介绍

Deep NLP

自然语言处理NLP是计算机科学、人工智能和语言学交叉领域的分支科学主要让计算机处理和理解自然语言如机器翻译、问答系统等。但因其在学习和使用语言的复杂性通常认为NLP是困难的近几年随着深度学习DL兴起人们不断的尝试将DL应用在NLP中被称为 Deep NLP并取得了很多突破。其中就有 Seq2Seq 模型。

莱由

Seq2Seq模型是序列到序列模型的简称也被称为一种编码器-解码器模型分别基于2014年发布的两篇论文

• Sequence to Sequence Learning with Neural Networks by Sutskever et al.,
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation by Cho et al.,
  作者分析了DNN因限制输入输出序列的长度无法处理未知长度和不定长序列并且很多重要的问题都使用未知长度序列表示的从而论证在处理未知长度的序列问题有必要提出新的解决方式于是创新型性的提出Seq2Seq模型下面让我们一起看看这个模型到底是什么?

Seq2Seq模型的不断探索

为什么说是创新性提出呢? 因为作者 Sutskever 经过了三次建模论证最终才确定下来 Seq2Seq 模型。而且模型的设计非常巧妙。让我们先回顾一下作者的探索经历。语言模型Language Model, LM是使用条件概率通过给定的词去计算下一个词。这是 Seq2Seq 模型的预测基础。由于序列之间是有上下文联系的类似句子的承上启下作用加上语言模型的特点条件概率作者首先选用了 RNN-LMRecurrent Neural Network Language Model, 循环神经网络语言模型。

上图是一个简单的RNN单元RNN循环往复的把前一步计算结果作为条件放进当前的输入中。
适合在任意长度的序列中对上下文依赖性进行建模但是有个问题那就是我们需要提前把输入和是输出序列对齐而且目前尚不清楚如何将RNN应用在不同长度有复杂非单一关系的序列中。为了解决对齐问题作者提出来一个理论的可行性方法使用两个RNN一个RNN将输入映射为一个固定长度的向量另一个RNN从这个向量中预测输出序列。

训练RNN是很困难的由于RNN的自身网络结构**其当前时刻的输出需要考虑前面所有时刻的输入**。那么在使用反向传播训练时一旦输入序列很长就极容易出现梯度消失问题为了解决RNN难训练问题作者使用$LSTM$网络。

上图是一个 LSTM 单元内部结构。LSTM 提出就是为了解决 RNN 梯度消失问题其创新性的加入了遗忘门让 LSTM 可以选择遗忘前面输入无关序列不用考虑全部输入序列。经过3次尝试最终加入 LSTM 后一个简单的 Seq2Seq 模型就建立了。

上图一个简单的 Seq2Seq 模型包括3个部分Encoder-LSTMDecoder-LSTMContext。输入序列是ABCEncoder-LSTM 将处理输入序列并在最后一个神经元返回整个输入序列的隐藏状态hidden state也被称为上下文ContextC。然后 Decoder-LSTM 根据隐藏状态一步一步的预测目标序列的下一个字符。最终输出序列wxyz。值得一提的是作者 Sutskever 根据其特定的任务具体设计特定的 Seq2Seq 模型。并对输入序列作逆序处理使模型能处理长句子也提高了准确率。

上图是作者 Sutskever 设计的真实模型并引以为傲一下三点。第一使用了两个 LSTM 一个用于编码一个用于解码。这也是作者探索并论证的结果。第二使用了深层的 LSTM 4层相比于浅层的网络每加一层模型困难程度就降低10% 。第三对输入序列使用了逆序操作提高了 LSTM 处理长序列能力。

中英文翻译

到了我们动手的时刻了理解了上面 Seq2Seq 模型让我们搭建一个简单的中英文翻译模型。

数据集

我们使用 manythings 网站的一个中英文数据集现已经上传到 Mo 平台了点击查看。该数据集格式为英文+tab+中文。

处理数据

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

batch_size = 64  # Batch size for training.
epochs = 100  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 10000  # Number of samples to train on.
# Path to the data txt file on disk.
data_path = 'cmn.txt'

# Vectorize the data.
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
    input_text, target_text = line.split('\t')
    # 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:
        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))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])

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)

Encoder-LSTM

# mapping token to index easily to vectors
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(
    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
    dtype='float32')
decoder_input_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')
decoder_target_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')

# 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
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.

Context

# 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]

Decoder-LSTM

# 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)

# 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
model.fit([encoder_input_data, decoder_input_data], 
          decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)
# Save model
model.save('seq2seq.h5')

解码序列

# Define sampling models
encoder_model = Model(encoder_inputs, encoder_states)
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
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)

# Reverse-lookup token index to decode sequences back to
# 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

预测

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('Input sentence:', input_texts[seq_index])
    print('Decoded sentence:', decoded_sentence)

经验

• 会自己比较两个模型的区别会自己进行整理与计算产出。都行啦的理由与打算。
• 对输入序列做逆序处理使模型能够处理长句子也提高了准确率。
• 对输入序列做逆序操作提高了LSTM处理长序列的能力。
• 慢慢的将各种代码都学会会自己调用LSTM库。会自己编写自己的notebook。会自己慢慢的将其搞定都行啦的样子与打算。