Code isn't executing for Test data
vinayakrajurs opened this issue · 2 comments
Hello,
I'm trying to run the Code for the ETTh1 dataset using the following run command in Google Colab:
!pythonrun_ETTh_10.py --data ETTh1 --features S --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 3 --lr 3e-3 --batch_size 8 --dropout 0.5 --model_name etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3
and it runs successfully for train before early stopping at epoch 17
`Args in experiment:
Namespace(INN=1, RIN=False, batch_size=8, c_out=1, checkpoints='exp/ETT_checkpoints/', cols=None, concat_len=0, data='ETTh1', data_path='ETTh1.csv', dec_in=1, detail_freq='h', devices='0', dilation=1, dropout=0.5, embed='timeF', enc_in=1, evaluate=False, features='S', freq='h', gpu=0, groups=1, hidden_size=4.0, inverse=False, itr=0, kernel=5, label_len=48, lastWeight=1.0, levels=3, loss='mae', lr=0.003, lradj=1, model='SCINet', model_name='etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3', num_decoder_layer=1, num_workers=0, patience=5, positionalEcoding=False, pred_len=48, resume=False, root_path='./datasets/', save=False, seq_len=96, single_step=0, single_step_output_One=0, stacks=1, target='OT', train_epochs=100, use_amp=False, use_gpu=True, use_multi_gpu=False, window_size=12)
SCINet(
(blocks1): EncoderTree(
(SCINet_Tree): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
(SCINet_Tree_odd): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
(SCINet_Tree_even): SCINet_Tree(
(workingblock): LevelSCINet(
(interact): InteractorLevel(
(level): Interactor(
(split): Splitting()
(phi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(psi): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(P): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
(U): Sequential(
(0): ReplicationPad1d((3, 3))
(1): Conv1d(1, 4, kernel_size=(5,), stride=(1,))
(2): LeakyReLU(negative_slope=0.01, inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Conv1d(4, 1, kernel_size=(3,), stride=(1,))
(5): Tanh()
)
)
)
)
)
)
)
)
(projection1): Conv1d(96, 48, kernel_size=(1,), stride=(1,), bias=False)
(div_projection): ModuleList()
)
start training : SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0>>>>>>>>>>>>>>>>>>>>>>>>>>
train 8497
val 2833
test 2833
exp/ETT_checkpoints/SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0
iters: 100, epoch: 1 | loss: 0.2635144
speed: 0.0918s/iter; left time: 9735.2018s
iters: 200, epoch: 1 | loss: 0.2746293
speed: 0.0640s/iter; left time: 6782.9779s
iters: 300, epoch: 1 | loss: 0.2532458
speed: 0.0641s/iter; left time: 6787.8207s
iters: 400, epoch: 1 | loss: 0.2308514
speed: 0.0644s/iter; left time: 6817.4635s
iters: 500, epoch: 1 | loss: 0.3040747
speed: 0.0651s/iter; left time: 6883.0310s
iters: 600, epoch: 1 | loss: 0.2578846
speed: 0.0644s/iter; left time: 6798.4408s
iters: 700, epoch: 1 | loss: 0.2459396
speed: 0.0634s/iter; left time: 6689.3999s
iters: 800, epoch: 1 | loss: 0.2914965
speed: 0.0653s/iter; left time: 6883.0213s
iters: 900, epoch: 1 | loss: 0.2554513
speed: 0.0641s/iter; left time: 6750.0606s
iters: 1000, epoch: 1 | loss: 0.2524573
speed: 0.0650s/iter; left time: 6838.8867s
Epoch: 1 cost time: 71.33614897727966
--------start to validate-----------
normed mse:0.0814, mae:0.2147, rmse:0.2852, mape:1.3623, mspe:25.9642, corr:0.8572
denormed mse:6.8514, mae:1.9706, rmse:2.6175, mape:0.1642, mspe:0.0813, corr:0.8572
--------start to test-----------
normed mse:0.0892, mae:0.2317, rmse:0.2987, mape:0.1735, mspe:0.0476, corr:0.8131
denormed mse:7.5120, mae:2.1258, rmse:2.7408, mape:inf, mspe:inf, corr:0.8131
Epoch: 1, Steps: 1062 | Train Loss: 0.2954810 valid Loss: 0.2147395 Test Loss: 0.2316557
Validation loss decreased (inf --> 0.214739). Saving model ...
Updating learning rate to 0.00285
iters: 100, epoch: 2 | loss: 0.2651560
speed: 0.2249s/iter; left time: 23618.5159s
iters: 200, epoch: 2 | loss: 0.3282328
speed: 0.0653s/iter; left time: 6857.5758s
iters: 300, epoch: 2 | loss: 0.2604796
speed: 0.0648s/iter; left time: 6791.7008s
iters: 400, epoch: 2 | loss: 0.2489426
speed: 0.0648s/iter; left time: 6790.2499s
iters: 500, epoch: 2 | loss: 0.3080887
speed: 0.0640s/iter; left time: 6695.5776s
iters: 600, epoch: 2 | loss: 0.2923984
speed: 0.0637s/iter; left time: 6657.3868s
iters: 700, epoch: 2 | loss: 0.3410122
speed: 0.0639s/iter; left time: 6673.1482s
iters: 800, epoch: 2 | loss: 0.2606724
speed: 0.0651s/iter; left time: 6792.7796s
iters: 900, epoch: 2 | loss: 0.2952042
speed: 0.0646s/iter; left time: 6730.5264s
iters: 1000, epoch: 2 | loss: 0.1823040
speed: 0.0639s/iter; left time: 6656.2354s
Epoch: 2 cost time: 68.4526846408844
--------start to validate-----------
normed mse:0.0799, mae:0.2147, rmse:0.2827, mape:1.4790, mspe:30.6527, corr:0.8621
denormed mse:6.7300, mae:1.9705, rmse:2.5942, mape:0.1594, mspe:0.0701, corr:0.8621
--------start to test-----------
normed mse:0.0586, mae:0.1883, rmse:0.2420, mape:0.1493, mspe:0.0399, corr:0.8178
denormed mse:4.9326, mae:1.7278, rmse:2.2210, mape:inf, mspe:inf, corr:0.8178
Epoch: 2, Steps: 1062 | Train Loss: 0.2776401 valid Loss: 0.2147304 Test Loss: 0.1882900
Validation loss decreased (0.214739 --> 0.214730). Saving model ...
Updating learning rate to 0.0027075
iters: 100, epoch: 3 | loss: 0.3098924
speed: 0.2221s/iter; left time: 23097.3052s
iters: 200, epoch: 3 | loss: 0.2770404
speed: 0.0643s/iter; left time: 6683.5361s
iters: 300, epoch: 3 | loss: 0.3261764
speed: 0.0639s/iter; left time: 6627.7702s
iters: 400, epoch: 3 | loss: 0.3463621
speed: 0.0645s/iter; left time: 6690.5621s
iters: 500, epoch: 3 | loss: 0.2016839
speed: 0.0669s/iter; left time: 6931.5046s
iters: 600, epoch: 3 | loss: 0.2773947
speed: 0.0660s/iter; left time: 6829.7108s
iters: 700, epoch: 3 | loss: 0.2436916
speed: 0.0645s/iter; left time: 6672.7252s
iters: 800, epoch: 3 | loss: 0.3100113
speed: 0.0636s/iter; left time: 6570.5793s
iters: 900, epoch: 3 | loss: 0.2406910
speed: 0.0637s/iter; left time: 6570.7141s
iters: 1000, epoch: 3 | loss: 0.2994752
speed: 0.0644s/iter; left time: 6641.0536s
Epoch: 3 cost time: 68.57215976715088
--------start to validate-----------
normed mse:0.0780, mae:0.2130, rmse:0.2793, mape:1.4954, mspe:33.6005, corr:0.8610
denormed mse:6.5708, mae:1.9548, rmse:2.5634, mape:0.1579, mspe:0.0679, corr:0.8610
--------start to test-----------
normed mse:0.0572, mae:0.1855, rmse:0.2392, mape:0.1437, mspe:0.0355, corr:0.8176
denormed mse:4.8174, mae:1.7019, rmse:2.1949, mape:inf, mspe:inf, corr:0.8176
Epoch: 3, Steps: 1062 | Train Loss: 0.2742680 valid Loss: 0.2130265 Test Loss: 0.1854673
Validation loss decreased (0.214730 --> 0.213026). Saving model ...
Updating learning rate to 0.0025721249999999998
iters: 100, epoch: 4 | loss: 0.3014244
speed: 0.2245s/iter; left time: 23101.4108s
iters: 200, epoch: 4 | loss: 0.2271728
speed: 0.0643s/iter; left time: 6606.3076s
iters: 300, epoch: 4 | loss: 0.3784584
speed: 0.0640s/iter; left time: 6569.4517s
iters: 400, epoch: 4 | loss: 0.2752601
speed: 0.0653s/iter; left time: 6696.5213s
iters: 500, epoch: 4 | loss: 0.3025605
speed: 0.0638s/iter; left time: 6536.2859s
iters: 600, epoch: 4 | loss: 0.2795481
speed: 0.0638s/iter; left time: 6538.9267s
iters: 700, epoch: 4 | loss: 0.2788646
speed: 0.0632s/iter; left time: 6465.8545s
iters: 800, epoch: 4 | loss: 0.2323274
speed: 0.0640s/iter; left time: 6545.1154s
iters: 900, epoch: 4 | loss: 0.2965076
speed: 0.0648s/iter; left time: 6620.8814s
iters: 1000, epoch: 4 | loss: 0.2785395
speed: 0.0643s/iter; left time: 6555.9024s
Epoch: 4 cost time: 68.27407765388489
--------start to validate-----------
normed mse:0.0787, mae:0.2138, rmse:0.2805, mape:1.5257, mspe:33.8453, corr:0.8624
denormed mse:6.6266, mae:1.9617, rmse:2.5742, mape:0.1578, mspe:0.0676, corr:0.8624
--------start to test-----------
normed mse:0.0564, mae:0.1850, rmse:0.2375, mape:0.1471, mspe:0.0391, corr:0.8203
denormed mse:4.7516, mae:1.6981, rmse:2.1798, mape:inf, mspe:inf, corr:0.8203
Epoch: 4, Steps: 1062 | Train Loss: 0.2719553 valid Loss: 0.2137705 Test Loss: 0.1850464
EarlyStopping counter: 1 out of 5
Updating learning rate to 0.0024435187499999996
iters: 100, epoch: 5 | loss: 0.3289642
speed: 0.2193s/iter; left time: 22341.4123s
iters: 200, epoch: 5 | loss: 0.2475609
speed: 0.0641s/iter; left time: 6520.0001s
iters: 300, epoch: 5 | loss: 0.2934369
speed: 0.0640s/iter; left time: 6501.5043s
iters: 400, epoch: 5 | loss: 0.3514774
speed: 0.0639s/iter; left time: 6488.8877s
iters: 500, epoch: 5 | loss: 0.3289756
speed: 0.0650s/iter; left time: 6592.7983s
iters: 600, epoch: 5 | loss: 0.3147124
speed: 0.0660s/iter; left time: 6684.8574s
iters: 700, epoch: 5 | loss: 0.2444675
speed: 0.0656s/iter; left time: 6642.7295s
iters: 800, epoch: 5 | loss: 0.2227931
speed: 0.0648s/iter; left time: 6558.8881s
iters: 900, epoch: 5 | loss: 0.2905650
speed: 0.0645s/iter; left time: 6519.5313s
iters: 1000, epoch: 5 | loss: 0.2011140
speed: 0.0643s/iter; left time: 6490.7155s
Epoch: 5 cost time: 68.49428033828735
--------start to validate-----------
normed mse:0.0795, mae:0.2143, rmse:0.2820, mape:1.5023, mspe:32.9689, corr:0.8631
denormed mse:6.6963, mae:1.9665, rmse:2.5877, mape:0.1583, mspe:0.0665, corr:0.8631
--------start to test-----------
normed mse:0.0568, mae:0.1897, rmse:0.2384, mape:0.1513, mspe:0.0402, corr:0.8214
denormed mse:4.7869, mae:1.7409, rmse:2.1879, mape:inf, mspe:inf, corr:0.8214
Epoch: 5, Steps: 1062 | Train Loss: 0.2680931 valid Loss: 0.2143007 Test Loss: 0.1897096
EarlyStopping counter: 2 out of 5
Updating learning rate to 0.0023213428124999992
iters: 100, epoch: 6 | loss: 0.2878237
speed: 0.2231s/iter; left time: 22487.2134s
iters: 200, epoch: 6 | loss: 0.3053960
speed: 0.0642s/iter; left time: 6463.6262s
iters: 300, epoch: 6 | loss: 0.2794231
speed: 0.0657s/iter; left time: 6608.5483s
iters: 400, epoch: 6 | loss: 0.1824071
speed: 0.0658s/iter; left time: 6613.3665s
iters: 500, epoch: 6 | loss: 0.3717845
speed: 0.0657s/iter; left time: 6599.7808s
iters: 600, epoch: 6 | loss: 0.2623390
speed: 0.0657s/iter; left time: 6589.1822s
iters: 700, epoch: 6 | loss: 0.2274510
speed: 0.0651s/iter; left time: 6523.5771s
iters: 800, epoch: 6 | loss: 0.2571564
speed: 0.0665s/iter; left time: 6657.4378s
iters: 900, epoch: 6 | loss: 0.2891446
speed: 0.0667s/iter; left time: 6670.0139s
iters: 1000, epoch: 6 | loss: 0.3868507
speed: 0.0665s/iter; left time: 6639.8010s
Epoch: 6 cost time: 69.7518322467804
--------start to validate-----------
normed mse:0.0777, mae:0.2116, rmse:0.2788, mape:1.5445, mspe:35.4777, corr:0.8636
denormed mse:6.5435, mae:1.9422, rmse:2.5580, mape:0.1556, mspe:0.0658, corr:0.8636
--------start to test-----------
normed mse:0.0489, mae:0.1677, rmse:0.2211, mape:0.1310, mspe:0.0322, corr:0.8212
denormed mse:4.1181, mae:1.5390, rmse:2.0293, mape:inf, mspe:inf, corr:0.8212
Epoch: 6, Steps: 1062 | Train Loss: 0.2668546 valid Loss: 0.2116495 Test Loss: 0.1677159
Validation loss decreased (0.213026 --> 0.211650). Saving model ...
Updating learning rate to 0.0022052756718749992
iters: 100, epoch: 7 | loss: 0.2596520
speed: 0.2240s/iter; left time: 22334.3117s
iters: 200, epoch: 7 | loss: 0.2324577
speed: 0.0640s/iter; left time: 6381.1203s
iters: 300, epoch: 7 | loss: 0.2214808
speed: 0.0651s/iter; left time: 6474.6273s
iters: 400, epoch: 7 | loss: 0.2045112
speed: 0.0632s/iter; left time: 6281.8838s
iters: 500, epoch: 7 | loss: 0.2396872
speed: 0.0636s/iter; left time: 6316.9631s
iters: 600, epoch: 7 | loss: 0.1907633
speed: 0.0644s/iter; left time: 6388.6488s
iters: 700, epoch: 7 | loss: 0.2620018
speed: 0.0747s/iter; left time: 7404.8432s
iters: 800, epoch: 7 | loss: 0.2821859
speed: 0.0652s/iter; left time: 6457.3174s
iters: 900, epoch: 7 | loss: 0.2233998
speed: 0.0641s/iter; left time: 6339.8982s
iters: 1000, epoch: 7 | loss: 0.2333842
speed: 0.0648s/iter; left time: 6402.4253s
Epoch: 7 cost time: 69.41319704055786
--------start to validate-----------
normed mse:0.0770, mae:0.2089, rmse:0.2776, mape:1.3294, mspe:25.3847, corr:0.8637
denormed mse:6.4876, mae:1.9170, rmse:2.5471, mape:0.1582, mspe:0.0734, corr:0.8637
--------start to test-----------
normed mse:0.0935, mae:0.2430, rmse:0.3058, mape:0.1754, mspe:0.0452, corr:0.8126
denormed mse:7.8770, mae:2.2295, rmse:2.8066, mape:inf, mspe:inf, corr:0.8126
Epoch: 7, Steps: 1062 | Train Loss: 0.2652045 valid Loss: 0.2089042 Test Loss: 0.2429530
Validation loss decreased (0.211650 --> 0.208904). Saving model ...
Updating learning rate to 0.0020950118882812493
iters: 100, epoch: 8 | loss: 0.2476663
speed: 0.2239s/iter; left time: 22095.3285s
iters: 200, epoch: 8 | loss: 0.3140231
speed: 0.0660s/iter; left time: 6502.9781s
iters: 300, epoch: 8 | loss: 0.2049506
speed: 0.0647s/iter; left time: 6368.1886s
iters: 400, epoch: 8 | loss: 0.2751644
speed: 0.0648s/iter; left time: 6373.9229s
iters: 500, epoch: 8 | loss: 0.3105533
speed: 0.0664s/iter; left time: 6520.4210s
iters: 600, epoch: 8 | loss: 0.2195727
speed: 0.0640s/iter; left time: 6287.0444s
iters: 700, epoch: 8 | loss: 0.2385361
speed: 0.0644s/iter; left time: 6313.8700s
iters: 800, epoch: 8 | loss: 0.2514920
speed: 0.0661s/iter; left time: 6476.8801s
iters: 900, epoch: 8 | loss: 0.2696154
speed: 0.0639s/iter; left time: 6250.8148s
iters: 1000, epoch: 8 | loss: 0.2994070
speed: 0.0646s/iter; left time: 6314.7932s
Epoch: 8 cost time: 69.0834846496582
--------start to validate-----------
normed mse:0.0786, mae:0.2097, rmse:0.2803, mape:1.3397, mspe:24.8040, corr:0.8618
denormed mse:6.6175, mae:1.9241, rmse:2.5725, mape:0.1595, mspe:0.0748, corr:0.8618
--------start to test-----------
normed mse:0.0803, mae:0.2185, rmse:0.2833, mape:0.1605, mspe:0.0409, corr:0.8191
denormed mse:6.7598, mae:2.0055, rmse:2.6000, mape:inf, mspe:inf, corr:0.8191
Epoch: 8, Steps: 1062 | Train Loss: 0.2626855 valid Loss: 0.2096790 Test Loss: 0.2185455
EarlyStopping counter: 1 out of 5
Updating learning rate to 0.0019902612938671868
iters: 100, epoch: 9 | loss: 0.2035707
speed: 0.2216s/iter; left time: 21633.7680s
iters: 200, epoch: 9 | loss: 0.1929587
speed: 0.0643s/iter; left time: 6273.6812s
iters: 300, epoch: 9 | loss: 0.2027658
speed: 0.0644s/iter; left time: 6275.3628s
iters: 400, epoch: 9 | loss: 0.1670165
speed: 0.0655s/iter; left time: 6372.5070s
iters: 500, epoch: 9 | loss: 0.3162191
speed: 0.0644s/iter; left time: 6264.6492s
iters: 600, epoch: 9 | loss: 0.2530913
speed: 0.0642s/iter; left time: 6233.9174s
iters: 700, epoch: 9 | loss: 0.2298067
speed: 0.0676s/iter; left time: 6557.6573s
iters: 800, epoch: 9 | loss: 0.2281127
speed: 0.0661s/iter; left time: 6406.0165s
iters: 900, epoch: 9 | loss: 0.2107817
speed: 0.0664s/iter; left time: 6426.1786s
iters: 1000, epoch: 9 | loss: 0.2355524
speed: 0.0656s/iter; left time: 6345.4314s
Epoch: 9 cost time: 69.29217648506165
--------start to validate-----------
normed mse:0.0747, mae:0.2069, rmse:0.2732, mape:1.4617, mspe:31.3834, corr:0.8653
denormed mse:6.2872, mae:1.8984, rmse:2.5074, mape:0.1508, mspe:0.0608, corr:0.8653
--------start to test-----------
normed mse:0.0819, mae:0.2269, rmse:0.2861, mape:0.1668, mspe:0.0438, corr:0.7987
denormed mse:6.8943, mae:2.0818, rmse:2.6257, mape:inf, mspe:inf, corr:0.7987
Epoch: 9, Steps: 1062 | Train Loss: 0.2619727 valid Loss: 0.2068796 Test Loss: 0.2268594
Validation loss decreased (0.208904 --> 0.206880). Saving model ...
Updating learning rate to 0.0018907482291738273
iters: 100, epoch: 10 | loss: 0.2934171
speed: 0.2292s/iter; left time: 22127.8739s
iters: 200, epoch: 10 | loss: 0.3041674
speed: 0.0652s/iter; left time: 6284.6789s
iters: 300, epoch: 10 | loss: 0.2558330
speed: 0.0646s/iter; left time: 6225.6793s
iters: 400, epoch: 10 | loss: 0.3225133
speed: 0.0643s/iter; left time: 6186.9587s
iters: 500, epoch: 10 | loss: 0.2021957
speed: 0.0646s/iter; left time: 6214.6386s
iters: 600, epoch: 10 | loss: 0.2634687
speed: 0.0644s/iter; left time: 6184.0262s
iters: 700, epoch: 10 | loss: 0.3601710
speed: 0.0649s/iter; left time: 6231.3502s
iters: 800, epoch: 10 | loss: 0.2715219
speed: 0.0643s/iter; left time: 6158.7077s
iters: 900, epoch: 10 | loss: 0.3285161
speed: 0.0662s/iter; left time: 6337.7003s
iters: 1000, epoch: 10 | loss: 0.2874655
speed: 0.0650s/iter; left time: 6218.2058s
Epoch: 10 cost time: 69.2235357761383
--------start to validate-----------
normed mse:0.0741, mae:0.2067, rmse:0.2721, mape:1.4288, mspe:32.2580, corr:0.8650
denormed mse:6.2362, mae:1.8968, rmse:2.4972, mape:0.1524, mspe:0.0658, corr:0.8650
--------start to test-----------
normed mse:0.1252, mae:0.2862, rmse:0.3538, mape:0.2056, mspe:0.0598, corr:0.7859
denormed mse:10.5420, mae:2.6266, rmse:3.2468, mape:inf, mspe:inf, corr:0.7859
Epoch: 10, Steps: 1062 | Train Loss: 0.2617135 valid Loss: 0.2067047 Test Loss: 0.2862312
Validation loss decreased (0.206880 --> 0.206705). Saving model ...
Updating learning rate to 0.001796210817715136
iters: 100, epoch: 11 | loss: 0.2604572
speed: 0.2211s/iter; left time: 21110.7798s
iters: 200, epoch: 11 | loss: 0.1902495
speed: 0.0639s/iter; left time: 6093.1127s
iters: 300, epoch: 11 | loss: 0.2706100
speed: 0.0645s/iter; left time: 6144.9446s
iters: 400, epoch: 11 | loss: 0.2700502
speed: 0.0641s/iter; left time: 6099.0807s
iters: 500, epoch: 11 | loss: 0.2039715
speed: 0.0644s/iter; left time: 6119.2977s
iters: 600, epoch: 11 | loss: 0.2211753
speed: 0.0644s/iter; left time: 6116.2928s
iters: 700, epoch: 11 | loss: 0.3060542
speed: 0.0641s/iter; left time: 6078.2290s
iters: 800, epoch: 11 | loss: 0.2073108
speed: 0.0636s/iter; left time: 6031.3409s
iters: 900, epoch: 11 | loss: 0.3166656
speed: 0.0642s/iter; left time: 6077.3573s
iters: 1000, epoch: 11 | loss: 0.2857897
speed: 0.0636s/iter; left time: 6020.0385s
Epoch: 11 cost time: 68.08029365539551
--------start to validate-----------
normed mse:0.0780, mae:0.2101, rmse:0.2792, mape:1.4069, mspe:27.8713, corr:0.8645
denormed mse:6.5642, mae:1.9282, rmse:2.5621, mape:0.1562, mspe:0.0672, corr:0.8645
--------start to test-----------
normed mse:0.0486, mae:0.1696, rmse:0.2206, mape:0.1346, mspe:0.0347, corr:0.8196
denormed mse:4.0965, mae:1.5563, rmse:2.0240, mape:inf, mspe:inf, corr:0.8196
Epoch: 11, Steps: 1062 | Train Loss: 0.2599722 valid Loss: 0.2101241 Test Loss: 0.1695920
EarlyStopping counter: 1 out of 5
Updating learning rate to 0.0017064002768293791
iters: 100, epoch: 12 | loss: 0.3560360
speed: 0.2200s/iter; left time: 20776.4963s
iters: 200, epoch: 12 | loss: 0.2906877
speed: 0.0638s/iter; left time: 6017.2672s
iters: 300, epoch: 12 | loss: 0.3136698
speed: 0.0640s/iter; left time: 6029.7119s
iters: 400, epoch: 12 | loss: 0.4521513
speed: 0.0637s/iter; left time: 5992.6421s
iters: 500, epoch: 12 | loss: 0.2683279
speed: 0.0635s/iter; left time: 5973.5166s
iters: 600, epoch: 12 | loss: 0.2095424
speed: 0.0632s/iter; left time: 5932.0850s
iters: 700, epoch: 12 | loss: 0.3217563
speed: 0.0646s/iter; left time: 6062.9666s
iters: 800, epoch: 12 | loss: 0.2670196
speed: 0.0635s/iter; left time: 5954.2641s
iters: 900, epoch: 12 | loss: 0.2306930
speed: 0.0639s/iter; left time: 5977.7809s
iters: 1000, epoch: 12 | loss: 0.2080201
speed: 0.0633s/iter; left time: 5915.4393s
Epoch: 12 cost time: 67.63420724868774
--------start to validate-----------
normed mse:0.0752, mae:0.2047, rmse:0.2742, mape:1.3057, mspe:24.8466, corr:0.8630
denormed mse:6.3301, mae:1.8782, rmse:2.5160, mape:0.1537, mspe:0.0697, corr:0.8630
--------start to test-----------
normed mse:0.1127, mae:0.2651, rmse:0.3357, mape:0.1911, mspe:0.0544, corr:0.7504
denormed mse:9.4922, mae:2.4327, rmse:3.0809, mape:inf, mspe:inf, corr:0.7504
Epoch: 12, Steps: 1062 | Train Loss: 0.2593013 valid Loss: 0.2046781 Test Loss: 0.2651025
Validation loss decreased (0.206705 --> 0.204678). Saving model ...
Updating learning rate to 0.00162108026298791
iters: 100, epoch: 13 | loss: 0.2679453
speed: 0.2223s/iter; left time: 20751.6236s
iters: 200, epoch: 13 | loss: 0.2244501
speed: 0.0640s/iter; left time: 5970.0265s
iters: 300, epoch: 13 | loss: 0.2729070
speed: 0.0638s/iter; left time: 5944.9959s
iters: 400, epoch: 13 | loss: 0.2141117
speed: 0.0642s/iter; left time: 5973.2411s
iters: 500, epoch: 13 | loss: 0.2737395
speed: 0.0649s/iter; left time: 6035.3685s
iters: 600, epoch: 13 | loss: 0.3773285
speed: 0.0655s/iter; left time: 6084.1809s
iters: 700, epoch: 13 | loss: 0.3060603
speed: 0.0651s/iter; left time: 6037.3794s
iters: 800, epoch: 13 | loss: 0.3271270
speed: 0.0639s/iter; left time: 5919.0781s
iters: 900, epoch: 13 | loss: 0.2570842
speed: 0.0644s/iter; left time: 5959.6727s
iters: 1000, epoch: 13 | loss: 0.1967695
speed: 0.0650s/iter; left time: 6008.8446s
Epoch: 13 cost time: 68.51006627082825
--------start to validate-----------
normed mse:0.0754, mae:0.2070, rmse:0.2745, mape:1.3459, mspe:27.9367, corr:0.8626
denormed mse:6.3463, mae:1.8996, rmse:2.5192, mape:0.1547, mspe:0.0686, corr:0.8626
--------start to test-----------
normed mse:0.1270, mae:0.2863, rmse:0.3563, mape:0.2047, mspe:0.0591, corr:0.7302
denormed mse:10.6924, mae:2.6270, rmse:3.2699, mape:inf, mspe:inf, corr:0.7302
Epoch: 13, Steps: 1062 | Train Loss: 0.2578851 valid Loss: 0.2070073 Test Loss: 0.2862706
EarlyStopping counter: 1 out of 5
Updating learning rate to 0.0015400262498385146
iters: 100, epoch: 14 | loss: 0.3828691
speed: 0.2242s/iter; left time: 20692.6053s
iters: 200, epoch: 14 | loss: 0.2255980
speed: 0.0655s/iter; left time: 6034.3079s
iters: 300, epoch: 14 | loss: 0.2057881
speed: 0.0651s/iter; left time: 5999.9542s
iters: 400, epoch: 14 | loss: 0.2044961
speed: 0.0654s/iter; left time: 6012.5291s
iters: 500, epoch: 14 | loss: 0.1950546
speed: 0.0659s/iter; left time: 6053.8845s
iters: 600, epoch: 14 | loss: 0.2513721
speed: 0.0663s/iter; left time: 6081.8917s
iters: 700, epoch: 14 | loss: 0.3617742
speed: 0.0676s/iter; left time: 6199.0184s
iters: 800, epoch: 14 | loss: 0.1818448
speed: 0.0660s/iter; left time: 6047.2192s
iters: 900, epoch: 14 | loss: 0.2921709
speed: 0.0637s/iter; left time: 5831.9500s
iters: 1000, epoch: 14 | loss: 0.4443547
speed: 0.0636s/iter; left time: 5812.0902s
Epoch: 14 cost time: 69.46138763427734
--------start to validate-----------
normed mse:0.0749, mae:0.2054, rmse:0.2737, mape:1.3964, mspe:29.3059, corr:0.8646
denormed mse:6.3101, mae:1.8853, rmse:2.5120, mape:0.1517, mspe:0.0638, corr:0.8646
--------start to test-----------
normed mse:0.0582, mae:0.1803, rmse:0.2413, mape:0.1373, mspe:0.0348, corr:0.7685
denormed mse:4.9051, mae:1.6544, rmse:2.2147, mape:inf, mspe:inf, corr:0.7685
Epoch: 14, Steps: 1062 | Train Loss: 0.2567903 valid Loss: 0.2054437 Test Loss: 0.1802866
EarlyStopping counter: 2 out of 5
Updating learning rate to 0.0014630249373465886
iters: 100, epoch: 15 | loss: 0.2464596
speed: 0.2214s/iter; left time: 20196.9599s
iters: 200, epoch: 15 | loss: 0.3060012
speed: 0.0648s/iter; left time: 5902.4050s
iters: 300, epoch: 15 | loss: 0.3132369
speed: 0.0647s/iter; left time: 5892.1457s
iters: 400, epoch: 15 | loss: 0.3352527
speed: 0.0645s/iter; left time: 5867.1592s
iters: 500, epoch: 15 | loss: 0.2343948
speed: 0.0640s/iter; left time: 5814.1662s
iters: 600, epoch: 15 | loss: 0.1983065
speed: 0.0636s/iter; left time: 5772.0216s
iters: 700, epoch: 15 | loss: 0.1803256
speed: 0.0638s/iter; left time: 5782.6625s
iters: 800, epoch: 15 | loss: 0.2608042
speed: 0.0640s/iter; left time: 5795.1071s
iters: 900, epoch: 15 | loss: 0.3982482
speed: 0.0656s/iter; left time: 5930.5014s
iters: 1000, epoch: 15 | loss: 0.2794555
speed: 0.0649s/iter; left time: 5859.7955s
Epoch: 15 cost time: 68.22825241088867
--------start to validate-----------
normed mse:0.0751, mae:0.2062, rmse:0.2740, mape:1.4129, mspe:29.4700, corr:0.8638
denormed mse:6.3221, mae:1.8922, rmse:2.5144, mape:0.1511, mspe:0.0607, corr:0.8638
--------start to test-----------
normed mse:0.0743, mae:0.2067, rmse:0.2726, mape:0.1542, mspe:0.0414, corr:0.7309
denormed mse:6.2598, mae:1.8971, rmse:2.5020, mape:inf, mspe:inf, corr:0.7309
Epoch: 15, Steps: 1062 | Train Loss: 0.2556333 valid Loss: 0.2062003 Test Loss: 0.2067328
EarlyStopping counter: 3 out of 5
Updating learning rate to 0.001389873690479259
iters: 100, epoch: 16 | loss: 0.4233045
speed: 0.2250s/iter; left time: 20289.9657s
iters: 200, epoch: 16 | loss: 0.2079662
speed: 0.0654s/iter; left time: 5887.5618s
iters: 300, epoch: 16 | loss: 0.2657562
speed: 0.0672s/iter; left time: 6045.0847s
iters: 400, epoch: 16 | loss: 0.1947590
speed: 0.0716s/iter; left time: 6432.6096s
iters: 500, epoch: 16 | loss: 0.2555844
speed: 0.0686s/iter; left time: 6157.6966s
iters: 600, epoch: 16 | loss: 0.2228586
speed: 0.0686s/iter; left time: 6153.4446s
iters: 700, epoch: 16 | loss: 0.2561190
speed: 0.0693s/iter; left time: 6211.2939s
iters: 800, epoch: 16 | loss: 0.2335158
speed: 0.0682s/iter; left time: 6099.8479s
iters: 900, epoch: 16 | loss: 0.1972947
speed: 0.0681s/iter; left time: 6082.7818s
iters: 1000, epoch: 16 | loss: 0.2210546
speed: 0.0684s/iter; left time: 6108.1396s
Epoch: 16 cost time: 72.44369554519653
--------start to validate-----------
normed mse:0.0767, mae:0.2066, rmse:0.2769, mape:1.3287, mspe:26.0432, corr:0.8614
denormed mse:6.4587, mae:1.8956, rmse:2.5414, mape:0.1549, mspe:0.0696, corr:0.8614
--------start to test-----------
normed mse:0.0722, mae:0.2028, rmse:0.2686, mape:0.1513, mspe:0.0397, corr:0.7538
denormed mse:6.0773, mae:1.8609, rmse:2.4652, mape:inf, mspe:inf, corr:0.7538
Epoch: 16, Steps: 1062 | Train Loss: 0.2538822 valid Loss: 0.2065719 Test Loss: 0.2027889
EarlyStopping counter: 4 out of 5
Updating learning rate to 0.001320380005955296
iters: 100, epoch: 17 | loss: 0.2435877
speed: 0.2235s/iter; left time: 19918.0769s
iters: 200, epoch: 17 | loss: 0.2681281
speed: 0.0643s/iter; left time: 5724.9103s
iters: 300, epoch: 17 | loss: 0.2386408
speed: 0.0645s/iter; left time: 5732.2681s
iters: 400, epoch: 17 | loss: 0.1940629
speed: 0.0645s/iter; left time: 5730.9041s
iters: 500, epoch: 17 | loss: 0.1982391
speed: 0.0647s/iter; left time: 5735.3843s
iters: 600, epoch: 17 | loss: 0.2039342
speed: 0.0645s/iter; left time: 5713.2374s
iters: 700, epoch: 17 | loss: 0.2077632
speed: 0.0648s/iter; left time: 5738.5635s
iters: 800, epoch: 17 | loss: 0.3183163
speed: 0.0640s/iter; left time: 5656.7522s
iters: 900, epoch: 17 | loss: 0.2791365
speed: 0.0648s/iter; left time: 5723.3435s
iters: 1000, epoch: 17 | loss: 0.2257991
speed: 0.0642s/iter; left time: 5660.2778s
Epoch: 17 cost time: 68.39220643043518
--------start to validate-----------
normed mse:0.0785, mae:0.2103, rmse:0.2803, mape:1.3704, mspe:27.2711, corr:0.8559
denormed mse:6.6141, mae:1.9294, rmse:2.5718, mape:0.1568, mspe:0.0703, corr:0.8559
--------start to test-----------
normed mse:0.1018, mae:0.2485, rmse:0.3191, mape:0.1800, mspe:0.0498, corr:0.7221
denormed mse:8.5757, mae:2.2802, rmse:2.9284, mape:inf, mspe:inf, corr:0.7221
Epoch: 17, Steps: 1062 | Train Loss: 0.2532137 valid Loss: 0.2102546 Test Loss: 0.2484858
EarlyStopping counter: 5 out of 5
Early stopping
save model in exp/ETT_checkpoints/SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0/ETTh148.bin
testing : SCINet_ETTh1_ftS_sl96_ll48_pl48_lr0.003_bs8_hid4.0_s1_l3_dp0.5_invFalse_itr0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
test 2833
normed mse:0.1127, mae:0.2651, rmse:0.3357, mape:0.1911, mspe:0.0544, corr:0.7504
TTTT denormed mse:9.4922, mae:2.4327, rmse:3.0809, mape:inf, mspe:inf, corr:0.7504
Final mean normed mse:0.1127,mae:0.2651,denormed mse:9.4922,mae:2.4327`
However Result Folder is not being formed containing trues.npy and preds.npy files.
Why is the code not executing for the test data ? Is there another separate script?
only the model is being saved in ETT_checkpoint
Please help, so the results could be plotted.
We run the model on the test set after every epoch as shown in the log:
--------start to test----------- normed mse:0.1018, mae:0.2485, rmse:0.3191, mape:0.1800, mspe:0.0498, corr:0.7221 denormed mse:8.5757, mae:2.2802, rmse:2.9284, mape:inf, mspe:inf, corr:0.7221 Epoch: 17, Steps: 1062 | Train Loss: 0.2532137 valid Loss: 0.2102546 Test Loss: 0.2484858.
If you want to run the test task separately, please pass --evaluate True as parameter, this will activate the line 114~119 in run_ETTh.py and calls exp.test() to run the test. You can add plot function in the test function then.
Hope this can help you.
Thank you, I figured how to plot the results.