系列文章
【如何训练一个中英翻译模型】LSTM机器翻译seq2seq字符编码（一）
【如何训练一个中英翻译模型】LSTM机器翻译模型训练与保存（二）
【如何训练一个中英翻译模型】LSTM机器翻译模型部署（三）
【如何训练一个中英翻译模型】LSTM机器翻译模型部署之onnx（python）（四）
目录
- 系列文章
- 1、加载训练集
- 2、训练集数据处理
- 3、网络搭建
- 4、启动训练
- 5、模型保存
- 6、模型加载与推理
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 npbatch_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 goodfor 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 1.0encoder_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 timestepdecoder_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, meansdecoder_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 optimizsm4、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 = Falsedecoded_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 charsampled_token_index = np.argmax(output_tokens[0, -1, :])# find char using indexsampled_char = reverse_target_char_index[sampled_token_index]# and append sentencedecoded_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 1.0target_seq = np.zeros((1, 1, num_decoder_tokens))target_seq[0, 0, sampled_token_index] = 1.# Update states# update states, frome the front partsstates_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
