具体代码

这里提供一个使用LSTM模型与GAN模型结合生成音乐和弦的示例代码，仅供参考：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, LSTM, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import RandomNormal

# LSTM模型
def build_lstm_model(seq_length, n_notes, n_layers=1, hidden_size=128):
    inputs = Input(shape=(seq_length, n_notes))
    x = inputs
    for i in range(n_layers):
        x = LSTM(hidden_size, return_sequences=True)(x)
    outputs = Dense(n_notes, activation='softmax')(x)
    model = Model(inputs=inputs, outputs=outputs)
    optimizer = Adam(lr=0.001)
    model.compile(loss='categorical_crossentropy', optimizer=optimizer)
    return model

# GAN模型
def build_gan_model(seq_length, n_notes, n_layers=1, hidden_size=128):
    # 生成器
    generator_inputs = Input(shape=(seq_length, n_notes))
    x = generator_inputs
    for i in range(n_layers):
        x = LSTM(hidden_size, return_sequences=True)(x)
    generator_outputs = Dense(n_notes, activation='softmax')(x)
    generator = Model(inputs=generator_inputs, outputs=generator_outputs)

    # 鉴别器
    discriminator_inputs = Input(shape=(seq_length, n_notes))
    x = discriminator_inputs
    for i in range(n_layers):
        x = LSTM(hidden_size, return_sequences=True)(x)
    x = tf.keras.layers.GlobalAveragePooling1D()(x)
    x = Dense(1, activation='sigmoid')(x)
    discriminator = Model(inputs=discriminator_inputs, outputs=x)
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))

    # GAN
    discriminator.trainable = False
    gan_inputs = Input(shape=(seq_length, n_notes))
    gan_outputs = discriminator(generator(gan_inputs))
    gan = Model(inputs=gan_inputs, outputs=gan_outputs)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))

    return generator, discriminator, gan

# 训练GAN模型
def train_gan(seq_length, n_notes, epochs=5000, batch_size=64):
    # 加载训练数据
    X_train = np.load('X_train.npy')
    Y_train = np.load('Y_train.npy')

    # 构建模型
    generator, discriminator, gan = build_gan_model(seq_length, n_notes)

    # 训练模型
    for epoch in range(epochs):
        # 训练鉴别器
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        real_seqs = X_train[idx]
        fake_seqs = generator.predict(np.random.randn(batch_size, seq_length, n_notes))
        x = np.concatenate([real_seqs, fake_seqs])
        y = np.zeros((2 * batch_size, 1))
        y[:batch_size] = 1
        y[batch_size:] = 0
        d_loss = discriminator.train_on_batch(x, y)

        # 训练生成器
        x = np.random.randn(batch_size, seq_length, n_notes)
        y = np.ones((batch_size, 1))
        g_loss = gan.train_on_batch(x, y)

        # 输出训练结果
        if epoch % 100 == 0:
            print('epoch: {}, d_loss: {}, g_loss: {}'.format(epoch, d_loss, g_loss))

        # 保存模型
        if epoch % 1000 == 0:
            generator.save('generator_{}.h5'.format(epoch))

# 使用LSTM模型生成音乐
def generate_music_with_lstm(model, seed_notes, n_notes, seq_length, temperature=1.0):
    generated_notes = []
    input_notes = seed_notes
    for i in range(n_notes):
        # 预测下一个音符
        x = np.reshape(input_notes, (1, seq_length, len(note_dict)))
        y_pred = model.predict(x)[0]
        # 根据温度调整预测结果，增加随机性
        y_pred = np.log(y_pred) / temperature
        y_pred = np.exp(y_pred) / np.sum(np.exp(y_pred))
        # 选择下一个音符
        idx = np.random.choice(len(note_dict), p=y_pred)
        next_note = note_dict[idx]
        generated_notes.append(next_note)
        # 更新当前输入
        input_notes = np.concatenate([input_notes[1:], np.eye(len(note_dict))[idx]])
    return generated_notes

# 使用GAN模型生成音乐
def generate_music_with_gan(generator, seq_length, n_notes, temperature=1.0):
    seed_noise = np.random.randn(1, seq_length, n_notes)
    generated_notes = []
    for i in range(n_notes):
        # 生成下一个音符
        x = generator.predict(seed_noise)[0]
        # 根据温度调整预测结果，增加随机性
        x = np.log(x) / temperature
        x = np.exp(x) / np.sum(np.exp(x))
        # 选择下一个音符
        idx = np.random.choice(len(note_dict), p=x[-1])
        next_note = note_dict[idx]
        generated_notes.append(next_note)
        # 更新当前输入
        seed_noise = np.concatenate([seed_noise[:, 1:, :], np.eye(len(note_dict))[idx][np.newaxis, np.newaxis, :]], axis=1)
    return generated_notes

以上代码中，`build_lstm_model()`函数用于构建LSTM模型，`build_gan_model()`函数用于构建GAN模型，`train_gan()`函数用于训练GAN模型，`generate_music_with_lstm()`函数用于使用LSTM模型生成音乐，`generate_music_with_gan()`函数用于使用GAN模型生成音乐。其中，`note_dict`为一个字典，将音符映射为一个整数，方便模型处理。