基于深度学习的文本生成模型研究代码

基于深度学习的文本生成模型在自然语言处理领域有广泛应用，其中比较常见的模型有循环神经网络 (RNN)、长短时记忆网络 (LSTM)、生成对抗网络 (GAN) 和变分自编码器 (VAE) 等。这些模型需要的代码实现和训练过程略有不同，但大多数框架都提供了相应的工具和API。

以下是一个简单的文本生成模型代码示例，使用LSTM实现：

import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import RMSprop
import numpy as np

# 载入数据集
path = tf.keras.utils.get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
text = open(path, 'rb').read().decode(encoding='utf-8')
text = text.lower()

# 构建字符索引映射表
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

# 预处理数据
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i: i + maxlen])
    next_chars.append(text[i + maxlen])
x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1

# 定义LSTM模型
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars), activation='softmax'))
optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)

# 训练模型
for epoch in range(1, 60):
    model.fit(x, y, batch_size=128, epochs=1)
    # 随机生成文本
    start_index = np.random.randint(0, len(text) - maxlen - 1)
    generated_text = text[start_index: start_index + maxlen]
    for temperature in [0.2, 0.5, 1.0, 1.2]:
        print('------temperature:', temperature)
        sys.stdout.write(generated_text)
        for i in range(400):
            sampled = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(generated_text):
                sampled[0, t, char_indices[char]] = 1.
            preds = model.predict(sampled, verbose=0)
            preds = np.asarray(preds).astype('float64')
            preds = np.log(preds) / temperature
            exp_preds = np.exp(preds)
            preds = exp_preds / np.sum(exp_preds)
            probas = np.random.multinomial(1, preds, 1)
            next_index = np.argmax(probas)
            next_char = indices_char[next_index]
            generated_text += next_char
            generated_text = generated_text[1:]