Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines.

Python packages:

• hpbandster 0.7.4
• jupyter 1.0.0
• matplotlib 3.2.1
• numpy 1.18.3
• pandas 0.24.2
• Pillow 6.2.0
• test-tube 0.7.5
• torch 1.3.1
• torchvision 0.4.2
• tqdm 4.44.1

Install the DUN package, and its requirements, by running the following commands in the root directory of the project:

pip install -r requirements.txt
pip install -e .

Change to the experiments directory:

cd experiments

First change to the toy subdirectory:

cd experiments/toy

All experiments with toy data can be produced with the following script. Plots are generated automatically.

python train_toy.py --help

For example:

python train_toy.py --inference DUN --N_layers 10  --overcount 1  --width 100  --n_epochs 1000 --dataset wiggle --lr 0.001 --wd 0.0001

First change to the regression subdirectory:

cd experiments/regression

The regression experiments require 4 stages of computation:

1. Hyperparameter optimisation
2. Training models with the optimal hyperparameters
3. Evaluating all models
4. Plotting the results

For stage 1, change to the hyperparams subdirectory:

cd experiments/regression/hyperparams

Then run the following script for all combinations of datasets, splits, and inference methods of interest.

python run_opt.py --help

For example:

python run_opt.py --dataset boston --n_split 0 --min_budget 200 --max_budget 2000 --early_stop 200 --n_iterations 20 --run_id 0 --method SGD 

Note that a unique run_id must be suplied for each run.

For stage 2, change to the retrain_best subdirectory:

cd experiments/regression/retrain_best

Then run the following script for all combinations of datasets, splits, and inference methods for which hyperparameter optimisation was run.

python final_run.py --help

For example:

python final_train.py --dataset boston --split 0 --method SGD

For stage 3, go to the regression subdirectory:

cd experiments/regression

Run the following script.

python evaluate_final_models_unnorm.py --help

This script shouldn't require any command line arguments to run, for example:

python evaluate_final_models_unnorm.py

For stage 4, go to the experiments directory:

cd experiments

The regression experiment plots can now be generated by executing the appropriate cells in the regression_and_image_PLOTS.ipynb notebook. Launch jupyter:

jupyter-notebook

Note that the flights dataset must first be un-zipped before it can be used.

First change to the image subdirectory:

cd experiments/image

The image experiments require 4 stages of computation:

1. Training baselines
2. Training DUNs
3. Evaluate the models
4. Plotting the results

For stage 1, run the train_baselines script, for each baseline configuration and dataset of interest:

python train_baselines.py --help

Note that, unlike the toy data and regression experiments, there are no flags to specify the inference method to use. Instead the inference method is implicit. For example to train an SGD model, we do not need to change any of the default arguments:

python train_baselines.py --dataset MNIST

To train a dropout model, just specify they p_drop and mcsamples arguments:

python train_baselines.py --dataset MNIST --p_drop 0.1 --mcsamples 10

To train an ensemble, simply train multiple SGD models. Each model will automatically be saved to a unique directory.

python train_baselines.py --dataset MNIST; 
python train_baselines.py --dataset MNIST; 
python train_baselines.py --dataset MNIST

For stage 2, run the train_DUN script, for each DUN configuration and dataset of interest:

python train_DUN.py --help

For example:

python train_DUN.py --dataset MNIST

For stage 3, run the run_final_image_experiments script for each inference method and dataset trained in the previous steps.

python run_final_image_experiments.py --help

For example:

python run_final_image_experiments.py --method=DUN --dataset=MNIST

or

python run_final_image_experiments.py --method=ensemble --dataset=CIFAR10

For stage 4, go to the experiments directory:

cd experiments

The image experiment plots can now be generated by executing the appropriate cells in the regression_and_image_PLOTS.ipynb notebook. Launch jupyter:

jupyter-notebook

If you find this code useful, please consider citing our paper:

Javier Antorán, James Urquhart Allingham, & José Miguel Hernández-Lobato. (2020). Depth Uncertainty in Neural Networks. [bibtex]

@misc{antoran2020depth,
    title={Depth Uncertainty in Neural Networks},
    author={Javier Antorán and James Urquhart Allingham and José Miguel Hernández-Lobato},
    year={2020},
    eprint={2006.08437},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}