This archive is the code supplementary materials of the paper Closed-form Continuous-depth Models
- Python3.6 or newer
- Tensorflow 2.4 or newer
- PyTorch 1.8 or newer
- pytorch-lightning 1.3.0 or newer
- scikit-learn 0.24.2 or newer
tf_cfc.py
Implementation of the CfC (various versions) in Tensorflow 2.xtorch_cfc.py
Implementation of the CfC (various versions) in PyTorchtrain_physio.py
Trains the CfC models on the Physionet 2012 dataset in PyTorch (code adapted from Rubanova et al. 2019)train_xor.py
Trains the CfC models on the XOR dataset in Tensorflow (code adapted from Lechner & Hasani, 2020)train_imdb.py
Trains the CfC models on the IMDB dataset in Tensorflow (code adapted from Keras examples website)train_walker.py
Trains the CfC models on the Walker2d dataset in Tensorflow (code adapted from Lechner & Hasani, 2020)irregular_sampled_datasets.py
Datasets (same splits) from Lechner & Hasani (2020)duv_physionet.py
andduv_utils.py
Physionet dataset (same split) from Rubanova et al. (2019)
All training scripts except the following three flags
no_gate
Runs the CfC without the (1-sigmoid) partminimal
Runs the CfC direct solutionuse_ltc
Runs an LTC with a semi-implicit ODE solver instead of a CfCuse_mixed
Mixes the CfC's RNN-state with a LSTM to avoid vanishing gradients
If none of these flags are provided, the full CfC model is used
For instance
python3 train_physio.py
train the full CfC model on the Physionet dataset.
Similarly
train_walker.py --minimal
runs the direct CfC solution on the walker2d dataset.
For downloading the Walker2d dataset of Lechner & Hasani 2020, run
source download_dataset.sh