Problem

The softmax_cross_entropy_with_logits_v2 function was silently accepting invalid label inputs where the probability vectors did not sum to 1. This is a common ML bug that can lead to incorrect loss calculations and poor model training results without any clear error indication.

Thought Process

1. Identified the issue: Found a TODO comment in the code indicating this exact problem needed to be addressed
2. Analyzed impact: Invalid probability distributions in labels can cause:
   • Incorrect gradient calculations
   • Misleading loss values
   • Silent failures that are hard to debug
3. Considered implementation approach: Needed to handle both eager and graph execution modes appropriately
4. Balanced validation vs. performance: Added tolerance for floating-point precision while maintaining strict validation

Solution

Added validation logic that:

• Checks label sums: Verifies each label vector sums to 1.0 (within 1e-5 tolerance)
• Handles execution modes:
  • Graph mode: Uses TensorFlow assertions with control dependencies
  • Eager mode: Direct numpy validation with immediate error raising
• Provides clear error messages: Explains the requirement for valid probability distributions
• Maintains performance: Minimal overhead with early validation

Long Term Effect

• Improved debugging: Developers will immediately catch invalid label inputs
• Better model reliability: Prevents silent failures that could lead to incorrect model behavior
• Educational value: Clear error messages help users understand proper label formatting
• Consistency: Aligns with TensorFlow's philosophy of catching errors early
• Future-proofing: Sets precedent for similar validation in other loss functions

Tech Stack/Resources Used

• TensorFlow Core: Used math_ops.reduce_sum, check_ops.assert_near, array_ops.ones_like
• Python: Standard library imports (numpy for eager mode validation)
• TensorFlow Execution Context: Leveraged context.executing_eagerly() for mode-specific handling
• Error Handling: Implemented both assertion ops (graph mode) and ValueError (eager mode)
• Code Analysis Tools: Used semantic search and grep to identify the TODO and understand the codebase structure