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Table 1 CORENup structure

From: CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Features extraction path
Layer Kernel Dim # Hidden Units stride Dim Output Dim # Params
Conv1D 5 50 1 147x50 1.050
MaxPool1D - - 2 73x50 0
Dropout 50% - - - 73x50 0
LSTM Path
Layer Kernel Dim # Hidden Units stride Dim Output Dim # Params
LSTM - 50 - 73x50 20.200
Dropout 50% - - - 73x50 0
Flatten - - - 3.650x1 0
Convolutional path
Layer Kernel Dim # Hidden Units stride Dim Output Dim # Params
Conv1D 10 50 1 73x50 25.050
MaxPool1D - - 2 36x50 0
Dropout 50% - - - 36x50 0
Flatten - - - 1.800x1 0
Dense path
Concatenate - - - 5.450x1 0
Dense - 370 - 370x1 2.016.870
Dropout 50% - - - 370x1 0
Dense - 1 - 1x1 371
Parameters count
  # Parameters
Features Extraction Path 1.050
LSTM Path 20.200
Convolutional Path 25.050
Dense Path 2.017.241
Total 2.063.541
  1. Notice that the dense layer contains the majority of the network parameters