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"layer_type": "Linear", "parameters": 387, "trainable": 387 }, { "layer_name": "price_pred_midterm.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "price_pred_midterm.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "price_pred_midterm.5", "layer_type": "Linear", "parameters": 387, "trainable": 387 }, { "layer_name": "price_pred_longterm.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "price_pred_longterm.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "price_pred_longterm.5", "layer_type": "Linear", "parameters": 387, "trainable": 387 }, { "layer_name": "price_pred_value.0", "layer_type": "Linear", "parameters": 393728, "trainable": 393728 }, { "layer_name": "price_pred_value.3", "layer_type": "Linear", "parameters": 131328, "trainable": 131328 }, { "layer_name": "price_pred_value.6", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "price_pred_value.8", "layer_type": "Linear", "parameters": 1032, "trainable": 1032 }, { "layer_name": "volatility_head.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "volatility_head.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "volatility_head.5", "layer_type": "Linear", "parameters": 645, "trainable": 645 }, { "layer_name": "support_resistance_head.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "support_resistance_head.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "support_resistance_head.5", "layer_type": "Linear", "parameters": 774, "trainable": 774 }, { "layer_name": "market_regime_head.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "market_regime_head.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "market_regime_head.5", "layer_type": "Linear", "parameters": 903, "trainable": 903 }, { "layer_name": "risk_head.0", "layer_type": "Linear", "parameters": 196864, "trainable": 196864 }, { "layer_name": "risk_head.3", "layer_type": "Linear", "parameters": 32896, "trainable": 32896 }, { "layer_name": "risk_head.5", "layer_type": "Linear", "parameters": 516, "trainable": 516 } ] } ] }, "saved_models": [ { "filename": "cnn_best.pt.pt", "path": "models/cnn_best.pt.pt", "size_mb": 33.12374496459961, "estimated_parameters": 2894410, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_BTC_USDT_20250329_021448.pt", "path": "models/cnn_BTC_USDT_20250329_021448.pt", "size_mb": 26.9183931350708, "estimated_parameters": 2350794, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_BTC_USDT_20250329_021800.pt", "path": "models/cnn_BTC_USDT_20250329_021800.pt", "size_mb": 26.9523286819458, "estimated_parameters": 2350794, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_BTC_USD_20250329_015217.pt", "path": "models/cnn_BTC_USD_20250329_015217.pt", "size_mb": 1.9763126373291016, "estimated_parameters": 170889, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_BTC_USD_20250329_020430.pt", "path": "models/cnn_BTC_USD_20250329_020430.pt", "size_mb": 32.90281295776367, "estimated_parameters": 2873740, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_BTC_USD_20250329_020711.pt", "path": "models/cnn_BTC_USD_20250329_020711.pt", "size_mb": 32.90281295776367, "estimated_parameters": 2873740, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes" ] }, { "filename": "cnn_final_20250331_001817.pt.pt", "path": "models/cnn_final_20250331_001817.pt.pt", "size_mb": 46.44105339050293, "estimated_parameters": 12168195, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes", "confidence_threshold", "max_consecutive_same_action", "action_counts", "last_actions", "model_version", "timestamp" ] }, { "filename": "trading_agent_best_net_pnl.pt", "path": "models/trading_agent_best_net_pnl.pt", "size_mb": 39.7817268371582, "estimated_parameters": 10424842, "checkpoint_keys": [ "policy_net", "target_net", "optimizer", "epsilon" ] }, { "filename": "trading_agent_best_pnl.pt", "path": "models/trading_agent_best_pnl.pt", "size_mb": 110.63929557800293, "estimated_parameters": "Error loading", "error": "Weights only load failed. In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\nPlease file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 149\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "trading_agent_best_reward.pt", "path": "models/trading_agent_best_reward.pt", "size_mb": 110.63994789123535, "estimated_parameters": "Error loading", "error": "Weights only load failed. In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\nPlease file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 149\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "trading_agent_final.pt", "path": "models/trading_agent_final.pt", "size_mb": 110.63858222961426, "estimated_parameters": "Error loading", "error": "Weights only load failed. In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\nPlease file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 149\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "trading_agent_live_trained.pt", "path": "models/trading_agent_live_trained.pt", "size_mb": 17.43359375, "estimated_parameters": "Error loading", "error": "PytorchStreamReader failed reading zip archive: failed finding central directory" }, { "filename": "best_rl_model.pth_agent_state.pt", "path": "NN/models/saved/best_rl_model.pth_agent_state.pt", "size_mb": 11.303743362426758, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state" ] }, { "filename": "best_rl_model.pth_policy.pt", "path": "NN/models/saved/best_rl_model.pth_policy.pt", "size_mb": 5.6540985107421875, "estimated_parameters": 1479751, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "conv2.weight", "conv2.bias", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "bn2.num_batches_tracked", "conv3.weight", "conv3.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "bn3.num_batches_tracked", "fc1.weight", "fc1.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias" ] }, { "filename": "best_rl_model.pth_target.pt", "path": "NN/models/saved/best_rl_model.pth_target.pt", "size_mb": 5.6540985107421875, "estimated_parameters": 1479751, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "conv2.weight", "conv2.bias", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "bn2.num_batches_tracked", "conv3.weight", "conv3.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "bn3.num_batches_tracked", "fc1.weight", "fc1.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias" ] }, { "filename": "cnn_model_best.pt", "path": "NN/models/saved/cnn_model_best.pt", "size_mb": 0.0011577606201171875, "estimated_parameters": "Error loading", "error": "Weights only load failed. In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\nPlease file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 80\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "dqn_agent_agent_state.pt", "path": "NN/models/saved/dqn_agent_agent_state.pt", "size_mb": 0.10158538818359375, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state", "best_reward", "avg_reward" ] }, { "filename": "dqn_agent_best_agent_state.pt", "path": "NN/models/saved/dqn_agent_best_agent_state.pt", "size_mb": 0.001384735107421875, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state", "best_reward", "avg_reward" ] }, { "filename": "dqn_agent_best_policy.pt", "path": "NN/models/saved/dqn_agent_best_policy.pt", "size_mb": 1.1685981750488281, "estimated_parameters": 304660, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "dqn_agent_best_target.pt", "path": "NN/models/saved/dqn_agent_best_target.pt", "size_mb": 1.1685981750488281, "estimated_parameters": 304660, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "dqn_agent_episode_100_agent_state.pt", "path": "NN/models/saved/dqn_agent_episode_100_agent_state.pt", "size_mb": 0.00135040283203125, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state" ] }, { "filename": "dqn_agent_episode_100_policy.pt", "path": "NN/models/saved/dqn_agent_episode_100_policy.pt", "size_mb": 11.874269485473633, "estimated_parameters": 3109003, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "conv2.weight", "conv2.bias", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "bn2.num_batches_tracked", "conv3.weight", "conv3.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "bn3.num_batches_tracked", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_bn.weight", "extrema_bn.bias", "extrema_bn.running_mean", "extrema_bn.running_var", "extrema_bn.num_batches_tracked", "fc1.weight", "fc1.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias" ] }, { "filename": "dqn_agent_episode_100_target.pt", "path": "NN/models/saved/dqn_agent_episode_100_target.pt", "size_mb": 11.874269485473633, "estimated_parameters": 3109003, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "bn1.num_batches_tracked", "conv2.weight", "conv2.bias", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "bn2.num_batches_tracked", "conv3.weight", "conv3.bias", "bn3.weight", "bn3.bias", "bn3.running_mean", "bn3.running_var", "bn3.num_batches_tracked", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_bn.weight", "extrema_bn.bias", "extrema_bn.running_mean", "extrema_bn.running_var", "extrema_bn.num_batches_tracked", "fc1.weight", "fc1.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias" ] }, { "filename": "dqn_agent_final_agent_state.pt", "path": "NN/models/saved/dqn_agent_final_agent_state.pt", "size_mb": 0.0176239013671875, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state", "best_reward", "avg_reward" ] }, { "filename": "dqn_agent_final_policy.pt", "path": "NN/models/saved/dqn_agent_final_policy.pt", "size_mb": 4.747499465942383, "estimated_parameters": 1242644, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "dqn_agent_final_target.pt", "path": "NN/models/saved/dqn_agent_final_target.pt", "size_mb": 4.747499465942383, "estimated_parameters": 1242644, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "dqn_agent_policy.pt", "path": "NN/models/saved/dqn_agent_policy.pt", "size_mb": 4.74730110168457, "estimated_parameters": 1242644, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "dqn_agent_target.pt", "path": "NN/models/saved/dqn_agent_target.pt", "size_mb": 4.74730110168457, "estimated_parameters": 1242644, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "enhanced_dqn_best_agent_state.pt", "path": "NN/models/saved/enhanced_dqn_best_agent_state.pt", "size_mb": 0.00756072998046875, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "enhanced_dqn_best_policy.pt", "path": "NN/models/saved/enhanced_dqn_best_policy.pt", "size_mb": 3.562204360961914, "estimated_parameters": 1085588, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim", "confidence_threshold" ] }, { "filename": "enhanced_dqn_best_target.pt", "path": "NN/models/saved/enhanced_dqn_best_target.pt", "size_mb": 3.562204360961914, "estimated_parameters": 1085588, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim", "confidence_threshold" ] }, { "filename": "enhanced_dqn_final_agent_state.pt", "path": "NN/models/saved/enhanced_dqn_final_agent_state.pt", "size_mb": 0.007564544677734375, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "enhanced_dqn_final_policy.pt", "path": "NN/models/saved/enhanced_dqn_final_policy.pt", "size_mb": 3.562246322631836, "estimated_parameters": 1085588, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim", "confidence_threshold" ] }, { "filename": "enhanced_dqn_final_target.pt", "path": "NN/models/saved/enhanced_dqn_final_target.pt", "size_mb": 3.562246322631836, "estimated_parameters": 1085588, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim", "confidence_threshold" ] }, { "filename": "improved_dqn_agent_best_agent_state.pt", "path": "NN/models/saved/improved_dqn_agent_best_agent_state.pt", "size_mb": 0.0016021728515625, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "improved_dqn_agent_best_policy.pt", "path": "NN/models/saved/improved_dqn_agent_best_policy.pt", "size_mb": 2.108156204223633, "estimated_parameters": 546571, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "norm1.batch_norm.weight", "norm1.batch_norm.bias", "norm1.batch_norm.running_mean", "norm1.batch_norm.running_var", "norm1.batch_norm.num_batches_tracked", "norm1.group_norm.weight", "norm1.group_norm.bias", "norm1.layer_norm.weight", "norm1.layer_norm.bias", "norm1.layer_norm_1d.weight", "norm1.layer_norm_1d.bias", "conv2.weight", "conv2.bias", "norm2.batch_norm.weight", "norm2.batch_norm.bias", "norm2.batch_norm.running_mean", "norm2.batch_norm.running_var", "norm2.batch_norm.num_batches_tracked", "norm2.group_norm.weight", "norm2.group_norm.bias", "norm2.layer_norm.weight", "norm2.layer_norm.bias", "norm2.layer_norm_1d.weight", "norm2.layer_norm_1d.bias", "conv3.weight", "conv3.bias", "norm3.batch_norm.weight", "norm3.batch_norm.bias", "norm3.batch_norm.running_mean", "norm3.batch_norm.running_var", "norm3.batch_norm.num_batches_tracked", "norm3.group_norm.weight", "norm3.group_norm.bias", "norm3.layer_norm.weight", "norm3.layer_norm.bias", "norm3.layer_norm_1d.weight", "norm3.layer_norm_1d.bias", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_norm.batch_norm.weight", "extrema_norm.batch_norm.bias", "extrema_norm.batch_norm.running_mean", "extrema_norm.batch_norm.running_var", "extrema_norm.batch_norm.num_batches_tracked", "extrema_norm.group_norm.weight", "extrema_norm.group_norm.bias", "extrema_norm.layer_norm.weight", "extrema_norm.layer_norm.bias", "extrema_norm.layer_norm_1d.weight", "extrema_norm.layer_norm_1d.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias", "fc1.weight", "fc1.bias" ] }, { "filename": "improved_dqn_agent_best_target.pt", "path": "NN/models/saved/improved_dqn_agent_best_target.pt", "size_mb": 2.108156204223633, "estimated_parameters": 546571, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "norm1.batch_norm.weight", "norm1.batch_norm.bias", "norm1.batch_norm.running_mean", "norm1.batch_norm.running_var", "norm1.batch_norm.num_batches_tracked", "norm1.group_norm.weight", "norm1.group_norm.bias", "norm1.layer_norm.weight", "norm1.layer_norm.bias", "norm1.layer_norm_1d.weight", "norm1.layer_norm_1d.bias", "conv2.weight", "conv2.bias", "norm2.batch_norm.weight", "norm2.batch_norm.bias", "norm2.batch_norm.running_mean", "norm2.batch_norm.running_var", "norm2.batch_norm.num_batches_tracked", "norm2.group_norm.weight", "norm2.group_norm.bias", "norm2.layer_norm.weight", "norm2.layer_norm.bias", "norm2.layer_norm_1d.weight", "norm2.layer_norm_1d.bias", "conv3.weight", "conv3.bias", "norm3.batch_norm.weight", "norm3.batch_norm.bias", "norm3.batch_norm.running_mean", "norm3.batch_norm.running_var", "norm3.batch_norm.num_batches_tracked", "norm3.group_norm.weight", "norm3.group_norm.bias", "norm3.layer_norm.weight", "norm3.layer_norm.bias", "norm3.layer_norm_1d.weight", "norm3.layer_norm_1d.bias", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_norm.batch_norm.weight", "extrema_norm.batch_norm.bias", "extrema_norm.batch_norm.running_mean", "extrema_norm.batch_norm.running_var", "extrema_norm.batch_norm.num_batches_tracked", "extrema_norm.group_norm.weight", "extrema_norm.group_norm.bias", "extrema_norm.layer_norm.weight", "extrema_norm.layer_norm.bias", "extrema_norm.layer_norm_1d.weight", "extrema_norm.layer_norm_1d.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias", "fc1.weight", "fc1.bias" ] }, { "filename": "improved_dqn_agent_final_agent_state.pt", "path": "NN/models/saved/improved_dqn_agent_final_agent_state.pt", "size_mb": 0.001605987548828125, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "improved_dqn_agent_final_policy.pt", "path": "NN/models/saved/improved_dqn_agent_final_policy.pt", "size_mb": 2.108224868774414, "estimated_parameters": 546571, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "norm1.batch_norm.weight", "norm1.batch_norm.bias", "norm1.batch_norm.running_mean", "norm1.batch_norm.running_var", "norm1.batch_norm.num_batches_tracked", "norm1.group_norm.weight", "norm1.group_norm.bias", "norm1.layer_norm.weight", "norm1.layer_norm.bias", "norm1.layer_norm_1d.weight", "norm1.layer_norm_1d.bias", "conv2.weight", "conv2.bias", "norm2.batch_norm.weight", "norm2.batch_norm.bias", "norm2.batch_norm.running_mean", "norm2.batch_norm.running_var", "norm2.batch_norm.num_batches_tracked", "norm2.group_norm.weight", "norm2.group_norm.bias", "norm2.layer_norm.weight", "norm2.layer_norm.bias", "norm2.layer_norm_1d.weight", "norm2.layer_norm_1d.bias", "conv3.weight", "conv3.bias", "norm3.batch_norm.weight", "norm3.batch_norm.bias", "norm3.batch_norm.running_mean", "norm3.batch_norm.running_var", "norm3.batch_norm.num_batches_tracked", "norm3.group_norm.weight", "norm3.group_norm.bias", "norm3.layer_norm.weight", "norm3.layer_norm.bias", "norm3.layer_norm_1d.weight", "norm3.layer_norm_1d.bias", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_norm.batch_norm.weight", "extrema_norm.batch_norm.bias", "extrema_norm.batch_norm.running_mean", "extrema_norm.batch_norm.running_var", "extrema_norm.batch_norm.num_batches_tracked", "extrema_norm.group_norm.weight", "extrema_norm.group_norm.bias", "extrema_norm.layer_norm.weight", "extrema_norm.layer_norm.bias", "extrema_norm.layer_norm_1d.weight", "extrema_norm.layer_norm_1d.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias", "fc1.weight", "fc1.bias" ] }, { "filename": "improved_dqn_agent_final_target.pt", "path": "NN/models/saved/improved_dqn_agent_final_target.pt", "size_mb": 2.108224868774414, "estimated_parameters": 546571, "checkpoint_keys": [ "conv1.weight", "conv1.bias", "norm1.batch_norm.weight", "norm1.batch_norm.bias", "norm1.batch_norm.running_mean", "norm1.batch_norm.running_var", "norm1.batch_norm.num_batches_tracked", "norm1.group_norm.weight", "norm1.group_norm.bias", "norm1.layer_norm.weight", "norm1.layer_norm.bias", "norm1.layer_norm_1d.weight", "norm1.layer_norm_1d.bias", "conv2.weight", "conv2.bias", "norm2.batch_norm.weight", "norm2.batch_norm.bias", "norm2.batch_norm.running_mean", "norm2.batch_norm.running_var", "norm2.batch_norm.num_batches_tracked", "norm2.group_norm.weight", "norm2.group_norm.bias", "norm2.layer_norm.weight", "norm2.layer_norm.bias", "norm2.layer_norm_1d.weight", "norm2.layer_norm_1d.bias", "conv3.weight", "conv3.bias", "norm3.batch_norm.weight", "norm3.batch_norm.bias", "norm3.batch_norm.running_mean", "norm3.batch_norm.running_var", "norm3.batch_norm.num_batches_tracked", "norm3.group_norm.weight", "norm3.group_norm.bias", "norm3.layer_norm.weight", "norm3.layer_norm.bias", "norm3.layer_norm_1d.weight", "norm3.layer_norm_1d.bias", "attention.query.weight", "attention.query.bias", "attention.key.weight", "attention.key.bias", "attention.value.weight", "attention.value.bias", "extrema_conv.weight", "extrema_conv.bias", "extrema_norm.batch_norm.weight", "extrema_norm.batch_norm.bias", "extrema_norm.batch_norm.running_mean", "extrema_norm.batch_norm.running_var", "extrema_norm.batch_norm.num_batches_tracked", "extrema_norm.group_norm.weight", "extrema_norm.group_norm.bias", "extrema_norm.layer_norm.weight", "extrema_norm.layer_norm.bias", "extrema_norm.layer_norm_1d.weight", "extrema_norm.layer_norm_1d.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias", "value_fc.weight", "value_fc.bias", "extrema_fc.weight", "extrema_fc.bias", "fc1.weight", "fc1.bias" ] }, { "filename": "optimized_short_term_model.pt", "path": "NN/models/saved/optimized_short_term_model.pt", "size_mb": 1.1817035675048828, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_best.pt", "path": "NN/models/saved/optimized_short_term_model_best.pt", "size_mb": 4.372953414916992, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_final.pt", "path": "NN/models/saved/optimized_short_term_model_final.pt", "size_mb": 4.373065948486328, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_realtime_best.pt", "path": "NN/models/saved/optimized_short_term_model_realtime_best.pt", "size_mb": 6.557572364807129, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_realtime_final.pt", "path": "NN/models/saved/optimized_short_term_model_realtime_final.pt", "size_mb": 6.557641983032227, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_ticks_best.pt", "path": "NN/models/saved/optimized_short_term_model_ticks_best.pt", "size_mb": 0.13934326171875, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "optimized_short_term_model_ticks_final.pt", "path": "NN/models/saved/optimized_short_term_model_ticks_final.pt", "size_mb": 0.13964271545410156, "estimated_parameters": "Error loading", "error": "Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` or the `torch.serialization.safe_globals([scalar])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html." }, { "filename": "rl_agent_best_agent_state.pt", "path": "NN/models/saved/rl_agent_best_agent_state.pt", "size_mb": 0.00925445556640625, "estimated_parameters": 0, "checkpoint_keys": [ "epsilon", "update_count", "losses", "optimizer_state", "best_reward", "avg_reward" ] }, { "filename": "rl_agent_best_policy.pt", "path": "NN/models/saved/rl_agent_best_policy.pt", "size_mb": 7.395586013793945, "estimated_parameters": 1936916, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "rl_agent_best_target.pt", "path": "NN/models/saved/rl_agent_best_target.pt", "size_mb": 7.395586013793945, "estimated_parameters": 1936916, "checkpoint_keys": [ "state_dict", "input_shape", "n_actions", "feature_dim" ] }, { "filename": "supervised_model_best.pt", "path": "NN/models/saved/supervised_model_best.pt", "size_mb": 0.157318115234375, "estimated_parameters": 12453, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes", "confidence_threshold", "max_consecutive_same_action", "action_counts", "last_actions", "model_version", "timestamp" ] }, { "filename": "supervised_model_best.pt.pt", "path": "NN/models/saved/supervised_model_best.pt.pt", "size_mb": 1.2264022827148438, "estimated_parameters": 105670, "checkpoint_keys": [ "model_state_dict", "optimizer_state_dict", "history", "window_size", "num_features", "output_size", "timeframes", "confidence_threshold", "max_consecutive_same_action", "action_counts", "last_actions", "model_version", "timestamp" ] } ], "summary": { "total_model_architectures": 2, "total_parameters_across_all": 504889098, "total_size_mb": 1926.6676025390625, "largest_model_parameters": 336592732, "smallest_model_parameters": 168296366, "saved_models_count": 52, "saved_models_total_size_mb": 720.3670511245728 } }