将DA-RNN中RNN换为GRU网络

好的，下面是将DA-RNN中的RNN换为GRU网络的代码实现：

import numpy as np
import tensorflow as tf

class DA_GRU(tf.keras.Model):
    def __init__(self, input_dim, output_dim, T, hidden_dim, attn_dim):
        super(DA_GRU, self).__init__()

        self.hidden_dim = hidden_dim
        self.T = T

        self.encoder = tf.keras.layers.Dense(hidden_dim, activation='relu')
        self.decoder = tf.keras.layers.Dense(output_dim, activation='linear')

        self.gru = tf.keras.layers.GRU(hidden_dim, return_sequences=True, return_state=True)
        self.W1 = tf.keras.layers.Dense(attn_dim)
        self.W2 = tf.keras.layers.Dense(attn_dim)
        self.v = tf.keras.layers.Dense(1)

    def call(self, inputs):
        x = inputs

        # Encode the input sequence
        encoded = self.encoder(x)

        # Split the encoded sequence into overlapping windows
        windows = []
        for i in range(self.T, encoded.shape[0]+1):
            windows.append(encoded[i-self.T:i, :])
        windows = np.array(windows)

        # Compute the attention weights and context vectors for each window
        context_vectors, attention_weights = [], []
        for i in range(windows.shape[0]):
            # Compute the attention weights for the current window
            score = tf.nn.tanh(self.W1(windows[i]) + self.W2(encoded))
            attention_weight = tf.nn.softmax(self.v(score), axis=0)

            # Compute the context vector for the current window
            context_vector = attention_weight * encoded
            context_vector = tf.reduce_sum(context_vector, axis=0)

            context_vectors.append(context_vector)
            attention_weights.append(attention_weight)

        context_vectors = np.array(context_vectors)
        attention_weights = np.array(attention_weights)

        # Pass the context vectors through the GRU
        output, state = self.gru(context_vectors)

        # Decode the output sequence
        decoded = self.decoder(output)

        return decoded, attention_weights

这段代码实现了一个基于GRU网络的DA-RNN模型。与标准的DA-RNN不同，这个模型使用了GRU层来代替RNN层。在调用模型时，输入数据应该是一个形状为 (seq_length, input_dim) 的张量 x，其中 seq_length 表示时间序列的长度，input_dim 表示每个时间步的输入维度。模型会根据输入计算出输出张量和注意力权重，然后返回它们。需要注意的是，在这个模型中，输入序列是被分割成了多个窗口，并且每个窗口都会计算对应的上下文向量和注意力权重。最终输出的是所有窗口的输出和注意力权重，而不是单个窗口的输出和注意力权重。