tcn结合自注意力keras代码，输入形状为（160，1）其中160是特征维数

下面是TCN结合自注意力的Keras代码，输入形状为（160，1）的示例：

from keras.layers import Input, Dense, Dropout, Lambda, Layer, Add, Multiply, Activation
from keras.models import Model
from keras.utils import to_categorical
from keras import backend as K
from keras import initializers, regularizers, constraints
from keras.layers import Conv1D, GlobalMaxPooling1D, GlobalAveragePooling1D, BatchNormalization, concatenate
from keras.layers import Add, Multiply, Activation, Lambda, Concatenate, Reshape, Permute
from keras.engine.topology import Layer
from keras.initializers import glorot_uniform
from keras.utils import conv_utils
from keras.regularizers import l2
import numpy as np


class SelfAttention(Layer):
    def __init__(self, output_dim, **kwargs):
        self.output_dim = output_dim
        super(SelfAttention, self).__init__(**kwargs)

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel',
                                      shape=(input_shape[-1], self.output_dim),
                                      initializer='glorot_uniform',
                                      trainable=True)
        self.bias = self.add_weight(name='bias',
                                    shape=(self.output_dim,),
                                    initializer='zeros',
                                    trainable=True)
        super(SelfAttention, self).build(input_shape)

    def call(self, x):
        eij = K.dot(x, self.kernel)
        eij = K.bias_add(eij, self.bias)
        eij = K.tanh(eij)
        a = K.exp(eij)
        a /= K.sum(a, axis=1, keepdims=True)
        return a

    def compute_output_shape(self, input_shape):
        return (input_shape[0], input_shape[1], self.output_dim)


class TCN(Layer):
    def __init__(self, nb_filters=64, kernel_size=2, nb_stacks=1,
                 dilations=None, activation='relu', use_skip_connections=True,
                 dropout_rate=0.0, kernel_initializer='he_normal',
                 padding='same', **kwargs):
        super(TCN, self).__init__(**kwargs)
        self.nb_filters = nb_filters
        self.kernel_size = kernel_size
        self.nb_stacks = nb_stacks
        self.dilations = dilations
        self.activation = activation
        self.use_skip_connections = use_skip_connections
        self.dropout_rate = dropout_rate
        self.kernel_initializer = kernel_initializer
        self.padding = padding
        self.skip_connections = []
        self.residual_blocks = []
        self.layers_output = []
        self.build_model()

    def build_model(self):
        self.layers_output = []
        self.residual_blocks = []
        self.dilations = self.dilations if self.dilations is not None else [1, 2, 4, 8, 16]
        self.nb_stacks = self.nb_stacks if self.nb_stacks is not None else 1
        self.kernel_size = self.kernel_size if self.kernel_size is not None else 3

        for s in range(self.nb_stacks):
            for d in self.dilations:
                res = self.residual_block(dilation_rate=d, stack_index=s)
                self.layers_output.append(res)
                self.residual_blocks.append(res)
        if self.use_skip_connections:
            self.skip_connections = self.skip_connection()
        else:
            self.skip_connections = [self.residual_blocks[-1]]

    def residual_block(self, dilation_rate):
        prev_x = inputs = Input(shape=(None, self.nb_filters))
        prev_x = Conv1D(filters=self.nb_filters, kernel_size=self.kernel_size,
                        dilation_rate=dilation_rate, padding=self.padding,
                        kernel_initializer=self.kernel_initializer)(prev_x)
        prev_x = BatchNormalization()(prev_x)
        prev_x = Activation(self.activation)(prev_x)
        prev_x = Dropout(self.dropout_rate)(prev_x)
        prev_x = Conv1D(filters=self.nb_filters, kernel_size=self.kernel_size,
                        dilation_rate=dilation_rate, padding=self.padding,
                        kernel_initializer=self.kernel_initializer)(prev_x)
        prev_x = BatchNormalization()(prev_x)
        x = Add()([inputs, prev_x])
        return Activation(self.activation)(x)

    def skip_connection(self):
        skip_connections = []
        for i in range(len(self.residual_blocks) - 1):
            if i % len(self.dilations) == 0:
                x = self.residual_blocks[i]
            x = Add()([x, self.residual_blocks[i + 1]])
            skip_connections.append(x)
        return skip_connections

    def call(self, inputs):
        x = inputs
        for layer in self.layers_output:
            x = layer(x)
        if self.use_skip_connections:
            x = Add()(self.skip_connections)
        return x

    def compute_output_shape(self, input_shape):
        return (input_shape[0], input_shape[1], self.nb_filters)

    def get_config(self):
        config = super().get_config().copy()
        config.update({
            'nb_filters': self.nb_filters,
            'kernel_size': self.kernel_size,
            'nb_stacks': self.nb_stacks,
            'dilations': self.dilations,
            'activation': self.activation,
            'use_skip_connections': self.use_skip_connections,
            'dropout_rate': self.dropout_rate,
            'kernel_initializer': self.kernel_initializer,
            'padding': self.padding,
        })
        return config


def tcn_attention_model(input_shape):
    inputs = Input(shape=input_shape)

    tcn_layer = TCN(nb_filters=64, kernel_size=3, nb_stacks=1, dilations=[1, 2, 4, 8, 16], padding='causal')(inputs)
    # Add self-attention layer
    att = SelfAttention(output_dim=64)(tcn_layer)
    att = Lambda(lambda x: K.sum(x, axis=1))(att)
    outputs = Dense(1, activation='sigmoid')(att)

    model = Model(inputs, outputs)
    return model

在上面的代码中，我们定义了一个带有TCN和自注意力层的Keras模型。输入形状为（160，1），输出形状为（1，）。其中TCN层是一个具有多个残差块的卷积神经网络，自注意力层用于对TCN层的输出进行加权平均。最后，我们使用一个全连接层将加权平均值转换为二元分类输出。