R 科学论文的“统计”引用

深度学习在自然语言生成中的应用

python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
# 构建语言模型
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length=max_length))
model.add(LSTM(128))
model.add(Dense(vocab_size, activation='softmax'))
model.summary()

python
# 加载预训练的语言模型
model.load_weights('language_model_weights.h5')
# 生成文本
seed_text = "今天天气很好"
generated_text = seed_text
for _ in range(num_words):
    encoded_text = tokenizer.texts_to_sequences([generated_text])[0]
    encoded_text = pad_sequences([encoded_text], maxlen=max_length-1, padding='pre')
    predicted_word_index = model.predict_classes(encoded_text, verbose=0)
    predicted_word = tokenizer.index_word[predicted_word_index[0]]
    generated_text += " " + predicted_word
print(generated_text)

python
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, LSTM, Dense
# 构建编码器
encoder_inputs = Input(shape=(max_length,))
encoder_embedding = Embedding(input_dim=vocab_size, output_dim=embedding_dim)(encoder_inputs)
encoder_outputs, state_h, state_c = LSTM(hidden_dim, return_state=True)(encoder_embedding)
encoder_states = [state_h, state_c]
# 构建解码器
decoder_inputs = Input(shape=(max_length,))
decoder_embedding = Embedding(input_dim=vocab_size, output_dim=embedding_dim)(decoder_inputs)
decoder_lstm = LSTM(hidden_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)
decoder_dense = Dense(vocab_size, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.summary()

python
# 加载预训练的对话模型
model.load_weights('chatbot_weights.h5')
# 生成回复
user_input = input("用户输入：")
encoder_input = tokenizer.texts_to_sequences([user_input])
encoder_input = pad_sequences(encoder_input, maxlen=max_length, padding='post')
decoder_input = np.zeros_like(encoder_input)
decoder_input[:, 0] = start_token_index
response = ''
for _ in range(max_length):
    output = model.predict([encoder_input, decoder_input])
    predicted_word_index = np.argmax(output[:, -1, :], axis=-1)
    predicted_word = tokenizer.index_word[predicted_word_index[0]]
    if predicted_word == end_token:
        break
    response += predicted_word + ' '
    decoder_input[:, 1] = predicted_word_index
print("生成回复：", response)