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Table 1 Architecture of our proposed 1D 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 32768*1*ch N/A N/A
conv1 c + r + p 8192*1*32 3*1, 32, 1 4*1
conv2 c + r + p 2048*1*64 3*1, 64, 1 4*1
conv3 c + r + p 512*1*128 3*1, 128, 1 4*1
conv4 c + r + p 128*1*256 3*1, 256, 1 4*1
conv5 c + r + p 32*1*512 3*1, 512, 1 4*1
conv6 c + r 1*1*4096 32*1, 4096, 0 N/A
fc7 fc + r + d 1*1*4096 1*1, 4096, 0 N/A
fc8 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