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Table 2 All layers with weights and trainable parameters in the proposed method

From: ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins

Layer Weights Parameters
Conv1d (20, 200, 3) ((200, 20, 3), (200,)) 12,200
AvgPool1d (3) 0 0
Conv1d (200, 200, 3) ((200, 200, 3), (200,)) 120,200
AvgPool1d (3) 0 0
GRU (200, 200, 1) ((600, 200), (600, 200), (600,), (600,)) 241,200
Linear (200, 32) ((32, 200), (32,)) 6432
Dropout (0.5) 0 0
Linear (32, 1) ((1, 32), (1,)) 33
Sigmoid () 0 0