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。