tensorflow_v2

TensorFlow 2.0 Examples

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

0 - Prerequisite

• Introduction to Machine Learning.
• Introduction to MNIST Dataset.

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.

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.

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.

Installation

To install TensorFlow 2.0, simply run:

pip install tensorflow==2.0.0-beta1

or (if you want GPU support):

pip install tensorflow_gpu==2.0.0-beta1