【如何训练一个中译英翻译器】LSTM机器翻译模型训练与保存（二）

系列文章

【如何训练一个中译英翻译器】LSTM机器翻译seq2seq字符编码（一）
【如何训练一个中译英翻译器】LSTM机器翻译模型训练与保存（二）
【如何训练一个中译英翻译器】LSTM机器翻译模型部署（三）
【如何训练一个中译英翻译器】LSTM机器翻译模型部署之onnx（python）（四）

基于LSTM训练一个翻译器，要怎么做呢？其实很简单，也没那么复杂。
主要包括以下几个步骤：
1、加载训练集
2、训练集数据处理（字符编码），可参见：
【如何训练一个中译英翻译器】LSTM机器翻译seq2seq字符编码（一）
3、网络搭建
4、启动训练
5、模型保存
6、模型加载与推理
下面来看下整个过程：

1、加载训练集

from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np

batch_size = 64  # Batch size for training.
epochs = 1000  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 12000  # Number of samples to train on.
data_path = 'cmn.txt'

2、训练集数据处理

数据集预处理

# Vectorize the data.
input_texts = [] # 保存英文数据集
target_texts = [] # 保存中文数据集
input_characters = set() # 保存英文字符，比如a,b，c
target_characters = set() # 保存中文字符,比如，你，我，她
with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')# 一行一行读取数据
for line in lines[: min(num_samples, len(lines) - 1)]: # 遍历每一行数据集（用min来防止越出）
    input_text, target_text = line.split('\t') # 分割中英文
    # We use "tab" as the "start sequence" character
    # for the targets, and "\n" as "end sequence" character.
    target_text = '\t' + target_text + '\n'
    input_texts.append(input_text)
    target_texts.append(target_text)
    for char in input_text: # 提取字符
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        if char not in target_characters:
            target_characters.add(char)
            
input_characters = sorted(list(input_characters)) # 排序一下
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters) # 英文字符数量
num_decoder_tokens = len(target_characters) # 中文文字数量
max_encoder_seq_length = max([len(txt) for txt in input_texts]) # 输入的最长句子长度
max_decoder_seq_length = max([len(txt) for txt in target_texts])# 输出的最长句子长度

print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique output tokens:', num_decoder_tokens)
print('Max sequence length for inputs:', max_encoder_seq_length)
print('Max sequence length for outputs:', max_decoder_seq_length)

数据集编码

# mapping token to index， easily to vectors
# 处理方便进行编码为向量
# {
#   'a': 0,
#   'b': 1,
#   'c': 2,
#   ...
#   'z': 25
# }
input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])

# np.zeros(shape, dtype, order)
# shape is an tuple, in here 3D
encoder_input_data = np.zeros( # (12000, 32, 73) （数据集长度、句子长度、字符数量）
    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
    dtype='float32')
decoder_input_data = np.zeros( # (12000, 22, 2751)
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')
decoder_target_data = np.zeros( # (12000, 22, 2751)
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')

# 遍历输入文本（input_texts）和目标文本（target_texts）中的每个字符，
# 并将它们转换为数值张量以供深度学习模型使用。
#编码如下
#我，你，他，这，国，是，家，人，中
#1  0  0   0  1   1  0   1  1，我是中国人
#1  0   1  0  0   1  1   1  0，他是我家人
# input_texts contain all english sentences
# output_texts contain all chinese sentences
# zip('ABC','xyz') ==> Ax By Cz, looks like that
# the aim is: vectorilize text, 3D
# zip(input_texts, target_texts)成对取出输入输出，比如input_text = 你好，target_text = you good

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
        # 3D vector only z-index has char its value equals 1.0
        encoder_input_data[i, t, input_token_index[char]] = 1.
    for t, char in enumerate(target_text):
        # decoder_target_data is ahead of decoder_input_data by one timestep
        decoder_input_data[i, t, target_token_index[char]] = 1.
        if t > 0:
            # decoder_target_data will be ahead by one timestep
            # and will not include the start character.
            # igone t=0 and start t=1, means
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.

3、网络搭建

编码器搭建

# Define an input sequence and process it.
# input prodocts keras tensor, to fit keras model!
# 1x73 vector
# encoder_inputs is a 1x73 tensor!
encoder_inputs = Input(shape=(None, num_encoder_tokens))

# units=256, return the last state in addition to the output
encoder_lstm = LSTM((latent_dim), return_state=True)

# LSTM(tensor) return output, state-history, state-current
encoder_outputs, state_h, state_c = encoder_lstm(encoder_inputs)

# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

解码器搭建

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None, num_decoder_tokens))

# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM((latent_dim), return_sequences=True, return_state=True)

# obtain output
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,initial_state=encoder_states)

整体网络模型

# dense 2580x1 units full connented layer
decoder_dense = Dense(num_decoder_tokens, activation='softmax')

# why let decoder_outputs go through dense ?
decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn, groups layers into an object
# with training and inference features
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
# model(input, output)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Run training
# compile -> configure model for training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
# model optimizsm

4、启动训练

model.fit([encoder_input_data, decoder_input_data],
          decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)

可在控制台看到训练的相关信息：

5、模型保存

首先，保存权重文件
这里将解码器与编码器分别保存下来

encoder_model = Model(encoder_inputs, encoder_states)
# tensor 73x1
decoder_state_input_h = Input(shape=(latent_dim,))
# tensor 73x1
decoder_state_input_c = Input(shape=(latent_dim,))
# tensor 146x1
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
# lstm
decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)
#
decoder_states = [state_h, state_c]
#
decoder_outputs = decoder_dense(decoder_outputs)
#
decoder_model = Model(
    [decoder_inputs] + decoder_states_inputs,
    [decoder_outputs] + decoder_states)
encoder_model.save('encoder_model.h5')
decoder_model.save('decoder_model.h5')

其次，保存编码文件

# 将 input_characters保存为 input_words.txt 文件
with open('input_words.txt', 'w', newline='') as f:
    for char in input_characters:
        if char == '\t':
            f.write('\\t\n')
        elif char == '\n':
            f.write('\\n\n')
        else:
            f.write(char + '\n')


# 将 target_characters保存为 target_words.txt 文件
with open('target_words.txt', 'w', newline='') as f:
    for char in target_characters:
        if char == '\t':
            f.write('\\t\n')
        elif char == '\n':
            f.write('\\n\n')
        else:
            f.write(char + '\n')

最后，保存一下配置文件：

import json

# 将数据保存到JSON文件
data = {
    "max_encoder_seq_length": max_encoder_seq_length,
    "max_decoder_seq_length": max_decoder_seq_length
}

with open('config.json', 'w') as file:
    json.dump(data, file)

所以保存下来我们可以看到一共有以下几个文件，这几个文件要保存好，我们后面的文章讲到部署的时候要用到：

6、模型加载与推理

加载模型权重

from keras.models import load_model
encoder_model = load_model('encoder_model.h5')
decoder_model = load_model('decoder_model.h5')

推理模型搭建

# something readable.
reverse_input_char_index = dict(
    (i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
    (i, char) for char, i in target_token_index.items())

def decode_sequence(input_seq):
    # Encode the input as state vectors.
    states_value = encoder_model.predict(input_seq)

    # Generate empty target sequence of length 1.
    target_seq = np.zeros((1, 1, num_decoder_tokens))
    # Populate the first character of target sequence with the start character.
    target_seq[0, 0, target_token_index['\t']] = 1.
    # this target_seq you can treat as initial state

    # Sampling loop for a batch of sequences
    # (to simplify, here we assume a batch of size 1).
    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)

        # Sample a token
        # argmax: Returns the indices of the maximum values along an axis
        # just like find the most possible char
        sampled_token_index = np.argmax(output_tokens[0, -1, :])
        # find char using index
        sampled_char = reverse_target_char_index[sampled_token_index]
        # and append sentence
        decoded_sentence += sampled_char

        # Exit condition: either hit max length
        # or find stop character.
        if (sampled_char == '\n' or len(decoded_sentence) > max_decoder_seq_length):
            stop_condition = True

        # Update the target sequence (of length 1).
        # append then ?
        # creating another new target_seq
        # and this time assume sampled_token_index to 1.0
        target_seq = np.zeros((1, 1, num_decoder_tokens))
        target_seq[0, 0, sampled_token_index] = 1.

        # Update states
        # update states, frome the front parts
        states_value = [h, c]

    return decoded_sentence

进行模型推理

for seq_index in range(100,200):
    # Take one sequence (part of the training set)
    # for trying out decoding.
    input_seq = encoder_input_data[seq_index: seq_index + 1]
    decoded_sentence = decode_sequence(input_seq)
    print('-')
    print('Input sentence:', input_texts[seq_index])
    print('Decoded sentence:', decoded_sentence)

可以看到输出：

以上的代码可在kaggle上运行：how-to-train-a-chinese-to-english-translator-ii