stevenlol / notebooks

Machine learning notebooks in different subjects optimized to run in google collaboratory

Notebooks

Training pix2pix

This notebook shows a simple pipeline for training pix2pix on a simple dataset.

One Place

This notebook shows how to train, test then deploy models in the browser directly from one notebook. We use a simple XOR example to prove this simple concept.

TPU vs GPU

Google recently allowed training on TPUs for free on colab. This notebook explains how to enable TPU training. Also, it reports some benchmarks using mnist dataset by comparing TPU and GPU performance.

Keras Custom Data Generator

This notebook shows to create a custom data genertor in keras.

Eager Execution and Gradient

As we know that TenosrFlow works with static graphs. So, first you have to create the graph then execute it later. This makes debugging a bit complicated. With Eager Execution you can now evalute operations directly without creating a session.

Eager Execution Enabled

In this notebook I explain different concepts in eager execution. I go over variables, ops, gradients, custom gradients, callbacks, metrics and creating models with tf.keras and saving/restoring them.

Sketcher

Create a simple app to recognize 100 drawings from the quickdraw dataset. A simple CNN model is created and served to deoploy in the browser to create a sketch recognizer app.

QuickDraw10

In this notebook we provide QuickDraw10 as an alternative for MNIST. A script is provided to download and load a preprocessed dataset for 10 classes with training and testing split. Also, a simple CNN model is implemented for training and testing.

Autoencoders

Autoencoders consists of two structures: the encoder and the decoder. The encoder network downsamples the data into lower dimensions and the decoder network reconstructs the original data from the lower dimension representation. The lower dimension representation is usually called latent space representation.

Weight Transfer

In this tutorial we explain how to transfer weights from a static graph model built with TensorFlow to a dynamic graph built with Keras. We will first train a model using Tensorflow then we will create the same model in keras and transfer the trained weights between the two models.

BigGan

Create some cool gifs by interpolation in the latent space of the BigGan model. The model is imported from tensorflow hub.

BigGanEx

In this notebook I give a basic introduction to bigGans. I also, how to interpolate between z-vector values. Moreover, I show the results of multiple experiments I made in the latent space of BigGans.