3D deep convolutional neural networks for amino acid environment similarity analysis

BMC Bioinformatics

Table 3 3DCNN and FEATURE Softmax Classifier Network Architecture

	3DCNN			FEATURE + SOFTMAX
Stage	Layer	Size	Output Volume	Layer	Size	Output Volume
Feature Extraction Stage	Input		42020*20	Input FEATURE program		480 features
	3D–Conv	333, 100 Filters	1001818*18
	Dropout (p = 0.3)
	3D–Conv	333, 200 Filters	2001616*16
	Dropout (p = 0.3)
	3D–Max Pooling	Stride of 2	20088*8
	3D–Conv	333, 400 Filters	40066*6
	Dropout (p = 0.3)
	3D–Max Pooling	Stride of 2	40033*3
Information Integration Stage	FC Layer	10800*1000 neurons	1000 neurons	FC Layer	480*100 neurons	100 neurons
	Dropout (p = 0.3)			Dropout (p = 0.3)
	FC Layer	1000*100 neurons	100 neurons	FC Layer	100*20 neurons	20 neurons
	Dropout (p = 0.3)			Dropout (p = 0.3)
Classification Stage	Softmax Classifier	100 neurons*20 classes	20 scores	Softmax Classifier	20 neurons* 20 classes	20 scores

The Stage column describes the component stages for the deep 3DCNN and FEATURE Softmax models. In our 3DCNN, the 3D convolution and max pooling layers, the fully connected layers, and the Softmax classifier correspond to the feature extraction, information integration, and classification stage respectively. In the FEATURE Softmax classifier, the feature extraction stage is completed by the FEATURE program in advance. The Layer column describes the type of layer employed in each stage for each model, where 3D–Conv represents 3D convolutional layer, 3D Max-Pooling represents 3D max pooling operation with stride of 2, Dropout represents dropout operation with p = 0.3, and FC Layer stands for fully-connected layer. The Size column further describes the parameters used in each layer. For 3D–Conv layers, the number of filters in each layer and the size of the receptive fields of the filters are specified. For 3D Max-Pooling layers, a stride of 2 is used. For FC Layers, M*N neurons specifies the number of input and output neurons, respectively. The Output volume column describes the size of output of each layer. For 3D–conv and 3D–Max Pool layers, the output is a 4D tensor, where the numbers describe the number of channels, output height, output width, and output depth, respectively. For FC Layer, the output is a vector, and the number describes the number of output neurons

ISSN: 1471-2105