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-short term memory）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，需要回传的残差会指数下降，导致网络权重更新缓慢，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LSTM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_train3.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 preds6.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。
原文：https://juejin.cn/post/7097065796454580254