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training fixes

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This commit is contained in:
Dobromir Popov
2025-10-31 01:29:05 +02:00
parent ba91740e4c
commit cefd30d2bd
2 changed files with 68 additions and 23 deletions
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30
NN/models/advanced_transformer_trading.py
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@@ -950,8 +950,9 @@ class TradingTransformerTrainer:
self.model.train()
self.optimizer.zero_grad()
# Move batch to device
batch = {k: v.to(self.device) for k, v in batch.items()}
# Clone and detach batch tensors before moving to device to avoid in-place operation issues
# This ensures each batch is independent and prevents gradient computation errors
batch = {k: v.detach().clone().to(self.device) for k, v in batch.items()}
# Forward pass
outputs = self.model(
@@ -965,24 +966,29 @@ class TradingTransformerTrainer:
action_loss = self.action_criterion(outputs['action_logits'], batch['actions'])
price_loss = self.price_criterion(outputs['price_prediction'], batch['future_prices'])
# Start with base losses - avoid inplace operations on computation graph
total_loss = action_loss + 0.1 * price_loss # Weight auxiliary task
# Add confidence loss if available
if 'confidence' in outputs and 'trade_success' in batch:
# Ensure both tensors have compatible shapes
# confidence: [batch_size, 1] -> squeeze last dim to [batch_size]
# trade_success: [batch_size] - ensure same shape
confidence_pred = outputs['confidence'].squeeze(-1) # Only remove last dimension
# Ensure both tensors have compatible shapes for BCELoss
# BCELoss requires both inputs to have the same shape
confidence_pred = outputs['confidence'] # Keep as [batch_size, 1]
trade_target = batch['trade_success'].float()
# Ensure shapes match (handle edge case where batch_size=1)
if confidence_pred.dim() == 0: # scalar case
confidence_pred = confidence_pred.unsqueeze(0)
if trade_target.dim() == 0: # scalar case
trade_target = trade_target.unsqueeze(0)
# Reshape target to match prediction shape [batch_size, 1]
if trade_target.dim() == 1:
trade_target = trade_target.unsqueeze(-1)
# Ensure both have same shape
if confidence_pred.shape != trade_target.shape:
# If shapes still don't match, squeeze both to 1D
confidence_pred = confidence_pred.view(-1)
trade_target = trade_target.view(-1)
confidence_loss = self.confidence_criterion(confidence_pred, trade_target)
total_loss += 0.1 * confidence_loss
# Use addition instead of += to avoid inplace operation
total_loss = total_loss + 0.1 * confidence_loss
# Backward pass
total_loss.backward()