💡 Learnergy is a Python library for energy-based machine learning models.

Last update: Nov 17, 2022

Overview

Learnergy: Energy-based Machine Learners

Welcome to Learnergy.

Did you ever reach a bottleneck in your computational experiments? Are you tired of implementing your own techniques? If yes, Learnergy is the real deal! This package provides an easy-to-go implementation of energy-based machine learning algorithms. From datasets to fully-customizable models, from internal functions to external communications, we will foster all research related to energy-based machine learning.

Use Learnergy if you need a library or wish to:

Create your energy-based machine learning algorithm;
Design or use pre-loaded learners;
Mix-and-match different strategies to solve your problem;
Because it is incredible to learn things.

Read the docs at learnergy.readthedocs.io.

Learnergy is compatible with: Python 3.6+.

Package guidelines

The very first information you need is in the very next section.
Installing is also easy if you wish to read the code and bump yourself into, follow along.
Note that there might be some additional steps in order to use our solutions.
If there is a problem, please do not hesitate, call us.

Citation

If you use Learnergy to fulfill any of your needs, please cite us:

@misc{roder2020learnergy,
    title={Learnergy: Energy-based Machine Learners},
    author={Mateus Roder and Gustavo Henrique de Rosa and João Paulo Papa},
    year={2020},
    eprint={2003.07443},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Getting started: 60 seconds with Learnergy

First of all. We have examples. Yes, they are commented. Just browse to examples/, choose your subpackage, and follow the example. We have high-level examples for most of the tasks we could think.

Alternatively, if you wish to learn even more, please take a minute:

Learnergy is based on the following structure, and you should pay attention to its tree:

- learnergy
    - core
        - dataset
        - model
    - math
        - metrics
        - scale
    - models
        - bernoulli
            - conv_rbm
            - discriminative_rbm
            - dropout_rbm
            - e_dropout_rbm
            - rbm
        - deep
            - conv_dbn
            - dbn
            - residual_dbn
        - extra
            - sigmoid_rbm
        - gaussian
            - gaussian_conv_rbm        
            - gaussian_rbm
    - utils
        - constants
        - exception
        - logging
    - visual
        - convergence
        - image
        - tensor

Core

Core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basis of our structure. They should provide variables and methods that will help to construct other modules.

Math

Just because we are computing stuff, it does not means that we do not need math. Math is the mathematical package, containing low-level math implementations. From random numbers to distributions generation, you can find your needs on this module.

Models

This is the heart. All models are declared and implemented here. We will offer you the most fantastic implementation of everything we are working with. Please take a closer look into this package.

Utils

This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use as you wish than re-implementing the same thing over and over again.

Visual

Everyone needs images and plots to help visualize what is happening, correct? This package will provide every visual-related method for you. Check a specific image, your fitness function convergence, plot reconstructions, weights, and much more.

Installation

We believe that everything has to be easy. Not tricky or daunting, Learnergy will be the one-to-go package that you will need, from the very first installation to the daily-tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever):

pip install learnergy

Alternatively, if you prefer to install the bleeding-edge version, please clone this repository and use:

pip install -e .

Environment configuration

Note that sometimes, there is a need for additional implementation. If needed, from here, you will be the one to know all of its details.

Ubuntu

No specific additional commands needed.

Windows

No specific additional commands needed.

MacOS

No specific additional commands needed.

Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository or [email protected] and [email protected].

Comments

Naming of models' class attributes
The variable names used in the getters and setters of each model class are different than what is initialized in the constructor.

Note that the name 'W' is used in init, but '_W' is used as the variable name in the getters and setters, both of which have the same name as the variable initialized in init 'W'. This issue is the same for all member variables. I have tested the DBN and GaussianVarianceRBM subclasses as well, and it looks like the issue persists across all other subclasses.

OS: CentOS 7.8

Virtual Environment conda 4.10.1

Python Version 3.8

This causes issues when trying to make copies of classes for things like multi-GPU training via torch.nn.DistributedDataParallel
bug
opened by nlahaye 14
Probabilistic Max Pooling

Hi Gustavo!

I hope you are doing well!

I was wondering if you had looked into / planned to add probabilistic max-pooling, as descibed in: H. Lee, et al., Convolutional Deep Belief Networksfor Scalable Unsupervised Learning of Hierarchical Representations Proceedings of the 26th annual international conference on machine learning (2009).

I am happy to help, but figured it would be best to consult you first.

Thanks! Nick
enhancement

opened by nlahaye 7
probability bug

When I use GaussianRBM to run rbm_classification.py on GPU, these errors appear.

11%|█ | 51/469 [00:01<00:07, 58.24it/s]C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [416,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [417,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [418,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [419,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [420,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [421,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [422,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [423,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [424,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [425,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [426,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [427,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [428,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [429,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [430,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [431,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [432,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [433,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [434,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [435,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [436,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [437,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [438,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [439,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [440,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [441,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [442,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [443,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [444,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [445,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [446,0,0] Assertion 0 <= p4 && p4 <= 1 failed. C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/DistributionTemplates.h:591: block: [6,0,0], thread: [447,0,0] Assertion 0 <= p4 && p4 <= 1 failed.

RuntimeError: CUDA error: device-side assert triggered

But if I run it on cpu, the error while like this: RuntimeError: Expected p_in >= 0 && p_in <= 1 to be true, but got false.

It seems like the function hidden_sampling dosen't run correctly.
bug question

opened by kyyongh 6
nan in probs
Hello,

Thank you very much for developing this superb library!!

I believe that I found an issue, but I am not able to find the root cause.

From time to time I receive this error:

Traceback (most recent call last): File "/Users/supermario/LNIssue/run_rbm.py", line 26, in epochs=1000000) File "/Users/supermario/LNIssue/venv/lib/python3.7/site-packages/learnergy/models/bernoulli/rbm.py", line 495, in fit _, _, _, _, visible_states = self.gibbs_sampling(samples) File "/Users/supermario/LNIssue/venv/lib/python3.7/site-packages/learnergy/models/bernoulli/rbm.py", line 379, in gibbs_sampling neg_hidden_states, True) File "/Users/supermario/LNIssue/venv/lib/python3.7/site-packages/learnergy/models/bernoulli/rbm.py", line 352, in visible_sampling states = torch.bernoulli(probs) RuntimeError: Expected p_in >= 0 && p_in <= 1 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

Here is the code to reproduce the error:

import numpy as np import torch from torch.utils.data import Dataset from learnergy.models.bernoulli.rbm import RBM

np.random.seed(16) torch.manual_seed(16)

class CustomDataset(Dataset):

def __init__(self, custom_data): self.custom_data = torch.from_numpy(custom_data) self.dummy_y = torch.from_numpy(np.array([0.0])) def __getitem__(self, index): return self.custom_data[index], self.dummy_y[0] def __len__(self): return self.custom_data.shape[0]

rbm = RBM(n_visible=32, n_hidden=64, steps=1, learning_rate=0.0001, momentum=0.9, decay=0.00001, temperature=0, use_gpu=False)

data = np.random.choice(a=[False, True], size=(5755, 32)).astype(np.float32) data_train = CustomDataset(data)

rbm.fit(dataset=data_train, batch_size=20, epochs=1000000)

And requirements: astroid==2.8.4 attrs==21.2.0 coverage==6.1.1 cycler==0.11.0 imageio==2.10.3 importlib-metadata==4.8.2 iniconfig==1.1.1 isort==5.10.1 kiwisolver==1.3.2 lazy-object-proxy==1.6.0 learnergy==1.1.1 matplotlib==3.4.3 mccabe==0.6.1 networkx==2.6.3 numpy==1.21.4 packaging==21.2 Pillow==8.4.0 platformdirs==2.4.0 pluggy==1.0.0 py==1.11.0 pylint==2.11.1 pyparsing==2.4.7 pytest==6.2.5 python-dateutil==2.8.2 PyWavelets==1.2.0 scikit-image==0.18.3 scipy==1.7.2 six==1.16.0 tifffile==2021.11.2 toml==0.10.2 torch==1.9.0 tqdm==4.62.3 typed-ast==1.4.3 typing-extensions==3.10.0.2 wrapt==1.13.3 zipp==3.6.0

The error should appear at epoch 221/1000000.

If I add the following torch.nan_to_num in visible_sampling() method, then the error is not present anymore.

probs = torch.nan_to_num(probs, nan=0.0) Sampling current states states = torch.bernoulli(probs)

macosx, python 3.7.0

Thank you! Please let me know if you need additional info.
bug
opened by citizenn19 4
[REG] Basis for classification architecture
Regarding the basic Bernoulli RBM for classification, could I check what is the basis of your implementation? From what I can tell, the training is broken up in two sections:

RBM will perform unsupervised learning to minimize energy (RBM::fit), basically like a feature extractor.

The whole model is then considered as a 2-layer neural network with a sigmoid layer (RBM::forward) and linear transformation layer.

So, it seems like the RBM is just used as an initializer for the neural network.

In addition, I noticed that RBM::fit's epoch defaults to 1. Increasing the epoch number does not seem to improve performance. Does this mean that there is no use in training the RBM?

Lastly, was there any prior work that implemented RBM in this way, or is this your original idea? Are there any published results or study into this architecture? Thank you!
general
opened by shotnothing 3
[REG] The purpose of applying Softplus function in RBM

I found in the comments of file learnergy/learnergy/models/bernoulli/rbm.py (line 401), you said

Creating a Softplus function for numerical stability

The form of Softplus function is f(x) = log(1+exp(x)), which fits the form of the log term of marginal energy of RBM

E(v)= \sum_i a_i v_i + \sum_j ( log( 1 + exp( \sum_i v_i \lambda_{ij} +b_j ) ) ).

So I suppose the Softplus function is not used to ensure the numerical stability, but to calculate the marginal energy. I am a beginner to RBM, if I have any wrong perceptions, please point it out.

Thank you!
general

opened by YemingMeng 3
Bump sphinx from 5.2.3 to 5.3.0
Bumps sphinx from 5.2.3 to 5.3.0.

Release notes

Sourced from sphinx's releases.

v5.3.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

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Release 5.3.0 (released Oct 16, 2022)

#10759: LaTeX: add :confval:latex_table_style and support the 'booktabs', 'borderless', and 'colorrows' styles. (thanks to Stefan Wiehler for initial pull requests #6666, #6671)

#10840: One can cross-reference including an option value like :option:`--module=foobar```, :option:--module[=foobar]``` or ``:option:--module foobar```. Patch by Martin Liska.

#10881: autosectionlabel: Record the generated section label to the debug log.

#10268: Correctly URI-escape image filenames.

#10887: domains: Allow sections in all the content of all object description directives (e.g. :rst:dir:py:function). Patch by Adam Turner

Commits

c1f2a52 Bump to 5.3.0 final

10db540 Allow sections in object description directives (#10919)

0fd4539 Simplify bug report template

b58eb61 Update all GitHub actions workflows to Ubuntu latest

3e04952 Add sphinx-build --bug-report

fa6d425 URI-escape image filenames (#10268)

e008e16 Revert "Fix detection for out of date files (#9360)" (#10908)

cef1a03 Update image for LaTeX job (#10916)

b5ba1ed s/5.2.0/5.3.0 in various code comments as #10759 got merged in 5.3.0

3deb527 Update CHANGES for PR #10759

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opened by dependabot[bot] 2
Bump sphinx from 5.2.3 to 5.3.0 in /docs
Bumps sphinx from 5.2.3 to 5.3.0.

Release notes

Sourced from sphinx's releases.

v5.3.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

Changelog

Sourced from sphinx's changelog.

Release 5.3.0 (released Oct 16, 2022)

#10759: LaTeX: add :confval:latex_table_style and support the 'booktabs', 'borderless', and 'colorrows' styles. (thanks to Stefan Wiehler for initial pull requests #6666, #6671)

#10840: One can cross-reference including an option value like :option:`--module=foobar```, :option:--module[=foobar]``` or ``:option:--module foobar```. Patch by Martin Liska.

#10881: autosectionlabel: Record the generated section label to the debug log.

#10268: Correctly URI-escape image filenames.

#10887: domains: Allow sections in all the content of all object description directives (e.g. :rst:dir:py:function). Patch by Adam Turner

Commits

c1f2a52 Bump to 5.3.0 final

10db540 Allow sections in object description directives (#10919)

0fd4539 Simplify bug report template

b58eb61 Update all GitHub actions workflows to Ubuntu latest

3e04952 Add sphinx-build --bug-report

fa6d425 URI-escape image filenames (#10268)

e008e16 Revert "Fix detection for out of date files (#9360)" (#10908)

cef1a03 Update image for LaTeX job (#10916)

b5ba1ed s/5.2.0/5.3.0 in various code comments as #10759 got merged in 5.3.0

3deb527 Update CHANGES for PR #10759

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opened by dependabot[bot] 2
Gaussian RBM with hidden layer SELU activation

Hi Gustavo, I hope you are having a great week. Over the last few months, I have made a couple of additions to my fork, and I figured I would share them to see if you found them useful for the base repo. No worries if not!

The first one was adding Gaussian RBM with hidden layer SELU activation. I have had some success using this model for my research, and figured it may be useful for others.

In addition to the SELU addition, added boolean flags to indicate whether or not to batch normalize and normalize the input within parent GaussianRBM class.

The batch normalization is removed for SELU RBM, as it self-normalizes. Also, I use a scaling functionality outside of learnergy for the input, so it would be useful to have the normalization internal to learnergy as an option.

By default input and batch normalization are turned on, as to not cause issues for other users.

Thanks! Nick

opened by nlahaye 2
Bump sphinx from 5.2.3 to 6.0.0
Bumps sphinx from 5.2.3 to 6.0.0.

Release notes

Sourced from sphinx's releases.

v6.0.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v6.0.0b2

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v6.0.0b1

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v5.3.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

Changelog

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Release 6.0.0 (released Dec 29, 2022)

Dependencies

#10468: Drop Python 3.6 support

#10470: Drop Python 3.7, Docutils 0.14, Docutils 0.15, Docutils 0.16, and Docutils 0.17 support. Patch by Adam Turner

Incompatible changes

#7405: Removed the jQuery and underscore.js JavaScript frameworks.

These frameworks are no longer be automatically injected into themes from Sphinx 6.0. If you develop a theme or extension that uses the jQuery, $, or $u global objects, you need to update your JavaScript to modern standards, or use the mitigation below.

The first option is to use the sphinxcontrib.jquery_ extension, which has been developed by the Sphinx team and contributors. To use this, add sphinxcontrib.jquery to the extensions list in conf.py, or call app.setup_extension("sphinxcontrib.jquery") if you develop a Sphinx theme or extension.

The second option is to manually ensure that the frameworks are present. To re-add jQuery and underscore.js, you will need to copy jquery.js and underscore.js from the Sphinx repository_ to your static directory, and add the following to your layout.html:

.. code-block:: html+jinja

{%- block scripts %} {{ super() }} {%- endblock %}

.. _sphinxcontrib.jquery: https://github.com/sphinx-contrib/jquery/

Patch by Adam Turner.

#10471, #10565: Removed deprecated APIs scheduled for removal in Sphinx 6.0. See :ref:dev-deprecated-apis for details. Patch by Adam Turner.

#10901: C Domain: Remove support for parsing pre-v3 style type directives and roles. Also remove associated configuration variables c_allow_pre_v3 and c_warn_on_allowed_pre_v3. Patch by Adam Turner.

Features added

... (truncated)

Commits

5b56a23 Bump to 6.0.0 final

f1d1e9c Update coverage workflow for Tox 4

66a738c Update coverage workflow for new configuration location

041e5f8 Add test coverage for 'today_fmt' reference substitution (#10980)

da25145 Remove unnecessary conditional import in sphinx.ext.napoleon (#11043)

45a0ea9 Migrate coveragepy config into pyproject.toml (#11025)

3ec54f1 Create a pydata_sphinx_theme section in usage examples (#11046)

32bce8f Copy edit the tutorial (#11049)

9844162 Fix example using add_config_value (#10937)

bf4a626 RTD builder: add graphviz depedendency (#11040)

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opened by dependabot[bot] 1
Bump sphinx from 5.2.3 to 6.0.0 in /docs
Bumps sphinx from 5.2.3 to 6.0.0.

Release notes

Sourced from sphinx's releases.

v6.0.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v6.0.0b2

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v6.0.0b1

Changelog: https://www.sphinx-doc.org/en/master/changes.html

v5.3.0

Changelog: https://www.sphinx-doc.org/en/master/changes.html

Changelog

Sourced from sphinx's changelog.

Release 6.0.0 (released Dec 29, 2022)

Dependencies

#10468: Drop Python 3.6 support

#10470: Drop Python 3.7, Docutils 0.14, Docutils 0.15, Docutils 0.16, and Docutils 0.17 support. Patch by Adam Turner

Incompatible changes

#7405: Removed the jQuery and underscore.js JavaScript frameworks.

These frameworks are no longer be automatically injected into themes from Sphinx 6.0. If you develop a theme or extension that uses the jQuery, $, or $u global objects, you need to update your JavaScript to modern standards, or use the mitigation below.

The first option is to use the sphinxcontrib.jquery_ extension, which has been developed by the Sphinx team and contributors. To use this, add sphinxcontrib.jquery to the extensions list in conf.py, or call app.setup_extension("sphinxcontrib.jquery") if you develop a Sphinx theme or extension.

The second option is to manually ensure that the frameworks are present. To re-add jQuery and underscore.js, you will need to copy jquery.js and underscore.js from the Sphinx repository_ to your static directory, and add the following to your layout.html:

.. code-block:: html+jinja

{%- block scripts %} {{ super() }} {%- endblock %}

.. _sphinxcontrib.jquery: https://github.com/sphinx-contrib/jquery/

Patch by Adam Turner.

#10471, #10565: Removed deprecated APIs scheduled for removal in Sphinx 6.0. See :ref:dev-deprecated-apis for details. Patch by Adam Turner.

#10901: C Domain: Remove support for parsing pre-v3 style type directives and roles. Also remove associated configuration variables c_allow_pre_v3 and c_warn_on_allowed_pre_v3. Patch by Adam Turner.

Features added

... (truncated)

Commits

5b56a23 Bump to 6.0.0 final

f1d1e9c Update coverage workflow for Tox 4

66a738c Update coverage workflow for new configuration location

041e5f8 Add test coverage for 'today_fmt' reference substitution (#10980)

da25145 Remove unnecessary conditional import in sphinx.ext.napoleon (#11043)

45a0ea9 Migrate coveragepy config into pyproject.toml (#11025)

3ec54f1 Create a pydata_sphinx_theme section in usage examples (#11046)

32bce8f Copy edit the tutorial (#11049)

9844162 Fix example using add_config_value (#10937)

bf4a626 RTD builder: add graphviz depedendency (#11040)

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opened by dependabot[bot] 1

Releases(v1.1.3)

v1.1.3(Oct 21, 2022)
Changelog

Description

Welcome to v1.1.3 release.

In this release, we have added the MaxPooling2D for convolutional models and optimized the training procedure for deeper models, i.e., DBNs. Such an optimization enables the DBN to train without loading all the dataset on the memory for each hidden layer in this phase.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

Source code(tar.gz)
Source code(zip)
v1.1.2(Apr 26, 2022)
Changelog

Description

Welcome to v1.1.2 release.

In this release, we have added pre-commit hooks and annotated typing.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

Source code(tar.gz)
Source code(zip)
v1.1.1(Dec 23, 2020)
Changelog

Description

Welcome to v1.1.1 release.

In this release, we added the DropConnectRBM and fixed some nasty bugs, as well as improved some unitary tests.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

models.bernoulli.dropout_rbm

Source code(tar.gz)
Source code(zip)
v1.1.0(Dec 2, 2020)
Changelog

Description

Welcome to v1.1.0 release.

In this release, we renamed some packages for a clearer description. Additionally, we have added a GaussianConvRBM class and made some minor adjustments along the package.

Note that this release might cause incompatibility with previous versions due to some packages being renamed.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

models

Source code(tar.gz)
Source code(zip)
v1.0.7(Nov 25, 2020)
Changelog

Description

Welcome to v1.0.7 release.

In this release, we added some CRBM variants and fixed some CRBM-related functions.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

models.real

Source code(tar.gz)
Source code(zip)
v1.0.6(Jul 10, 2020)
Changelog

Description

Welcome to v1.0.6 release.

In this release, we have added a new model, known as Convolutional RBM. Additionally, we have reworked our modules to provide a cleaner environment.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

Source code(tar.gz)
Source code(zip)
v1.0.5(May 28, 2020)
Changelog

Description

Welcome to v1.0.5 release.

In this release, we have added a new model, known as Gaussian ReLU RBM. Additionally, we have added progress bars in an attempt to construct a clearer visualization of the training process.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy.models.gaussian_rbm

Source code(tar.gz)
Source code(zip)
v1.0.4(May 7, 2020)
Changelog

Description

Welcome to v1.0.4 release.

In this release, we have facilitated some classes imports, added the Residual DBN, and corrected some nasty bugs.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

learnergy.models.residual_dbn

Source code(tar.gz)
Source code(zip)
v1.0.3(Mar 31, 2020)
Changelog

Description

Welcome to v1.0.3 release.

In this release, we have improved the code readability, as well as some bug fixes.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

Source code(tar.gz)
Source code(zip)
v1.0.2(Feb 18, 2020)
Changelog

Description

Welcome to v1.0.2 release.

In this release, we have rebranded the package to Learnergy. We have also improved all of your packages, including GPU-based versions of our models.

Please read the docs at: learnergy.readthedocs.io

Also, stay tuned for our next updates!

Includes (or changes)

learnergy

Source code(tar.gz)
Source code(zip)
v1.0.1(Sep 12, 2019)
Changelog

Description

Welcome to Recogners v1.0.1. We fixed some issues related to the auto docstringing API, added the Dropout-based RBM and a method to dump important information to the model's history.

Note that the Gaussian RBM is not yet guaranteed to work. Please await for our next release.

Includes (or changes)

models/dropout_rbm

tests

Source code(tar.gz)
Source code(zip)
v1.0.0(Jul 4, 2019)
Changelog

Description

This is the initial release of Recogners. It includes all basic modules in order to work with it. One can create and use a Restricted Boltzmann Machine, along with some extra functionalities. Please check examples folder or read the docs in order to know how to use this library.

Includes

core

datasets

math

models

utils

visual

Source code(tar.gz)
Source code(zip)