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九江专业的企业网站建设公司,百度推广计划,团队拓展总结,欧洲ip地址目录实验目的实验代码实验内容实验结果实验目的基于Pytorch搭建一个生成对抗网络，使用MNIST数据集。实验代码 import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data…

实验目的

实验代码

实验内容

实验结果

实验目的

基于Pytorch搭建一个生成对抗网络，使用MNIST数据集。

实验代码

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
import os# 设置环境变量
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"# 创建保存生成图像的文件夹
output_path = r"xxxxxxxxxxxxxxxxxx"
os.makedirs(output_path, exist_ok=True)# 生成器网络
class Generator(nn.Module):def __init__(self, latent_dim):super(Generator, self).__init__()self.network = nn.Sequential(nn.Linear(latent_dim, 256),nn.LeakyReLU(0.2),nn.Linear(256, 512),nn.LeakyReLU(0.2),nn.Linear(512, 1024),nn.LeakyReLU(0.2),nn.Linear(1024, 784),nn.Tanh())def forward(self, z):img = self.network(z)return img.view(img.size(0), 1, 28, 28)# 判别器网络
class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.network = nn.Sequential(nn.Linear(784, 512),nn.LeakyReLU(0.2),nn.Linear(512, 256),nn.LeakyReLU(0.2),nn.Linear(256, 1),nn.Sigmoid())def forward(self, img):return self.network(img.view(img.size(0), -1))def generate_and_save_images(generator, test_input, epoch, img_path):with torch.no_grad():generated_images = generator(test_input).cpu().numpy()fig, axes = plt.subplots(4, 4, figsize=(4, 4))for i, ax in enumerate(axes.flat):# 将图像从形状 (1, 28, 28) 转换为 (28, 28)，去除通道维度ax.imshow(np.squeeze(generated_images[i]), cmap='gray')ax.axis('off')img_filename = os.path.join(img_path, f"generated_epoch_{epoch}.png")plt.tight_layout()plt.savefig(img_filename)plt.close()# 设置设备（使用GPU或CPU）
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 超参数
lr = 0.0001
batch_size = 128
latent_dim = 100
epochs = 2000# 数据预处理和加载
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))
])train_dataset = datasets.MNIST(root='./MNIST_data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)# 测试数据：随机噪声作为输入
test_data = torch.randn(batch_size, latent_dim).to(device)# 初始化生成器和判别器，并定义损失函数和优化器
generator = Generator(latent_dim).to(device)
discriminator = Discriminator().to(device)
adversarial_loss = nn.BCELoss()
optimizer_G = optim.Adam(generator.parameters(), lr=lr)
optimizer_D = optim.Adam(discriminator.parameters(), lr=lr)# 记录损失
D_losses = []
G_losses = []# 训练过程
for epoch in range(epochs):for i, (imgs, _) in enumerate(train_loader):real_imgs = imgs.to(device)batch_size = real_imgs.size(0)# 判别器训练z = torch.randn(batch_size, latent_dim).to(device)fake_imgs = generator(z)real_labels = torch.ones(batch_size, 1).to(device)fake_labels = torch.zeros(batch_size, 1).to(device)# 计算损失real_loss = adversarial_loss(discriminator(real_imgs), real_labels)fake_loss = adversarial_loss(discriminator(fake_imgs.detach()), fake_labels)d_loss = (real_loss + fake_loss) / 2optimizer_D.zero_grad()d_loss.backward()optimizer_D.step()# 生成器训练z = torch.randn(batch_size, latent_dim).to(device)fake_imgs = generator(z)g_loss = adversarial_loss(discriminator(fake_imgs), real_labels)optimizer_G.zero_grad()g_loss.backward()optimizer_G.step()# 记录损失D_losses.append(d_loss.item())G_losses.append(g_loss.item())# 打印每2000个步骤的迭代信息if (epoch * len(train_loader) + i) % 2000 == 0:print(f"Iter: {epoch * len(train_loader) + i}")print(f"D_loss: {d_loss.item():.4f}")print(f"G_loss: {g_loss.item():.4f}")# 每个epoch保存生成的图像generate_and_save_images(generator, test_data, epoch, output_path)# 保存生成器和判别器的模型torch.save(generator.state_dict(), "Generator_mnist.pth")torch.save(discriminator.state_dict(), "Discriminator_mnist.pth")# 绘制损失曲线
plt.figure(figsize=(10, 5))
plt.plot(D_losses, label='Discriminator Loss')
plt.plot(G_losses, label='Generator Loss')
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.legend()
plt.title('Loss Curve')
plt.savefig('loss_curve.png')  # 保存图像
plt.show()  # 显示图像