Implementation of Teacher-Student Model in PyTorch

Abdulkader Helwan
6 min readMar 18, 2023

With a pre-trained “teacher” network, teacher-student training is a method for accelerating training and enhancing the convergence of a neural network. It is widely used to train smaller, less expensive networks from more expensive, larger ones since it is both popular and effective. In a previous post, we discussed the concept of Knowlege distillation as the idea behind the Teacher-Student model. In this post, we’ll discuss the fundamentals of teacher-student training, demonstrate how to do it in PyTorch, and examine the results of using this approach. If you’re not familiar with softmax cross entropy, our introduction to it might be a helpful pre-read. This is a part of our series on training targets.

Main Concept

The concept is basic. Start by training a sizable neural network (the teacher) with training data as per normal. Then, build a second, smaller network (the student), and train it to replicate the teacher’s outcomes. For instance, teacher preparation might look like this:

for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # the predictors / inputs
Y = batch[1] # the targets
out = teacher(X)
. . .

But training the student looks like:

for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # the predictors / inputs
Y = teacher(X) # outputs from the teacher
out = student(X)
. . .

The teacher-student technique can be applied in a variety of ways because it is only a basic idea rather than a predetermined procedure. I’ve already looked at teacher-student relationships, but I wanted to review the concepts. I applied one of my typical instances of multi-class classification, where the objective is to predict a person’s political leaning (conservative, moderate, or liberal) based on their gender, age, state (Michigan, Nebraska, or Oklahoma), and income. The data after normalization and encoding looks like:

# sex  age  state       income  politics
1 0.24 1 0 0 0.2950 2
-1 0.39 0 0 1 0.5120 1
1 0.63 0 1 0 0.7580 0
-1 0.36 1 0 0 0.4450 1
. . .

We created a large teacher network with 6-(10–10)-3 architecture and trained it using NLLLoss(). Then we created a…

--

--