Example Description
addition_rnn Implementation of sequence to sequence learning for performing addition of two numbers (as strings).
babi_memnn Trains a memory network on the bAbI dataset for reading comprehension.
babi_rnn Trains a two-branch recurrent network on the bAbI dataset for reading comprehension.
cifar10_cnn Trains a simple deep CNN on the CIFAR10 small images dataset.
cifar10_densenet Trains a DenseNet-40-12 on the CIFAR10 small images dataset.
conv_lstm Demonstrates the use of a convolutional LSTM network.
deep_dream Deep Dreams in Keras.
eager_dcgan Generating digits with generative adversarial networks and eager execution.
eager_image_captioning Generating image captions with Keras and eager execution.
eager_pix2pix Image-to-image translation with Pix2Pix, using eager execution.
eager_styletransfer Neural style transfer with eager execution.
fine_tuning Fine tuning of a image classification model.
imdb_bidirectional_lstm Trains a Bidirectional LSTM on the IMDB sentiment classification task.
imdb_cnn Demonstrates the use of Convolution1D for text classification.
imdb_cnn_lstm Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task.
imdb_fasttext Trains a FastText model on the IMDB sentiment classification task.
imdb_lstm Trains a LSTM on the IMDB sentiment classification task.
lstm_text_generation Generates text from Nietzsche’s writings.
lstm_seq2seq This script demonstrates how to implement a basic character-level sequence-to-sequence model.
mnist_acgan Implementation of AC-GAN (Auxiliary Classifier GAN ) on the MNIST dataset
mnist_antirectifier Demonstrates how to write custom layers for Keras
mnist_cnn Trains a simple convnet on the MNIST dataset.
mnist_cnn_embeddings Demonstrates how to visualize embeddings in TensorBoard.
mnist_irnn Reproduction of the IRNN experiment with pixel-by-pixel sequential MNIST in “A Simple Way to Initialize Recurrent Networks of Rectified Linear Units” by Le et al.
mnist_mlp Trains a simple deep multi-layer perceptron on the MNIST dataset.
mnist_hierarchical_rnn Trains a Hierarchical RNN (HRNN) to classify MNIST digits.
mnist_tfrecord MNIST dataset with TFRecords, the standard TensorFlow data format.
mnist_transfer_cnn Transfer learning toy example.
neural_style_transfer Neural style transfer (generating an image with the same “content” as a base image, but with the “style” of a different picture).
nmt_attention Neural machine translation with an attention mechanism.
quora_siamese_lstm Classifying duplicate quesitons from Quora using Siamese Recurrent Architecture.
reuters_mlp Trains and evaluatea a simple MLP on the Reuters newswire topic classification task.
stateful_lstm Demonstrates how to use stateful RNNs to model long sequences efficiently.
text_explanation_lime How to use lime to explain text data.
variational_autoencoder Demonstrates how to build a variational autoencoder.
variational_autoencoder_deconv Demonstrates how to build a variational autoencoder with Keras using deconvolution layers.
tfprob_vae A variational autoencoder using TensorFlow Probability on Kuzushiji-MNIST.
vq_vae Discrete Representation Learning with VQ-VAE and TensorFlow Probability.