Skip to content
Simple tutorials for building neural networks with TensorFlow Eager mode.
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
datasets Finished 6th tutorial and updated names of the rest of the tutorials Jun 5, 2018
models_checkpoints Added new tutorial and updated name of tutorials May 13, 2018
tutorials_graphics Updated READMEand added flowcharts to tutorials Jun 5, 2018
.gitignore Finished 5th tutorial and added dummy images Jun 4, 2018
01_simple_feedforward_neural_network.ipynb
02_using_metrics_in_eager_mode.ipynb
03_save_and_restore_model.ipynb Updated READMEand added flowcharts to tutorials Jun 5, 2018
04_text_data_to_tfrecords.ipynb
05_images_to_tfrecords.ipynb
06_read_data_in_batches_from_tfrecords.ipynb Updated READMEand added flowcharts to tutorials Jun 5, 2018
07_convolutional_neural_networks_for_emotion_recognition.ipynb Finished 6th tutorial and updated names of the rest of the tutorials Jun 5, 2018
08_dynamic_recurrent_neural_networks_for_sequence_classification.ipynb Finished 6th tutorial and updated names of the rest of the tutorials Jun 5, 2018
09_recurrent_neural_networks_for_time_series_regression.ipynb Finished 6th tutorial and updated names of the rest of the tutorials Jun 5, 2018
README.md
data_utils.py Updated tutorials to latest API changes Apr 22, 2018

README.md

Simple tutorials on deep learning using TensorFlow Eager

This repo aims to help people who would like to start getting hands-on experience with deep learning using the TensorFlow Eager mode. TensorFlow Eager mode lets you build neural networks as easy as you would do with Numpy, with the huge advantage that it provides automatic differentiation (no more handwritten backprop. YAAAY!). It can ran also on GPUs making the neural networks training significantly faster.

I will try to make the tutorials accessible for everyone, thus I will try to work on problems that do not require a GPU to work on.

TensorFlow Version used in the tutorials - 1.7

List of tutorials available:

Getting started


  • 01. Build a simple neural network - This tutorial shows you how to build and train a one-hidden layer neural network using the Eager mode of TensorFlow, on a synthetically generated dataset.

  • 02. Using metrics in Eager mode - This tutorial shows you how to use metrics that are compatible with Eager mode, for three types of machine learning problems (multi-classification, imbalanced dataset and regression).

Simple but useful stuff


  • 03. Save and restore a trained model - Simple tutorial on how you can save a trained model and restore it at a later time to make predictions on new data.

  • 04. Transfer text data to TFRecords - This tutorial shows you how to store text data of variable sequence length to TFRecords. The data can be easily padded on the fly, within a batch, when reading the dataset with an iterator.

  • 05. Transfer image data to TFRecords - Easy and simple tutorial on how to transfew image data and its metadata (e.g. target) to TFRecords.

  • 06. How to read TFRecords data in batches - This tutorial shows you how to read either variable length sequence data or image data, in batches, from TFRecords.

Convolutional neural networks


  • 07. Build a CNN for emotion recognition - This tutorial shows you how to build a CNN from scratch using the TensorFlow Eager API and the FER2013 dataset. At the end of the tutorial you will be able to test the network on yourself using a webcam. Very fun exercise!

Recurrent neural networks


  • 08. Build a dynamic RNN for sequence classification - Learn how to work with variable sequence input data. This tutorial shows you how to build a dynamic RNN using the TensorFlow Eager API and the Stanford Large Movie Review Dataset.

  • 09. Build a RNN for time series regression - Learn how to build a RNN for timeseries forecasting.

Requests for tutorials:

  • If you have any requests for a specific tutorial please let me know.

Improvement advice:

  • Please let me know if you have any suggestions to improve these tutorials. The aim is to help you getting a good grasp of this framework but I am also looking to improve my programming skills so any feedback will be really appreciated :)!
You can’t perform that action at this time.