请将Self attention Gan单头注意力机制改进为多头注意力机制并给出模型的全部代码

好的，以下是Self-Attention GAN多头注意力机制的代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate, Conv2D, Conv2DTranspose, LeakyReLU, BatchNormalization, LayerNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.constraints import max_norm
from tensorflow.keras.initializers import RandomNormal
from tensorflow.keras import backend

# 定义多头注意力层
class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, n_heads, n_features):
        super(MultiHeadAttention, self).__init__()
        self.n_heads = n_heads
        self.n_features = n_features
        
        assert n_features % n_heads == 0
        
        self.head_dim = n_features // n_heads
        
        self.query_dense = Dense(n_features, kernel_constraint=max_norm(1.), bias_constraint=max_norm(1.))
        self.key_dense = Dense(n_features, kernel_constraint=max_norm(1.), bias_constraint=max_norm(1.))
        self.value_dense = Dense(n_features, kernel_constraint=max_norm(1.), bias_constraint=max_norm(1.))
        self.combine_heads = Dense(n_features, kernel_constraint=max_norm(1.), bias_constraint=max_norm(1.))
        
    def call(self, inputs):
        query, key, value = inputs
        
        # 分离头
        query = tf.stack(tf.split(self.query_dense(query), self.n_heads, axis=-1))
        key = tf.stack(tf.split(self.key_dense(key), self.n_heads, axis=-1))
        value = tf.stack(tf.split(self.value_dense(value), self.n_heads, axis=-1))
        
        # 计算注意力
        attention = tf.matmul(query, key, transpose_b=True)
        attention = tf.nn.softmax(attention / backend.sqrt(float(self.head_dim)))
        attention = tf.matmul(attention, value)
        
        # 合并头
        attention = tf.concat(tf.split(attention, self.n_heads, axis=0), axis=-1)
        attention = self.combine_heads(attention)
        
        return attention

# 定义生成器
def define_generator(latent_dim):
    init = RandomNormal(stddev=0.02)
    
    in_lat = Input(shape=(latent_dim,))
    n_nodes = 128 * 7 * 7
    gen = Dense(n_nodes, kernel_initializer=init)(in_lat)
    gen = Reshape((7, 7, 128))(gen)
    
    gen = Conv2D(128, (4,4), strides=(1,1), padding='same', kernel_initializer=init)(gen)
    gen = LayerNormalization()(gen)
    gen = LeakyReLU(alpha=0.2)(gen)
    
    gen = MultiHeadAttention(8, 128)([gen, gen, gen])
    
    gen = Conv2DTranspose(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(gen)
    gen = LayerNormalization()(gen)
    gen = LeakyReLU(alpha=0.2)(gen)
    
    gen = MultiHeadAttention(8, 128)([gen, gen, gen])
    
    gen = Conv2DTranspose(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(gen)
    gen = LayerNormalization()(gen)
    gen = LeakyReLU(alpha=0.2)(gen)
    
    gen = Conv2D(1, (7,7), activation='tanh', padding='same', kernel_initializer=init)(gen)
    model = Model(in_lat, gen)
    return model

# 定义判别器
def define_discriminator(in_shape=(28,28,1)):
    init = RandomNormal(stddev=0.02)
    
    in_image = Input(shape=in_shape)
    
    dis = Conv2D(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(in_image)
    dis = LeakyReLU(alpha=0.2)(dis)
    
    dis = MultiHeadAttention(8, 64)([dis, dis, dis])
    
    dis = Conv2D(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(dis)
    dis = LayerNormalization()(dis)
    dis = LeakyReLU(alpha=0.2)(dis)
    
    dis = MultiHeadAttention(8, 128)([dis, dis, dis])
    
    dis = Flatten()(dis)
    dis = Dropout(0.4)(dis)
    dis = Dense(1, activation='sigmoid')(dis)
    model = Model(in_image, dis)
    model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5), metrics=['accuracy'])
    return model

# 定义GAN模型
def define_gan(g_model, d_model):
    d_model.trainable = False
    gan_output = d_model(g_model.output)
    model = Model(g_model.input, gan_output)
    model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return model

# 加载数据集
def load_real_samples():
    (trainX, _), (_, _) = tf.keras.datasets.mnist.load_data()
    X = trainX.astype('float32')
    X = (X - 127.5) / 127.5
    X = np.expand_dims(X, axis=-1)
    return X

# 生成真实样本
def generate_real_samples(dataset, n_samples):
    ix = np.random.randint(0, dataset.shape[0], n_samples)
    X = dataset[ix]
    y = np.ones((n_samples, 1))
    return X, y

# 生成潜在空间
def generate_latent_points(latent_dim, n_samples):
    x_input = np.random.randn(latent_dim * n_samples)
    x_input = x_input.reshape(n_samples, latent_dim)
    return x_input

# 生成虚假样本
def generate_fake_samples(g_model, latent_dim, n_samples):
    x_input = generate_latent_points(latent_dim, n_samples)
    X = g_model.predict(x_input)
    y = np.zeros((n_samples, 1))
    return X, y

# 训练GAN
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=100, n_batch=128):
    bat_per_epo = int(dataset.shape[0] / n_batch)
    half_batch = int(n_batch / 2)
    for i in range(n_epochs):
        for j in range(bat_per_epo):
            X_real, y_real = generate_real_samples(dataset, half_batch)
            d_loss1, _ = d_model.train_on_batch(X_real, y_real)
            X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
            d_loss2, _ = d_model.train_on_batch(X_fake, y_fake)
            X_gan = generate_latent_points(latent_dim, n_batch)
            y_gan = np.ones((n_batch, 1))
            g_loss = gan_model.train_on_batch(X_gan, y_gan)
            print('Epoch %d, Batch %d/%d, d_loss=%.3f, g_loss=%.3f' % (i+1, j+1, bat_per_epo, d_loss1+d_loss2, g_loss))
        if (i+1) % 10 == 0:
            summarize_performance(i, g_model, d_model, dataset, latent_dim)

# 评估模型
def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=100):
    X_real, y_real = generate_real_samples(dataset, n_samples)
    _, acc_real = d_model.evaluate(X_real, y_real, verbose=0)
    X_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_samples)
    _, acc_fake = d_model.evaluate(X_fake, y_fake, verbose=0)
    print('Epoch %d, Acc_real=%.3f, Acc_fake=%.3f' % (epoch+1, acc_real, acc_fake))
    save_plot(X_fake, epoch)
    filename = 'generator_model_%03d.h5' % (epoch+1)
    g_model.save(filename)

# 保存生成器生成的样本
def save_plot(examples, epoch, n=10):
    examples = (examples + 1) / 2.0
    for i in range(n * n):
        pyplot.subplot(n, n, 1 + i)
        pyplot.axis('off')
        pyplot.imshow(examples[i, :, :, 0], cmap='gray_r')
    filename = 'generated_plot_e%03d.png' % (epoch+1)
    pyplot.savefig(filename)
    pyplot.close()

# 主函数
def main():
    latent_dim = 100
    d_model = define_discriminator()
    g_model = define_generator(latent_dim)
    gan_model = define_gan(g_model, d_model)
    dataset = load_real_samples()
    train(g_model, d_model, gan_model, dataset, latent_dim)
    
if __name__ == '__main__':
    main()