如何用Python画兔子

以下是一个基于PyTorch的多个条件的条件对抗生成网络（Conditional GAN）的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable

class Generator(nn.Module):
    def __init__(self, input_dim, output_dim, num_filters):
        super(Generator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.num_filters = num_filters
        self.net = nn.Sequential(
            nn.Linear(input_dim, num_filters),
            nn.ReLU(),
            nn.Linear(num_filters, num_filters*2),
            nn.ReLU(),
            nn.Linear(num_filters*2, num_filters*4),
            nn.ReLU(),
            nn.Linear(num_filters*4, output_dim),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.net(x)
        return x

class Discriminator(nn.Module):
    def __init__(self, input_dim, num_filters):
        super(Discriminator, self).__init__()
        self.input_dim = input_dim
        self.num_filters = num_filters
        self.net = nn.Sequential(
            nn.Linear(input_dim, num_filters*4),
            nn.LeakyReLU(0.2),
            nn.Linear(num_filters*4, num_filters*2),
            nn.LeakyReLU(0.2),
            nn.Linear(num_filters*2, num_filters),
            nn.LeakyReLU(0.2),
            nn.Linear(num_filters, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.net(x)
        return x

class ConditionalGAN(object):
    def __init__(self, input_dim, output_dim, num_filters, learning_rate):
        self.generator = Generator(input_dim, output_dim, num_filters)
        self.discriminator = Discriminator(input_dim+1, num_filters)
        self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate)
        self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate)

    def train(self, data_loader, num_epochs):
        for epoch in range(num_epochs):
            for i, (inputs, labels) in enumerate(data_loader):
                # Train discriminator with real data
                real_inputs = Variable(inputs)
                real_labels = Variable(labels)
                real_labels = real_labels.view(real_labels.size(0), 1)
                real_inputs = torch.cat((real_inputs, real_labels), 1)
                real_outputs = self.discriminator(real_inputs)
                real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size()))

                # Train discriminator with fake data
                noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim))
                fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10))
                fake_labels = fake_labels.view(fake_labels.size(0), 1)
                fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1))
                fake_inputs = torch.cat((fake_inputs, fake_labels), 1)
                fake_outputs = self.discriminator(fake_inputs)
                fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size()))

                # Backpropagate and update weights for discriminator
                discriminator_loss = real_loss + fake_loss
                self.discriminator.zero_grad()
                discriminator_loss.backward()
                self.optimizer_D.step()

                # Train generator
                noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim))
                fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,