trainStep

Executes asynchronous neural network training step from user interaction data.

This method processes a single user interaction, converts it to neural network training data, and performs one iteration of supervised learning via backpropagation.

Training Pipeline:

1. Validation: Verify engine initialization state

2. Preprocessing: Convert InteractionData to feature vector

3. Target Generation: Create expected output values for supervision

4. Forward Pass: Compute current model prediction

5. Backward Pass: Calculate gradients and update weights

6. Bookkeeping: Update training statistics and history

7. Cleanup: Maintain bounded memory usage

Feature Engineering:

input[0] = action_type_hash    // Normalized interaction type [-1, 1]
input[1] = timestamp_normalized // Time-of-day feature [0, 1]
input[2] = user_hash           // User identification [-1, 1]
input[3] = context_indicator   // Context presence [0, 1]

Asynchronous Execution:

• Returns immediately with Job for non-blocking operation

• Training executes in background using engineScope

• Multiple training steps can execute concurrently

• Mutex ensures thread-safe weight updates

Error Handling:

• Initialization check prevents invalid operations

• Exception handling with comprehensive logging

• Graceful degradation on training failures

• No-op job returned for uninitialized engine

Performance Optimizations:

• Bounded training history (1000 examples max)

• Periodic logging to reduce I/O overhead

• Memory-efficient data structures

• Lazy evaluation of expensive operations

Training Metrics:

• Incremental training step counter

• Loss calculation and logging every 10 steps

• Interaction count tracking for analytics

• Training example timestamp recording

Parameters

See also