基于keras的时域卷积网络（TCN）
1 前言
时域卷积网络（Temporal Convolutional Network，TCN）属于卷积神经网络（CNN）家族，于2017年被提出，目前已在多项时间序列数据任务中击败循环神经网络（RNN）家族。
TCN 网络结构
图中，xi 表示第 i 个时刻的特征，可以是多维的。
TCN源码见-->GitHub - philipperemy/keras-tcn: Keras Temporal Convolutional Network.，由于源码过于复杂，新手不易上手，笔者参照源码，手撕了个简洁版的TCN，与君共享。
本文以 MNIST 手写数字分类为例，讲解 TCN 模型。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-->时域卷积网络（TCN）案例模型
2 实验
TCN.py
from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Model
from keras.layers import add,Input,Conv1D,Activation,Flatten,Dense
#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
#残差块
def ResBlock(x,filters,kernel_size,dilation_rate):
r=Conv1D(filters,kernel_size,padding='same',dilation_rate=dilation_rate,activation='relu')(x) #第一卷积
r=Conv1D(filters,kernel_size,padding='same',dilation_rate=dilation_rate)(r) #第二卷积
if x.shape[-1]==filters:
shortcut=x
else:
shortcut=Conv1D(filters,kernel_size,padding='same')(x) #shortcut（捷径）
o=add([r,shortcut])
o=Activation('relu')(o) #激活函数
return o
#序列模型
def TCN(train_x,train_y,valid_x,valid_y,test_x,test_y):
inputs=Input(shape=(28,28))
x=ResBlock(inputs,filters=32,kernel_size=3,dilation_rate=1)
x=ResBlock(x,filters=32,kernel_size=3,dilation_rate=2)
x=ResBlock(x,filters=16,kernel_size=3,dilation_rate=4)
x=Flatten()(x)
x=Dense(10,activation='softmax')(x)
model=Model(input=inputs,output=x)
#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=30,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')
TCN(train_x,train_y,valid_x,valid_y,test_x,test_y)
网络各层输出尺寸：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 28, 28) 0
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 28, 32) 2720 input_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 28, 32) 3104 conv1d_1[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 28, 32) 2720 input_1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 28, 32) 0 conv1d_2[0][0]
conv1d_3[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 28, 32) 0 add_1[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 28, 32) 3104 activation_1[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 28, 32) 3104 conv1d_4[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 28, 32) 0 conv1d_5[0][0]
activation_1[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 28, 32) 0 add_2[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 28, 16) 1552 activation_2[0][0]
__________________________________________________________________________________________________
conv1d_7 (Conv1D) (None, 28, 16) 784 conv1d_6[0][0]
__________________________________________________________________________________________________
conv1d_8 (Conv1D) (None, 28, 16) 1552 activation_2[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 28, 16) 0 conv1d_7[0][0]
conv1d_8[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 28, 16) 0 add_3[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 448) 0 activation_3[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 10) 4490 flatten_1[0][0]
==================================================================================================
Total params: 23,130
Trainable params: 23,130
Non-trainable params: 0
网络训练结果：
Epoch 28/30
- 6s - loss: 0.0112 - acc: 0.9966 - val_loss: 0.0539 - val_acc: 0.9854
Epoch 29/30
- 6s - loss: 0.0080 - acc: 0.9977 - val_loss: 0.0536 - val_acc: 0.9872
Epoch 30/30
- 6s - loss: 0.0099 - acc: 0.9965 - val_loss: 0.0486 - val_acc: 0.9892
test_loss: 0.055041389787220396 - test_acc: 0.9855000048875808
可以看到，TCN模型的预测精度为 0.9855， 超越了 seq2seq模型案例分析 中 AttSeq2Seq 模型（0.9825）、基于keras的双层LSTM网络和双向LSTM网络 中 DoubleLSTM 模型（0.9789）和 BiLSTM 模型（0.9795）、基于keras的残差网络 中 ResNet 模型（0.9721）。
3 拓展延申
有时候，并不需要最后一层 TCN 输出序列的所有步，而只需要最后一层 TCN 输出序列的第一步或最后一步。这时候，需要借助 lambda 关键字定义 Lambda 层，取代 Flatten 层。如下：
from keras.layers import Lambda
......
x=ResBlock(x,filters=16,kernel_size=3,dilation_rate=4)
x=Lambda(lambda x: x[:,0,:])(x) #此前是：x=Flatten()(x)
x=Dense(10,activation='softmax')(x)
......
lambda 关键字用于定义匿名函数，应用如下：
import numpy as np
f=lambda x: x*x+x+1
x=np.array([1,2,3])
y=f(x)
print(y) #输出：[ 3 7 13]
声明：本文转自基于keras的时域卷积网络（TCN）
