我在MNIST数据集上训练了TensorFlow（TF）CNN模型，并在训练后使用tf.train.Saver（）存储了模型 . 现在，我想恢复这个模型并在重新训练之前在两个层之间添加一个操作，例如，在第一个卷积层（名为'conv1'）的输出和第一个汇集层（名为'pool1'）之间添加tf.stop_gradient（） . 那可能吗？如果是这样，我怎么能这样做？

这是我的代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/home/frr/MNIST_data", one_hot=True)

learning_rate = 0.001
training_iters = 100000
batch_size = 64
display_step = 20
ImVecDim = 784
NumOfClasses = 10
dropout = 0.8

g = tf.get_default_graph()

with tf.Session() as sess:
   LoadMod = tf.train.import_meta_graph('simple_mnist.ckpt.meta')
   LoadMod.restore(sess, tf.train.latest_checkpoint('./'))

#############################################################################
#     Adding a tf.stop_gradient() operation between 'conv1' and 'pool1'     #
#############################################################################

   x = g.get_tensor_by_name('ImageIn:0')
   y = g.get_tensor_by_name('LabelIn:0')
   keep_prob = g.get_tensor_by_name('KeepProb:0')
   accuracy = g.get_tensor_by_name('NetAccuracy:0')
   optimizer = g.get_operation_by_name('Adam')
   step = 1
   while step * batch_size < training_iters:
       batch_xs, batch_ys = mnist.train.next_batch(batch_size)

       sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
       if step % display_step == 0:
          acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
          print("Iter " + str(step*batch_size) + ", Training Accuracy= " + "{:.5f}".format(acc))
   step += 1
   print("Optimization Finished!")
   print("Testing Accuracy:",
   sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))
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