python之在 TensorFlow 中修改恢复的 CNN 模型的权重和偏差

我最近开始使用 TensorFlow (TF)，遇到了一个问题，需要一些帮助。基本上，我恢复了一个预训练模型，我需要在重新测试其准确性之前修改其中一层的权重和偏差。现在，我的问题如下: 如何使用 TF 中的 assign 方法更改权重和偏差？甚至可以在 TF 中修改恢复模型的权重吗？

这是我的代码:

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data # Imports the MINST dataset 
 
# Data Set: 
# --------- 
mnist = input_data.read_data_sets("/home/frr/MNIST_data", one_hot=True)# An object where data is stored 
 
ImVecDim = 784# The number of elements in a an image vector (flattening a 28x28 2D image) 
NumOfClasses = 10 
 
g = tf.get_default_graph() 
 
with tf.Session() as sess: 
  LoadMod = tf.train.import_meta_graph('simple_mnist.ckpt.meta')  # This object loads the model 
  LoadMod.restore(sess, tf.train.latest_checkpoint('./'))# Loading weights and biases and other stuff to the model 
 
  # ( Here I'd like to modify the weights and biases of layer 1, set them to one for example, before I go ahead and test the accuracy ) # 
 
  # Testing the acuracy of the model: 
  X = g.get_tensor_by_name('ImageIn:0') 
  Y = g.get_tensor_by_name('LabelIn:0') 
  KP = g.get_tensor_by_name('KeepProb:0') 
  Accuracy = g.get_tensor_by_name('NetAccuracy:0') 
  feed_dict = { X: mnist.test.images[:256], Y: mnist.test.labels[:256], KP: 1.0 } 
  print( 'Model Accuracy = ' ) 
  print( sess.run( Accuracy, feed_dict ) ) 

请您参考如下方法:

除了现有答案外，张量更新还可以通过 tf.assign 执行功能。

v1 = sess.graph.get_tensor_by_name('v1:0') 
print(sess.run(v1))   # 1.0 
sess.run(tf.assign(v1, v1 + 1)) 
print(sess.run(v1))   # 2.0