Newbee needs help!!LSTM training code ...

[slevinlee]

print("Training......")
start_time=time.time()
for epoch in range(1000):
    net = net.train()
    out = net(batch_x)
    Loss = loss(out, batch_y)

    optimizer.zero_grad()
    Loss.backward()
    optimizer.step()

    # scheduler.step()
    scheduler.step(Loss)#lr_scheduler.ReduceLROnPlateau

    if epoch % 10 == 0:
        Loss_valid=0
        net = net.eval()
        with torch.no_grad():
            out_valid = net(batch_validx)
            Loss_valid = loss(out_valid, batch_validy)
        print('Epoch: {:4}, Loss: {:.5f} , Loss_valid: {:.5f} , lr: {:.5f} '.format(epoch, Loss.item(), Loss_valid.item(), optimizer.param_groups[0]["lr"]))


end_time=time.time()
print(f'Training took {end_time-start_time} seconds')

Training……
Epoch: 0, Loss: 1.00316 , Loss_valid: 1.87190 , lr: 0.10000
Epoch: 10, Loss: 1.00277 , Loss_valid: 0.84588 , lr: 0.09000
Epoch: 20, Loss: 0.99660 , Loss_valid: 0.81552 , lr: 0.09000
Epoch: 30, Loss: 0.98478 , Loss_valid: 0.79688 , lr: 0.08100
Epoch: 40, Loss: 0.94413 , Loss_valid: 0.81124 , lr: 0.08100
Epoch: 50, Loss: 0.96319 , Loss_valid: 0.82703 , lr: 0.08100
Epoch: 60, Loss: 0.97517 , Loss_valid: 0.83291 , lr: 0.07290
Epoch: 70, Loss: 0.95339 , Loss_valid: 0.81459 , lr: 0.05905
Epoch: 80, Loss: 0.95462 , Loss_valid: 0.82126 , lr: 0.05314
Epoch: 90, Loss: 0.95244 , Loss_valid: 0.81611 , lr: 0.04783
Epoch: 100, Loss: 0.91957 , Loss_valid: 0.82782 , lr: 0.04305
Epoch: 110, Loss: 0.88055 , Loss_valid: 0.84757 , lr: 0.04305
Epoch: 120, Loss: 0.88261 , Loss_valid: 0.82991 , lr: 0.04305
Epoch: 130, Loss: 0.84414 , Loss_valid: 0.83797 , lr: 0.03874
Epoch: 140, Loss: 0.82690 , Loss_valid: 0.84577 , lr: 0.03874
Epoch: 150, Loss: 0.82871 , Loss_valid: 0.82727 , lr: 0.03487
Epoch: 160, Loss: 0.81299 , Loss_valid: 0.83291 , lr: 0.03487
Epoch: 170, Loss: 0.80848 , Loss_valid: 0.81171 , lr: 0.03487
Epoch: 180, Loss: 0.82118 , Loss_valid: 0.82955 , lr: 0.03138
Epoch: 190, Loss: 0.84017 , Loss_valid: 0.84859 , lr: 0.02824
Epoch: 200, Loss: 0.81144 , Loss_valid: 0.85816 , lr: 0.02542
Epoch: 210, Loss: 0.79448 , Loss_valid: 0.85410 , lr: 0.02288
Epoch: 220, Loss: 0.77297 , Loss_valid: 0.89438 , lr: 0.02288
Epoch: 230, Loss: 0.76242 , Loss_valid: 0.87608 , lr: 0.02288
Epoch: 240, Loss: 0.75231 , Loss_valid: 0.89328 , lr: 0.02288
Epoch: 250, Loss: 0.73848 , Loss_valid: 0.89923 , lr: 0.02288
Epoch: 260, Loss: 0.72244 , Loss_valid: 0.92185 , lr: 0.02288
Epoch: 270, Loss: 0.71837 , Loss_valid: 0.89441 , lr: 0.02288
Epoch: 280, Loss: 0.70244 , Loss_valid: 0.91994 , lr: 0.02288
Epoch: 290, Loss: 0.68920 , Loss_valid: 0.91606 , lr: 0.02288
Epoch: 300, Loss: 0.68744 , Loss_valid: 0.94420 , lr: 0.02288
Epoch: 310, Loss: 0.68034 , Loss_valid: 0.92912 , lr: 0.02059
Epoch: 320, Loss: 0.66862 , Loss_valid: 0.97463 , lr: 0.02059
Epoch: 330, Loss: 0.66328 , Loss_valid: 0.96139 , lr: 0.02059
Epoch: 340, Loss: 0.66524 , Loss_valid: 0.92839 , lr: 0.02059
Epoch: 350, Loss: 0.64902 , Loss_valid: 0.97426 , lr: 0.01853
Epoch: 360, Loss: 0.63730 , Loss_valid: 0.97161 , lr: 0.01853
Epoch: 370, Loss: 0.63587 , Loss_valid: 0.96699 , lr: 0.01853
Epoch: 380, Loss: 0.63489 , Loss_valid: 0.94800 , lr: 0.01668
Epoch: 390, Loss: 0.62138 , Loss_valid: 0.98730 , lr: 0.01668
Epoch: 400, Loss: 0.61612 , Loss_valid: 1.00091 , lr: 0.01501
Epoch: 410, Loss: 0.60611 , Loss_valid: 1.00102 , lr: 0.01501
Epoch: 420, Loss: 0.60122 , Loss_valid: 1.01488 , lr: 0.01501
Epoch: 430, Loss: 0.59859 , Loss_valid: 1.03265 , lr: 0.01501
Epoch: 440, Loss: 0.60626 , Loss_valid: 0.98629 , lr: 0.01351
Epoch: 450, Loss: 0.59467 , Loss_valid: 1.02511 , lr: 0.01351
Epoch: 460, Loss: 0.58322 , Loss_valid: 1.04381 , lr: 0.01351
Epoch: 470, Loss: 0.57967 , Loss_valid: 1.05414 , lr: 0.01351
Epoch: 480, Loss: 0.59828 , Loss_valid: 1.01195 , lr: 0.01216
Epoch: 490, Loss: 0.57783 , Loss_valid: 1.04054 , lr: 0.01094
Epoch: 500, Loss: 0.56883 , Loss_valid: 1.04576 , lr: 0.01094
Epoch: 510, Loss: 0.56206 , Loss_valid: 1.06330 , lr: 0.01094
Epoch: 520, Loss: 0.55730 , Loss_valid: 1.08246 , lr: 0.01094
Epoch: 530, Loss: 0.55305 , Loss_valid: 1.11677 , lr: 0.01094
Epoch: 540, Loss: 0.55478 , Loss_valid: 1.12357 , lr: 0.00985
Epoch: 550, Loss: 0.54799 , Loss_valid: 1.12652 , lr: 0.00985
Epoch: 560, Loss: 0.54452 , Loss_valid: 1.13231 , lr: 0.00985
Epoch: 570, Loss: 0.53961 , Loss_valid: 1.14211 , lr: 0.00886
Epoch: 580, Loss: 0.53510 , Loss_valid: 1.15727 , lr: 0.00886
Epoch: 590, Loss: 0.53428 , Loss_valid: 1.16417 , lr: 0.00886
Epoch: 600, Loss: 0.53007 , Loss_valid: 1.17492 , lr: 0.00886
Epoch: 610, Loss: 0.52665 , Loss_valid: 1.19026 , lr: 0.00886
Epoch: 620, Loss: 0.52201 , Loss_valid: 1.19882 , lr: 0.00798
Epoch: 630, Loss: 0.51790 , Loss_valid: 1.19867 , lr: 0.00798
Epoch: 640, Loss: 0.51407 , Loss_valid: 1.23578 , lr: 0.00798
Epoch: 650, Loss: 0.51048 , Loss_valid: 1.24741 , lr: 0.00798
Epoch: 660, Loss: 0.50739 , Loss_valid: 1.24768 , lr: 0.00798
Epoch: 670, Loss: 0.50299 , Loss_valid: 1.25863 , lr: 0.00798
Epoch: 680, Loss: 0.50126 , Loss_valid: 1.28079 , lr: 0.00798
Epoch: 690, Loss: 0.50005 , Loss_valid: 1.28577 , lr: 0.00798
Epoch: 700, Loss: 0.49493 , Loss_valid: 1.30260 , lr: 0.00718
Epoch: 710, Loss: 0.49159 , Loss_valid: 1.33307 , lr: 0.00718
Epoch: 720, Loss: 0.48919 , Loss_valid: 1.31761 , lr: 0.00718
Epoch: 730, Loss: 0.48820 , Loss_valid: 1.35105 , lr: 0.00718
Epoch: 740, Loss: 0.48467 , Loss_valid: 1.36124 , lr: 0.00718
Epoch: 750, Loss: 0.48053 , Loss_valid: 1.36916 , lr: 0.00718
Epoch: 760, Loss: 0.47790 , Loss_valid: 1.40329 , lr: 0.00718
Epoch: 770, Loss: 0.47623 , Loss_valid: 1.39230 , lr: 0.00646
Epoch: 780, Loss: 0.47302 , Loss_valid: 1.42231 , lr: 0.00646
Epoch: 790, Loss: 0.47060 , Loss_valid: 1.44392 , lr: 0.00646
Epoch: 800, Loss: 0.46743 , Loss_valid: 1.45224 , lr: 0.00646
Epoch: 810, Loss: 0.46663 , Loss_valid: 1.45919 , lr: 0.00581
Epoch: 820, Loss: 0.46373 , Loss_valid: 1.46976 , lr: 0.00581
Epoch: 830, Loss: 0.46277 , Loss_valid: 1.47158 , lr: 0.00523
Epoch: 840, Loss: 0.46052 , Loss_valid: 1.49458 , lr: 0.00523
Epoch: 850, Loss: 0.45873 , Loss_valid: 1.49603 , lr: 0.00523
Epoch: 860, Loss: 0.45715 , Loss_valid: 1.50548 , lr: 0.00523
Epoch: 870, Loss: 0.45689 , Loss_valid: 1.51772 , lr: 0.00523
Epoch: 880, Loss: 0.45414 , Loss_valid: 1.51268 , lr: 0.00471
Epoch: 890, Loss: 0.45319 , Loss_valid: 1.51110 , lr: 0.00471
Epoch: 900, Loss: 0.44999 , Loss_valid: 1.54060 , lr: 0.00471
Epoch: 910, Loss: 0.44626 , Loss_valid: 1.56482 , lr: 0.00471
Epoch: 920, Loss: 0.44512 , Loss_valid: 1.58074 , lr: 0.00471
Epoch: 930, Loss: 0.44234 , Loss_valid: 1.59516 , lr: 0.00471
Epoch: 940, Loss: 0.44033 , Loss_valid: 1.59582 , lr: 0.00471
Epoch: 950, Loss: 0.43926 , Loss_valid: 1.60843 , lr: 0.00471
Epoch: 960, Loss: 0.44000 , Loss_valid: 1.61440 , lr: 0.00471
Epoch: 970, Loss: 0.43613 , Loss_valid: 1.60503 , lr: 0.00424
Epoch: 980, Loss: 0.43454 , Loss_valid: 1.62839 , lr: 0.00424
Epoch: 990, Loss: 0.43331 , Loss_valid: 1.63874 , lr: 0.00424
Training took 20.11829423904419 seconds

What's wrong with the training code？？？