Quick Action Summary - Training Effectiveness

What Was Wrong

Only epoch 1 was training, epochs 2-10 were skipping with 0.0 loss

The batch dictionaries were being modified in-place during training, so by epoch 2 the data was corrupted.

What Was Fixed

1. Batch Generator (ANNOTATE/core/real_training_adapter.py)

# ❌ BEFORE - Same batch object reused
for batch in grouped_batches:
    yield batch

# ✅ AFTER - New dict each time
for batch in grouped_batches:
    batch_copy = {k: v for k, v in batch.items()}
    yield batch_copy

2. Train Step (NN/models/advanced_transformer_trading.py)

# ❌ BEFORE - Modifies input batch
batch = batch_gpu  # Overwrites input

# ✅ AFTER - Creates new dict
batch_on_device = {}  # New dict, preserves input
for k, v in batch.items():
    batch_on_device[k] = v

Expected Result

✅ All 10 epochs should now train with real loss values
✅ No more "No timeframe data" warnings after epoch 1
✅ Loss should decrease across epochs
✅ Model should actually learn

Still Need to Address

GPU utilization 0% - Might be monitoring issue or single-sample batches
Occasional inplace errors - Caught and recovered, but losing training steps
Single sample batches - Need to accumulate more samples for better training

Test It

Run your realtime training again and check if:

Epoch 2 shows non-zero loss (not 0.000000)
All epochs train successfully
Loss decreases over time

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