目前网络上的深度学习，是如何通过给定关键词，去生成一张图片？请列举代码

以下是使用PyTorch实现基于文本描述生成图片的示例代码：

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# 设置随机种子，保证结果可重复
torch.manual_seed(42)

# 定义生成器模型
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.linear = nn.Linear(embedding_dim, 4*4*1024)
        self.bn1 = nn.BatchNorm2d(1024)
        self.deconv1 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(512)
        self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        self.deconv4 = nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1)
        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.embedding(x)
        x = self.linear(x)
        x = x.view(-1, 1024, 4, 4)
        x = self.bn1(x)
        x = nn.functional.relu(x)
        x = self.deconv1(x)
        x = self.bn2(x)
        x = nn.functional.relu(x)
        x = self.deconv2(x)
        x = self.bn3(x)
        x = nn.functional.relu(x)
        x = self.deconv3(x)
        x = self.bn4(x)
        x = nn.functional.relu(x)
        x = self.deconv4(x)
        x = self.tanh(x)
        return x

# 定义判别器模型
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.linear = nn.Linear(512*4*4, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.functional.leaky_relu(x, 0.2)
        x = self.conv2(x)
        x = self.bn2(x)
        x = nn.functional.leaky_relu(x, 0.2)
        x = self.conv3(x)
        x = self.bn3(x)
        x = nn.functional.leaky_relu(x, 0.2)
        x = self.conv4(x)
        x = self.bn4(x)
        x = nn.functional.leaky_relu(x, 0.2)
        x = x.view(-1, 512*4*4)
        x = self.linear(x)
        x = self.sigmoid(x)
        return x

# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizerG = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizerD = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 加载预训练的图像分类模型，并设置为不需要梯度更新
vgg16 = models.vgg16(pretrained=True)
for param in vgg16.parameters():
    param.requires_grad = False

# 定义图像预处理函数
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载文本描述并转换为词向量
text = "a red car on a city street"
words = text.split()
word2idx = {"<pad>": 0, "<start>": 1, "<end>": 2, "<unk>": 3}
for word in words:
    if word not in word2idx:
        word2idx[word] = len(word2idx)
idx2word = {i: w for w, i in word2idx.items()}
vocab_size = len(word2idx)
embedding_dim = 128
text = ["<start>"] + words + ["<end>"]
text = [word2idx[w] for w in text]
text = torch.tensor(text).unsqueeze(0)

# 生成图片
generator = Generator()
generator.eval()
with torch.no_grad():
    fake_images = generator(text)
    fake_images = fake_images.detach().cpu()

# 使用预训练模型计算图片的特征
features = vgg16(transform(Image.fromarray((fake_images[0].numpy()*255).astype('uint8').transpose((1, 2, 0)))).unsqueeze(0)).squeeze()

# 输出特征向量
print(features.detach().numpy())

以上代码是一个简单的GAN模型，可以通过文本描述生成图片，并计算生成图片的特征向量。其中，生成器模型和判别器模型分别定义了神经网络的结构，损失函数使用二元交叉熵，优化器使用Adam算法，图像预处理使用了PyTorch提供的transforms库，预训练的图像分类模型使用了PyTorch自带的VGG16模型。在输入文本描述时，需要将其转换为词向量，并添加起始和结束标记。最终，通过生成器生成图片，并使用预训练模型计算特征向量。