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training fixes

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This commit is contained in:
Dobromir Popov
2025-07-14 00:47:44 +03:00
parent ebf65494a8
commit e76b1b16dc
2 changed files with 37 additions and 21 deletions
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17
NN/models/enhanced_cnn.py
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@ -494,11 +494,8 @@ class EnhancedCNN(nn.Module):
return q_values, extrema_pred, price_predictions, features_refined, advanced_predictions
def act(self, state, explore=True):
def act(self, state, explore=True) -> Tuple[int, float, List[float]]:
"""Enhanced action selection with ultra massive model predictions"""
if explore and np.random.random() < 0.1: # 10% random exploration
return np.random.choice(self.n_actions)
self.eval()
# Accept both NumPy arrays and already-built torch tensors
@ -511,14 +508,16 @@ class EnhancedCNN(nn.Module):
state_tensor = torch.as_tensor(state, dtype=torch.float32, device=self.device)
if state_tensor.dim() == 1:
state_tensor = state_tensor.unsqueeze(0)
with torch.no_grad():
q_values, extrema_pred, price_predictions, features, advanced_predictions = self(state_tensor)
# Apply softmax to get action probabilities
action_probs = torch.softmax(q_values, dim=1)
action = torch.argmax(action_probs, dim=1).item()
action_probs_tensor = torch.softmax(q_values, dim=1)
action_idx = int(torch.argmax(action_probs_tensor, dim=1).item())
confidence = float(action_probs_tensor[0, action_idx].item()) # Confidence of the chosen action
action_probs = action_probs_tensor.squeeze(0).tolist() # Convert to list of floats for return
# Log advanced predictions for better decision making
if hasattr(self, '_log_predictions') and self._log_predictions:
# Log volatility prediction
@ -547,7 +546,7 @@ class EnhancedCNN(nn.Module):
logger.info(f" Market Regime: {regime_labels[regime_class]} ({regime[regime_class]:.3f})")
logger.info(f" Risk Level: {risk_labels[risk_class]} ({risk[risk_class]:.3f})")
return action
return action_idx, confidence, action_probs
def save(self, path):
"""Save model weights and architecture"""