用TensorFlow实现NLP小说扩写代码如何实现

# 加载文本数据
text = open(‘novel.txt’, ‘r’).read()
chars = list(set(text))
char_to_idx = {ch:i for i, ch in enumerate(chars)}
idx_to_char = {i:ch for i, ch in enumerate(chars)}

# 数据预处理
max_len = 100
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) – max_len, step):
sentences.append(text[i:i+max_len])
next_chars.append(text[i+max_len])
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 t, char in enumerate(sentence):
x[i, t, char_to_idx[char]] = 1
y[i, char_to_idx[next_chars[i]]] = 1

# 构建LSTM模型
model = tf.keras.Sequential([
tf.keras.layers.LSTM(128, input_shape=(max_len, len(chars))),
tf.keras.layers.Dense(len(chars), activation=‘softmax’)
])
model.compile(loss=‘categorical_crossentropy’, optimizer=‘adam’)

# 模型训练
model.fit(x, y, batch_size=128, epochs=20)

# 模型使用
def generate_text(model, start_text, length=100, diversity=0.5):
generated_text = start_text
for i in range(length):
x_pred = np.zeros((1, max_len, len(chars)))
for t, char in enumerate(start_text):
x_pred[0, t, char_to_idx[char]] = 1
preds = model.predict(x_pred, verbose=0)[0]
next_idx = sample(preds, diversity)
next_char = idx_to_char[next_idx]
generated_text += next_char
start_text = start_text[1:] + next_char
return generated_text

def sample(preds, temperature=1.0):
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)
return np.argmax(probas)

# 生成文本
generated_text = generate_text(model, start_text=‘The night was dark and’, length=500, diversity=0.5)
print(generated_text)