Recurrent encoder-decoder networks for time-varying dense prediction

Implementation for submitted paper of KDD 2017 : " Recurrent encoder-decoder networks for time-varying dense prediction", which combined FCN (U-net, DeepEM-net(DenseNet)) and CRNN(convolution LSTM and GRU, deconvolution versions(DRNN)) as an integrated networks for en-to-end training.

Required libraries

Keras, Theano

Data

(1). Register first at: http://brainiac2.mit.edu/SNEMI3D/user/register

(2). Login in and download data at: http://brainiac2.mit.edu/SNEMI3D/downloads

(3) Convert image files into h5 file that contains \data and \label sets.

Code

Run train_predict.py for training or prediction. To predict/train a specific model, you need change mode_name accordingly.

Note:

To use convolutional GRU and Deconvolutional LSTM/GRU layers, copy extra_conv_recurrent.py to Keras's layer folder and run " python setup.py install" to install Keras again.

Code reference:

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
(http://arxiv.org/pdf/1506.04214v1.pdf)

https://github.com/fchollet/keras/blob/master/keras/layers/convolutional_recurrent.py