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        "description": "# Keras LR Multiplier\n\n[![Travis](https://travis-ci.org/CyberZHG/keras-lr-multiplier.svg)](https://travis-ci.org/CyberZHG/keras-lr-multiplier)\n[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-lr-multiplier/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-lr-multiplier)\n[![Version](https://img.shields.io/pypi/v/keras-lr-multiplier.svg)](https://pypi.org/project/keras-lr-multiplier/)\n![Downloads](https://img.shields.io/pypi/dm/keras-lr-multiplier.svg)\n\n![](https://img.shields.io/badge/keras-tensorflow-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/eager-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/2.0_beta-blue.svg)\n\n\\[[\u4e2d\u6587](https://github.com/CyberZHG/keras-lr-multiplier/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-lr-multiplier/blob/master/README.md)\\]\n\nLearning rate multiplier wrapper for optimizers.\n\n## Install\n\n```bash\npip install keras-lr-multiplier\n```\n\n## Usage\n\n### Basic\n\n`LRMultiplier` is a wrapper for optimizers to assign different learning rates to specific layers (or weights). The first argument is the original optimizer which could be either an identifier (e.g. `'Adam'`) or an initialized object (e.g. `Adam(lr=1e-2)`). The second argument is a dict that maps prefixes to learning rate multipliers. The multiplier for a weight is the value mapped from the __longest matched prefix__ in the given dict, and the default multiplier `1.0` will be used if there is no prefix matched.\n\n```python\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras_lr_multiplier import LRMultiplier\n\nmodel = Sequential()\nmodel.add(Dense(\n    units=5,\n    input_shape=(5,),\n    activation='tanh',\n    name='Dense',\n))\nmodel.add(Dense(\n    units=2,\n    activation='softmax',\n    name='Output',\n))\nmodel.compile(\n    optimizer=LRMultiplier('adam', {'Dense': 0.5, 'Output': 1.5}),\n    loss='sparse_categorical_crossentropy',\n)\n```\n\n### Lambda\n\nThe multiplier can be a callable object. The input argument is the number of steps starting from 0.\n\n```python\nfrom keras import backend as K\nfrom keras_lr_multiplier import LRMultiplier\n\nLRMultiplier('adam', {'Dense': lambda t: 2.0 - K.minimum(1.9, t * 1e-4)})\n```",
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