Bayesian dessert for Lasagne

Overview

Gelato

Coverage Status

Bayesian dessert for Lasagne

Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the best ways to deal with uncertainty, overfitting but still having good performance. Gelato will help to use bayes for neural networks. Library heavily relies on Theano, Lasagne and PyMC3.

Installation

  • from github (assumes bleeding edge pymc3 installed)
    # pip install git+git://github.com/pymc-devs/pymc3.git
    pip install git+https://github.com/ferrine/gelato.git
  • from source
    git clone https://github.com/ferrine/gelato
    pip install -r gelato/requirements.txt
    pip install -e gelato

Usage

I use generic approach for decorating all Lasagne at once. Thus, for using Gelato you need to replace import statements for layers only. For constructing a network you need to be the in pm.Model context environment.

Warning

  • lasagne.layers.noise is not supported
  • lasagne.layers.normalization is not supported (theano problems with default updates)
  • functions from lasagne.layers are hidden in gelato as they use Lasagne classes. Some exceptions are done for lasagne.layers.helpers. I'll try to solve the problem generically in future.

Examples

For comprehensive example of using Gelato you can reference this notebook

Life Hack

Any spec class can be used standalone so feel free to use it everywhere

References

Charles Blundell et al: "Weight Uncertainty in Neural Networks" (arXiv preprint arXiv:1505.05424)

You might also like...
Bayesian optimization in PyTorch

BoTorch is a library for Bayesian Optimization built on PyTorch. BoTorch is currently in beta and under active development! Why BoTorch ? BoTorch Prov

Safe Bayesian Optimization
Safe Bayesian Optimization

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Bayesian Optimization using GPflow

Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP

Code for
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha

(under submission) Bayesian Integration of a Generative Prior for Image Restoration
(under submission) Bayesian Integration of a Generative Prior for Image Restoration

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Sรผsstrunk {Note: p

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning Warning: This is a rapidly evolving research prototype.

Bayesian Image Reconstruction using Deep Generative Models
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

Supporting code for the paper
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Comments
  • Exception in example NB

    Exception in example NB

    I'm up-to-date on pymc3 and gelato.

    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    /Users/twiecki/anaconda/lib/python3.6/site-packages/theano/gof/op.py in __call__(self, *inputs, **kwargs)
        624                 try:
    --> 625                     storage_map[ins] = [self._get_test_value(ins)]
        626                     compute_map[ins] = [True]
    
    /Users/twiecki/anaconda/lib/python3.6/site-packages/theano/gof/op.py in _get_test_value(cls, v)
        580         detailed_err_msg = utils.get_variable_trace_string(v)
    --> 581         raise AttributeError('%s has no test value %s' % (v, detailed_err_msg))
        582 
    
    AttributeError: Softmax.0 has no test value  
    Backtrace when that variable is created:
    
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
        return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
        interactivity=interactivity, compiler=compiler, result=result)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
        if self.run_code(code, result):
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "<ipython-input-18-7dd01309b711>", line 37, in <module>
        prediction = gelato.layers.get_output(network)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/layers/helper.py", line 190, in get_output
        all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/layers/dense.py", line 124, in get_output_for
        return self.nonlinearity(activation)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/nonlinearities.py", line 44, in softmax
        return theano.tensor.nnet.softmax(x)
    
    
    During handling of the above exception, another exception occurred:
    
    ValueError                                Traceback (most recent call last)
    <ipython-input-18-7dd01309b711> in <module>()
         44                    prediction,
         45                    observed=target_var,
    ---> 46                    total_size=total_size)
    
    /Users/twiecki/working/projects/pymc/pymc3/distributions/distribution.py in __new__(cls, name, *args, **kwargs)
         35                 raise TypeError("observed needs to be data but got: {}".format(type(data)))
         36             total_size = kwargs.pop('total_size', None)
    ---> 37             dist = cls.dist(*args, **kwargs)
         38             return model.Var(name, dist, data, total_size)
         39         else:
    
    /Users/twiecki/working/projects/pymc/pymc3/distributions/distribution.py in dist(cls, *args, **kwargs)
         46     def dist(cls, *args, **kwargs):
         47         dist = object.__new__(cls)
    ---> 48         dist.__init__(*args, **kwargs)
         49         return dist
         50 
    
    /Users/twiecki/working/projects/pymc/pymc3/distributions/discrete.py in __init__(self, p, *args, **kwargs)
        429         super(Categorical, self).__init__(*args, **kwargs)
        430         try:
    --> 431             self.k = tt.shape(p)[-1].tag.test_value
        432         except AttributeError:
        433             self.k = tt.shape(p)[-1]
    
    /Users/twiecki/anaconda/lib/python3.6/site-packages/theano/gof/op.py in __call__(self, *inputs, **kwargs)
        637                         raise ValueError(
        638                             'Cannot compute test value: input %i (%s) of Op %s missing default value. %s' %
    --> 639                             (i, ins, node, detailed_err_msg))
        640                     elif config.compute_test_value == 'ignore':
        641                         # silently skip test
    
    ValueError: Cannot compute test value: input 0 (Softmax.0) of Op Shape(Softmax.0) missing default value.  
    Backtrace when that variable is created:
    
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
        return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
        interactivity=interactivity, compiler=compiler, result=result)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
        if self.run_code(code, result):
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "<ipython-input-18-7dd01309b711>", line 37, in <module>
        prediction = gelato.layers.get_output(network)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/layers/helper.py", line 190, in get_output
        all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/layers/dense.py", line 124, in get_output_for
        return self.nonlinearity(activation)
      File "/Users/twiecki/anaconda/lib/python3.6/site-packages/lasagne/nonlinearities.py", line 44, in softmax
        return theano.tensor.nnet.softmax(x)
    
    opened by twiecki 12
  • Integrate opvi

    Integrate opvi

    I'm currently integrating recent changes in PyMC3 to gelato. There are a lot of changes. Everyone is welcome for discussion.

    Here are the most remarkable features:

    • no more with context when using gelato layers
    from gelato.layers import *
    import pymc3 as pm
    # get data somehow
    inp = InputLayer(shape)
    out = DenseLayer(inp, 1, W=NormalSpec(sd=LognormalSpec(sd=.1)))
    out = DenseLayer(out, 1, W=NormalSpec(sd=LognormalSpec(sd=.1)))
    with out.root:
        pm.Normal('y', mu=get_output(out, {inp:x}),
                  observed=y)
        approx = pm.fit(10000)
    
    • Flexible Specs you can do almost everything. What to do if we want different shapes there is an open question
    from gelato import *
    import theano.tensor as tt
    import pymc3 as pm
    func = as_spec_op(tt.nlinalg.matrix_power)
    expr0= func(NormalSpec() * LaplaceSpec(), 2)
    expr1 = expr0 / 100 - NormalSpec()
    with Model() as model:
        var = expr((10, 10))
        assert var.tag.test_value.shape == (10, 10)
        assert len(model.free_RVs) == 3
        fit(100)
    U = NormalSpec()
    V = UniformSpec()
    V = V / V.norm(2)
    W = U*V
    with pm.Model() as model:
        result = W((3, 2), name='weight_normalization')
    
    opened by ferrine 2
  • Fix example

    Fix example

    refere to #7. I've updated example using new pm.Minibatch API. All was running good with the following theanorc:

    [global]
    device=cpu
    floatX=float32
    mode=FAST_RUN
    optimizer_including=cudnn
    
    [lib]
    cnmem=0.95
    
    [nvcc]
    fastmath=True
    flags = -I/usr/local/cuda-8.0-cudnnv5.1/include -L/usr/local/cuda-8.0-cudnnv5.1/lib64
    
    [blas]
    ldflag = -L/usr/lib/openblas-base -Lusr/local/cuda-8.0-cudnnv5.1/lib64 -lopenblas
    
    [DebugMode]
    check_finite=1
    
    [cuda]
    root=/usr/local/cuda-8.0-cudnnv5.1/
    

    pip freeze output

    alabaster==0.7.10
    algopy==0.5.3
    Babel==2.4.0
    bleach==2.0.0
    CommonMark==0.5.4
    cycler==0.10.0
    Cython==0.25.2
    decorator==4.0.11
    docutils==0.13.1
    entrypoints==0.2.2
    -e git+https://github.com/ferrine/[email protected]#egg=gelato
    h5py==2.7.0
    html5lib==0.999999999
    imagesize==0.7.1
    ipykernel==4.6.1
    ipython==6.0.0
    ipython-genutils==0.2.0
    ipywidgets==6.0.0
    Jinja2==2.9.6
    joblib==0.11
    jsonschema==2.6.0
    jupyter==1.0.0
    jupyter-client==5.0.1
    jupyter-console==5.1.0
    jupyter-core==4.3.0
    Keras==2.0.4
    Lasagne==0.2.dev1
    Mako==1.0.6
    MarkupSafe==1.0
    matplotlib==2.0.0
    mistune==0.7.4
    more-itertools==3.1.0
    nbconvert==5.1.1
    nbformat==4.3.0
    nbsphinx==0.2.13
    nose==1.3.7
    notebook==5.0.0
    numdifftools==0.9.20
    numpy==1.13.0
    pandas==0.20.1
    pandocfilters==1.4.1
    patsy==0.4.1
    pexpect==4.2.1
    pickleshare==0.7.4
    prompt-toolkit==1.0.14
    ptyprocess==0.5.1
    Pygments==2.2.0
    pygpu==0.6.5
    -e git+https://github.com/ferrine/[email protected]#egg=pymc3
    pymongo==3.4.0
    pyparsing==2.2.0
    python-dateutil==2.6.0
    pytz==2017.2
    PyYAML==3.12
    pyzmq==16.0.2
    qtconsole==4.3.0
    recommonmark==0.4.0
    requests==2.13.0
    scikit-learn==0.18.1
    scipy==0.19.1
    seaborn==0.7.1
    simplegeneric==0.8.1
    six==1.10.0
    sklearn==0.0
    snowballstemmer==1.2.1
    Sphinx==1.5.5
    terminado==0.6
    testpath==0.3
    Theano==0.10.0.dev1
    tornado==4.5.1
    tqdm==4.11.2
    traitlets==4.3.2
    wcwidth==0.1.7
    webencodings==0.5.1
    widgetsnbextension==2.0.0
    xmltodict==0.11.0
    
    opened by ferrine 0
  • Not compatible with latest version of pymc3

    Not compatible with latest version of pymc3

    When I attempt to import gelato, it fails with the following error message:

    ---> 19 class LayerModelMeta(pm.model.InitContextMeta):
         20     """Magic comes here
         21     """
    
    AttributeError: module 'pymc3.model' has no attribute 'InitContextMeta'
    

    I believe that InitContextMeta no longer exists in pymc3; it's been merged with ContextMeta.

    I don't know if there are plans to update this repository anytime soon, although it does seem like a useful tool, so it would be great if it worked with the latest pymc3.

    opened by quevivasbien 2
Releases(v0.1.0)
Owner
Maxim Kochurov
Researcher @ NTechLab; MSU/Skoltech; Core Dev @ PyMC3, Geoopt
Maxim Kochurov
Code repo for EMNLP21 paper "Zero-Shot Information Extraction as a Unified Text-to-Triple Translation"

Zero-Shot Information Extraction as a Unified Text-to-Triple Translation Source code repo for paper Zero-Shot Information Extraction as a Unified Text

cgraywang 88 Dec 31, 2022
GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs

GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs [Paper, Slides, Video Talk] at USENIX OSDI'21 @inproceedings{GNNAdvisor, title=

YUKE WANG 47 Jan 03, 2023
PyTorch Implementation of Region Similarity Representation Learning (ReSim)

ReSim This repository provides the PyTorch implementation of Region Similarity Representation Learning (ReSim) described in this paper: @Article{xiao2

Tete Xiao 74 Jan 03, 2023
Complex Answer Generation For Conversational Search Systems.

Complex Answer Generation For Conversational Search Systems. Code for Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex

Hanane Djeddal 0 Dec 06, 2021
A package for "Procedural Content Generation via Reinforcement Learning" OpenAI Gym interface.

Readme: Illuminating Diverse Neural Cellular Automata for Level Generation This is the codebase used to generate the results presented in the paper av

Sam Earle 27 Jan 05, 2023
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Oral)

Pixel-Perfect Structure-from-Motion (ICCV 2021 Oral) We introduce a framework that improves the accuracy of Structure-from-Motion by refining keypoint

Computer Vision and Geometry Lab 831 Dec 29, 2022
An Artificial Intelligence trying to drive a car by itself on a user created map

An Artificial Intelligence trying to drive a car by itself on a user created map

Akhil Sahukaru 17 Jan 13, 2022
A LiDAR point cloud cluster for panoptic segmentation

Divide-and-Merge-LiDAR-Panoptic-Cluster A demo video of our method with semantic prior: More information will be coming soon! As a PhD student, I don'

YimingZhao 65 Dec 22, 2022
To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts

JaxTon ๐Ÿ’ฏ JAX exercises Mission ๐Ÿš€ To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beg

Rohan Rao 512 Jan 01, 2023
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale

EgonNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale Paper: EgoNN: Egocentric Neural Network for Point Cloud

19 Sep 20, 2022
A light weight data augmentation tool for training CNNs and Viola Jones detectors

hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six

Jaiyam Sharma 2 Nov 23, 2019
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

NeoDTI NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (Bioinformatics).

62 Nov 26, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
StarGAN - Official PyTorch Implementation (CVPR 2018)

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Yunjey Choi 5.1k Dec 30, 2022
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Documentation: https://mmgeneration.readthedocs.io/ Introduction English | ็ฎ€ไฝ“ไธญๆ–‡ MMGeneration is a powerful toolkit for generative models, especially f

OpenMMLab 1.3k Dec 29, 2022
[์ œ 13ํšŒ ํˆฌ๋น…์Šค ์ปจํผ๋Ÿฐ์Šค] OK Mugle! - ์žฅ๋ฅด๋ถ€ํ„ฐ ๋ฉœ๋กœ๋””๊นŒ์ง€, Content-based Music Recommendation

Ok Mugle! ๐ŸŽต ์žฅ๋ฅด๋ถ€ํ„ฐ ๋ฉœ๋กœ๋””๊นŒ์ง€, Content-based Music Recommendation 'Ok Mugle!'์€ ์ œ13ํšŒ ํˆฌ๋น…์Šค ์ปจํผ๋Ÿฐ์Šค(2022.01.15)์—์„œ ์ง„ํ–‰ํ•œ ์Œ์•… ์ถ”์ฒœ ํ”„๋กœ์ ํŠธ์ž…๋‹ˆ๋‹ค. Description ๐Ÿ“– ๋ณธ ํ”„๋กœ์ ํŠธ์—์„œ๋Š” Kakao

SeongBeomLEE 5 Oct 09, 2022
Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer) Introduction By applying the

Son Gyo Jung 1 Jul 09, 2022
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited b

Facebook Research 61 Nov 28, 2022
Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

ลžebnem 6 Jan 18, 2022