Metadata-Version: 2.1
Name: keras-targeted-dropout
Version: 0.5.0
Summary: Targeted dropout implemented in Keras
Home-page: https://github.com/CyberZHG/keras-targeted-dropout
Author: CyberZHG
Author-email: CyberZHG@gmail.com
License: MIT
Description: # Keras Targeted Dropout
        
        [![Travis](https://travis-ci.org/CyberZHG/keras-targeted-dropout.svg)](https://travis-ci.org/CyberZHG/keras-targeted-dropout)
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        Unofficial implementation of [Targeted Dropout](https://openreview.net/pdf?id=HkghWScuoQ) with tensorflow backend.
        Note that there is no model compression in this implementation.
        
        ## Install
        
        ```bash
        pip install keras-targeted-dropout
        ```
        
        ## Usage
        
        ```python
        import keras
        from keras_targeted_dropout import TargetedDropout
        
        model = keras.models.Sequential()
        model.add(TargetedDropout(
            layer=keras.layers.Dense(units=2, activation='softmax'),
            drop_rate=0.8,
            target_rate=0.2,
            drop_patterns=['kernel'],
            mode=TargetedDropout.MODE_UNIT,
            input_shape=(5,),
        ))
        model.compile(optimizer='adam', loss='mse')
        model.summary()
        ```
        
        * `drop_rate`: Dropout rate for each pixel.
        * `target_rate`: The proportion of bottom weights selected as candidates
        * `drop_patterns`: A list of names of weights to be dropped.
        * `mode`: `TargetedDropout.MODE_UNIT` or `TargetedDropout.MODE_WEIGHT`.
        
        The final dropout rate will be `drop_rate` times `target_rate`.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
