具体代码

由于生成音乐和弦涉及到的模型比较复杂，代码实现也比较长，这里提供一份使用TensorFlow实现的代码供参考：

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 定义常量
NUM_EPOCHS = 100
BATCH_SIZE = 128
LATENT_DIM = 100
SEQ_LEN = 32
NUM_CLASSES = 12

# 加载数据集
def load_data():
    root_dir = 'data'
    files = os.listdir(root_dir)
    data = []
    for file in files:
        if file.endswith('.npy'):
            path = os.path.join(root_dir, file)
            data.append(np.load(path))
    data = np.concatenate(data, axis=0)
    return data

# 预处理数据
def preprocess_data(data):
    # 将音符和和弦分开
    notes, chords = [], []
    for seq in data:
        notes.append(seq[:, :NUM_CLASSES])
        chords.append(seq[:, NUM_CLASSES:])
    notes = np.array(notes)
    chords = np.array(chords)
    # 对音符序列进行one-hot编码
    notes = to_categorical(notes, num_classes=NUM_CLASSES)
    # 对和弦序列进行编码
    chords = np.argmax(chords, axis=-1)
    # 对音符和和弦序列进行填充
    notes = pad_sequences(notes, maxlen=SEQ_LEN, padding='pre', truncating='pre')
    chords = pad_sequences(chords, maxlen=SEQ_LEN, padding='pre', truncating='pre')
    return notes, chords

# 定义生成器模型
def define_generator():
    # 定义输入
    inputs = layers.Input(shape=(LATENT_DIM,))
    x = layers.Dense(256)(inputs)
    x = layers.LeakyReLU()(x)
    x = layers.BatchNormalization()(x)
    x = layers.Dense(512)(x)
    x = layers.LeakyReLU()(x)
    x = layers.BatchNormalization()(x)
    x = layers.Dense(SEQ_LEN * NUM_CLASSES, activation='tanh')(x)
    outputs = layers.Reshape((SEQ_LEN, NUM_CLASSES))(x)
    # 定义模型
    model = tf.keras.Model(inputs, outputs, name='generator')
    return model

# 定义判别器模型
def define_discriminator():
    # 定义输入
    inputs = layers.Input(shape=(SEQ_LEN, NUM_CLASSES))
    x = layers.Flatten()(inputs)
    x = layers.Dense(512)(x)
    x = layers.LeakyReLU()(x)
    x = layers.Dense(256)(x)
    x = layers.LeakyReLU()(x)
    # 注意这里输出为1，表示真假
    outputs = layers.Dense(1, activation='sigmoid')(x)
    # 定义模型
    model = tf.keras.Model(inputs, outputs, name='discriminator')
    return model

# 定义GAN模型
def define_gan(generator, discriminator):
    # 将判别器设置为不可训练
    discriminator.trainable = False
    # 定义输入
    inputs = layers.Input(shape=(LATENT_DIM,))
    # 生成音符和和弦
    outputs = generator(inputs)
    # 判断音符和和弦是否为真实的
    real_or_fake = discriminator(outputs)
    # 定义模型
    model = tf.keras.Model(inputs, real_or_fake, name='gan')
    return model

# 定义损失函数和优化器
def define_loss_and_optimizer():
    loss_fn = tf.keras.losses.BinaryCrossentropy()
    generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
    discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
    return loss_fn, generator_optimizer, discriminator_optimizer

# 训练模型
def train(notes, chords, generator, discriminator, gan, loss_fn, generator_optimizer, discriminator_optimizer):
    num_batches = notes.shape[0] // BATCH_SIZE
    for epoch in range(NUM_EPOCHS):
        for batch in range(num_batches):
            # 训练判别器
            for _ in range(1):
                # 生成随机的噪声
                noise = np.random.normal(0, 1, size=(BATCH_SIZE, LATENT_DIM))
                # 随机选择一个真实的样本
                idx = np.random.randint(0, notes.shape[0], size=BATCH_SIZE)
                real_notes, real_chords = notes[idx], chords[idx]
                # 生成假的样本
                fake_notes = generator(noise)
                # 计算判别器的损失函数
                real_loss = loss_fn(tf.ones((BATCH_SIZE, 1)), discriminator([real_notes, real_chords]))
                fake_loss = loss_fn(tf.zeros((BATCH_SIZE, 1)), discriminator([fake_notes, chords]))
                total_loss = real_loss + fake_loss
                # 计算判别器的梯度并更新参数
                grads = tf.gradients(total_loss, discriminator.trainable_variables)
                discriminator_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables)))
            # 训练生成器
            for _ in range(1):
                # 生成随机的噪声
                noise = np.random.normal(0, 1, size=(BATCH_SIZE, LATENT_DIM))
                # 计算生成器的损失函数
                fake_notes = generator(noise)
                fake_loss = loss_fn(tf.ones((BATCH_SIZE, 1)), discriminator([fake_notes, chords]))
                # 计算生成器的梯度并更新参数
                grads = tf.gradients(fake_loss, generator.trainable_variables)
                generator_optimizer.apply_gradients(zip(grads, generator.trainable_variables)))
            # 打印损失函数和精度
            print('Epoch {}, Batch {}/{}: Loss={:.4f}'.format(epoch+1, batch+1, num_batches, total_loss))
            # 保存模型
            if (epoch+1) % 10 == 0:
                generator.save('generator.h5')
                discriminator.save('discriminator.h5')
                gan.save('gan.h5')

# 加载数据集
data = load_data()
# 预处理数据
notes, chords = preprocess_data(data)
# 定义模型
generator = define_generator()
discriminator = define_discriminator()
gan = define_gan(generator, discriminator)
# 定义损失函数和优化器
loss_fn, generator_optimizer, discriminator_optimizer = define_loss_and_optimizer()
# 训练模型
train(notes, chords, generator, discriminator, gan, loss_fn, generator_optimizer, discriminator_optimizer)

这个代码仅仅是为了提供一个参考，实际应用中还需要进行更多的调试和优化，才能得到满意的结果。