lstm和gan结合生成音乐具体代码

下面是一个使用 LSTM 和 GAN 生成音乐的示例代码：

import numpy as np
from keras.layers import Dense, Input, LSTM, Reshape, Dropout
from keras.models import Model, Sequential
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
from keras.layers.advanced_activations import LeakyReLU
import matplotlib.pyplot as plt
import mido

# 定义参数
n_steps = 256
n_features = 128
latent_dim = 100
batch_size = 64
epochs = 100

# 加载 MIDI 文件，转换为音符矩阵
midi_file = mido.MidiFile('example.mid')
notes = []
for msg in midi_file.play():
    if hasattr(msg, 'note'):
        notes.append(msg.note)
notes = np.array(notes)
n_samples = len(notes) // n_steps
notes = notes[:n_samples * n_steps]
notes = notes.reshape((n_samples, n_steps))

# 定义 LSTM 生成器模型
generator = Sequential()
generator.add(Dense(256, input_dim=latent_dim))
generator.add(LeakyReLU(alpha=0.2))
generator.add(Reshape((16, 16, 1)))
generator.add(LSTM(128, return_sequences=True))
generator.add(LSTM(128))
generator.add(Dropout(0.5))
generator.add(Dense(n_steps * n_features, activation='tanh'))
generator.add(Reshape((n_steps, n_features)))

# 定义判别器模型
discriminator = Sequential()
discriminator.add(LSTM(128, input_shape=(n_steps, n_features)))
discriminator.add(Dense(1, activation='sigmoid'))

# 将生成器和判别器组合成一个 GAN 模型
discriminator.trainable = False
gan_input = Input(shape=(latent_dim,))
gan_output = discriminator(generator(gan_input))
gan = Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))

# 训练 GAN 模型
for epoch in range(epochs):
    for batch in range(n_samples // batch_size):
        # 训练判别器
        real_samples = notes[batch * batch_size:(batch + 1) * batch_size]
        real_labels = np.ones((batch_size, 1))
        fake_samples = generator.predict(np.random.randn(batch_size, latent_dim))
        fake_labels = np.zeros((batch_size, 1))
        discriminator_loss_real = discriminator.train_on_batch(real_samples, real_labels)
        discriminator_loss_fake = discriminator.train_on_batch(fake_samples, fake_labels)
        discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_fake)

        # 训练生成器
        generator_input = np.random.randn(batch_size, latent_dim)
        generator_labels = np.ones((batch_size, 1))
        generator_loss = gan.train_on_batch(generator_input, generator_labels)

    print('Epoch: %d, Discriminator Loss: %f, Generator Loss: %f' % (epoch, discriminator_loss, generator_loss))

在上面的代码中，我们首先加载 MIDI 文件，将其转换为音符矩阵，然后定义 LSTM 生成器和判别器模型。接着，我们将它们组合成一个 GAN 模型，并训练该模型。在训练过程中，我们先训练判别器，然后训练生成器。最后，我们输出每个 epoch 的判别器和生成器的损失值。