如何在tensorflow中执行图像的线性单应性

import tensorflow as tf
import numpy as np

input_image = tf.placeholder(tf.float32, shape=[None, height, width, channels])
target_image = tf.placeholder(tf.float32, shape=[None, height, width, channels])

def linear_homography(input_image):
    # 定义模型的网络结构
    # ...
    return output_image

output_image = linear_homography(input_image)
loss = tf.reduce_mean(tf.square(output_image - target_image))

optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        # 获取训练数据
        # ...
        # 执行训练操作
        _, current_loss = sess.run([train_op, loss], feed_dict={input_image: input_data, target_image: target_data})
        print("Epoch: {}, Loss: {}".format(epoch, current_loss))