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    "info": {
        "author": "Piotr Migda\u0142",
        "author_email": "pmigdal@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Framework :: Jupyter",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "# Live Loss Plot\n\n![PyPI version](https://img.shields.io/pypi/pyversions/livelossplot.svg)\n![PyPI license](https://img.shields.io/pypi/l/livelossplot.svg)\n![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg)\n[![Downloads](http://pepy.tech/badge/livelossplot)](http://pepy.tech/count/livelossplot)\n\nDon't train deep learning models blindfolded! Be impatient and look at each epoch of your training!\n\nA live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open source Python package by [Piotr Migda\u0142](https://p.migdal.pl/), and [others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :))\n\n```\nfrom livelossplot.keras import PlotLossesCallback\n\nmodel.fit(X_train, Y_train,\n          epochs=10,\n          validation_data=(X_test, Y_test),\n          callbacks=[PlotLossesCallback()],\n          verbose=0)\n```\n\n![](livelossplot.gif)\n\nSo remember, [log your loss](https://twitter.com/pmigdal/status/943764924983017473)!\n\n* (The most FA)Q: Why not TensorBoard?\n* A: Jupyter Notebook compatibility (for exploration and teaching). Simplicity of use.\n\n## Installation\n\nTo install [this verson from PyPI](https://pypi.org/project/livelossplot/), type:\n\n```\npip install livelossplot\n```\n\nTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:\n\n```\npip install git+git://github.com/stared/livelossplot.git\n```\n\n## Examples\n\nLook at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/):\n\n* [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback\n* [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere\n* [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch\n* [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+)\n* [pytoune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytoune.ipynb) - a PyToune callback ([PyToune](https://pytoune.org/) is a Keras-like framework for PyTorch)\n* [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch)\n* [neptune-minimal-terminal.py](https://github.com/stared/livelossplot/blob/master/examples/neptune-minimal-terminal.py) - a [Neptune.ML](https://neptune.ml/) Python script (so far the only way to use livelossplot outside of Jupyter)\n* [neptune-minimal-jupyter.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune-minimal-jupyter.ipynb) - a [Neptune.ML](https://neptune.ml/) Jupyter Notebook integration\n\n## Overview\n\nText logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting?\n\nVisual feedback allows us to keep track of the training process. Now there is one for Jupyter.\n\nIf you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) or even better - [Neptune - Machine Learning Lab](https://neptune.ml/) (as it allows to compare between models, in a Kaggle leaderboard style). Or, well use `tensorboard_dir=\"./logs\"` or `target='neptune'`. Now these are included as well!\n\nBut what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component.\n\nIt started as [this gist](https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e). Since it went popular, I decided to rewrite it as a package.\n\n## To do\n\nIf you want more functionality - open an [Issue](https://github.com/stared/livelossplot/issues) or even better - prepare a [Pull Request](https://github.com/stared/livelossplot/pulls).\n\n\n",
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