I have recently started using TensorFlow (TF), and I have come across a problem that I need some help with. Basically, I've restored a pre-trained model, and I need to modify the weights and biases of one of its layers before I retest its accuracy. Now, my problem is the following: how can I change the weights and biases using the assign method in TF? Is modifying the weights of a restored modeled even possible in TF?

Here is my code:

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 storedImVecDim = 784# The number of elements in a an image vector (flattening a 28x28 2D image)
NumOfClasses = 10g = tf.get_default_graph()with tf.Session() as sess:LoadMod = tf.train.import_meta_graph('simple_mnist.ckpt.meta')  # This object loads the modelLoadMod.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 ) )

In addition to an existing answer, tensor update can be performed via tf.assign function.

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