{
    "info": {
        "author": "Yasha Bubnov",
        "author_email": "girokompass@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "# Keras Metrics\n\n[![Build Status][BuildStatus]](https://travis-ci.org/netrack/keras-metrics)\n\nThis package provides metrics for evaluation of Keras classification models.\nThe metrics are safe to use for batch-based model evaluation.\n\n## Installation\n\nTo install the package from the PyPi repository you can execute the following\ncommand:\n```sh\npip install keras-metrics\n```\n\n## Usage\n\nThe usage of the package is simple:\n```py\nimport keras\nimport keras_metrics as km\n\nmodel = models.Sequential()\nmodel.add(keras.layers.Dense(1, activation=\"sigmoid\", input_dim=2))\nmodel.add(keras.layers.Dense(1, activation=\"softmax\"))\n\nmodel.compile(optimizer=\"sgd\",\n              loss=\"binary_crossentropy\",\n              metrics=[km.binary_precision(), km.binary_recall()])\n```\n\nSimilar configuration for multi-label binary crossentropy:\n```py\nimport keras\nimport keras_metrics as km\n\nmodel = models.Sequential()\nmodel.add(keras.layers.Dense(1, activation=\"sigmoid\", input_dim=2))\nmodel.add(keras.layers.Dense(2, activation=\"softmax\"))\n\n# Calculate precision for the second label.\nprecision = km.binary_precision(label=1)\n\n# Calculate recall for the first label.\nrecall = km.binary_recall(label=0)\n\nmodel.compile(optimizer=\"sgd\",\n              loss=\"binary_crossentropy\",\n              metrics=[precision, recall])\n```\n\nKeras metrics package also supports metrics for categorical crossentropy and\nsparse categorical crossentropy:\n```py\nimport keras_metrics as km\n\nc_precision = km.categorical_precision()\nsc_precision = km.sparse_categorical_precision()\n\n# ...\n```\n\n## Tensorflow Keras\n\nTensorflow library provides the ```keras``` package as parts of its API, in\norder to use ```keras_metrics``` with Tensorflow Keras, you are advised to\nperform model training with initialized global variables:\n```py\nimport numpy as np\nimport keras_metrics as km\nimport tensorflow as tf\nimport tensorflow.keras as keras\n\nmodel = keras.Sequential()\nmodel.add(keras.layers.Dense(1, activation=\"softmax\"))\nmodel.compile(optimizer=\"sgd\",\n              loss=\"binary_crossentropy\",\n              metrics=[km.binary_true_positive()])\n\nx = np.array([[0], [1], [0], [1]])\ny = np.array([1, 0, 1, 0]\n\n# Wrap model.fit into the session with global\n# variables initialization.\nwith tf.Session() as s:\n    s.run(tf.global_variables_initializer())\n    model.fit(x=x, y=y)\n```\n\n[BuildStatus]: https://travis-ci.org/netrack/keras-metrics.svg?branch=master\n\n\n",
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        "name": "keras-metrics",
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