C:\Users\adminstor\anaconda3\envs\python39\python.exe D:\daima\KalmanNet_TSP-main\main_lor_DT_NLobs.py
Pipeline Start
Current Time = 07.24.23_12:19:44
Using GPU
1/r2 [dB]: tensor(30.)
1/q2 [dB]: tensor(30.)
Start Data Gen
Data Load
data_lor_v0_rq3030_T20.pt
no chopping
trainset size: torch.Size([1000, 3, 20])
cvset size: torch.Size([100, 3, 20])
testset size: torch.Size([200, 3, 20])
Evaluate EKF full
Extended Kalman Filter - MSE LOSS: tensor(-26.4659) [dB]
Extended Kalman Filter - STD: tensor(1.6740) [dB]
Inference Time: 37.115127086639404
KalmanNet start
Number of trainable parameters for KNet: 19938
Composition Loss: True
Traceback (most recent call last):
File "D:\daima\KalmanNet_TSP-main\main_lor_DT_NLobs.py", line 146, in <module>
[MSE_cv_linear_epoch, MSE_cv_dB_epoch, MSE_train_linear_epoch, MSE_train_dB_epoch] = KalmanNet_Pipeline.NNTrain(sys_model, cv_input, cv_target, train_input, train_target, path_results)
File "D:\daima\KalmanNet_TSP-main\Pipelines\Pipeline_EKF.py", line 150, in NNTrain
MSE_trainbatch_linear_LOSS = self.alpha * self.loss_fn(x_out_training_batch, train_target_batch)+(1-self.alpha)*self.loss_fn(y_hat, y_training_batch)
File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\modules\loss.py", line 520, in forward
return F.mse_loss(input, target, reduction=self.reduction)
File "C:\Users\adminstor\anaconda3\envs\python39\lib\site-packages\torch\nn\functional.py", line 3112, in mse_loss
return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
赵智明于2018年7月24日分享了他在STM32单片机上编写点亮LED灯程序的学习经历。在开发过程中，尽管遇到不少困难，但最终成功完成了任务，并在熟悉单片机寄存器等方面有所收获。然而，他也意识到自己在STM32基础知识方面仍需加强。
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