53 lines
1.5 KiB
Markdown
53 lines
1.5 KiB
Markdown
# 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)
|
|
```python
|
|
# ❌ 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)
|
|
```python
|
|
# ❌ 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
|
|
|
|
1. **GPU utilization 0%** - Might be monitoring issue or single-sample batches
|
|
2. **Occasional inplace errors** - Caught and recovered, but losing training steps
|
|
3. **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
|