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    "info": {
        "author": "Johannes Filter",
        "author_email": "hi@jfilter.de",
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            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "# Deep Plots [![Build Status](https://travis-ci.com/jfilter/deep-plots.svg?branch=master)](https://travis-ci.com/jfilter/deep-plots) [![PyPI](https://img.shields.io/pypi/v/deep-plots.svg)](https://pypi.org/project/deep-plots/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/deep-plots.svg)](https://pypi.org/project/deep-plots/)\n\nVisualize Your Deep Learning Training in Static Graphics.\n\n<div align=\"center\">\n  <img src=\"demo/loss.png\" alt=\"Plot Loss\">\n</div>\n\n**Why?** [Analyzing learning curves](https://www.coursera.org/lecture/machine-learning/learning-curves-Kont7) are a standard way to evaluate the learning performances of machine learning models. There exist [several tools](#Related) for creating live plots. This Python package focuses on producing beautiful static graphics only.\n\nCurrently, only plotting from [Keras CSV log file](https://keras.io/callbacks/#csvlogger) format is supported.\n\nFor creating the graphics, [plotnine](https://github.com/has2k1/plotnine) is used which is build upon [Matplotlib](https://matplotlib.org/).\n\n## Installation\n\n```bash\npip install deep_plots\n```\n\nUnfortunately, you may need to:\n\n```bash\npip install numpy\n```\n\nbefore because a [depedency implicitly assumes numpy is installed](https://github.com/statsmodels/statsmodels/issues/3207).\n\n## Usage\n\n```python\n# create a Keras callback to log your training\ncsv_logger = keras.callbacks.CSVLogger('log.csv')\n\n# train your model\nmodel.fit(X, y, ..., callbacks=[csv_logger, ...])\n\n# after finishing training, plot the learning curves with Deep Plots\ndeep_plots.from_keras_log('log.csv', 'output_dir')\n```\n\n## Related\n\n-   [TensorBoard](https://github.com/tensorflow/tensorboard): Live plots for TensorFlow, [Keras](https://keras.io/callbacks/#tensorboard).\n-   [tensorboardX](https://github.com/lanpa/tensorboardX): Live plots for PyTorch, Chainer etc..\n-   [Live Loss Plot](https://github.com/stared/livelossplot): Live plots in Jupyter Notebooks for Keras, PyTorch etc..\n\n## Contributing\n\nIf you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/deep-plots/issues).\n\n**Pull requests** are especially welcomed when they fix bugs or improve the code quality.\n\n## License\n\nMIT.\n\n\n",
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