要通過代碼實現AI圖像生成，可以使用深度學習框架如TensorFlow、PyTorch或GANs等技術。下面是一個簡單的示例代碼，演示如何使用GANs生成手寫數字圖像：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
from torchvision.utils import save_image
import os# 數據預處理
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])# 加載MNIST數據集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)# 定義生成器和判別器網絡
class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.model = nn.Sequential(nn.Linear(100, 256),nn.ReLU(),nn.Linear(256, 784),nn.Tanh())def forward(self, x):return self.model(x)class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.model = nn.Sequential(nn.Linear(784, 256),nn.LeakyReLU(0.2),nn.Linear(256, 1),nn.Sigmoid())def forward(self, x):return self.model(x)# 初始化網絡和優化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
gen = Generator().to(device)
disc = Discriminator().to(device)
criterion = nn.BCELoss()
gen_optimizer = optim.Adam(gen.parameters(), lr=0.0002)
disc_optimizer = optim.Adam(disc.parameters(), lr=0.0002)# 訓練GANs模型
num_epochs = 50
for epoch in range(num_epochs):for i, data in enumerate(trainloader, 0):real_images, _ = datareal_images = real_images.view(real_images.size(0), -1).to(device)real_labels = torch.ones(real_images.size(0), 1).to(device)fake_labels = torch.zeros(real_images.size(0), 1).to(device)# 訓練判別器disc.zero_grad()real_outputs = disc(real_images)real_loss = criterion(real_outputs, real_labels)real_score = real_outputsz = torch.randn(real_images.size(0), 100).to(device)fake_images = gen(z)fake_outputs = disc(fake_images)fake_loss = criterion(fake_outputs, fake_labels)fake_score = fake_outputsd_loss = real_loss + fake_lossd_loss.backward()disc_optimizer.step()# 訓練生成器gen.zero_grad()z = torch.randn(real_images.size(0), 100).to(device)fake_images = gen(z)outputs = disc(fake_images)g_loss = criterion(outputs, real_labels)g_loss.backward()gen_optimizer.step()print('Epoch [%d/%d], Step [%d/%d], d_loss: %.4f, g_loss: %.4f, D(x): %.2f, D(G(z)): %.2f'% (epoch, num_epochs, i, len(trainloader), d_loss.item(), g_loss.item(), real_score.mean().item(), fake_score.mean().item()))if epoch % 10 == 0:if not os.path.exists('images'):os.mkdir('images')save_image(fake_images.view(fake_images.size(0), 1, 28, 28), 'images/{}.png'.format(epoch))

這段代碼實現了一個簡單的基于GANs的手寫數字生成器。在訓練過程中，生成器和判別器交替訓練，以使生成器生成更逼真的手寫數字圖像。注意，這只是一個簡單的示例，實際應用中可能需要更復雜的網絡結構和更多的訓練數據。