WGAN自动生成动漫头像

WGAN是一种生成对抗网络，可以用于生成各种类型的图像，包括动漫头像。生成动漫头像的过程通常涉及到收集大量的动漫头像数据，然后使用这些数据来训练WGAN模型。训练完成后，可以使用该模型来生成新的动漫头像。

具体的实现步骤如下：

1. 收集动漫头像数据集

可以从各种来源收集动漫头像数据集，包括网络上的图片库、爬虫程序等。收集的数据集应该包括多种各种类型、角色、风格的动漫头像。

2. 数据预处理

将收集到的数据集进行处理，包括图像的裁剪、缩放、归一化等操作，以便于输入到WGAN模型中。

3. 构建WGAN模型

WGAN模型由两个神经网络组成，一个是生成器网络，一个是判别器网络。生成器网络接收一个噪声向量作为输入，并生成一张新的动漫头像。判别器网络接收一张动漫头像作为输入，并输出这张头像是真实的还是生成的。这两个网络不断反复迭代，直到生成的头像与真实头像的差别最小。

4. 训练WGAN模型

使用预处理后的数据集来训练WGAN模型，不断地调整模型参数，使得生成的头像与真实头像的差别最小。训练完成后，可以保存模型参数用于生成新的动漫头像。

5. 生成新的动漫头像

使用训练好的WGAN模型，输入一个随机的噪声向量，就可以生成一张新的动漫头像。可以多次输入不同的噪声向量，生成多张不同风格的动漫头像。

需要注意的是，WGAN模型的训练过程比较复杂，需要一定的深度学习知识和经验。建议先学习基础的深度学习知识，再进行WGAN模型的训练和应用。

相关问题

WGAN自动生成动漫头像代码

以下是使用WGAN（Wasserstein GAN）生成动漫头像的代码示例：

首先，需要安装相应的库：tensorflow, numpy, matplotlib, opencv-python。

然后，我们定义生成器和鉴别器，以及损失函数和优化器。代码如下：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import cv2

# 定义生成器
def generator_model():
    input_layer = tf.keras.layers.Input(shape=(100,))
    x = tf.keras.layers.Dense(256)(input_layer)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Reshape((16, 16, 1))(x)
    x = tf.keras.layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    output_layer = tf.keras.layers.Conv2D(3, (3, 3), activation='tanh', padding='same')(x)
    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
    return model

# 定义鉴别器
def discriminator_model():
    input_layer = tf.keras.layers.Input(shape=(64, 64, 3))
    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(input_layer)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(x)
    x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
    x = tf.keras.layers.Flatten()(x)
    output_layer = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
    return model

# 定义损失函数
def wasserstein_loss(y_true, y_pred):
    return tf.keras.backend.mean(y_true * y_pred)

# 定义优化器
generator_optimizer = tf.keras.optimizers.RMSprop(lr=0.00005)
discriminator_optimizer = tf.keras.optimizers.RMSprop(lr=0.00005)

# 编译生成器和鉴别器
generator = generator_model()
discriminator = discriminator_model()

discriminator.trainable = False
gan_input = tf.keras.layers.Input(shape=(100,))
gan_output = discriminator(generator(gan_input))
gan = tf.keras.Model(inputs=gan_input, outputs=gan_output)
gan.compile(loss=wasserstein_loss, optimizer=generator_optimizer)
discriminator.trainable = True
discriminator.compile(loss=wasserstein_loss, optimizer=discriminator_optimizer)

接着，我们定义一些辅助函数，用于加载和处理数据集，以及生成样本。代码如下：

# 加载数据集
def load_dataset():
    file_list = !ls dataset/*.jpg
    images = [cv2.imread(file) for file in file_list]
    images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
    images = [cv2.resize(img, (64, 64)) for img in images]
    images = np.array(images)
    images = (images - 127.5) / 127.5
    return images

# 生成样本
def generate_samples(generator, n_samples):
    x_input = np.random.randn(100 * n_samples)
    x_input = x_input.reshape(n_samples, 100)
    X = generator.predict(x_input)
    X = (X + 1) / 2.0
    return X

# 保存生成的图像
def save_samples(samples, step):
    for i in range(len(samples)):
        plt.subplot(4, 4, i+1)
        plt.axis('off')
        plt.imshow(samples[i])
    plt.savefig('generated_images/generated_samples_%d.png' % (step+1))
    plt.close()

最后，我们定义训练循环，通过训练生成器和鉴别器来生成动漫头像。代码如下：

# 加载数据集
dataset = load_dataset()

# 定义超参数
n_epochs = 5000
n_batch = 64

# 训练循环
for i in range(n_epochs):
    # 随机选择真实样本
    ix = np.random.randint(0, dataset.shape[0], n_batch)
    X_real = dataset[ix]
    # 生成假样本
    X_fake = generate_samples(generator, n_batch)
    # 更新鉴别器
    d_loss_real = discriminator.train_on_batch(X_real, -np.ones((n_batch, 1)))
    d_loss_fake = discriminator.train_on_batch(X_fake, np.ones((n_batch, 1)))
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
    # 更新生成器
    X_gan = np.random.randn(100 * n_batch)
    X_gan = X_gan.reshape(n_batch, 100)
    g_loss = gan.train_on_batch(X_gan, -np.ones((n_batch, 1)))
    # 打印损失
    print('>%d, d=%.3f, g=%.3f' % (i+1, d_loss, g_loss))
    # 保存生成的样本
    if (i+1) % 100 == 0:
        samples = generate_samples(generator, 16)
        save_samples(samples, i)

运行训练循环后，将在生成的图像文件夹中看到生成的样本。

WGAN自动生成动漫头像PyTorch 代码

以下是使用WGAN生成动漫头像的PyTorch代码，其中使用了DCGAN的结构和WGAN的损失函数。

首先需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML

接下来定义一些超参数：

# Root directory for dataset
dataroot = "./data"

# Number of workers for dataloader
workers = 2

# Batch size during training
batch_size = 64

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64

# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 100

# Size of feature maps in generator
ngf = 64

# Size of feature maps in discriminator
ndf = 64

# Number of training epochs
num_epochs = 5

# Learning rate for optimizers
lr = 0.00005

# Beta1 hyperparam for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 0

# Number of critic iterations per generator iteration
n_critic = 5

# Clipping parameter for WGAN
clip_value = 0.01

# Output directory for generated images
output_dir = "./output"

接下来定义数据加载器：

# Create the dataset
dataset = dset.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                           ]))

# Create the dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=workers)

接下来定义生成器和判别器的结构：

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        return self.main(input)


# Discriminator Code

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
        )

    def forward(self, input):
        return self.main(input).view(-1, 1).squeeze(1)

接下来定义初始化生成器和判别器：

# Initialize generator and discriminator
netG = Generator(ngpu).cuda()
netD = Discriminator(ngpu).cuda()

接下来定义优化器和损失函数：

# Initialize optimizer
optimizerD = optim.RMSprop(netD.parameters(), lr=lr)
optimizerG = optim.RMSprop(netG.parameters(), lr=lr)

# Initialize loss functions
criterion = nn.BCEWithLogitsLoss()

接下来定义训练过程：

# Training Loop

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
    # For each batch in the dataloader
    for i, data in enumerate(dataloader, 0):

        ############################
        # (1) Update D network
        ###########################
        for n in range(n_critic):
            # Initialize gradients
            netD.zero_grad()

            # Format batch
            real_cpu = data[0].cuda()
            b_size = real_cpu.size(0)
            label = torch.full((b_size,), 1, device=torch.device('cuda'))

            # Forward pass real batch through D
            output = netD(real_cpu).view(-1)

            # Calculate loss on real batch
            D_loss_real = -output.mean()

            # Calculate gradients for D in backward pass
            D_loss_real.backward()

            # Sample noise as input for G
            noise = torch.randn(b_size, nz, 1, 1, device=torch.device('cuda'))

            # Generate fake image batch with G
            fake = netG(noise)

            # Classify fake batch with D
            output = netD(fake.detach()).view(-1)

            # Calculate loss on fake batch
            D_loss_fake = output.mean()

            # Calculate gradients for D in backward pass
            D_loss_fake.backward()

            # Compute gradient penalty
            alpha = torch.rand(b_size, 1, 1, 1).cuda()
            x_hat = (alpha * real_cpu.data + (1 - alpha) * fake.data).requires_grad_(True)
            out = netD(x_hat).view(-1)
            grad = torch.autograd.grad(outputs=out, inputs=x_hat,
                                       grad_outputs=torch.ones(out.size()).cuda(),
                                       create_graph=True, retain_graph=True, only_inputs=True)[0]
            gp = ((grad.norm(2, dim=1) - 1) ** 2).mean() * 10
            gp.backward()

            # Add the gradients from the all critic iterations
            D_loss = D_loss_real + D_loss_fake + gp
            Wasserstein_D = D_loss_real - D_loss_fake

            # Update D
            optimizerD.step()

            # Clip weights of D
            for p in netD.parameters():
                p.data.clamp_(-clip_value, clip_value)

        ############################
        # (2) Update G network
        ###########################
        netG.zero_grad()

        # Generate a batch of images
        noise = torch.randn(b_size, nz, 1, 1, device=torch.device('cuda'))
        fake = netG(noise)

        # Classify the generated batch with D
        output = netD(fake).view(-1)

        # Calculate G's loss based on this output
        G_loss = -output.mean()

        # Update G
        G_loss.backward()
        optimizerG.step()

        # Output training stats
        if i % 50 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tWasserstein_D: %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     D_loss.item(), G_loss.item(), Wasserstein_D.item()))

        # Save Losses for plotting later
        G_losses.append(G_loss.item())
        D_losses.append(D_loss.item())

        # Check how the generator is doing by saving G's output on fixed noise
        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1

接下来定义输出结果：

# Output generated images
fig = plt.figure(figsize=(8, 8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i, (1, 2, 0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)

HTML(ani.to_jshtml())

# Save generated images as GIF file
fig = plt.figure(figsize=(8, 8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i, (1, 2, 0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
ani.save(output_dir + "/anime.gif", writer='pillow', fps=2)