Keras 生成式文本模型训练代码示例

下面是一个使用 Keras 框架训练生成式文本模型的示例代码：

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense, Embedding

# 加载文本数据
with open('text_data.txt', 'r') as file:
    text_data = file.read()

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

# 准备训练数据
max_len = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text_data) - max_len, step):
    sentences.append(text_data[i:i+max_len])
    next_chars.append(text_data[i+max_len])
    
# 将训练数据转换为向量表示
X = np.zeros((len(sentences), max_len, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for j, char in enumerate(sentence):
        X[i, j, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1

# 构建模型
model = Sequential()
model.add(LSTM(128, input_shape=(max_len, len(chars))))
model.add(Dense(len(chars), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

# 训练模型
model.fit(X, y, batch_size=128, epochs=20)

# 生成文本
start_index = np.random.randint(0, len(text_data) - max_len - 1)
seed_text = text_data[start_index:start_index+max_len]
generated_text = seed_text

for _ in range(400):
    x_pred = np.zeros((1, max_len, len(chars)))
    for t, char in enumerate(seed_text):
        x_pred[0, t, char_indices[char]] = 1.
        
    preds = model.predict(x_pred, verbose=0)[0]
    next_index = np.argmax(preds)
    next_char = indices_char[next_index]
    
generated_text += next_char
    seed_text = seed_text[1:] + next_char
    
print(generated_text)

这个例子使用一个 LSTM 模型来训练生成文本。首先，加载文本数据并构建字符索引映射表。然后，将文本数据切分为训练样本，并将其转换为向量表示。接下来，构建模型并编译。最后，使用训练好的模型生成文本。