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Table 2 Architecture of our proposed 2D CNN

From: Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks

Layer Type Output size Conv (size, channel, pad) Max pooling
input in 176*176*ch N/A N/A
conv1 c + r + p 88*88*32 3*3, 32, 1 2*2
conv2 c + r + p 44*44*64 3*3, 64, 1 2*2
conv3 c + r + p 22*22*128 3*3128, 1 2*2
conv4 c + r + p 11*11*256 3*3, 256, 1 2*2
conv5 c + r 1*1*1024 11*11, 1024, 0 N/A
fc6 fc + r + d 1*1*1024 1*1, 1024, 0 N/A
fc7 fc 1*1*25 1*1, 25, 0 N/A
loss sm + log 1*1 N/A N/A
  1. Annotations - in: input layer; c: convolutional layer; r: ReLU layer; p: pooling layer; fc: fully connected layer; d: dropout layer; sm: softmax layer; log: log loss layer; ch: number of input channels (depending on whether the HiC data is used); asterisk(*): multiplication