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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["library","machine-learning","neural-network","python"],"created_at":"2026-01-14T07:49:04.779Z","updated_at":"2026-01-14T07:49:05.432Z","avatar_url":"https://github.com/gvinciguerra.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://gvinciguerra.github.io/nnweaver/_static/logo.svg\"\u003e\n\u003c/p\u003e\n\n# NNWeaver #\n\n[![Coverage Status](https://coveralls.io/repos/github/gvinciguerra/nnweaver/badge.svg?branch=master)](https://coveralls.io/github/gvinciguerra/nnweaver?branch=master)\n[![Build Status](https://travis-ci.org/gvinciguerra/nnweaver.svg?branch=master)](https://travis-ci.org/gvinciguerra/nnweaver)\n\nNNWeaver is a *tiny* Python library to create and train feedforward neural networks. We developed this library as a project for a Machine Learning course.\n\nSome of its features are:\n\n  1. Simple API, easy to learn.\n  2. Validation functions included.\n  3. Lightweight and with few dependencies.\n  4. Live loss/epoch curve display.\n\n## Installation ##\n\nYou can install NNWeaver from the GitHub source with the following commands:\n\n    git clone https://github.com/gvinciguerra/nnweaver.git\n    cd nnweaver\n    python3 setup.py install\n\nYou can also run the test suite with `python3 setup.py test`.\n\n## Getting started ##\n\n### Specify a Neural Network Topology ###\n\nYou can create a feedforward neural network specifying the number of inputs as the argument of [`NN`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.nn.NN), and the number of outputs by adding a [`Layer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.nn.Layer):\n\n    from nnweaver import *\n    nn = NN(3)\n    nn.add_layer(Layer(5, Linear))\n\nYou can always add more layers, specify an activation function and a weights initializer, as the following lines of code show:\n\n    nn.add_layer(Layer(7, Sigmoid))\n    nn.add_layer(Layer(6, Rectifier, uniform(0, 0.05)))\n    nn.add_layer(Layer(42, TanH, glorot_uniform()))\n\nSee [`activations`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#module-nnweaver.activations) for the list of available activation functions.\n\n### Train the Neural Network ###\n\nNow, choose a [`Loss`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.losses.Loss) function, pass it to an [`Optimizer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.Optimizer) (like the stochastic gradient descent) and start the training:\n\n    sgd = SGD(MSE)\n    sgd.train(nn, x, y, learning_rate=0.3)\n\nThere are other arguments to pass to the [`SGD.train()`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.SGD.train) method, for example:\n\n    sgd.train(nn, x_train, y_train,\n              learning_rate_time_based(0.25, 0.001),\n              batch_size=10, epochs=100, momentum=0.85)\n\nAlso, you may want to control the model complexity. [`SGD.train()`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.optimizers.SGD.train) has a `regularizer` argument, that accepts an instance of the [`L1L2Regularizer`](https://gvinciguerra.github.io/nnweaver/nnweaver.html#nnweaver.regularizers.L1L2Regularizer) class.\n\n### A very, very simple example ###\n\n\u003cimg src=\"https://github.com/gvinciguerra/nnweaver/blob/gh-pages/_images/nnweaver.gif?raw=true\" width=\"550\" /\u003e\n\n## Documentation ##\n\nFor more information, tutorials, and API reference, please visit [NNweaver's online documentation](https://gvinciguerra.github.io/nnweaver/index.html) or build your own offline copy executing `python3 setup.py docs`.\n\n## License ##\n\nThis project is licensed under the terms of the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgvinciguerra%2Fnnweaver","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgvinciguerra%2Fnnweaver","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgvinciguerra%2Fnnweaver/lists"}