Awesome Keras | Massive Resources & Collection ⭐

Awesome Keras | Massive Resources & Collection :star:

Quiet fire — that’s Aina. · 2021-01-25T02:57:30Z

![image|652x235](upload://gnv0UKEHQ66PWv14EjNZpoARP2M.png) # Awesome Keras This is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library. If you have a high-quality tutorial or project to add, please open a PR. ## Official starter resources * [keras.io](http://keras.io/) - Keras documentation * [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide/) * [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide/) * [Keras FAQ](http://keras.io/getting-started/faq/) ## Tutorials * [Quick start: the Iris dataset in Keras and scikit-learn](https://github.com/fastforwardlabs/keras-hello-world/blob/master/kerashelloworld.ipynb) * [Using pre-trained word embeddings in a Keras model](http://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) * [Building powerful image classification models using very little data](http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) * [Building Autoencoders in Keras](http://blog.keras.io/building-autoencoders-in-keras.html) * [A complete guide to using Keras as part of a TensorFlow workflow](http://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html) * Introduction to Keras, from University of Waterloo: [video](https://www.youtube.com/watch?v=Tp3SaRbql4k) - [slides](https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/keras_tutorial.pdf) * Introduction to Deep Learning with Keras, from CERN: [video](http://cds.cern.ch/record/2157570?ln=en) - [slides](https://indico.cern.ch/event/506145/contributions/2132944/attachments/1258124/1858154/NNinKeras_MPaganini.pdf) * [Installing Keras for deep learning](http://www.pyimagesearch.com/2016/07/18/installing-keras-for-deep-learning/) * [Develop Your First Neural Network in Python With Keras Step-By-Step](http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/) * [Practical Neural Networks with Keras: Classifying Yelp Reviews](http://www.developintelligence.com/blog/2017/06/practical-neural-networks-keras-classifying-yelp-reviews/) (Shows basic classification and how to set up a GPU instance on AWS) * [Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/) * [Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/) * [Keras video tutorials from Dan Van Boxel](https://www.youtube.com/playlist?list=PLFxrZqbLojdKuK7Lm6uamegEFGW2wki6P) * [Keras Deep Learning Tutorial for Kaggle 2nd Annual Data Science Bowl](https://github.com/jocicmarko/kaggle-dsb2-keras/) * [Collection of tutorials setting up DNNs with Keras](http://ml4a.github.io/guides/) * [Fast.AI - Practical Deep Learning For Coders, Part 1](http://course.fast.ai/) (great information on deep learning in general, heavily uses Keras for the labs) * [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511) * [A Bit of Deep Learning and Keras](https://www.youtube.com/watch?v=UOEhojCzWrY&list=PLgJhDSE2ZLxaPX0jteHZG4skdj8ZrST9d): a multipart video introduction to deep learning and keras * [Five simple examples of the Keras Functional API](http://www.puzzlr.org/the-keras-functional-api-five-simple-examples/) * [Keras video tutorials playlist from Deeplizard](https://www.youtube.com/watch?v=RznKVRTFkBY&list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL) ## Books based on Keras * [Deep Learning with Python](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/) * [Deep Learning with Keras](https://www.amazon.com/Deep-Learning-Keras-Implementing-learning/dp/1787128423/) * [Deep Learning and the Game of Go (MEAP)](https://www.manning.com/books/deep-learning-and-the-game-of-go) ## Code examples ### Working with text * [Reuters topic classification](https://github.com/fchollet/keras/blob/master/examples/reuters_mlp.py) * [LSTM on the IMDB dataset (text sentiment classification)](https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py) * [Bidirectional LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_bidirectional_lstm.py) * [1D CNN on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn.py) * [1D CNN-LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn_lstm.py) * [LSTM-based network on the bAbI dataset](https://github.com/fchollet/keras/blob/master/examples/babi_rnn.py) * [Memory network on the bAbI dataset (reading comprehension question answering)](https://github.com/fchollet/keras/blob/master/examples/babi_memnn.py) * [Sequence to sequence learning for performing additions of strings of digits](https://github.com/fchollet/keras/blob/master/examples/addition_rnn.py) * [LSTM text generation](https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py) * [Using pre-trained word embeddings](https://github.com/fchollet/keras/blob/master/examples/pretrained_word_embeddings.py) * [Monolingual and Multilingual Image Captioning](https://github.com/elliottd/GroundedTranslation) * [FastText on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_fasttext.py) * [Structurally constrained recurrent nets text generation](https://github.com/nzw0301/keras-examples/blob/master/SCRNLM.ipynb) * [Character-level convolutional neural nets for text classification](https://github.com/johnb30/py_crepe) * [LSTM to predict gender of a name](https://github.com/divamgupta/lstm-gender-predictor) * [Language/dialect identification with multiple character-level CNNs](https://github.com/boknilev/dsl-char-cnn) ### Working with images * [Simple CNN on MNIST](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py) * [Simple CNN on CIFAR10 with data augmentation](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py) * [Inception v3](https://github.com/fchollet/keras/blob/master/examples/inception_v3.py) * [VGG 16 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3) * [VGG 19 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/8d096f48a1be4a2d660d) * ResNet 50 (with pre-trained weights): [1](https://github.com/fchollet/keras/pull/3266/files) - [2](https://github.com/raghakot/keras-resnet) * [FractalNet](https://github.com/snf/keras-fractalnet) * [AlexNet, VGG 16, VGG 19, and class heatmap visualization](https://github.com/heuritech/convnets-keras) * [Visual-Semantic Embedding](https://github.com/awentzonline/keras-visual-semantic-embedding) * Variational Autoencoder: [with deconvolutions](https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder_deconv.py) - [with upsampling](https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py) * [Visual question answering](https://github.com/avisingh599/visual-qa) * [Deep Networks with Stochastic Depth](https://github.com/dblN/stochastic_depth_keras) * [Smile detection with a CNN](https://github.com/kylemcdonald/SmileCNN) * [VGG-CAM](https://github.com/tdeboissiere/VGG16CAM-keras) * [t-SNE of image CNN fc7 activations](https://github.com/ml4a/ml4a-guides/blob/master/notebooks/tsne-images.ipynb) * [VGG16 Deconvolution network](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DeconvNet) * Wide Residual Networks (with pre-trained weights): [1](https://github.com/asmith26/wide_resnets_keras) - [2](https://github.com/titu1994/Wide-Residual-Networks) * Ultrasound nerve segmentation: [1](https://github.com/jocicmarko/ultrasound-nerve-segmentation) - [2](https://github.com/raghakot/ultrasound-nerve-segmentation) * [DeepMask object segmentation](https://github.com/abbypa/NNProject_DeepMask) * Densely Connected Convolutional Networks: [1](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet) - [2](https://github.com/titu1994/DenseNet) * [Snapshot Ensembles: Train 1, Get M for Free](https://github.com/titu1994/Snapshot-Ensembles) * [Single Shot MultiBox Detector](https://github.com/rykov8/ssd_keras) * [Popular Image Segmentation Models : FCN, Segnet, U-Net etc.](https://github.com/divamgupta/image-segmentation-keras) ### Creative visual applications * [Real-time style transfer](https://github.com/awentzonline/keras-rtst) * Style transfer: [1](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py) - [2](https://github.com/titu1994/Neural-Style-Transfer) * [Image analogies](https://github.com/awentzonline/image-analogies): Generate image analogies using neural matching and blending. * [Visualizing the filters learned by a CNN](https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py) * [Deep dreams](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py) * GAN / DCGAN: [1](https://github.com/phreeza/keras-GAN) - [2](https://github.com/jacobgil/keras-dcgan) - [3](https://github.com/osh/KerasGAN) - [4](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/GAN) * [InfoGAN](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/InfoGAN) * [pix2pix](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix) * [DFI](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DFI): Deep Feature Interpolation * [Colorful Image colorization](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful): B&W to color ### Reinforcement learning * [DQN](https://github.com/sherjilozair/dqn) * [FlappyBird DQN](https://github.com/yanpanlau/Keras-FlappyBird) * [async-RL](https://github.com/coreylynch/async-rl): Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning" * [keras-rl](https://github.com/matthiasplappert/keras-rl): A library for state-of-the-art reinforcement learning. Integrates with OpenAI Gym and implements DQN, double DQN, Continuous DQN, and DDPG. ### Miscallenous architecture blueprints * [Stateful LSTM](https://github.com/fchollet/keras/blob/master/examples/stateful_lstm.py) * [Siamese network](https://github.com/fchollet/keras/blob/master/examples/mnist_siamese_graph.py) * [Pretraining on a different dataset](https://github.com/fchollet/keras/blob/master/examples/mnist_transfer_cnn.py) * [Neural programmer-interpreter](https://github.com/mokemokechicken/keras_npi) ## Third-party libraries * [Elephas](https://github.com/maxpumperla/elephas): Distributed Deep Learning with Keras & Spark * [Hyperas](https://github.com/maxpumperla/hyperas): Hyperparameter optimization * [Hera](https://github.com/jakebian/hera): in-browser metrics dashboard for Keras models * [Kerlym](https://github.com/osh/kerlym): reinforcement learning with Keras and OpenAI Gym * [Qlearning4K](https://github.com/farizrahman4u/qlearning4k): reinforcement learning add-on for Keras * [seq2seq](https://github.com/farizrahman4u/seq2seq): Sequence to Sequence Learning with Keras * [Seya](https://github.com/EderSantana/seya): Keras extras * [Keras Language Modeling](https://github.com/codekansas/keras-language-modeling): Language modeling tools for Keras * [Recurrent Shop](https://github.com/datalogai/recurrentshop): Framework for building complex recurrent neural networks with Keras * [Keras.js](https://github.com/transcranial/keras-js): Run trained Keras models in the browser, with GPU support * [keras-vis](https://github.com/raghakot/keras-vis): Neural network visualization toolkit for keras. ## Projects built with Keras * [RocAlphaGo](https://github.com/Rochester-NRT/RocAlphaGo): An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" * [BetaGo](https://github.com/maxpumperla/betago): Deep Learning Go bots using Keras * [DeepJazz](https://github.com/jisungk/deepjazz): Deep learning driven jazz generation using Keras * [dataset-sts](https://github.com/brmson/dataset-sts): Semantic Text Similarity Dataset Hub * [snli-entailment](https://github.com/shyamupa/snli-entailment): Independent implementation of attention model for textual entailment from the paper ["Reasoning about Entailment with Neural Attention"](http://arxiv.org/abs/1509.06664). * [Headline generator](https://github.com/udibr/headlines): independent implementation of [Generating News Headlines with Recurrent Neural Networks](http://arxiv.org/abs/1512.01712) * [LipNet](https://github.com/rizkiarm/LipNet): independent implementation of [LipNet: End-to-End Sentence-level Lipreading](https://arxiv.org/abs/1611.01599). * [caption_generator](https://github.com/anuragmishracse/caption_generator): An implementation of image caption generation in natural language inspired from [Show and Tell: A Neural Image Caption Generator](https://arxiv.org/pdf/1411.4555.pdf). * [NMT-Keras](https://github.com/lvapeab/nmt-keras): Neural Machine Translation using Keras. * [Conx](https://conx.readthedocs.io/) - easy-to-use layer on top of Keras, with visualizations (eg, no knowledge of numpy needed) ## Modeling Tools/Utilities 1. https://github.com/joeddav/devol 2. https://github.com/maxpumperla/hyperas 3. https://github.com/keras-team/keras-contrib 4. [Accelerating Deep Learning with Multiprocess Image Augmentation in Keras](https://github.com/stratospark/keras-multiprocess-image-data-generator) ([accompanying blog post](http://blog.stratospark.com/multiprocess-image-augmentation-keras.html)) 5. [ml-tools: Tools for common machine learning tasks using Tensorflow and Keras](https://github.com/triagemd/ml-tools) 6. https://github.com/kuza55/keras-extras 7. [keras-multi-gpu: Multi-GPU data-parallel training in Keras](https://github.com/rossumai/keras-multi-gpu) 8. [keras_callbacks_example: Keras Callback Examples](https://github.com/keunwoochoi/keras_callbacks_example) ## Implementation (from Scratch)/Pre-trained Models 1. https://github.com/raghakot/keras-resnet 2. https://github.com/XifengGuo/CapsNet-Keras 3. https://github.com/kentsommer/keras-inceptionV4 4. https://github.com/fchollet/deep-learning-models 5. https://github.com/titu1994/DenseNet 6. [BatchRenormalization: Batch Renormalization algorithm implementation in Keras](https://github.com/titu1994/BatchRenormalization) 7. [mlp: Multilayer Perceptron Keras wrapper for sklearn](https://github.com/alvarouc/mlp) 8. [Image-Classification-Mobile: Sandbox for training large-scale image classification networks for embedded systems, including collection of pretrained classification models for Keras with MXNet backend](https://github.com/osmr/imgclsmob) ## Visualization Tools 1. https://github.com/merantix/picasso 2. https://github.com/raghakot/keras-vis 3. https://github.com/fchollet/hualos 4. [quiver: Interactive convnet features visualization for Keras](https://github.com/keplr-io/quiver) ([homepage](https://keplr-io.github.io/quiver/)) 5. [hera: Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser](https://github.com/keplr-io/hera) 6. [picard: Easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them](https://github.com/keplr-io/picard) ([homepage](http://picard.libs.keplr.io/)) 7. [keras-visualize-activations: Activation Maps Visualization for Keras](https://github.com/philipperemy/keras-visualize-activations) ## Recurrent Networks 1. [recurrentshop: Framework for building complex recurrent neural networks with Keras](https://github.com/farizrahman4u/recurrentshop) ## Tutorial 1. https://github.com/sachinruk/deepschool.io 2. https://github.com/leriomaggio/deep-learning-keras-tensorflow 3. https://github.com/kailashahirwar/cheatsheets-ai 4. https://github.com/donnemartin/data-science-ipython-notebooks 5. https://github.com/xingkongliang/Keras-Tutorials 6. https://github.com/anujgupta82/DeepNets/tree/master/Keras/Keras_from_scratch 7. https://github.com/chibuk/simple-cnn-keras-colaboratory ## Art 1. https://github.com/OsciiArt/DeepAA 2. https://github.com/titu1994/Neural-Style-Transfer ## Interoperating with other frameworks 1. https://github.com/Microsoft/MMdnn 2. [model-converters: Tools for converting Keras models for use with other ML frameworks](https://github.com/triagemd/model-converters) 3. [PyTorch to Keras model converter](https://github.com/nerox8664/pytorch2keras) 4. [Gluon to Keras model converter](https://github.com/nerox8664/gluon2keras) `Source: GitHub` ENJOY & HAPPY LEARNING! :heart: -

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