Metadata-Version: 1.1
Name: keras-multi-head
Version: 0.4
Summary: A wrapper layer for stacking layers horizontally
Home-page: https://github.com/CyberZHG/keras-multi-head
Author: CyberZHG
Author-email: CyberZHG@gmail.com
License: MIT
Description: 
        Keras Multi-Head
        ================
        
        
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           :target: https://travis-ci.org/CyberZHG/keras-multi-head
           :alt: Travis
        
        
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           :target: https://coveralls.io/github/CyberZHG/keras-multi-head
           :alt: Coverage
        
        
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           :alt: PyPI
        
        
        A wrapper layer for stacking layers horizontally.
        
        
        .. image:: https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png
           :target: https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png
           :alt: 
        
        
        Install
        -------
        
        .. code-block:: bash
        
           pip install keras-multi-head
        
        Usage
        -----
        
        Duplicate Layers
        ^^^^^^^^^^^^^^^^
        
        The layer will be duplicated if only a single layer is provided. The ``layer_num`` argument controls how many layers will be duplicated eventually.
        
        .. code-block:: python
        
           import keras
           from keras_multi_head import MultiHead
        
        
           model = keras.models.Sequential()
           model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
           model.add(MultiHead(keras.layers.LSTM(units=32), layer_num=5, name='Multi-LSTMs'))
           model.add(keras.layers.Flatten(name='Flatten'))
           model.add(keras.layers.Dense(units=4, activation='softmax', name='Dense'))
           model.build()
           model.summary()
        
        Use Multiple-Layers
        ^^^^^^^^^^^^^^^^^^^
        
        The first argument could also be a list of layers with different configurations, however, they must have the same output shapes.
        
        .. code-block:: python
        
           import keras
           from keras_multi_head import MultiHead
        
        
           model = keras.models.Sequential()
           model.add(keras.layers.Embedding(input_dim=100, output_dim=20, name='Embedding'))
           model.add(MultiHead([
               keras.layers.Conv1D(filters=32, kernel_size=3, padding='same'),
               keras.layers.Conv1D(filters=32, kernel_size=5, padding='same'),
               keras.layers.Conv1D(filters=32, kernel_size=7, padding='same'),
           ], name='Multi-CNNs'))
           model.build()
           model.summary()
        
        Regularization
        ^^^^^^^^^^^^^^
        
        
        .. image:: https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png
           :target: https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png
           :alt: 
        
        
        The regularization is used when you expect to extract different features from the parallel layers. You can customize the indices of weights in the layers, the intervals represent the parts of the weights and the factor of the regularization.
        
        For example, the bidirectional LSTM layer has 6 weights by default, and the first 3s belong to the forward layer. The 2nd weight (recurrent kernel) in the forward layer controls the computation of gates for recurrent connections. The kernel for computing cell states lays in units x 2 to units x 3 of the recurrent kernel. We can used the regularization for the kernels:
        
        .. code-block:: python
        
           import keras
           from keras_multi_head import MultiHead
        
        
           model = keras.models.Sequential()
           model.add(keras.layers.Embedding(input_dim=5, output_dim=3, name='Embed'))
           model.add(MultiHead(
               layer=keras.layers.Bidirectional(keras.layers.LSTM(units=16), name='LSTM'),
               layer_num=5,
               reg_index=[1, 4],
               reg_slice=(slice(None, None), slice(32, 48)),
               reg_factor=0.1,
               name='Multi-Head-Attention',
           ))
           model.add(keras.layers.Flatten(name='Flatten'))
           model.add(keras.layers.Dense(units=2, activation='softmax', name='Dense'))
           model.build()
        
        
        * ``reg_index``\ : The indices of ``layer.get_weights()``\ , a single integer or a list of integers.
        * ``reg_slice``\ : ``slice``\ s or a tuple of ``slice``\ s or a list of the previous choices. If multiple indices are provided in ``reg_index`` and ``reg_slice`` is not a list, then ``reg_slice`` is assumed to be equal for all the indices. The whole array will be used if you leave this argument to ``None``.
        * ``reg_factor``\ : The factor of the regularization, a float or a list of floats.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
