加载

        # 标签
        real_labels = np.ones(batch_size)
        fake_labels = np.zeros(batch_size)

        # 训练判别器
        d_loss_real = discriminator.train_on_batch(real_images, real_labels)
        d_loss_fake = discriminator.train_on_batch(generated_images, fake_labels)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # 训练生成器
        noise = np.random.normal(0, 1, size=[batch_size, latent_dim])
        g_loss = gan_model.train_on_batch(noise, real_labels)  # 使用真实的标签来欺骗判别器

        if epoch % 1000 == 0:
            print(f'Epoch {epoch}, D Loss: {d_loss[0]}, G Loss: {g_loss}')

# 设置训练参数并开始训练
train_gan(epochs=10000, batch_size=32)

# main.py
import torch

def check_cuda():
    if torch.cuda.is_available():
        print("CUDA is available! GPU will be used.")
    else:
        print("CUDA is not available. CPU will be used.")

if __name__ == "__main__":
    check_cuda()

# utils/data_loader.py

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

def get_mnist_loader(batch_size=64):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])

    mnist_data = MNIST(root='./data', train=True, download=True, transform=transform)
    mnist_loader = DataLoader(mnist_data, batch_size=batch_size, shuffle=True)
    
    return mnist_loader

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Reshape, LeakyReLU, BatchNormalization

def build_generator():
    model = Sequential()
    
    # 输入层：100维随机噪声
    model.add(Dense(256, input_dim=100))
    model.add(LeakyReLU(alpha=0.2))  # 使用Leaky ReLU
        
    model.add(Dense(512))              # 第二层：512个神经元
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())    # 批量归一化
     
    model.add(Dense(1024))             # 第三层：1024个神经元
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())
    
    model.add(Dense(28 * 28 * 1, activation='tanh'))  # 输出层，28x28的图像
    model.add(Reshape((28, 28, 1)))  # 重塑为28x28的形状
    
    return model

# 创建生成器模型
generator = build_generator()
generator.summary()  # 打印模型概述

import matplotlib.pyplot as plt

# 生成随机噪声
noise = np.random.normal(0, 1, (1, 100))

# 生成图像
generated_image = generator.predict(noise)

# 将值从[-1, 1]转换为[0, 1]
generated_image = (generated_image + 1) / 2.0

# 显示图像
plt.imshow(generated_image[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.show()

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

def build_discriminator(img_shape):
    model = models.Sequential()

    # 第一卷积层
    model.add(layers.Conv2D(64, kernel_size=3, strides=2, padding='same', input_shape=img_shape))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dropout(0.3))

    # 第二卷积层
    model.add(layers.Conv2D(128, kernel_size=3, strides=2, padding='same'))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dropout(0.3))

    # 第三卷积层
    model.add(layers.Conv2D(256, kernel_size=3, strides=2, padding='same'))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dropout(0.3))

    # 第四卷积层
    model.add(layers.Conv2D(512, kernel_size=3, strides=2, padding='same'))
    model.add(layers.LeakyReLU(alpha=0.2))
    model.add(layers.Dropout(0.3))

    # 展平层
    model.add(layers.Flatten())
    model.add(layers.Dense(1, activation='sigmoid'))  # 输出层

    return model

# 定义图像形状
img_shape = (64, 64, 3)
discriminator = build_discriminator(img_shape)
discriminator.summary()

import torch
from torchvision import datasets, transforms

# 数据预处理的转换流程
transform = transforms.Compose([
    transforms.ToTensor(),  # 将图像转换为Tensor
    transforms.Normalize((0.5,), (0.5,))  # 归一化，将数据标准化到[-1, 1]
])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)

transform = transforms.Compose([
    transforms.RandomRotation(10),  # 随机旋转
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 使用train_test_split将数据划分为训练集和验证集
from sklearn.model_selection import train_test_split

train_data, val_data = train_test_split(train_dataset, test_size=0.2, random_state=42)
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=64, shuffle=True)
val_loader = torch.utils.data.DataLoader(dataset=val_data, batch_size=64, shuffle=False)

for epoch in range(num_epochs):
    for i, (real_data, _) in enumerate(data_loader):
        # 生成器的训练
        noise = torch.randn(batch_size, z_dim)  # 噪声输入
        fake_data = generator(noise)             # 生成伪造数据
        d_loss, g_loss = train_gan(real_data, fake_data)

        # 打印损失值
        if (i+1) % log_interval == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{total_steps}], D Loss: {d_loss:.4f}, G Loss: {g_loss:.4f}')

import torch
import torch.nn as nn
import torch.optim as optim

# 伪造数据使用的网络
class Generator(nn.Module):
    # 定义生成器结构
    ...

class Discriminator(nn.Module):
    # 定义判别器结构
    ...

generator = Generator()
discriminator = Discriminator()

criterion = nn.BCELoss() 
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)

def train_gan(real_data, fake_data):
    # 判别器训练
    d_optimizer.zero_grad()

    # 标签设置
    real_labels = torch.ones(real_data.size(0), 1)  # 真实样本标签
    fake_labels = torch.zeros(fake_data.size(0), 1) # 伪造样本标签

    # 计算判别器对真实数据的损失
    outputs = discriminator(real_data)
    d_loss_real = criterion(outputs, real_labels)

    # 计算判别器对伪造数据的损失
    outputs = discriminator(fake_data.detach())
    d_loss_fake = criterion(outputs, fake_labels)

    # 反向传播并优化判别器
    d_loss = d_loss_real + d_loss_fake
    d_loss.backward()
    d_optimizer.step()

    # 生成器训练
    g_optimizer.zero_grad()

    # 生成器损失
    outputs = discriminator(fake_data)
    g_loss = criterion(outputs, real_labels)
    
    # 反向传播并优化生成器
    g_loss.backward()
    g_optimizer.step()

    return d_loss.item(), g_loss.item()

import torch
from torchvision import models

def calculate_fid(real_images, fake_images):
    # 使用Inception模型提取特征
    inception_model = models.inception_v3(pretrained=True, transform_input=True)
    real_features = inception_model(real_images).detach().numpy()
    fake_features = inception_model(fake_images).detach().numpy()

    # 计算均值和协方差
    mu1, sigma1 = real_features.mean(axis=0), np.cov(real_features, rowvar=False)
    mu2, sigma2 = fake_features.mean(axis=0), np.cov(fake_features, rowvar=False)

    # 计算FID
    fid_value = ... # 计算FID的公式
    return fid_value

for epoch in range(num_epochs):
    # ...训练代码...
    
    # 每10个周期评估一次
    if epoch % 10 == 0:
        fake_images = generator(noise)
        fid = calculate_fid(real_images, fake_images)
        print(f'Epoch {epoch}, FID: {fid}')