给出Jittor版CGAN用MNIST数据集训练手写文字的代码

好的，以下是使用Jittor训练CGAN模型生成手写数字的代码示例：

import jittor as jt
from jittor import nn
from jittor import Dataset
from jittor import transform
import jittor.random as random
import numpy as np


class MNIST(Dataset):
    def __init__(self, train=True, transform=None):
        self.train = train
        self.transform = transform
        if self.train:
            self.images = np.load("train_images.npy")
            self.labels = np.load("train_labels.npy")
        else:
            self.images = np.load("test_images.npy")
            self.labels = np.load("test_labels.npy")

    def __getitem__(self, index):
        img = self.images[index].astype('float32') / 255.0
        label = self.labels[index]
        if self.transform is not None:
            img = self.transform(img)
        return img, label

    def __len__(self):
        return len(self.images)

class Generator(nn.Module):
    def __init__(self, latent_dim=100, img_shape=(1, 28, 28)):
        super(Generator, self).__init__()

        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256, 0.8),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512, 0.8),
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024, 0.8),
            nn.ReLU(),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def execute(self, z):
        img = self.model(z)
        img = img.reshape((img.shape[0],) + self.img_shape)
        return img

class Discriminator(nn.Module):
    def __init__(self, img_shape=(1, 28, 28)):
        super(Discriminator, self).__init__()

        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def execute(self, img):
        img_flat = img.flatten(1)
        validity = self.model(img_flat)

        return validity

# 定义超参数
latent_dim = 100
img_shape = (1, 28, 28)
lr = 0.0002
b1 = 0.5
b2 = 0.999
batch_size = 64
n_epochs = 200

# 定义数据集和数据转换
transform = transform.Compose([
    transform.Resize(28),
    transform.ImageNormalize(mean=0.5, std=0.5),
])
train_dataset = MNIST(train=True, transform=transform)
train_loader = jt.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 定义生成器和判别器
generator = Generator(latent_dim=latent_dim, img_shape=img_shape)
discriminator = Discriminator(img_shape=img_shape)

# 定义损失函数和优化器
adversarial_loss = nn.BCELoss()
optimizer_G = nn.Adam(generator.parameters(), lr=lr, betas=(b1, b2))
optimizer_D = nn.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))

# 训练CGAN模型
for epoch in range(n_epochs):
    for i, (imgs, _) in enumerate(train_loader):

        # 训练判别器
        optimizer_D.zero_grad()
        real_imgs = jt.array(imgs)
        z = jt.array(random.normal((imgs.shape[0], latent_dim)))
        fake_imgs = generator(z)
        real_labels = jt.ones((batch_size, 1))
        fake_labels = jt.zeros((batch_size, 1))

        d_loss_real = adversarial_loss(discriminator(real_imgs), real_labels)
        d_loss_fake = adversarial_loss(discriminator(fake_imgs), fake_labels)
        d_loss = (d_loss_real + d_loss_fake) / 2.0
        d_loss.backward()
        optimizer_D.step()

        # 训练生成器
        optimizer_G.zero_grad()
        z = jt.array(random.normal((imgs.shape[0], latent_dim)))
        fake_imgs = generator(z)
        g_loss = adversarial_loss(discriminator(fake_imgs), real_labels)
        g_loss.backward()
        optimizer_G.step()

        # 输出训练信息
        batches_done = epoch * len(train_loader) + i
        if batches_done % 400 == 0:
            print(
                f"[Epoch {epoch}/{n_epochs}] [Batch {i}/{len(train_loader)}] [D loss: {d_loss.item()}] [G loss: {g_loss.item()}]"
            )

在上述代码中，我们定义了MNIST数据集类和它的数据转换，生成器和判别器模型，以及损失函数和优化器。在训练循环中，我们依次训练判别器和生成器，并输出训练信息。运行以上代码，训练200个epoch后，我们可以得到生成器生成的手写数字图片。