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LSTM Network Pro
Interactive Long Short-Term Memory network simulator with comprehensive features for sequential data modeling
📊 LSTM Network Summary
Sequence Length:
20
Hidden Size:
128
Input Size:
64
Layers:
1
Total Parameters:
98,816
Dropout Rate:
20%
Network Configuration
Training Parameters
Typical range: 0.0001 - 0.01
0%90%
Regularization to prevent overfitting
Sample Configurations
LSTM Cell Architecture
Forget Gate (ft)
Decides what to forget from previous cell state
Input Gate (it)
Decides what new information to store
Cell State (Ct)
Long-term memory storage
Output Gate (ot)
Decides what to output from cell state
💡 Configuration Tips
- • Hidden Size: Typically 64, 128, 256, or 512 for most applications
- • Number of Layers: 1-2 layers for simple tasks, 2-4 for complex sequences
- • Sequence Length: Depends on your data; longer sequences capture more context
- • Dropout: Use 0.2-0.5 to prevent overfitting on training data
- • Learning Rate: Start with 0.001 and adjust based on training performance
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