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README.md

TensorFlow 2.0 Examples

*** More examples to be added later... ***

0 - Prerequisite

1 - Introduction

  • Hello World (notebook). Very simple example to learn how to print "hello world" using TensorFlow 2.0.
  • Basic Operations (notebook). A simple example that cover TensorFlow 2.0 basic operations.

2 - Basic Models

  • Linear Regression (notebook). Implement a Linear Regression with TensorFlow 2.0.
  • Logistic Regression (notebook). Implement a Logistic Regression with TensorFlow 2.0.
  • Word2Vec (Word Embedding) (notebook). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0.
  • GBDT (Gradient Boosted Decision Trees) (notebooks). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset.

3 - Neural Networks

Supervised
  • Simple Neural Network (notebook). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset.
  • Simple Neural Network (low-level) (notebook). Raw implementation of a simple neural network to classify MNIST digits dataset.
  • Convolutional Neural Network (notebook). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset.
  • Convolutional Neural Network (low-level) (notebook). Raw implementation of a convolutional neural network to classify MNIST digits dataset.
  • Recurrent Neural Network (LSTM) (notebook). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API.
  • Bi-directional Recurrent Neural Network (LSTM) (notebook). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API.
  • Dynamic Recurrent Neural Network (LSTM) (notebook). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API.
Unsupervised
  • Auto-Encoder (notebook). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
  • DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.

4 - Utilities

  • Save and Restore a model (notebook). Save and Restore a model with TensorFlow 2.0.
  • Build Custom Layers & Modules (notebook). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models.
  • Tensorboard (notebook). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard.

5 - Data Management

  • Load and Parse data (notebook). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...).
  • Build and Load TFRecords (notebook). Convert data into TFRecords format, and load them with TensorFlow 2.0.
  • Image Transformation (i.e. Image Augmentation) (notebook). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training.

6 - Hardware

  • Multi-GPU Training (notebook). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset.

Installation

To install TensorFlow 2.0, simply run:

pip install tensorflow==2.0.0

or (if you want GPU support):

pip install tensorflow_gpu==2.0.0