keras-ordered-neurons's Introduction

Keras Ordered Neurons LSTM

Unofficial implementation of ON-LSTM.

Install

pip install keras-ordered-neurons

Usage

Basic

Same as LSTM except that an extra argument chunk_size should be given:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, Dense

from keras_ordered_neurons import ONLSTM

model = Sequential()
model.add(Embedding(input_shape=(None,), input_dim=10, output_dim=100))
model.add(Bidirectional(ONLSTM(units=50, chunk_size=5)))
model.add(Dense(units=2, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary()

DropConnect

Set recurrent_dropconnect to a non-zero value to enable drop-connect for recurrent weights:

from keras_ordered_neurons import ONLSTM

ONLSTM(units=50, chunk_size=5, recurrent_dropconnect=0.2)

Expected Split Points

Set return_splits to True if you want to know the expected split points of master forget gate and master input gate.

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding

from keras_ordered_neurons import ONLSTM

inputs = Input(shape=(None,))
embed = Embedding(input_dim=10, output_dim=100)(inputs)
outputs, splits = ONLSTM(units=50, chunk_size=5, return_sequences=True, return_splits=True)(embed)
model = Model(inputs=inputs, outputs=splits)
model.compile(optimizer='adam', loss='mse')
model.summary(line_length=120)

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