Table 1 Parameters of DeEPsnap.
From: A deep ensemble framework for human essential gene prediction by integrating multi-omics data
Shape | Activation | Dropout | ||
|---|---|---|---|---|
DNN Structure | Input | 258 | – | – |
Hidden Layer 1 | 1024 | ReLU | 0.3 | |
Hidden Layer 2 | 512 | ReLU | 0.3 | |
Hidden Layer 3 | 256 | ReLU | 0.3 | |
Output Layer | 1 | Sigmoid | – |
Initial Learning Rate (\(lr_0\)) | #Epochs per Cycle (C) | |||
|---|---|---|---|---|
Snapshot ensemble | 0.001 | 5 | ||
\(\#\)Epochs (T) | Optimizer | Batch size | Class weights | |
|---|---|---|---|---|
Training | 50 | Adam | 32 | 1 : 4.5 |
