首先说一下训练时loss值不收敛的情况,就是loss值没有慢慢变小。 这种情况有几种原因。首先是loss值收敛到了0.15以下,后续没有继续收敛,这种时候我们可以将学习率调低一半再试一下。有可能是学习率太高了。如果是loss值越来越大,直到loss=nan。一般是你数据集比较大,要调高batchsize。 需要注意的是,batchsize越...
Avr_loss= :平均损失函数值。即预测值与样本值之间的差异。定理:loss值越低,拟合度越高。正常的loss值应该在初期体现出较高的数值,后面慢慢降低。最后保持一个相对低位的“震荡”。反之,如果一直提高或者高低乱飘,说明训练欠拟合或根本无拟合。如果loss值一直固定,说明过拟合。
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break if args.logging_dir is not None: logs = {"loss/epoch": loss_total / len(train_dataloader)} ...
With the aim of testing the operation of a general purpose device, a standard low-power system architecture was designed, integrating a low-power 8-bit AVR ATtiny84 microcontroller by Microchip, a MCP9700-E/TO temperature sensor by Microchip and an RFM95 LoRa module by HopeRF equipped with ...
LMFSim used part of LoRaSim, which set the distance between node randomly based on the Path Loss model [18]. Either using the PoW or LMF platform, the Path Loss model used in the LMFSim is the same. Figure 13 shows that the greater the number of broadcast domains (Nbroker), the ...