输入时间序列数据为（none，20）none是样本数/时间步，通过大小为128的时间窗对数据分割，构建一个tcn网络，batch大小等于时间窗，求keras代码

以下是基于Keras实现的TCN网络代码，用于处理输入时间序列数据为（none，20）none是样本数/时间步，通过大小为128的时间窗对数据分割的情况，batch大小等于时间窗：

from keras.layers import Input, Conv1D, Dense, Activation, Dropout
from keras.layers import Layer, Add, Multiply
from keras.models import Model
from keras.optimizers import Adam

from keras import backend as K

# 定义TCN的基本块
class ResidualBlock(Layer):
    def __init__(self, dilation_rate, nb_filters, kernel_size, padding, activation='relu', dropout_rate=0, name='residual_block'):
        super(ResidualBlock, self).__init__(name=name)
        
        self.dilation_rate = dilation_rate
        self.nb_filters = nb_filters
        self.kernel_size = kernel_size
        self.padding = padding
        self.activation = activation
        self.dropout_rate = dropout_rate
        
        self.conv1 = Conv1D(filters=nb_filters, kernel_size=kernel_size,
                            dilation_rate=dilation_rate, padding=padding,
                            name=name+'_conv1')
        self.conv2 = Conv1D(filters=nb_filters, kernel_size=kernel_size,
                            dilation_rate=dilation_rate, padding=padding,
                            name=name+'_conv2')
        self.drop1 = Dropout(dropout_rate, name=name+'_dropout1')
        self.drop2 = Dropout(dropout_rate, name=name+'_dropout2')
        self.add1 = Add(name=name+'_add1')
        self.act1 = Activation(activation, name=name+'_act1')
        
    def call(self, inputs):
        x = inputs
        x = self.conv1(x)
        x = self.act1(x)
        x = self.drop1(x)
        x = self.conv2(x)
        x = self.act1(x)
        x = self.drop2(x)
        x = self.add1([inputs, x])
        return x

# 定义TCN网络
def build_tcn_model(nb_filters=64, kernel_size=2, padding='causal', activation='relu', dropout_rate=0.0, dilation_rates=[1, 2, 4, 8, 16], name='tcn'):
    input_layer = Input(shape=(128, 20), name=name+'_input')
    x = input_layer
    
    # 添加多个TCN的基本块
    for i, dilation_rate in enumerate(dilation_rates):
        x = ResidualBlock(dilation_rate=dilation_rate, nb_filters=nb_filters,
                          kernel_size=kernel_size, padding=padding,
                          activation=activation, dropout_rate=dropout_rate,
                          name=name+'_residual_block_'+str(i))(x)
    
    # 添加多个全连接层
    x = Conv1D(filters=128, kernel_size=1, padding=padding, name=name+'_conv1')(x)
    x = Activation(activation, name=name+'_act1')(x)
    x = Dropout(dropout_rate, name=name+'_dropout1')(x)
    x = Conv1D(filters=20, kernel_size=1, padding=padding, name=name+'_conv2')(x)
    x = Activation('linear', name=name+'_output')(x)
    
    # 构建模型
    model = Model(inputs=[input_layer], outputs=[x], name=name)
    return model

# 编译模型
tcn_model = build_tcn_model()
tcn_model.compile(optimizer=Adam(lr=0.001), loss='mse')

这段代码定义了一个由多个TCN块和全连接层组成的网络，可以通过输入（none，128，20）的时间序列数据进行训练和预测。其中，ResidualBlock是TCN块的基本组成部分，用于实现带有空洞卷积的残差网络。build_tcn_model函数使用输入的参数构建了TCN网络，包括多个TCN块和全连接层，并返回构建好的模型。在主函数中，我们使用上述定义的TCN网络进行模型的编译。