Keras Multi-Head
![License](https://img.shields.io/pypi/l/keras-multi-head.svg)
A wrapper layer for stacking layers horizontally.
![](https://user-images.githubusercontent.com/853842/45797517-867b8580-bcd8-11e8-9ec6-39d6508cf438.png)
Install
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.
from tensorflow 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.
from tensorflow 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()
Linear Transformation
The input data will be mapped to different values of the same shape for each layer when hidden_dim
is given.
Regularization
![](https://user-images.githubusercontent.com/853842/45857922-8b4e4100-bd8d-11e8-905a-4eb07da31418.png)
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:
from tensorflow 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.
Multi-Head Attention
A more specific multi-head layer is provided (since the general one is harder to use). The layer uses scaled dot product attention layers as its sub-layers and only head_num
is required:
from tensorflow import keras
from keras_multi_head import MultiHeadAttention
input_layer = keras.layers.Input(
shape=(2, 3),
name='Input',
)
att_layer = MultiHeadAttention(
head_num=3,
name='Multi-Head',
)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=att_layer)
model.compile(
optimizer='adam',
loss='mse',
metrics={},
)
model.summary()
The shapes of input and output tensors would be the same if only one layer is presented as input. The input layers will be considered as query, key and value when a list is given:
from tensorflow import keras
from keras_multi_head import MultiHeadAttention
input_query = keras.layers.Input(
shape=(2, 3),
name='Input-Q',
)
input_key = keras.layers.Input(
shape=(4, 5),
name='Input-K',
)
input_value = keras.layers.Input(
shape=(4, 6),
name='Input-V',
)
att_layer = MultiHeadAttention(
head_num=3,
name='Multi-Head',
)([input_query, input_key, input_value])
model = keras.models.Model(inputs=[input_query, input_key, input_value], outputs=att_layer)
model.compile(
optimizer='adam',
loss='mse',
metrics={},
)
model.summary()