Implementation of zhang2016dependency
Project description
zhang2016dependency
This package provides a simple implementation of the models proposed in the paper:
Zhang, R., Lee, H., & Radev, D. (2016). Dependency sensitive convolutional neural networks for modeling sentences and documents. arXiv preprint arXiv:1611.02361.
Installation
This package depends on the Keras library. This means you will need to install a backend library in order to use this module. Take a look to Keras installation to get more information.
After having installed the backend of yout choice, you just need to install this package using pip:
pip install zhang2016dependency
Usage
This package only provides a single model. To get detailed information on the parameters the model accepts, take a look to the documentation included with the module class.
Here is a complete example of instantiation of the model proposed in the original paper using two channel of randomly initialized word embeddings:
import numpy as np
import numpy.random as rng
vocabulary_size = 1000
embedding_size = 300
value = np.sqrt(6/embedding_size)
weights_shape = (vocabulary_size+1, embedding_size)
weights = rng.uniform(low=-value, high=value, size=weights_shape)
channels = [
{
'weights': [weights],
'trainable': False,
'input_dim': vocabulary_size + 1,
'output_dim': embedding_size,
'name': 'random-embedding-1'
},
{
'weights': [weights],
'trainable': True,
'input_dim': vocabulary_size + 1,
'output_dim': embedding_size,
'name': 'random-embedding-2'
}
]
windows = [
{
'filters': 100,
'kernel_size': 3,
'activation': 'relu',
'name': '3-grams'
},
{
'filters': 100,
'kernel_size': 4,
'activation': 'relu',
'name': '4-grams'
},
{
'filters': 100,
'kernel_size': 5,
'activation': 'relu',
'name': '5-grams'
}
]
from zhang2016dependency import Model
model = Model(channels=channels,
windows=windows,
sentence_length=37,
num_classes=6,
dropout_rate=0.5,
classifier_activation='softmax',
include_top=True,
name='DSCNN')
model.compile(optimizer='adadelta',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
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