Transfer learning within Tensorflow's inception model I want to freeze the layers, except the first three layers, in the Inception v3 model with TensorFlow in Python 3, and modify the weights of these three layers to be able to re-initialize and re-train only the three first layers of the network.  

If this can't be done within the inception model, is there any other network (in TensorFlow) with which this could be done?
To freeze the lower layers during training, the simplest solution is to give the optimizer the list of variables to train, excluding the variables from the lower layers:



train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                               scope="hidden[34]|outputs")
training_op = optimizer.minimize(loss, var_list=train_vars)


The first line gets the list of all trainable variables in hidden layers 3 and 4 and in the output layer. This leaves out the variables in the hidden layers 1 and 2.