keras中seq2seq实现

• 这里只是简单的一个例子

输入序列	目标序列
[13, 28, 18, 7, 9, 5]	[18, 28, 13]
[29, 44, 38, 15, 26, 22]	[38, 44, 29]
[27, 40, 31, 29, 32, 1]	[31, 40, 27]

1.输入序列与目标序列向量化

• 设置GPU进行计算

import tensorflow as tf
tf.debugging.set_log_device_placement(True)

• 导入需要的函数和包

from numpy import array
from numpy import argmax
from keras.utils import to_categorical
import numpy as np

• 定义数据生成函数

# 随机产生在(1,n_features)区间的整数序列，序列长度为n_steps_in
def generate_sequence(length, n_unique):
    return [np.random.randint(1, n_unique-1) for _ in range(length)]

这个函数的目的是产生长度为length，数据范围为（1，n_unique）之间的整数。

generate_sequence(10,100)

产生10个在（1，100）之间的整数

[9, 37, 38, 73, 4, 1, 42, 97, 48, 17]

• 构造LSTM模型所需要的数据

def get_dataset(n_in, n_out, cardinality, n_samples):
    X1, X2, y = list(), list(), list()
    for _ in range(n_samples):
        # 生成输入序列
        source = generate_sequence(n_in, cardinality) #n_in就是代表生成序列的个数，生成n个1到n_unique-1的数
        # 定义目标序列，这里就是输入序列的前n_out个数据
        target = source[:n_out]
        target.reverse() #对数据做一个逆序（前后调转）
        # 向前偏移一个时间步目标序列
        target_in = [0] + target[:-1]
        # 直接使用to_categorical函数进行on_hot编码
        src_encoded = to_categorical(source, num_classes=cardinality) #这里就是将向量进行one_hot编码的
        tar_encoded = to_categorical(target, num_classes=cardinality)
        tar2_encoded = to_categorical(target_in, num_classes=cardinality)

        X1.append(src_encoded)
        X2.append(tar2_encoded)
        y.append(tar_encoded)
    return array(X1), array(X2), array(y)

to_categorical(list,num)的作用是对一个数进行one_hot编码，其输入的参数有两个，list表示的是一个整数列表，例如[3,2,5,1,2],num就是one_hot编码时向量的长度，其有一个要求就是num>max(list)。

# one_hot解码,看一下那些位置不为0
def one_hot_decode(encoded_seq):
    return [argmax(vector) for vector in encoded_seq]

这里用这个one_hot解码的主要作用是来进行还原，看一下那些位置不为0。

• 输入参数

n_features = 50 + 1   #在输入时可以理解为，每个向量的长度
n_steps_in = 6        #可以看作时间不
n_steps_out = 3
# 生成处理后的输入序列与目标序列，这里测试产生了一个序列样本
X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 100)

X1作为encoder的输入，X2作为decoder的输入，且encoder的输出同时也作为decoder的输入，y时最终的标签

one_hot_decode(X1[1])
one_hot_decode(X2[1])
one_hot_decode(y[1])

[21, 49, 21, 34, 48, 46]
[0, 21, 49]
[21, 49, 21]

2.构造seq2seq模型

• 导入所需的网络

from numpy import array
from numpy import argmax
from numpy import array_equal
from keras.utils import to_categorical
from keras.models import Model
from keras.layers import Input
from keras.layers import LSTM
from keras.layers import Dense

• 构造网络

# 构造Seq2Seq训练模型model, 以及进行新序列预测时需要的的Encoder模型:encoder_model 与Decoder模型:decoder_model
def define_models(n_input, n_output, n_units):
    # 训练模型中的encoder
    encoder_inputs = Input(shape=(None, n_input))
    encoder = LSTM(n_units, return_state=True)

    #上述参数下，encoder_outputs和state_h都是最后一步的hidden_state
    # state_c 存放最后一个时间步的cell_state
    encoder_outputs, state_h, state_c = encoder(encoder_inputs) 

    encoder_states = [state_h, state_c]   #仅保留编码状态向量
    # 训练模型中的decoder
    decoder_inputs = Input(shape=(None, n_output))
    decoder_lstm = LSTM(n_units, return_sequences=True, return_state=True)

    #上述参数下，decoder_outputs是全部时间步的hidden_state
    #第一个_存放的是最后一个时间步的hidden_state
    #第二个_存放的是最后一个时间步的cell state

    decoder_outputs, _, _ = decoder_lstm(decoder_inputs,initial_state=encoder_states)
    decoder_dense = Dense(n_output, activation='softmax')
    decoder_outputs = decoder_dense(decoder_outputs)
    model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
    # 新序列预测时需要的encoder
    encoder_model = Model(encoder_inputs, encoder_states)
    # 新序列预测时需要的decoder
    decoder_state_input_h = Input(shape=(n_units,))
    decoder_state_input_c = Input(shape=(n_units,))
    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)
    # 返回需要的三个模型
    return model, encoder_model, decoder_model

其中model是训练的网络，encoder_model是预测时的encoder模型，decoder_model是预测时的decoder模型。其中在LSTM模型中return_sequence和return_state的参数设置得到不同结果见另一个博客。https://www.jianshu.com/p/a74bb5a623dd

model, encoder_model, decoder_model = define_models(10,5,15)
model.summary()

Model: "model_19"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_25 (InputLayer)           (None, None, 10)     0
__________________________________________________________________________________________________
input_26 (InputLayer)           (None, None, 5)      0
__________________________________________________________________________________________________
lstm_13 (LSTM)                  [(None, 15), (None,  1560        input_25[0][0]
__________________________________________________________________________________________________
lstm_14 (LSTM)                  [(None, None, 15), ( 1260        input_26[0][0]
                                                                 lstm_13[0][1]
                                                                 lstm_13[0][2]
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, None, 5)      80          lstm_14[0][0]
==================================================================================================
Total params: 2,900
Trainable params: 2,900
Non-trainable params: 0
__________________________________________________________________________________________________

encoder包含了一个lstm结构，decoder包含一个lstm结构和一个全连接层，encoder的输出作为decoder的输入，在模型中可以看见，lstm_14中与之相关联的为input,lstm_13,其中lstm_13是encoder的输出。

def predict_sequence(infenc, infdec, source, n_steps, cardinality):
    """
    infenc:encoder_model
    infdec:decoder_model
    """
    # 输入序列编码得到编码状态向量
    state = infenc.predict(source)
    # 初始目标序列输入：通过开始字符计算目标序列第一个字符，这里是0
    target_seq = array([0.0 for _ in range(cardinality)]).reshape(1, 1, cardinality)
    # 输出序列列表
    output = list()
    for t in range(n_steps):
        # predict next char
        yhat, h, c = infdec.predict([target_seq] + state)
        # 截取输出序列，取后三个
        output.append(yhat[0,0,:])
        # 更新状态
        state = [h, c]
        # 更新目标序列(用于下一个词预测的输入)
        target_seq = yhat
    return array(output)

``

3 评估模型效果

total, correct = 100, 0
for _ in range(total):
    X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1)
    target = predict_sequence(infenc, infdec, X1, n_steps_out, n_features)
    if array_equal(one_hot_decode(y[0]), one_hot_decode(target)):
        correct += 1
print('Accuracy: %.2f%%' % (float(correct)/float(total)*100.0))

4 完整代码

from numpy import array
from numpy import argmax
from numpy import array_equal
from keras.utils import to_categorical
from keras.models import Model
from keras.layers import Input
from keras.layers import LSTM
from keras.layers import Dense
import numpy
import tensorflow as tf
tf.debugging.set_log_device_placement(True)

# 随机产生在(1,n_features)区间的整数序列，序列长度为n_steps_in
def generate_sequence(length, n_unique):
    return [np.random.randint(1, n_unique-1) for _ in range(length)]

# 构造LSTM模型输入需要的训练数据
def get_dataset(n_in, n_out, cardinality, n_samples):
    X1, X2, y = list(), list(), list()
    for _ in range(n_samples):
        # 生成输入序列
        source = generate_sequence(n_in, cardinality)
        # 定义目标序列，这里就是输入序列的前三个数据
        target = source[:n_out]
        target.reverse()
        # 向前偏移一个时间步目标序列
        target_in = [0] + target[:-1]
        # 直接使用to_categorical函数进行on_hot编码
        src_encoded = to_categorical(source, num_classes=cardinality)
        tar_encoded = to_categorical(target, num_classes=cardinality)
        tar2_encoded = to_categorical(target_in, num_classes=cardinality)

        X1.append(src_encoded)
        X2.append(tar2_encoded)
        y.append(tar_encoded)
    return array(X1), array(X2), array(y)

# 构造Seq2Seq训练模型model, 以及进行新序列预测时需要的的Encoder模型:encoder_model 与Decoder模型:decoder_model
def define_models(n_input, n_output, n_units):
    # 训练模型中的encoder
    encoder_inputs = Input(shape=(None, n_input))
    encoder = LSTM(n_units, return_state=True)
    encoder_outputs, state_h, state_c = encoder(encoder_inputs)
    encoder_states = [state_h, state_c]   #仅保留编码状态向量
    # 训练模型中的decoder
    decoder_inputs = Input(shape=(None, n_output))
    decoder_lstm = LSTM(n_units, return_sequences=True, return_state=True)
    decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
    decoder_dense = Dense(n_output, activation='softmax')
    decoder_outputs = decoder_dense(decoder_outputs)
    model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
    # 新序列预测时需要的encoder
    encoder_model = Model(encoder_inputs, encoder_states)
    # 新序列预测时需要的decoder
    decoder_state_input_h = Input(shape=(n_units,))
    decoder_state_input_c = Input(shape=(n_units,))
    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)
    # 返回需要的三个模型
    return model, encoder_model, decoder_model

def predict_sequence(infenc, infdec, source, n_steps, cardinality):
    # 输入序列编码得到编码状态向量
    state = infenc.predict(source)
    # 初始目标序列输入：通过开始字符计算目标序列第一个字符，这里是0
    target_seq = array([0.0 for _ in range(cardinality)]).reshape(1, 1, cardinality)
    # 输出序列列表
    output = list()
    for t in range(n_steps):
        # predict next char
        yhat, h, c = infdec.predict([target_seq] + state)
        # 截取输出序列，取后三个
        output.append(yhat[0,0,:])
        # 更新状态
        state = [h, c]
        # 更新目标序列(用于下一个词预测的输入)
        target_seq = yhat
    return array(output)

# one_hot解码
def one_hot_decode(encoded_seq):
    return [argmax(vector) for vector in encoded_seq]

# 参数设置
n_features = 10 + 1
n_steps_in = 6
n_steps_out = 3
# 定义模型
train, infenc, infdec = define_models(n_features, n_features, 16)
train.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
# 生成训练数据
import numpy as np
X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1000)
print(X1.shape,X2.shape,y.shape)

# 训练模型
train.fit([X1, X2], y, epochs=500)

# 评估模型效果
total, correct = 100, 0
for _ in range(total):
    X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1)
    target = predict_sequence(infenc, infdec, X1, n_steps_out, n_features)
    if array_equal(one_hot_decode(y[0]), one_hot_decode(target)):
        correct += 1
print('Accuracy: %.2f%%' % (float(correct)/float(total)*100.0))
# 查看预测结果
for _ in range(10):
    X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1)
    target = predict_sequence(infenc, infdec, X1, n_steps_out, n_features)
    print('X=%s y=%s, yhat=%s' % (one_hot_decode(X1[0]), one_hot_decode(y[0]), one_hot_decode(target)))