用tesnsorflow的keras实现attention_3d_block+TCN模型预测波士顿房价，过去5步预测未来1步

以下是使用TensorFlow的Keras实现带有注意力机制和TCN的模型来预测波士顿房价，并使用过去5步来预测未来1步的示例代码：

import numpy as np
import pandas as pd
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Conv1D, GlobalMaxPooling1D, concatenate, Activation, Dropout
from tensorflow.keras.layers import Permute, Lambda, RepeatVector, Multiply, Add, Flatten
from tensorflow.keras import backend as K

# 读取数据并进行预处理
boston_housing = pd.read_csv('https://storage.googleapis.com/tf-datasets/tf_datasets/boston_housing.csv')
train_data = boston_housing.values[:, :-1]
train_target = boston_housing.values[:, -1]
train_data = (train_data - np.mean(train_data, axis=0)) / np.std(train_data, axis=0)

# 定义输入的形状
seq_len = 5
input_shape = (seq_len, train_data.shape[1])
input_layer = Input(shape=input_shape)

# 定义注意力机制
def attention_3d_block(inputs):
    a = Permute((2, 1))(inputs)
    a = Dense(seq_len, activation='softmax')(a)
    a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)
    a = RepeatVector(train_data.shape[1])(a)
    a_probs = Permute((2, 1), name='attention_vec')(a)
    output_attention_mul = Multiply()([inputs, a_probs])
    return output_attention_mul

# 定义TCN模型
def tcn_block(input_layer, n_filters, kernel_size, dilation_rate):
    d = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=dilation_rate, padding='causal')(input_layer)
    d = Activation('relu')(d)
    d = Dropout(0.2)(d)
    return d

n_filters = 64
kernel_size = 2
dilation_rates = [2**i for i in range(3)]
dilated_layers = []
for dilation_rate in dilation_rates:
    dilated_layers.append(tcn_block(input_layer, n_filters, kernel_size, dilation_rate))
concatenated_layers = concatenate(dilated_layers, axis=-1)

# 添加注意力机制
attention_layer = attention_3d_block(concatenated_layers)

# 全局最大池化
pooling_layer = GlobalMaxPooling1D()(attention_layer)

# 输出层
output_layer = Dense(1)(pooling_layer)

# 构建模型
model = Model(inputs=input_layer, outputs=output_layer)

# 编译模型
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(train_data[:-seq_len-1], train_target[seq_len+1:], epochs=50, batch_size=32, validation_split=0.2)

# 预测未来1步
test_data = train_data[-seq_len:]
predicted_price = model.predict(np.expand_dims(test_data, axis=0))[0][0]
print("Predicted price for the next time step: ", predicted_price)

这个模型包含了一个注意力机制和TCN模块，可以用来预测波士顿房价。在这个例子中，我们使用过去5步来预测未来1步。