IPSO-GRU算法 python代码

我可以回答这个问题，以下是IPSO-GRU算法的Python代码：

import numpy as np
import tensorflow as tf

class IPSO_GRU:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim

        self.x = tf.placeholder(tf.float32, [None, None, input_dim])
        self.y = tf.placeholder(tf.float32, [None, None, output_dim])

        self.gru_cell = tf.nn.rnn_cell.GRUCell(hidden_dim)
        self.outputs, self.states = tf.nn.dynamic_rnn(self.gru_cell, self.x, dtype=tf.float32)

        self.w = tf.Variable(tf.random_normal([hidden_dim, output_dim]))
        self.b = tf.Variable(tf.random_normal([output_dim]))

        self.pred = tf.matmul(self.outputs, self.w) + self.b

        self.loss = tf.reduce_mean(tf.square(self.pred - self.y))
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)

    def train(self, x_train, y_train, epochs):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())

            for i in range(epochs):
                _, loss = sess.run([self.optimizer, self.loss], feed_dict={self.x: x_train, self.y: y_train})
                print("Epoch: {}, Loss: {}".format(i+1, loss))

    def predict(self, x_test):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())

            return sess.run(self.pred, feed_dict={self.x: x_test})

希望能对你有所帮助！