1. 語料準備
? 壹***四萬多首古詩，壹行壹首詩
2. 預處理
? 將漢字表示為 One-Hot 的形式
? 在每行末尾加上 ] 符號是為了標識這首詩已經結束，說明 ] 符號之前的語句和之後的語句是沒有關聯關系的，後面會舍棄掉包含 ] 符號的訓練數據。
? puncs = [']', '[', '（', '）', '{', '}', '：', '《', '》']
def preprocess_file(Config):
# 語料文本內容
files_content = ''
with open(Config.poetry_file, 'r', encoding='utf-8') as f:
for line in f:
# 每行的末尾加上"]"符號代表壹首詩結束
for char in puncs:
line = line.replace(char, "")
files_content += line.strip() + "]"
words = sorted(list(files_content))
words.remove(']')
counted_words = {}
for word in words:
if word in counted_words:
counted_words[word] += 1
else:
counted_words[word] = 1
# 去掉低頻的字
erase = []
for key in counted_words:
if counted_words[key] <= 2:
erase.append(key)
for key in erase:
del counted_words[key]
del counted_words[']']
wordPairs = sorted(counted_words.items(), key=lambda x: -x[1])
words, _ = zip(*wordPairs)
# word到id的映射
word2num = dict((c, i + 1) for i, c in enumerate(words))
num2word = dict((i, c) for i, c in enumerate(words))
word2numF = lambda x: word2num.get(x, 0)
return word2numF, num2word, words, files_content
3. 模型參數配置
? class Config(object):
poetry_file = 'poetry.txt'
weight_file = 'poetry_model.h5'
# 根據前六個字預測第七個字
max_len = 6
batch_size = 512
learning_rate = 0.001
4. 構建模型
? 通過 PoetryModel 類實現
? class PoetryModel(object):
def __init__(self, config):
pass
def build_model(self):
pass
def sample(self, preds, temperature=1.0):
pass
def generate_sample_result(self, epoch, logs):
pass
def predict(self, text):
pass
def data_generator(self):
pass
def train(self):
pass
? （1）init 函數
? 加載 Config 配置信息，進行語料預處理和模型加載
? def __init__(self, config):
self.model = None
self.do_train = True
self.loaded_model = False
self.config = config
# 文件預處理
self.word2numF, self.num2word, self.words, self.files_content = preprocess_file(self.config)
if os.path.exists(self.config.weight_file):
self.model = load_model(self.config.weight_file)
self.model.summary()
else:
self.train()
self.do_train = False
self.loaded_model = True
? （2）build_model 函數
? GRU 模型建立
? def build_model(self):
'''建立模型'''
input_tensor = Input(shape=(self.config.max_len,))
embedd = Embedding(len(self.num2word)+1, 300, input_length=self.config.max_len)(input_tensor)
lstm = Bidirectional(GRU(128, return_sequences=True))(embedd)
dropout = Dropout(0.6)(lstm)
lstm = Bidirectional(GRU(128, return_sequences=True))(embedd)
dropout = Dropout(0.6)(lstm)
flatten = Flatten()(lstm)
dense = Dense(len(self.words), activation='softmax')(flatten)
self.model = Model(inputs=input_tensor, outputs=dense)
optimizer = Adam(lr=self.config.learning_rate)
self.model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
? （3）sample 函數
? def sample(self, 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)
? （4）訓練模型
? def generate_sample_result(self, epoch, logs):?
print("\n==================Epoch {}=====================".format(epoch))
for diversity in [0.5, 1.0, 1.5]:
print("------------Diversity {}--------------".format(diversity))
start_index = random.randint(0, len(self.files_content) - self.config.max_len - 1)
generated = ''
sentence = self.files_content[start_index: start_index + self.config.max_len]
generated += sentence
for i in range(20):
x_pred = np.zeros((1, self.config.max_len))
for t, char in enumerate(sentence[-6:]):
x_pred[0, t] = self.word2numF(char)
preds = self.model.predict(x_pred, verbose=0)[0]
next_index = self.sample(preds, diversity)
next_char = self.num2word[next_index]
generated += next_char
sentence = sentence + next_char
print(sentence)
? （5）predict 函數
? 根據給出的文字，生成詩句
? def predict(self, text):
if not self.loaded_model:
return
with open(self.config.poetry_file, 'r', encoding='utf-8') as f:
file_list = f.readlines()
random_line = random.choice(file_list)
# 如果給的text不到四個字，則隨機補全
if not text or len(text) != 4:
for _ in range(4 - len(text)):
random_str_index = random.randrange(0, len(self.words))
text += self.num2word.get(random_str_index) if self.num2word.get(random_str_index) not in [',', '。',
? '，'] else self.num2word.get(
random_str_index + 1)
seed = random_line[-(self.config.max_len):-1]
res = ''
seed = 'c' + seed
for c in text:
seed = seed[1:] + c
for j in range(5):
x_pred = np.zeros((1, self.config.max_len))
for t, char in enumerate(seed):
x_pred[0, t] = self.word2numF(char)
preds = self.model.predict(x_pred, verbose=0)[0]
next_index = self.sample(preds, 1.0)
next_char = self.num2word[next_index]
seed = seed[1:] + next_char
res += seed
return res
? （6） data_generator 函數
? 生成數據，提供給模型訓練時使用
def data_generator(self):
i = 0
while 1:
x = self.files_content[i: i + self.config.max_len]
y = self.files_content[i + self.config.max_len]
puncs = [']', '[', '（', '）', '{', '}', '：', '《', '》', ':']
if len([i for i in puncs if i in x]) != 0:
i += 1
continue
if len([i for i in puncs if i in y]) != 0:
i += 1
continue
y_vec = np.zeros(
shape=(1, len(self.words)),
dtype=np.bool
)
y_vec[0, self.word2numF(y)] = 1.0
x_vec = np.zeros(
shape=(1, self.config.max_len),
dtype=np.int32
)
for t, char in enumerate(x):
x_vec[0, t] = self.word2numF(char)
yield x_vec, y_vec
i += 1
? （7）train 函數
? def train(self):
#number_of_epoch = len(self.files_content) // self.config.batch_size
number_of_epoch = 10
if not self.model:
self.build_model()
self.model.summary()
self.model.fit_generator(
generator=self.data_generator(),
verbose=True,
steps_per_epoch=self.config.batch_size,
epochs=number_of_epoch,
callbacks=[
keras.callbacks.ModelCheckpoint(self.config.weight_file, save_weights_only=False),
LambdaCallback(on_epoch_end=self.generate_sample_result)
]
)
5. 進行模型訓練
? model = PoetryModel(Config)
6. 作詩
? text = input("text:")
sentence = model.predict(text)
print(sentence)
學習資料：
《中文自然語言處理入門實戰》