源码地址
LSTM模型简介
如果不想看原理的可以略过，直接看下面的代码部分。LSTM具体实现原理网上有很多，这里就不具体讲了。
长短期记忆模型（long-shor梯度下降法原理t term m算法的特征emory）是一种特殊的RNN模型，是为了解决RNN模型梯度弥散的问题而提出的；在传统的RNN中，训练算法使用的是BPTT，当时间比较长时，算法的空间复杂度是指需要回传的残差会指数下降，导致工商银行网络权重更新缓慢，无法体现出RNN的长期记忆的效果，因此需要一个存储单元来存储记忆，因此Lgithub中文社区STM模型被提出
代码
1.构建中文词典tokenizer
class NameTokenizer():
def __init__(self, path, required_gender="男"):
self.names = []
self._tokens = []
self._tokens += ['0', 'n']
with open(path, mode='r', encoding="utf-8-sig") as f:
for idx, line in enumerate(f.readlines()):
line = line.strip()
line = line.split(",")
gender = line[1]
if gender == required_gender or gender == "未知":
name = line[0]
self.names.append(name)
for char in name:
self._tokens.append(char)
# 创建词典 token->id映射关系
self._tokens = sorted(list(set(self._tokens)))
self.token_id_dict = dict((c, i) for i, c in enumerate(self._tokens))
self.id_token_dict = dict((i, c) for i, c in enumerate(self._tokens))
self.vocab = len(self._tokens)
2.构建训练数据集
def generate_data(tokenizer: NameTokenizer):
total = len(tokenizer.names)
while True:
for start in range(0, total, BATCH_SIZE):
end = min(start + BATCH_SIZE, total)
sequences = []
next_chars = []
for name in tokenizer.names[start:end]:
s = name + (MAX_LEN - len(name))*'0'
sequences.append(s)
next_chars.append('n')
for it, j in enumerate(name):
if (it >= len(name)-1):
continue
s = name[:-1-it]+(MAX_LEN - len(name[:-1-it]))*'0'
sequences.append(s)
next_chars.append(name[-1-it])
# print(sequences[:10])
# print(next_chars[:10])
x_train = np.zeros((len(sequences), MAX_LEN, tokenizer.vocab))
y_train = np.zeros((len(next_chars), tokenizer.vocab))
for idx, seq in enumerate(sequences):
for t, char in enumerate(seq):
x_train[idx, t, tokenizer.token_id_dict[char]] = 1
for idx, char in enumerate(next_chars):
y_train[idx, tokenizer.token_id_dict[char]] = 1
yield x_train, y_train
del sequences,next_chars,x_train,y_train
3.训练
def train():
tokenizer = NameTokenizer(path=PATH_DATA, required_gender=GENDER)
steps = int(math.floor(len(tokenizer.names) / BATCH_SIZE))
print("step:"+steps)
# # 构建模型
model = keras.Sequential([
keras.Input(shape=(MAX_LEN, tokenizer.vocab)),
# 第一个LSTM层，返回序列作为下一层的输入
layers.LSTM(STATE_DIM, dropout=DROP_OUT, return_sequences=False),
layers.Dense(tokenizer.vocab, activation='softmax')
])
model.summary()
optimizer = keras.optimizers.Adam(learning_rate=LEARNING_RATE)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
model.fit_generator(generate_data(tokenizer),
steps_per_epoch=steps, epochs=EPOCHS)
model.save("mymodel")
4.训练结果
5.预测
def sample(preds, temperature=1):
# helper function to sample an index from a probability array
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)
def generateNames(prefix,size):
tokenizer = NameTokenizer(path=PATH_DATA, required_gender=GENDER)
model = keras.models.load_model(PATH_MODEL)
preds = set()
tmp_generated = prefix
for char in tmp_generated:
if char not in tokenizer.token_id_dict:
print("字典中没有这个字")
return
sequence = ('{0:0<' + str(MAX_LEN) + '}').format(prefix).lower()
while len(preds) < size:
x_pre = np.zeros((1, MAX_LEN, tokenizer.vocab))
for t, char in enumerate(sequence):
x_pre[0, t, tokenizer.token_id_dict[char]] = 1
output = model.predict(x_pre, verbose=0)[0]
index = sample(output)
char = tokenizer.id_token_dict[index]
if(char == '0' or char == 'n'):
preds.add(tmp_generated)
tmp_generated = prefix
sequence = ('{0:0<' + str(MAX_LEN) + '}').format(prefix).lower()
else:
tmp_generated += char
sequence = (
'{0:0<' + str(MAX_LEN) + '}').format(tmp_generated).lower()
print(sequence)
if(len(sequence) > MAX_LEN):
tmp_generated = prefix
sequence = ('{0:0<' + str(MAX_LEN) + '}').format(prefix).lower()
return preds
6.预测结果
完整代码已经上传到了github上，开源不易，希望多多点赞。
