NeuralODE_SDE

Neural ODE with Flax

This is the result of project "Reproduce Neural ODE and SDE" in HuggingFace Flax/JAX community week.

<code>main.py</code> will execute training of ResNet or OdeNet for MNIST dataset.

Dependency

JAX and Flax

For JAX installation, please follow here.

or simply, type

pip install jax jaxlib

For Flax installation,

pip install flax

Tensorflow-datasets will download MNIST dataset to environment.

How to run training

For (small) ResNet training,

python main.py --model=resnet --lr=1e-4 --n_epoch=20 --batch_size=64 

For Neural ODE training,

python main.py --model=odenet --lr=1e-4 --n_epoch=20 --batch_size=64

For Continuous Normalizing Flow,

python main.py --model=cnf --sample_dataset=circles

Sample datasets can be chosen as circles, moons, or scurve.

Sample Results

Bird Call generation Score SDE

These are the codes for the bird call generation score sde model.

<code>core-sde-sampler.py</code> will execute the sampler. The sampler uses pretrained weight to generate bird calls. The weight can be found here

For using different sample generation parameters change the argument values. For example,

python main.py --sigma=25 --num_steps=500 --signal_to_noise_ratio=0.10 --etol=1e-5 --sample_batch_size = 128 --sample_no = 47

In order to generate the audios, these dependencies are required,

pip install librosa
pip install soundfile

In order to train the model from scratch, please generate the dataset using this link. The dataset is generated in kaggle. Therefore, during training your username and api key is required in the specified section inside the script.

python main.py --sigma=35 --n_epochs=1000 --batch_size=512 --lr=1e-3 --num_steps=500 --signal_to_noise_ratio=0.15 --etol=1e-5 --sample_batch_size = 64 --sample_no = 23

Generated samples can be found here and here