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توضیحات

Train Multilayer Perceptrons with Genetic Algorithms.

ویژگی	مقدار
سیستم عامل	-
نام فایل	DeepGProp-1.0.8
نام	DeepGProp
نسخه کتابخانه	1.0.8
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Luis Liñán
ایمیل نویسنده	luislivilla@gmail.com
آدرس صفحه اصلی	https://github.com/lulivi/dgp-lib
آدرس اینترنتی	https://pypi.org/project/DeepGProp/
مجوز	GPLv3

*DeepGProp* ================================= Neural Networks optimization with Genetic Algorithms Based on the BSc thesis by - **Author: Luis Liñán Villafranca** - **Mentor: Juan Julián Merelo Guervós** ---- .. contents:: **Table of contents** :depth: 2 ---- Installation ------------ The first prerequisite is to have `Python 3.6, 3.7 or 3.8 <https://www.python.org/downloads/>`_ and pip_ installed on the system. It is recommended to create a virtual environment to isolate the used package versions. For more information about pip_ and venv_ check the `official tutorial <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/>`_. If you are using `pyenv`, remember to compile your version with the `--enable-shared` configuration option: First, you need to install a version of python that’s been compiled with ``-fPIC``. ``pyenv`` versions by default are not, so you will need to issue something like this: .. code:: shell env PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install 3.8.6 Virtual environment creation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ We can use a core module to create the virtual environment, it’s been working since version 3.3 .. code:: shell python -m venv .venv .. Please make sure when you do this that all ``__pycache__`` directories have been deleted; otherwise, it might fail in some unexpected place. This will create a virtual environment in the ``.venv`` directory. Once that’s been done, we need to activate it; use one of the following commands (depending on the interpreter) (obtained from the official venv_ documentation): +--------+---------------+---------------------------------------+ |Platform|Shell |Command to activate virtual environment| +========+===============+=======================================+ |POSIX |bash/zsh |``$ source <venv>/bin/activate`` | | +---------------+---------------------------------------+ | |fish |``$ . <venv>/bin/activate.fish`` | | +---------------+---------------------------------------+ | |csh/tcsh |``$ source <venv>/bin/activate.csh`` | | +---------------+---------------------------------------+ | |PowerShell Core|``$ <venv>/bin/Activate.ps1`` | +--------+---------------+---------------------------------------+ |Windows |cmd.exe |``C:\> <venv>\Scripts\activate.bat`` | | +---------------+---------------------------------------+ | |PowerShell |``PS C:\> <venv>\Scripts\Activate.ps1``| +--------+---------------+---------------------------------------+ Table 1.1: *Activating the virtual environment.* You won’t need to create the virtual environment in the case you’re using global installation of modules via version managers such as ``pyenv``. Installing the DeepGProp CLI ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To run DeepGProp first we need to install its cli. You can install it with ``pip``: .. code:: shell pip install -U DeepGProp Or downloading the repository with: .. code:: shell pip install . On the other hand, if we want the code to be updated as we change it, we will need to install DeepGProp in editable mode. To do this, we need to add the option ``-e/--editable`` to the installation command: .. code:: shell pip install -e . All the available options can be listed using: .. code:: shell dgp --help Extra modules ~~~~~~~~~~~~~ I’ve divided all the used packages in different groups to avoid installing undesirable ones for specific use of the repository: +---------+-------------------------+--------------------------------------------------------------------------------------------------+ | Purpose | File path | Description | +=========+=========================+==================================================================================================+ | Test | requirements/tests.txt | Necessary packages for tests. Nox_ installs them automaticly when running the tests. | +---------+-------------------------+--------------------------------------------------------------------------------------------------+ | Lint | requirements/lint.txt | Necessary packages for linting. Nox_ installs them automaticly when linting the code. | +---------+-------------------------+--------------------------------------------------------------------------------------------------+ | Format | requirements/format.txt | Necessary packages for formatting. Nox_ installs them automaticly when running format command. | +---------+-------------------------+--------------------------------------------------------------------------------------------------+ | Dev | requirements/dev.txt | All above packages. | +---------+-------------------------+--------------------------------------------------------------------------------------------------+ To install any of these packages you can run: .. code:: shell pip install -r <file path> If you are not using any virtual environment, make sure you install these packages so that they are available in the required Python version. Tutorials --------- * `Runing DGP on the MNIST dataset <docs/using_mnist_dataset.rst>`_ Tests and formatting -------------------- .. note:: To be able to run the DeepGProp tests, you will need to install it in editable mode. checkout in `Installing the DeepGProp CLI <#installing-the-deepgprop-cli>`_ section how to do it. First, we need to install the Nox_ tool: .. code:: shell pip install -U nox To run all the tests: .. code:: shell nox -k test To run the linters: .. code:: shell nox -k lint You can check all the possible sessions with the following command: .. code:: shell nox -l Frameworks ---------- - `Keras <https://keras.io/>`_ - base library to create and run the neural networks. - `DEAP <https://deap.readthedocs.io/en/master/>`_ - genetic algorithms library used to optimize the models hyper parametters. Utilities --------- - Automation: - Nox_ - automation tool to run different tasks as the tests or the code formatting check. - Tests: - `pytest <https://docs.pytest.org/en/latest/>`_ - Python test framework to run the tests. Datasets -------- All datasets need to have a first row with the column names, and one of the columns needs to be named `class`. For the time being, it's prepared to run only classification problems. Licence ------- The original code can be found in the `DeepGProp <https://github.com/lulivi/dgp-lib>`_ repo under GPLv3 License. .. _pip: https://pypi.org/project/pip/ .. _Nox: https://nox.thea.codes/en/stable .. _venv: https://docs.python.org/3/library/venv.html

نیازمندی

مقدار	نام
-	tensorflow
>=1.18.2	numpy
>=1.0.3	pandas
>=1.3.1	deap
>=0.22.2.post1	scikit-learn
==1.0.10	Theano-PyMC
==2.3.1	Keras
==7.1.2	click
==0.8.7	tabulate

زبان مورد نیاز

مقدار	نام
>=3.6,<3.9	Python

نحوه نصب

نصب پکیج whl DeepGProp-1.0.8:

pip install DeepGProp-1.0.8.whl

نصب پکیج tar.gz DeepGProp-1.0.8:

pip install DeepGProp-1.0.8.tar.gz