Examples

Keras

Name	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.
conv_lstm	Demonstrates the use of a convolutional LSTM network.
deep_dream	Deep Dreams in Keras.
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.
mnist_acgan	Implementation of AC-GAN (Auxiliary Classifier GAN ) on the MNIST dataset
mnist_cnn	Trains a simple convnet on the MNIST dataset.
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_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).
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.
variational_autoencoder	Demonstrates how to build a variational autoencoder.
variational_autoencoder_deconv	Demonstrates how to build a variational autoencoder with Keras using deconvolution layers.

Estimators

Name	Description
mnist	Train a deep learning model with the MNIST dataset.
iris_dnn_classifier	Construct a DNN classifier using the iris data set.
iris_custom_decay_dnn	Create a deep neural network with learning rate decay for iris dataset.
wide_and_deep	Building a wide & deep learning model
custom_estimator	Creating a custom estimator for abalone age prediction
tensorflow_layers	Building an estimator using TensorFlow layers

Core

Name	Description
hello_tensorflow	Simplest possible TensorFlow program illustrating creation a session, evaluating constants, and performing basic arithmetic.
linear_regression_simple	Simple model that learns W and b by minimizing mean squared errors via gradient descent.
linear_regression_multiple	Illustrate how a multiple linear regression (Y ~ XW + b) might be fit using TensorFlow.
mnist_softmax	Use softmax regression to train a model to look at MNIST images and predict what digits they are.
mnist_with_summaries	A simple MNIST classifier which displays summaries in TensorBoard.
mnist_fully_connected_feed	Trains and Evaluates the MNIST network using a feed dictionary.