It’s been around 5 months since I released my last blog on DCGAN Review and Implementation using PyTorch C++ API and I’ve missed writing blogs badly! Straight the to the point, I’m back!

I’m pleased to start a series of blogs on GANs and their implementation with PyTorch C++ API. We’ll be starting with one of the initial GANs - DCGANs (Deep Convolutional Generative Adversarial Networks).

Introduction to Xeus Cling

Today, we are going to run our C++ codes in the Jupyter Notebook. Sounds ambitious? Not much. Let’s see how we do it using Xeus Cling.

Transfer Learning

Before we go ahead and discuss the Why question of Transfer Learning, let’s have a look at What is Transfer Learning? Let’s have a look at the Notes from CS231n on Transfer Learning:

Hi Everyone! So excited to be back with another blog in the series of PyTorch C++ Blogs.

Today, we are going to see a practical example of applying a CNN to a Custom Dataset - Dogs vs Cats. This is going to be a short post of showing results and discussion about hyperparameters and loss functions for the task, as code snippets and explanation has been provided here, here and here.

Recap of the last blog

Before we move on, it’s important what we covered in the last blog. We’ll be going forward from loading Custom Dataset to now using the dataset to train our VGG-16 Network. Previously, we were able to load our custom dataset using the following template:

Hi everyone! If you’ve been following my blog, you would have noticed a couple of PyTorch Blogs (PyTorch C++ API: Installation and MNIST Digit Classification using VGG-16, PyTorch C++ API: Using Custom Data). I initially started in a motive to help people getting started with, as there are not a lot of tutorials available on Libtorch (PyTorch C++ API).

Overview: How C++ API loads data?

Environment Setup [Ubuntu 16.04, 18.04]