Keras-2.4.3.tar.gz

# Keras examples directory ## Vision models examples [mnist_mlp.py](mnist_mlp.py) Trains a simple deep multi-layer perceptron on the MNIST dataset. [mnist_cnn.py](mnist_cnn.py) Trains a simple convnet on the MNIST dataset. [cifar10_cnn.py](cifar10_cnn.py) Trains a simple deep CNN on the CIFAR10 small images dataset. [cifar10_cnn_capsule.py](cifar10_cnn_capsule.py) Trains a simple CNN-Capsule Network on the CIFAR10 small images dataset. [cifar10_resnet.py](cifar10_resnet.py) Trains a ResNet on the CIFAR10 small images dataset. [conv_lstm.py](conv_lstm.py) Demonstrates the use of a convolutional LSTM network. [image_ocr.py](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](mnist_acgan.py) Implementation of AC-GAN (Auxiliary Classifier GAN) on the MNIST dataset [mnist_hierarchical_rnn.py](mnist_hierarchical_rnn.py) Trains a Hierarchical RNN (HRNN) to classify MNIST digits. [mnist_siamese.py](mnist_siamese.py) Trains a Siamese multi-layer perceptron on pairs of digits from the MNIST dataset. [mnist_swwae.py](mnist_swwae.py) Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset. [mnist_transfer_cnn.py](mnist_transfer_cnn.py) Transfer learning toy example on the MNIST dataset. [mnist_denoising_autoencoder.py](mnist_denoising_autoencoder.py) Trains a denoising autoencoder on the MNIST dataset. ---- ## Text & sequences examples [addition_rnn.py](addition_rnn.py) Implementation of sequence to sequence learning for performing addition of two numbers (as strings). [babi_rnn.py](babi_rnn.py) Trains a two-branch recurrent network on the bAbI dataset for reading comprehension. [babi_memnn.py](babi_memnn.py) Trains a memory network on the bAbI dataset for reading comprehension. [imdb_bidirectional_lstm.py](imdb_bidirectional_lstm.py) Trains a Bidirectional LSTM on the IMDB sentiment classification task. [imdb_cnn.py](imdb_cnn.py) Demonstrates the use of Convolution1D for text classification. [imdb_cnn_lstm.py](imdb_cnn_lstm.py) Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task. [imdb_fasttext.py](imdb_fasttext.py) Trains a FastText model on the IMDB sentiment classification task. [imdb_lstm.py](imdb_lstm.py) Trains an LSTM model on the IMDB sentiment classification task. [lstm_stateful.py](lstm_stateful.py) Demonstrates how to use stateful RNNs to model long sequences efficiently. [lstm_seq2seq.py](lstm_seq2seq.py) Trains a basic character-level sequence-to-sequence model. [lstm_seq2seq_restore.py](lstm_seq2seq_restore.py) Restores a character-level sequence to sequence model from disk (saved by [lstm_seq2seq.py](lstm_seq2seq.py)) and uses it to generate predictions. [pretrained_word_embeddings.py](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](reuters_mlp.py) Trains and evaluate a simple MLP on the Reuters newswire topic classification task. ---- ## Generative models examples [lstm_text_generation.py](lstm_text_generation.py) Generates text from Nietzsche's writings. [conv_filter_visualization.py](conv_filter_visualization.py) Visualization of the filters of VGG16, via gradient ascent in input space. [deep_dream.py](deep_dream.py) Deep Dreams in Keras. [neural_doodle.py](neural_doodle.py) Neural doodle. [neural_style_transfer.py](neural_style_transfer.py) Neural style transfer. [variational_autoencoder.py](variational_autoencoder.py) Demonstrates how to build a variational autoencoder. [variational_autoencoder_deconv.py](variational_autoencoder_deconv.py) Demonstrates how to build a variational autoencoder with Keras using deconvolution layers. ---- ## Examples demonstrating specific Keras functionality [antirectifier.py](antirectifier.py) Demonstrates how to write custom layers for Keras. [mnist_sklearn_wrapper.py](mnist_sklearn_wrapper.py) Demonstrates how to use the sklearn wrapper. [mnist_irnn.py](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](mnist_net2net.py) Reproduction of the Net2Net experiment with MNIST in "Net2Net: Accelerating Learning via Knowledge Transfer". [reuters_mlp_relu_vs_selu.py](reuters_mlp_relu_vs_selu.py) Compares self-normalizing MLPs with regular MLPs. [mnist_tfrecord.py](mnist_tfrecord.py) MNIST dataset with TFRecords, the standard TensorFlow data format. [mnist_dataset_api.py](mnist_dataset_api.py) MNIST dataset with TensorFlow's Dataset API. [cifar10_cnn_tfaugment2d.py](cifar10_cnn_tfaugment2d.py) Trains a simple deep CNN on the CIFAR10 small images dataset using Tensorflow internal augmentation APIs. [tensorboard_embeddings_mnist.py](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.