『深度应用』NLP机器翻译深度学习实战课程·壹(RNN base)

0. 项目背景

在上个文章中,我们已经简单介绍了NLP机器翻译,这次我们将用实战的方式讲解基于RNN的翻译模型。

0.1 基于RNN的seq2seq架构翻译模型介绍

seq2seq结构

基于RNN的seq2seq架构包含encoder和decoder,decoder部分又分train和inference两个过程,具体结构如下面两图所示:

可以看出结构很简单(相较于CNN与Attention base),下面我们就通过代码的形式实现,来进一步探究理解模型内在原理。


1. 数据准备

1.1 下载数据

此网站http://www.manythings.org/anki/上有许多翻译数据,包含多种语言,这里此教程选择的是中文到英语的数据集。

训练下载地址:http://www.manythings.org/anki/cmn-eng.zip

解压cmn-eng.zip,可以找到cmn.txt文件,内容如下:

# ========读取原始数据========
with open('cmn.txt', 'r', encoding='utf-8') as f:
    data = f.read()
data = data.split('\n')
data = data[:100]
print(data[-5:])
['Tom died.\t汤姆去世了。', 'Tom quit.\t汤姆不干了。', 'Tom swam.\t汤姆游泳了。', 'Trust me.\t相信我。', 'Try hard.\t努力。']

可以发现,每对翻译数据在同一行,左边是英文,右边是中文使用 \t 作为英语与中文的分界。

1.2 数据预处理

使用网络训练,需要我们把数据处理成网络可以接收的格式。

针对这个数据,具体来说就是需要把字符转换为数字(句子数字化),句子长度归一化处理。

句子数字化

可以参考我的这博客:『深度应用』NLP命名实体识别(NER)开源实战教程,数据预处理的实现。

分别对英语与汉字做处理。

英文处理

因为英语每个单词都是用空格分开的(除了缩写词,这里缩写词当做一个词处理),还有就是标点符号和单词没有分开,也需要特殊处理一下

这里我用的是一个简单方法处理下,实现在标点前加空格:

def split_dot(strs,dots=", . ! ?"):
    for d in dots.split(" "):
        #print(d)
        strs = strs.replace(d," "+d)
        #print(strs)
    return(strs)

使用这个方法来把词个字典化:

ef get_eng_dicts(datas):
    w_all_dict = {}
    for sample in datas:
        for token in sample.split(" "):
            if token not in w_all_dict.keys():
                w_all_dict[token] = 1
            else:
                w_all_dict[token] += 1
 
    sort_w_list = sorted(w_all_dict.items(),  key=lambda d: d[1], reverse=True)


    w_keys = [x for x,_ in sort_w_list[:7000-2]]
    w_keys.insert(0,"<PAD>")
    w_keys.insert(0,"<UNK>")
    
 
    w_dict = { x:i for i,x in enumerate(w_keys) }
    i_dict = { i:x for i,x in enumerate(w_keys) }
    return w_dict,i_dict

中文处理

在处理中文时可以发现,有繁体也有简体,所以最好转换为统一形式:(参考地址

# 安装
pip install opencc-python-reimplemented

# t2s - 繁体转简体(Traditional Chinese to Simplified Chinese)
# s2t - 简体转繁体(Simplified Chinese to Traditional Chinese)
# mix2t - 混合转繁体(Mixed to Traditional Chinese)
# mix2s - 混合转简体(Mixed to Simplified Chinese)

使用方法,把繁体转换为简体:

import opencc
cc = opencc.OpenCC('t2s')
s = cc.convert('這是什麼啊?')
print(s)
#这是什么啊?

再使用jieba分词的方法来从句子中分出词来:

def get_chn_dicts(datas):
    w_all_dict = {}
    for sample in datas:
        for token in jieba.cut(sample):
            if token not in w_all_dict.keys():
                w_all_dict[token] = 1
            else:
                w_all_dict[token] += 1
 
    sort_w_list = sorted(w_all_dict.items(),  key=lambda d: d[1], reverse=True)

    w_keys = [x for x,_ in sort_w_list[:10000-4]]
    w_keys.insert(0,"<EOS>")
    w_keys.insert(0,"<GO>")
    w_keys.insert(0,"<PAD>")
    w_keys.insert(0,"<UNK>")
    w_dict = { x:i for i,x in enumerate(w_keys) }
    i_dict = { i:x for i,x in enumerate(w_keys) }
    return w_dict,i_dict

下面进行padding

def get_val(keys,dicts):
    if keys in dicts.keys():
        val = dicts[keys]
    else:
        keys = "<UNK>"
        val = dicts[keys]
    return(val)

def padding(lists,lens=LENS):
    list_ret = []
    for l in lists:
        
        while(len(l)<lens):
            l.append(1)

        if len(l)>lens:
            l = l[:lens]
        list_ret.append(l)
    
    return(list_ret)

最后统一运行处理一下:

if __name__ == "__main__":
    df = read2df("cmn-eng/cmn.txt")
    eng_dict,id2eng = get_eng_dicts(df["eng"])
    chn_dict,id2chn = get_chn_dicts(df["chn"])
    print(list(eng_dict.keys())[:20])
    print(list(chn_dict.keys())[:20])

    enc_in = [[get_val(e,eng_dict) for e in eng.split(" ")] for eng in df["eng"]]
    dec_in = [[get_val("<GO>",chn_dict)]+[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]
    dec_out = [[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]

    enc_in_ar = np.array(padding(enc_in,32))
    dec_in_ar = np.array(padding(dec_in,30))
    dec_out_ar = np.array(padding(dec_out,30))

输出结果如下:

(TF_GPU) D:\Files\Prjs\Pythons\Kerases\MNT_RNN>C:/Datas/Apps/RJ/Miniconda3/envs/TF_GPU/python.exe d:/Files/Prjs/Pythons/Kerases/MNT_RNN/mian.py        
Using TensorFlow backend.
       eng    chn
0     Hi .     嗨。
1     Hi .    你好。
2    Run .  你用跑的。
3   Wait !    等等!
4  Hello !    你好。
save csv
Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\xiaos\AppData\Local\Temp\jieba.cache
Loading model cost 0.788 seconds.
Prefix dict has been built succesfully.
['<UNK>', '<PAD>', '.', 'I', 'to', 'the', 'you', 'a', '?', 'is', 'Tom', 'He', 'in', 'of', 'me', ',', 'was', 'for', 'have', 'The']
['<UNK>', '<PAD>', '<GO>', '<EOS>', '。', '我', '的', '了', '你', '他', '?', '在', '汤姆', '是', '她', '吗', '我们', ',', '不', '很']

2. 构建模型与训练

2.1 构建模型与超参数

用的是双层LSTM网络

# =======预定义模型参数========
EN_VOCAB_SIZE = 7000
CH_VOCAB_SIZE = 10000
HIDDEN_SIZE = 256

LEARNING_RATE = 0.001
BATCH_SIZE = 50
EPOCHS = 100

# ======================================keras model==================================
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Embedding,CuDNNLSTM
from keras.optimizers import Adam
import numpy as np

def get_model():
    # ==============encoder=============
    encoder_inputs = Input(shape=(None,))
    emb_inp = Embedding(output_dim=128, input_dim=EN_VOCAB_SIZE)(encoder_inputs)
    encoder_h1, encoder_state_h1, encoder_state_c1 = CuDNNLSTM(HIDDEN_SIZE, return_sequences=True, return_state=True)(emb_inp)
    encoder_h2, encoder_state_h2, encoder_state_c2 = CuDNNLSTM(HIDDEN_SIZE, return_state=True)(encoder_h1)

    # ==============decoder=============
    decoder_inputs = Input(shape=(None,))

    emb_target = Embedding(output_dim=128, input_dim=CH_VOCAB_SIZE)(decoder_inputs)
    lstm1 = CuDNNLSTM(HIDDEN_SIZE, return_sequences=True, return_state=True)
    lstm2 = CuDNNLSTM(HIDDEN_SIZE, return_sequences=True, return_state=True)
    decoder_dense = Dense(CH_VOCAB_SIZE, activation='softmax')

    decoder_h1, _, _ = lstm1(emb_target, initial_state=[encoder_state_h1, encoder_state_c1])
    decoder_h2, _, _ = lstm2(decoder_h1, initial_state=[encoder_state_h2, encoder_state_c2])
    decoder_outputs = decoder_dense(decoder_h2)

    model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

    # encoder模型和训练相同
    encoder_model = Model(encoder_inputs, [encoder_state_h1, encoder_state_c1, encoder_state_h2, encoder_state_c2])

    # 预测模型中的decoder的初始化状态需要传入新的状态
    decoder_state_input_h1 = Input(shape=(HIDDEN_SIZE,))
    decoder_state_input_c1 = Input(shape=(HIDDEN_SIZE,))
    decoder_state_input_h2 = Input(shape=(HIDDEN_SIZE,))
    decoder_state_input_c2 = Input(shape=(HIDDEN_SIZE,))

    # 使用传入的值来初始化当前模型的输入状态
    decoder_h1, state_h1, state_c1 = lstm1(emb_target, initial_state=[decoder_state_input_h1, decoder_state_input_c1])
    decoder_h2, state_h2, state_c2 = lstm2(decoder_h1, initial_state=[decoder_state_input_h2, decoder_state_input_c2])
    decoder_outputs = decoder_dense(decoder_h2)

    decoder_model = Model([decoder_inputs, decoder_state_input_h1, decoder_state_input_c1, decoder_state_input_h2, decoder_state_input_c2], 
                        [decoder_outputs, state_h1, state_c1, state_h2, state_c2])


    return(model,encoder_model,decoder_model)

2.2 模型配置与训练

自定义了一个acc,便于显示效果,keras内置的acc无法使用

import keras.backend as K
from keras.models import load_model
 
def my_acc(y_true, y_pred):
    acc = K.cast(K.equal(K.max(y_true,axis=-1),K.cast(K.argmax(y_pred,axis=-1),K.floatx())),K.floatx())
    return acc


Train = True

if __name__ == "__main__":
    df = read2df("cmn-eng/cmn.txt")
    eng_dict,id2eng = get_eng_dicts(df["eng"])
    chn_dict,id2chn = get_chn_dicts(df["chn"])
    print(list(eng_dict.keys())[:20])
    print(list(chn_dict.keys())[:20])

    enc_in = [[get_val(e,eng_dict) for e in eng.split(" ")] for eng in df["eng"]]
    dec_in = [[get_val("<GO>",chn_dict)]+[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]
    dec_out = [[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]

    enc_in_ar = np.array(padding(enc_in,32))
    dec_in_ar = np.array(padding(dec_in,30))
    dec_out_ar = np.array(padding(dec_out,30))

    #dec_out_ar = covt2oh(dec_out_ar)


    
    if Train:


        model,encoder_model,decoder_model = get_model()

        model.load_weights('e2c1.h5')

        opt = Adam(lr=LEARNING_RATE, beta_1=0.9, beta_2=0.99, epsilon=1e-08)
        model.compile(optimizer=opt, loss='sparse_categorical_crossentropy',metrics=[my_acc])
        model.summary()
        print(dec_out_ar.shape)
        model.fit([enc_in_ar, dec_in_ar], np.expand_dims(dec_out_ar,-1),
                batch_size=50,
                epochs=64,
                initial_epoch=0,
                validation_split=0.1)
        model.save('e2c1.h5')
        encoder_model.save("enc1.h5")
        decoder_model.save("dec1.h5")

64Epoch训练结果如下:

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            (None, None)         0
__________________________________________________________________________________________________
input_2 (InputLayer)            (None, None)         0
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, None, 128)    896000      input_1[0][0]
__________________________________________________________________________________________________
embedding_2 (Embedding)         (None, None, 128)    1280000     input_2[0][0]
__________________________________________________________________________________________________
cu_dnnlstm_1 (CuDNNLSTM)        [(None, None, 256),  395264      embedding_1[0][0]
__________________________________________________________________________________________________
cu_dnnlstm_3 (CuDNNLSTM)        [(None, None, 256),  395264      embedding_2[0][0]
                                                                 cu_dnnlstm_1[0][1]
                                                                 cu_dnnlstm_1[0][2]
__________________________________________________________________________________________________
cu_dnnlstm_2 (CuDNNLSTM)        [(None, 256), (None, 526336      cu_dnnlstm_1[0][0]
__________________________________________________________________________________________________
cu_dnnlstm_4 (CuDNNLSTM)        [(None, None, 256),  526336      cu_dnnlstm_3[0][0]
                                                                 cu_dnnlstm_2[0][1]
                                                                 cu_dnnlstm_2[0][2]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, None, 10000)  2570000     cu_dnnlstm_4[0][0]
==================================================================================================
Non-trainable params: 0
__________________________________________________________________________________________________
...
...
19004/19004 [==============================] - 98s 5ms/step - loss: 0.1371 - my_acc: 0.9832 - val_loss: 2.7299 - val_my_acc: 0.7412
Epoch 58/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.1234 - my_acc: 0.9851 - val_loss: 2.7378 - val_my_acc: 0.7410
Epoch 59/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.1132 - my_acc: 0.9867 - val_loss: 2.7477 - val_my_acc: 0.7419
Epoch 60/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.1050 - my_acc: 0.9879 - val_loss: 2.7660 - val_my_acc: 0.7426
Epoch 61/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.0983 - my_acc: 0.9893 - val_loss: 2.7569 - val_my_acc: 0.7408
Epoch 62/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.0933 - my_acc: 0.9903 - val_loss: 2.7775 - val_my_acc: 0.7414
Epoch 63/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.0885 - my_acc: 0.9911 - val_loss: 2.7885 - val_my_acc: 0.7420
Epoch 64/64
19004/19004 [==============================] - 96s 5ms/step - loss: 0.0845 - my_acc: 0.9920 - val_loss: 2.7914 - val_my_acc: 0.7423

3. 模型应用与预测

从训练集选取部分数据进行测试

Train = False

if __name__ == "__main__":
    df = read2df("cmn-eng/cmn.txt")
    eng_dict,id2eng = get_eng_dicts(df["eng"])
    chn_dict,id2chn = get_chn_dicts(df["chn"])
    print(list(eng_dict.keys())[:20])
    print(list(chn_dict.keys())[:20])

    enc_in = [[get_val(e,eng_dict) for e in eng.split(" ")] for eng in df["eng"]]
    dec_in = [[get_val("<GO>",chn_dict)]+[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]
    dec_out = [[get_val(e,chn_dict) for e in jieba.cut(eng)]+[get_val("<EOS>",chn_dict)] for eng in df["chn"]]

    enc_in_ar = np.array(padding(enc_in,32))
    dec_in_ar = np.array(padding(dec_in,30))
    dec_out_ar = np.array(padding(dec_out,30))

    #dec_out_ar = covt2oh(dec_out_ar)


    
    if Train:


        pass
    
    else:

        encoder_model,decoder_model = load_model("enc1.h5",custom_objects={"my_acc":my_acc}),load_model("dec1.h5",custom_objects={"my_acc":my_acc})

        for k in range(16000-20,16000):
            test_data = enc_in_ar[k:k+1]
            h1, c1, h2, c2 = encoder_model.predict(test_data)
            target_seq = np.zeros((1,1))
            
            outputs = []
            target_seq[0, len(outputs)] = chn_dict["<GO>"]
            while True:
                output_tokens, h1, c1, h2, c2 = decoder_model.predict([target_seq, h1, c1, h2, c2])
                sampled_token_index = np.argmax(output_tokens[0, -1, :])
                #print(sampled_token_index)
                outputs.append(sampled_token_index)
                #target_seq = np.zeros((1, 30))
                target_seq[0, 0] = sampled_token_index
                #print(target_seq)
                if sampled_token_index == chn_dict["<EOS>"] or len(outputs) > 28: break
            
            print("> "+df["eng"][k])
            print("< "+' '.join([id2chn[i] for i in outputs[:-1]]))
            print()

测试结果如下:基本上都翻译正确了。

> I can understand you to some extent .
< 在 某种程度 上 我 能 了解 你 。

> I can't recall the last time we met .
< 我 想不起来 我们 上次 见面 的 情况 了 。

> I can't remember which is my racket .
< 我 不 记得 哪个 是 我 的 球拍 。

> I can't stand that noise any longer .
< 我 不能 再 忍受 那 噪音 了 。

> I can't stand this noise any longer .
< 我 无法 再 忍受 这个 噪音 了 。

> I caught the man stealing the money .
< 我 抓 到 了 这个 男人 正在 偷钱 。

> I could not afford to buy a bicycle .
< 我 买不起 自行车 。

> I couldn't answer all the questions .
< 我 不能 回答 所有 的 问题 。

> I couldn't think of anything to say .
< 我 想不到 要说 什么 话 。

> I cry every time I watch this movie .
< 我 每次 看 这部 电影 都 会 哭 。

> I did not participate in the dialog .
< 我 没有 参与 对话 。

> I didn't really feel like going out .
< 我 不是 很想 出去 。

> I don't care a bit about the future .
< 我 不在乎 将来 。

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