TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

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This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2.

It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as

`layers`

,

`estimator`

,

`dataset`

, ...).

**Update (05/16/2020):** Moving all default examples to TF2. For TF v1 examples: check here.

**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.

**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.

**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.

**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.

**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.

**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.

The tutorial index for TF v1 is available here: TensorFlow v1.15 Examples. Or see below for a list of the examples.

Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

Official Website: http://yann.lecun.com/exdb/mnist/.

To download all the examples, simply clone this repository:

`git clone https://github.com/aymericdamien/TensorFlow-Examples`

To run them, you also need the latest version of TensorFlow. To install it:

`pip install tensorflow`

or (with GPU support):

`pip install tensorflow\_gpu`

For more details about TensorFlow installation, you can check TensorFlow Installation Guide

The tutorial index for TF v1 is available here: TensorFlow v1.15 Examples.

**Hello World**(notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.**Basic Operations**(notebook) (code). A simple example that cover TensorFlow basic operations.**TensorFlow Eager API basics**(notebook) (code). Get started with TensorFlow's Eager API.

**Linear Regression**(notebook) (code). Implement a Linear Regression with TensorFlow.**Linear Regression (eager api)**(notebook) (code). Implement a Linear Regression using TensorFlow's Eager API.**Logistic Regression**(notebook) (code). Implement a Logistic Regression with TensorFlow.**Logistic Regression (eager api)**(notebook) (code). Implement a Logistic Regression using TensorFlow's Eager API.**Nearest Neighbor**(notebook) (code). Implement Nearest Neighbor algorithm with TensorFlow.**K-Means**(notebook) (code). Build a K-Means classifier with TensorFlow.**Random Forest**(notebook) (code). Build a Random Forest classifier with TensorFlow.**Gradient Boosted Decision Tree (GBDT)**(notebook) (code). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow.**Word2Vec (Word Embedding)**(notebook) (code). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow.

**Simple Neural Network**(notebook) (code). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation.**Simple Neural Network (tf.layers/estimator api)**(notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.**Simple Neural Network (eager api)**(notebook) (code). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.**Convolutional Neural Network**(notebook) (code). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.**Convolutional Neural Network (tf.layers/estimator api)**(notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.**Recurrent Neural Network (LSTM)**(notebook) (code). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.**Bi-directional Recurrent Neural Network (LSTM)**(notebook) (code). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.**Dynamic Recurrent Neural Network (LSTM)**(notebook) (code). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.

**Auto-Encoder**(notebook) (code). Build an auto-encoder to encode an image to a lower dimension and re-construct it.**Variational Auto-Encoder**(notebook) (code). Build a variational auto-encoder (VAE), to encode and generate images from noise.**GAN (Generative Adversarial Networks)**(notebook) (code). Build a Generative Adversarial Network (GAN) to generate images from noise.**DCGAN (Deep Convolutional Generative Adversarial Networks)**(notebook) (code). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.

**Save and Restore a model**(notebook) (code). Save and Restore a model with TensorFlow.**Tensorboard - Graph and loss visualization**(notebook) (code). Use Tensorboard to visualize the computation Graph and plot the loss.**Tensorboard - Advanced visualization**(notebook) (code). Going deeper into Tensorboard; visualize the variables, gradients, and more...

**Build an image dataset**(notebook) (code). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.**TensorFlow Dataset API**(notebook) (code). Introducing TensorFlow Dataset API for optimizing the input data pipeline.**Load and Parse data**(notebook). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...).**Build and Load TFRecords**(notebook). Convert data into TFRecords format, and load them.**Image Transformation (i.e. Image Augmentation)**(notebook). Apply various image augmentation techniques, to generate distorted images for training.