Neural DSL v0.2.2 Release Notes

🚀 Major Changes

Fixed Parameter Parsing

Layer parameter handling has been significantly improved:

# Now correctly handles both styles:
Dense(64, "relu")     # Positional params
Dense(units=64, activation="relu") # Named params

Validation Enhancements

• Strict positive integer validation for critical parameters

# These will now raise clear validation errors:
Conv2D(filters=-32)  # ERROR: filters must be positive
Dense(units=0)       # ERROR: units must be positive

Improved Error Messages

• Added line/column information for better debugging

ERROR at line 4, column 32: Conv2D filters must be positive integer, got -32

🛠️ Technical Improvements

Layer Parameter Processing

• Unified parameter merging across layers:
  • Dense
  • LSTM
  • GRUCell
  • GaussianNoise

Grammar Refinements

• Resolved token conflicts between:
  • NUMBER
  • FLOAT
  • INT
• Simplified param_style1 rules

HPO Support Updates

# Now correctly supports:
HPO(choice(32, 64, 128))  # Units choice
HPO(choice("relu", "tanh"))  # Activation choice

🐛 Bug Fixes

Layer-Specific Fixes

• Fixed nested list flattening in GaussianNoise
• Corrected STRING token regex for activation functions
• Resolved VisitError wrapping issues

Macro System

• Fixed parameter override logic during expansion

# Now correctly handles:
define MyBlock {
    Dense(64)
    Dropout(0.5)
}
MyBlock(units=128)  # Properly overrides Dense units

🚧 Known Issues

1. PyTorch Support: Limited layer support (work in progress)
2. Macro Stability: Potential parser issues with nested layer blocks
3. HPO Limitations: log_range() requires explicit integer casting

📝 Migration Guide

Updating from v0.2.1

# Old style (might fail):
network MyNet {
    layers: Dense("64")  # String number
}

# New style (recommended):
network MyNet {
    layers: Dense(64)    # Integer number
}

🔜 Next Steps

1. Complete PyTorch layer support
2. Stabilize macro system
3. Enhance HPO functionality

For full changelog, see CHANGELOG.md
For documentation, visit docs/