Tensorflow: how to use pretrained weights in new graph?

Question

I\'m trying to build an object detector with CNN using tensorflow with python framework. I would like to train my model to do just object recognition (classification) at fir

Answer 1

Although I agree with Aechlys to restore variables. The problem is harder when we want to fix these variables. For example, we trained these variables and we want to use them in another model, but this time without training them (training new variables like in transfer-learning). You can see the answer I posted here.

Quick example:

 with tf.session() as sess:
    new_saver = tf.train.import_meta_graph(pathToMeta)
    new_saver.restore(sess, pathToNonMeta) 

    weight1 = sess.run(sess.graph.get_tensor_by_name("w1:0")) 


 tf.reset_default_graph() #this will eliminate the variables we restored


 with tf.session() as sess:
    weights = 
       {
       '1': tf.Variable(weight1 , name='w1-bis', trainable=False)
       }
...

We are now sure the restored variables are not a part of the graph.