tcn、lstm、attention结合的时序预测的tensorflow的代码

以下是一个结合了TCN、LSTM和Attention的时序预测模型的TensorFlow代码示例：

import tensorflow as tf

class TCN_Attention_LSTM(tf.keras.Model):
  
  def __init__(self, tcn_layers, lstm_units, attention_units, input_shape):
    super(TCN_Attention_LSTM, self).__init__()

    self.tcn_layers = tcn_layers
    self.lstm_units = lstm_units
    self.attention_units = attention_units
    self.input_shape = input_shape

    self.tcn_layer = []
    for i in range(self.tcn_layers):
      self.tcn_layer.append(tf.keras.layers.Conv1D(filters=64, kernel_size=3, dilation_rate=2**i, padding='same', activation=tf.nn.relu))
    
    self.attention_layer = tf.keras.layers.Dense(units=self.attention_units, activation=tf.nn.tanh)
    self.lstm_layer = tf.keras.layers.LSTM(units=self.lstm_units, return_sequences=True)
    self.dense_layer = tf.keras.layers.Dense(units=1)

  def call(self, inputs):

    # TCN
    tcn_input = inputs
    for i in range(self.tcn_layers):
      tcn_output = self.tcn_layer[i](tcn_input)
      tcn_input = tcn_output + tcn_input
    
    # Attention
    attention_output = self.attention_layer(tcn_output)
    attention_weights = tf.nn.softmax(attention_output, axis=1)
    attention_output = tf.reduce_sum(tf.multiply(tcn_output, attention_weights), axis=1)

    # LSTM
    lstm_output = self.lstm_layer(tcn_output)

    # Concatenate LSTM and attention output
    lstm_attention_output = tf.concat([lstm_output, attention_output[:, tf.newaxis, :]], axis=1)

    # Dense layer
    output = self.dense_layer(lstm_attention_output)

    return output

在这个模型中，首先使用TCN进行特征提取，然后使用Attention层对TCN的输出进行加权，再将加权后的结果与LSTM的输出进行拼接，最后通过一个全连接层输出预测结果。在这个模型中，TCN、LSTM和Attention的超参数可以根据实际情况进行调整。