95 lines
3.5 KiB
Markdown
95 lines
3.5 KiB
Markdown
# Keras examples directory
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[addition_rnn.py](addition_rnn.py)
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Implementation of sequence to sequence learning for performing addition of two numbers (as strings).
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[antirectifier.py](antirectifier.py)
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Demonstrates how to write custom layers for Keras.
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[babi_memnn.py](babi_memnn.py)
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Trains a memory network on the bAbI dataset for reading comprehension.
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[babi_rnn.py](babi_rnn.py)
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Trains a two-branch recurrent network on the bAbI dataset for reading comprehension.
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[cifar10_cnn.py](cifar10_cnn.py)
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Trains a simple deep CNN on the CIFAR10 small images dataset.
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[conv_filter_visualization.py](conv_filter_visualization.py)
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Visualization of the filters of VGG16, via gradient ascent in input space.
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[deep_dream.py](deep_dream.py)
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Deep Dreams in Keras.
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[image_ocr.py](image_ocr.py)
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Trains a convolutional stack followed by a recurrent stack and a CTC logloss function to perform optical character recognition (OCR).
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[imdb_bidirectional_lstm.py](imdb_bidirectional_lstm.py)
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Trains a Bidirectional LSTM on the IMDB sentiment classification task.
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[imdb_cnn.py](imdb_cnn.py)
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Demonstrates the use of Convolution1D for text classification.
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[imdb_cnn_lstm.py](imdb_cnn_lstm.py)
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Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task.
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[imdb_fasttext.py](imdb_fasttext.py)
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Trains a FastText model on the IMDB sentiment classification task.
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[imdb_lstm.py](imdb_lstm.py)
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Trains a LSTM on the IMDB sentiment classification task.
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[lstm_benchmark.py](lstm_benchmark.py)
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Compares different LSTM implementations on the IMDB sentiment classification task.
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[lstm_text_generation.py](lstm_text_generation.py)
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Generates text from Nietzsche's writings.
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[mnist_cnn.py](mnist_cnn.py)
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Trains a simple convnet on the MNIST dataset.
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[mnist_hierarchical_rnn.py](mnist_hierarchical_rnn.py)
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Trains a Hierarchical RNN (HRNN) to classify MNIST digits.
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[mnist_irnn.py](mnist_irnn.py)
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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.
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[mnist_mlp.py](mnist_mlp.py)
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Trains a simple deep multi-layer perceptron on the MNIST dataset.
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[mnist_net2net.py](mnist_net2net.py)
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Reproduction of the Net2Net experiment with MNIST in "Net2Net: Accelerating Learning via Knowledge Transfer".
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[mnist_siamese_graph.py](mnist_siamese_graph.py)
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Trains a Siamese multi-layer perceptron on pairs of digits from the MNIST dataset.
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[mnist_sklearn_wrapper.py](mnist_sklearn_wrapper.py)
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Demonstrates how to use the sklearn wrapper.
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[mnist_swwae.py](mnist_swwae.py)
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Trains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.
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[mnist_transfer_cnn.py](mnist_transfer_cnn.py)
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Transfer learning toy example.
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[neural_doodle.py](neural_doodle.py)
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Neural doodle.
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[neural_style_transfer.py](neural_style_transfer.py)
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Neural style transfer.
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[pretrained_word_embeddings.py](pretrained_word_embeddings.py)
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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.
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[reuters_mlp.py](reuters_mlp.py)
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Trains and evaluate a simple MLP on the Reuters newswire topic classification task.
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[stateful_lstm.py](stateful_lstm.py)
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Demonstrates how to use stateful RNNs to model long sequences efficiently.
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[variational_autoencoder.py](variational_autoencoder.py)
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Demonstrates how to build a variational autoencoder.
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[variational_autoencoder_deconv.py](variational_autoencoder_deconv.py)
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Demonstrates how to build a variational autoencoder with Keras using deconvolution layers.
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