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        "description": "# Keras Targeted Dropout\n\n[![Travis](https://travis-ci.org/CyberZHG/keras-targeted-dropout.svg)](https://travis-ci.org/CyberZHG/keras-targeted-dropout)\n[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-targeted-dropout/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-targeted-dropout)\n[![Version](https://img.shields.io/pypi/v/keras-targeted-dropout.svg)](https://pypi.org/project/keras-targeted-dropout/)\n![Downloads](https://img.shields.io/pypi/dm/keras-targeted-dropout.svg)\n![License](https://img.shields.io/pypi/l/keras-targeted-dropout.svg)\n\nUnofficial implementation of [Targeted Dropout](https://openreview.net/pdf?id=HkghWScuoQ) with tensorflow backend.\nNote that there is no model compression in this implementation.\n\n## Install\n\n```bash\npip install keras-targeted-dropout\n```\n\n## Usage\n\n```python\nimport keras\nfrom keras_targeted_dropout import TargetedDropout\n\nmodel = keras.models.Sequential()\nmodel.add(TargetedDropout(\n    layer=keras.layers.Dense(units=2, activation='softmax'),\n    drop_rate=0.8,\n    target_rate=0.2,\n    drop_patterns=['kernel'],\n    mode=TargetedDropout.MODE_UNIT,\n    input_shape=(5,),\n))\nmodel.compile(optimizer='adam', loss='mse')\nmodel.summary()\n```\n\n* `drop_rate`: Dropout rate for each pixel.\n* `target_rate`: The proportion of bottom weights selected as candidates\n* `drop_patterns`: A list of names of weights to be dropped.\n* `mode`: `TargetedDropout.MODE_UNIT` or `TargetedDropout.MODE_WEIGHT`.\n\nThe final dropout rate will be `drop_rate` times `target_rate`.",
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