基于keras的双层LSTM网络和双向LSTM网络

1 前言

基于keras的双层LSTM网络和双向LSTM网络中，都会用到 LSTM层，主要参数如下：

LSTM(units,input_shape,return_sequences=False)

• units：隐藏层神经元个数
• input_shape=(time_step, input_feature)：time_step是序列递归的步数，input_feature是输入特征维数
• return_sequences： 取值为True，表示每个时间步的值都返回；取值为False，表示只返回最后一个时间步的取值

本文以MNIST手写数字分类为例，讲解双层LSTM网络和双向LSTM网络的实现。关于MNIST数据集的说明，见使用TensorFlow实现MNIST数据集分类。

笔者工作空间如下：

代码资源见--> 双隐层LSTM和双向LSTM

2 双层LSTM网络

双层LSTM网络结构

DoubleLSTM.py

from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Sequential
from keras.layers import Dense,LSTM

#载入数据
def read_data(path):
    mnist=input_data.read_data_sets(path,one_hot=True)
    train_x,train_y=mnist.train.images.reshape(-1,28,28),mnist.train.labels,
    valid_x,valid_y=mnist.validation.images.reshape(-1,28,28),mnist.validation.labels,
    test_x,test_y=mnist.test.images.reshape(-1,28,28),mnist.test.labels
    return train_x,train_y,valid_x,valid_y,test_x,test_y

#双层LSTM模型
def DoubleLSTM(train_x,train_y,valid_x,valid_y,test_x,test_y):
    #创建模型
    model=Sequential()
    model.add(LSTM(64,input_shape=(28,28),return_sequences=True))  #返回所有节点的输出
    model.add(LSTM(32,return_sequences=False))  #返回最后一个节点的输出
    model.add(Dense(10,activation='softmax'))
    #查看网络结构
    model.summary()
    #编译模型
    model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
    #训练模型
    model.fit(train_x,train_y,batch_size=500,nb_epoch=15,verbose=2,validation_data=(valid_x,valid_y))
    #评估模型
    pre=model.evaluate(test_x,test_y,batch_size=500,verbose=2)
    print('test_loss:',pre[0],'- test_acc:',pre[1])

train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data')
DoubleLSTM(train_x,train_y,valid_x,valid_y,test_x,test_y)

每层网络输出尺寸：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
lstm_5 (LSTM)                (None, 28, 64)            23808
_________________________________________________________________
lstm_6 (LSTM)                (None, 32)                12416
_________________________________________________________________
dense_5 (Dense)              (None, 10)                330
=================================================================
Total params: 36,554
Trainable params: 36,554
Non-trainable params: 0

由于第一个LSTM层设置了 return_sequences=True，每个节点的输出值都会返回，因此输出尺寸为 (None, 28, 64)

由于第二个LSTM层设置了 return_sequences=False，只有最后一个节点的输出值会返回，因此输出尺寸为 (None, 32)

训练结果：

Epoch 13/15
 - 17s - loss: 0.0684 - acc: 0.9796 - val_loss: 0.0723 - val_acc: 0.9792
Epoch 14/15
 - 18s - loss: 0.0633 - acc: 0.9811 - val_loss: 0.0659 - val_acc: 0.9822
Epoch 15/15
 - 17s - loss: 0.0597 - acc: 0.9821 - val_loss: 0.0670 - val_acc: 0.9812
test_loss: 0.0714278114028275 - test_acc: 0.9789000034332276

3 双向LSTM网络

双向LSTM网络结构

from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Sequential
from keras.layers import Dense,LSTM,Bidirectional

#载入数据
def read_data(path):
    mnist=input_data.read_data_sets(path,one_hot=True)
    train_x,train_y=mnist.train.images.reshape(-1,28,28),mnist.train.labels,
    valid_x,valid_y=mnist.validation.images.reshape(-1,28,28),mnist.validation.labels,
    test_x,test_y=mnist.test.images.reshape(-1,28,28),mnist.test.labels
    return train_x,train_y,valid_x,valid_y,test_x,test_y

#双向LSTM模型
def BiLSTM(train_x,train_y,valid_x,valid_y,test_x,test_y):
    #创建模型
    model=Sequential()
    lstm=LSTM(64,input_shape=(28,28),return_sequences=False)  #返回最后一个节点的输出
    model.add(Bidirectional(lstm))  #双向LSTM
    model.add(Dense(10,activation='softmax'))
    #编译模型
    model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
    #训练模型
    model.fit(train_x,train_y,batch_size=500,nb_epoch=15,verbose=2,validation_data=(valid_x,valid_y))
    #查看网络结构
    model.summary()
    #评估模型
    pre=model.evaluate(test_x,test_y,batch_size=500,verbose=2)
    print('test_loss:',pre[0],'- test_acc:',pre[1])

train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data')
BiLSTM(train_x,train_y,valid_x,valid_y,test_x,test_y)

每层网络输出尺寸：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
bidirectional_5 (Bidirection (None, 128)               47616
_________________________________________________________________
dense_6 (Dense)              (None, 10)                1290
=================================================================
Total params: 48,906
Trainable params: 48,906
Non-trainable params: 0

由于LSTM层设置了 return_sequences=False，只有最后一个节点的输出值会返回，每层LSTM返回64维向量，两层合并共128维，因此输出尺寸为 (None, 128)

训练结果：

Epoch 13/15
 - 22s - loss: 0.0512 - acc: 0.9839 - val_loss: 0.0632 - val_acc: 0.9790
Epoch 14/15
 - 22s - loss: 0.0453 - acc: 0.9865 - val_loss: 0.0534 - val_acc: 0.9832
Epoch 15/15
 - 22s - loss: 0.0418 - acc: 0.9869 - val_loss: 0.0527 - val_acc: 0.9830
test_loss: 0.06457789749838412 - test_acc: 0.9795000076293945

​ 声明：本文转自基于keras的双层LSTM网络和双向LSTM网络