Keras 框架对抗性网络生成式文本训练代码示例

以下是一个基于 Keras 框架使用对抗性网络（GAN）进行生成式文本训练的示例代码：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding, Bidirectional
from keras.optimizers import Adam
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.layers.advanced_activations import LeakyReLU
from keras.utils import to_categorical
from keras import backend as K

def build_generator(latent_dim, num_words):
    model = Sequential()
    model.add(Dense(256, input_dim=latent_dim))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(num_words, activation='softmax'))
    return model

def build_discriminator(num_words):
    model = Sequential()
    model.add(Embedding(num_words, 256, input_length=seq_length))
    model.add(Bidirectional(LSTM(256)))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(1, activation='sigmoid'))
    return model

def build_gan(generator, discriminator):
    discriminator.trainable = False
    gan_input = generator.input
    gan_output = discriminator(generator.output)
    model = Model(inputs=gan_input, outputs=gan_output)
    model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return model

def generate_text(generator, tokenizer, max_length):
    seed_text = 'The'
    for _ in range(max_length):
        sequence = tokenizer.texts_to_sequences([seed_text])[0]
        sequence = pad_sequences([sequence], maxlen=max_length)
        predicted = np.argmax(generator.predict(sequence), axis=-1)
        output_word = ''
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        seed_text += ' ' + output_word
    return seed_text

# 载入数据集
text = open('text_data.txt').read()
tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
sequences = tokenizer.texts_to_sequences([text])[0]

# 构建生成器和判别器
latent_dim = 100
num_words = len(tokenizer.word_index) + 1
seq_length = 10

generator = build_generator(latent_dim, num_words)
discriminator = build_discriminator(num_words)

# 构建GAN模型
gan = build_gan(generator, discriminator)

# 训练GAN模型
batch_size = 64
epochs = 10000

for epoch in range(epochs):
    # 训练判别器
    random_indices = np.random.randint(0, len(sequences) - seq_length - 1, size=batch_size)
    real_sequences = []
    for index in random_indices:
        real_sequences.append(sequences[index:index+seq_length])
    real_sequences = np.array(real_sequences)
    real_labels = np.ones((batch_size, 1))

    noise = np.random.normal(0, 1, size=(batch_size, latent_dim))
    generated_sequences = generator.predict(noise)
    generated_labels = np.zeros((batch_size, 1))

    discriminator_loss_real = discriminator.train_on_batch(real_sequences, real_labels)
    discriminator_loss_generated = discriminator.train_on_batch(generated_sequences, generated_labels)

    discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_generated)

    # 训练生成器
    noise = np.random.normal(0, 1, size=(batch_size, latent_dim))
    generator_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))

    # 输出损失
    if epoch % 100 == 0:
        print('Epoch:', epoch, 'Discriminator Loss:', discriminator_loss, 'Generator Loss:', generator_loss)

# 生成文本
generated_text = generate_text(generator, tokenizer, max_length=100)
print('Generated Text:', generated_text)

这个示例代码中，我们首先构建了生成器和判别器模型。生成器模型将一个潜在的噪声向量映射到一个文本序列，而判别器模型则尝试区分真实的文本序列和生成器生成的假文本序列。然后，我们将生成器和判别器组合成一个 GAN 模型，其中生成器的目标是让判别器无法区分生成的文本和真实的文本。最后，我们使用训练好的生成器生成一段文本。

请注意，这只是一个简单的示例，实际的 GAN 模型可能需要更复杂的架构和更大的数据集来获得更好的结果。此外，还可以尝试使用更先进的技术来改进生成器和判别器的性能，如改进的 LSTM 结构、注意力机制等。