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Table 1 Different network architectures of DeepEnhancer

From: Predicting enhancers with deep convolutional neural networks

Layer ID Layer Type Size Output shape
0 Input 4x1x300
1 Conv 128x4x1x8 128x1x293
2 Batchnorm 128x1x293
3 Conv 128x128x1x8 128x1x286
4 Batchnorm 128x1x286
5 Maxpooling 1 × 2 128x1x143
6 Conv 64x128x1x3 64x1x141
7 Batchnorm 64x1x141
8 Conv 64x64x1x3 64x1x139
9 Batchnorm 64x1x139
10 Maxpooling 1 × 2 64x1x69
11 Dense 256 256
12 Dropout 256
13 Dense 128 128
14 Softmax 2 2
  1. The size column records the convolutional kernel size, the max-pooling window size and the fully connected layer size. The output shape depicts the change of data’s shape in the flow