Squeezed Very Deep Convolutional Neural Networks for Text Classification *Authors contributed equally and are both first writers.
Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs
['[EMPTY]', 'SVDCNN 9', 'SVDCNN 17', 'SVDCNN 29', 'VDCNN 9', 'VDCNN 17', 'VDCNN 29', 'Char-CNN 6']
The use of TDSCs promoted a significant reduction in convolutional parameters compared to VDCNN. For the most in-depth network evaluated, which contains 29 convolutional layers (depth 29), the number of parameters of these convolutional layers had a reduction of 66.08%, from 4.6 to 1.56 million parameters. This quantity is slightly larger than the one obtained from the Char-CNN, 1.40 million parameters, but this network has only six convolutional layers (depth 6). Regarding accuracy results, usually, a model with such parameter reduction should present some loss of accuracy in comparison to the original model. Nevertheless, the performance difference between VDCNN and SVDCNN models varies between 0.4 and 1.3%, which is pretty modest considering the parameters and storage size reduction aforementioned. Another two fundamental results obtained are a) The base property of VDCNN model is preserved on its squeezed model: the performance still increasing up with the depth and b) The performance evaluated for the most extensive dataset, i.e., Yelp Review (62.30%), still overcomes the accuracy of the Char-CNN model (62.05%).