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README.md

Keras examples directory

Vision models examples

mnist_mlp.py Trains a simple deep multi-layer perceptron on the MNIST dataset.

mnist_cnn.py Trains a simple convnet on the MNIST dataset.

cifar10_cnn.py Trains a simple deep CNN on the CIFAR10 small images dataset.

cifar10_cnn_capsule.py Trains a simple CNN-Capsule Network on the CIFAR10 small images dataset.

cifar10_resnet.py Trains a ResNet on the CIFAR10 small images dataset.

conv_lstm.py Demonstrates the use of a convolutional LSTM network.

image_ocr.py Trains a convolutional stack followed by a recurrent stack and a CTC logloss function to perform optical character recognition (OCR).

mnist_acgan.py Implementation of AC-GAN (Auxiliary Classifier GAN) on the MNIST dataset

mnist_hierarchical_rnn.py Trains a Hierarchical RNN (HRNN) to classify MNIST digits.

mnist_siamese.py Trains a Siamese multi-layer perceptron on pairs of digits from the MNIST dataset.

mnist_swwae.py Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.

mnist_transfer_cnn.py Transfer learning toy example on the MNIST dataset.

mnist_denoising_autoencoder.py Trains a denoising autoencoder on the MNIST dataset.


Text & sequences examples

addition_rnn.py Implementation of sequence to sequence learning for performing addition of two numbers (as strings).

babi_rnn.py Trains a two-branch recurrent network on the bAbI dataset for reading comprehension.

babi_memnn.py Trains a memory network on the bAbI dataset for reading comprehension.

imdb_bidirectional_lstm.py Trains a Bidirectional LSTM on the IMDB sentiment classification task.

imdb_cnn.py Demonstrates the use of Convolution1D for text classification.

imdb_cnn_lstm.py Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task.

imdb_fasttext.py Trains a FastText model on the IMDB sentiment classification task.

imdb_lstm.py Trains an LSTM model on the IMDB sentiment classification task.

lstm_stateful.py Demonstrates how to use stateful RNNs to model long sequences efficiently.

lstm_seq2seq.py Trains a basic character-level sequence-to-sequence model.

lstm_seq2seq_restore.py Restores a character-level sequence to sequence model from disk (saved by lstm_seq2seq.py) and uses it to generate predictions.

pretrained_word_embeddings.py Loads pre-trained word embeddings (GloVe embeddings) into a frozen Keras Embedding layer, and uses it to train a text classification model on the 20 Newsgroup dataset.

reuters_mlp.py Trains and evaluate a simple MLP on the Reuters newswire topic classification task.


Generative models examples

lstm_text_generation.py Generates text from Nietzsche's writings.

conv_filter_visualization.py Visualization of the filters of VGG16, via gradient ascent in input space.

deep_dream.py Deep Dreams in Keras.

neural_doodle.py Neural doodle.

neural_style_transfer.py Neural style transfer.

variational_autoencoder.py Demonstrates how to build a variational autoencoder.

variational_autoencoder_deconv.py Demonstrates how to build a variational autoencoder with Keras using deconvolution layers.


Examples demonstrating specific Keras functionality

antirectifier.py Demonstrates how to write custom layers for Keras.

mnist_sklearn_wrapper.py Demonstrates how to use the sklearn wrapper.

mnist_irnn.py 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_net2net.py Reproduction of the Net2Net experiment with MNIST in "Net2Net: Accelerating Learning via Knowledge Transfer".

reuters_mlp_relu_vs_selu.py Compares self-normalizing MLPs with regular MLPs.

mnist_tfrecord.py MNIST dataset with TFRecords, the standard TensorFlow data format.

mnist_dataset_api.py MNIST dataset with TensorFlow's Dataset API.

cifar10_cnn_tfaugment2d.py Trains a simple deep CNN on the CIFAR10 small images dataset using Tensorflow internal augmentation APIs.

tensorboard_embeddings_mnist.py Trains a simple convnet on the MNIST dataset and embeds test data which can be later visualized using TensorBoard's Embedding Projector.