Python is an interpreting language. It means there is an interpreter to run our program, rather than compiling the code and running natively. In Python, a REPL (read-eval-print loop) can run commands line by line. Together with some inspection tools provided by Python, it helps to develop codes.
In the following, you will see how to make use of the Python interpreter to inspect an object and develop a program.
After finishing this tutorial, you will learn:How to work in the Python interpreter
How to use the inspection functions in Python
How to develop a solution step by step with the help of inspection functions
Tutorial Overview
This tutorial is in four parts; they are:PyTorch and TensorFlow
Looking for Clues
Learning from the Weights
Making a Copier
PyTorch and TensorFlow
PyTorch and TensorFlow are the two biggest neural network libraries in Python. Their code is different, but the things they can do are similar.
Consider the classic MNIST handwritten digit recognition problem; you can build a LeNet-5 model to classify the digits as follows:
Looking for Clues
If you understand what the above neural networks are doing, you should be able to tell that there is nothing but many multiply and add calculations in each layer. Mathematically, there is a matrix multiplication between the input and the kernel of each fully-connected layer before adding the bias to the result. In the convolutional layers, there is the element-wise multiplication of the kernel to a portion of the input matrix before taking the sum of the result and adding the bias as one output element of the feature map.
While developing the same LeNet-5 model using two different frameworks, it should be possible to make them work identically if their weights are the same. How can you copy over the weight from one model to another, given their architectures are identical?
You can load the saved models as follows:
www.dprg.co.in
