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 Süsstrunk {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
Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation"

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use

57 Dec 27, 2022
TVNet: Temporal Voting Network for Action Localization

TVNet: Temporal Voting Network for Action Localization This repo holds the codes of paper: "TVNet: Temporal Voting Network for Action Localization". P

hywang 5 Jul 26, 2022
A free, multiplatform SDK for real-time facial motion capture using blendshapes, and rigid head pose in 3D space from any RGB camera, photo, or video.

mocap4face by Facemoji mocap4face by Facemoji is a free, multiplatform SDK for real-time facial motion capture based on Facial Action Coding System or

Facemoji 591 Dec 27, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
PyoMyo - Python Opensource Myo library

PyoMyo Python module for the Thalmic Labs Myo armband. Cross platform and multithreaded and works without the Myo SDK. pip install pyomyo Documentati

PerlinWarp 81 Jan 08, 2023
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon 연애혁명 and tried to transfer human faces to webtoon domain.

이상윤 64 Oct 19, 2022
WSDM‘2022: Knowledge Enhanced Sports Game Summarization

Knowledge Enhanced Sports Game Summarization Cooming Soon! :) Data will be released after approval process. Code will be published once the author of

Jiaan Wang 14 Jul 13, 2022
Auditing Black-Box Prediction Models for Data Minimization Compliance

Data-Minimization-Auditor An auditing tool for model-instability based data minimization that is introduced in "Auditing Black-Box Prediction Models f

Bashir Rastegarpanah 2 Mar 24, 2022
This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.

GMPQ: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation This is the pytorch implementation for the paper: Generalizable Mix

18 Sep 02, 2022
A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021)

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

4 Sep 13, 2022
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

DeepMind 44 Nov 20, 2022
Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

DTI-Sprites Pytorch implementation of "Unsupervised Layered Image Decomposition into Object Prototypes" paper Check out our paper and webpage for deta

40 Dec 22, 2022
Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

Transformer-vocabulary-transfer Implementation of the paper "Fine-Tuning Transfo

LEYA 13 Nov 30, 2022
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment

Hailo Model Zoo The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can mea

Hailo 50 Dec 07, 2022
RSNA Intracranial Hemorrhage Detection with python

RSNA Intracranial Hemorrhage Detection This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challeng

24 Nov 30, 2022
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

GalaXC GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification @InProceedings{Saini21, author = {Saini, D. and Jain,

Extreme Classification 28 Dec 05, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"

ElasticGNN This repository includes the official implementation of ElasticGNN in the paper "Elastic Graph Neural Networks" [ICML 2021]. Xiaorui Liu, W

liuxiaorui 34 Dec 04, 2022
MISSFormer: An Effective Medical Image Segmentation Transformer

MISSFormer Code for paper "MISSFormer: An Effective Medical Image Segmentation Transformer". Please read our preprint at the following link: paper_add

Fong 22 Dec 24, 2022