Install

CPU

conda env create -f env_cpu.yaml

source activate sfr

NVIDIA GPU

conda env create -f env_nvidia.yaml

source activate sfr

Useage

Image Classification

Example

importsrcimporttorchtorch.set_default_dtype(torch.float64)deffunc(x,noise=True):returntorch.sin(x*5)/x+torch.cos(x*10)# Toy data setX_train=torch.rand((100,1))*2Y_train=func(X_train,noise=True)data= (X_train,Y_train)# Training configwidth=64num_epochs=1000batch_size=16learning_rate=1e-3delta=0.00005# prior precisiondata_loader=torch.utils.data.DataLoader(torch.utils.data.TensorDataset(*data),batch_size=batch_size)# Create a neural networknetwork=torch.nn.Sequential(torch.nn.Linear(1,width),torch.nn.Tanh(),torch.nn.Linear(width,width),torch.nn.Tanh(),torch.nn.Linear(width,1),)# Instantiate SFR (handles NN training/prediction as they're coupled via the prior/likelihood)sfr=src.SFR(network=network,prior=src.priors.Gaussian(params=network.parameters,delta=delta),likelihood=src.likelihoods.Gaussian(sigma_noise=2),output_dim=1,num_inducing=32,dual_batch_size=None,# this reduces the memory required for computing dual parametersjitter=1e-4,)sfr.train()optimizer=torch.optim.Adam([{"params":sfr.parameters()}],lr=learning_rate)forepoch_idxinrange(num_epochs):forbatch_idx,batchinenumerate(data_loader):x,y=batchloss=sfr.loss(x,y)optimizer.zero_grad()loss.backward()optimizer.step()sfr.set_data(data)# This builds the dual parameters# Make predictions in function spaceX_test=torch.linspace(-0.7,3.5,300,dtype=torch.float64).reshape(-1,1)f_mean,f_var=sfr.predict_f(X_test)# Make predictions in output spacey_mean,y_var=sfr.predict(X_test)

Development

pre-commit install

Citation

@inproceedings{scannellFunction2024,title           ={Function-space Prameterization of Neural Networks for Sequential Learning},booktitle       ={Proceedings of The Twelth International Conference on Learning Representations (ICLR 2024)},author          ={Aidan Scannell and Riccardo Mereu and Paul Chang and Ella Tami and Joni Pajarinen and Arno Solin},year            ={2024},month           ={5},}

@inproceedings{scannellSparse2023,title           ={Sparse Function-space Representation of Neural Networks},maintitle       ={ICML 2023 Workshop on Duality Principles for Modern Machine Learning},author          ={Aidan Scannell and Riccardo Mereu and Paul Chang and Ella Tami and Joni Pajarinen and Arno Solin},year            ={2023},month           ={7},}