Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: ArticularyWordRecognitionModel: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/ArticularyWordRecognition
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 128
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
275
300
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_ArticularyWordRecognition_MambaSingleLayer_UEA_ftM_sl144_ll0_pl0_dm128_ds8_expand1_dc4_nk3_tvdt0_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
300
loading model
test shape: torch.Size([300, 25]) torch.Size([300, 1])
accuracy:0.9933333333333333
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: AtrialFibrillation Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/AtrialFibrillation
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 13
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
15
15
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_AtrialFibrillation_MambaSingleLayer_UEA_ftM_sl640_ll0_pl0_dm32_ds16_expand1_dc4_nk13_tvdt1_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
15
loading model
test shape: torch.Size([15, 3]) torch.Size([15, 1])
accuracy:0.5333333333333333
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: BasicMotions Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/BasicMotions
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
40
40
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_BasicMotions_MambaSingleLayer_UEA_ftM_sl100_ll0_pl0_dm32_ds1_expand1_dc4_nk3_tvdt0_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
40
loading model
test shape: torch.Size([40, 4]) torch.Size([40, 1])
accuracy:1.0
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: CharacterTrajectoriesModel: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/CharacterTrajectories
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 4
Enc In: 7 Dec In: 7
C Out: 7 d model: 128
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
1422
1436
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_CharacterTrajectories_MambaSingleLayer_UEA_ftM_sl182_ll0_pl0_dm128_ds1_expand1_dc4_nk4_tvdt1_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
1436
loading model
test shape: torch.Size([1436, 20]) torch.Size([1436, 1])
accuracy:0.9972144846796658
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: Cricket Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/Cricket
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 24
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
108
72
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_Cricket_MambaSingleLayer_UEA_ftM_sl1197_ll0_pl0_dm32_ds4_expand1_dc4_nk24_tvdt0_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
72
loading model
test shape: torch.Size([72, 12]) torch.Size([72, 1])
accuracy:1.0
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: DuckDuckGeese Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/DuckDuckGeese
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 6
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 2
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
50
50
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_DuckDuckGeese_MambaSingleLayer_UEA_ftM_sl270_ll0_pl0_dm1024_ds2_expand1_dc4_nk6_tvdt0_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
50
loading model
test shape: torch.Size([50, 5]) torch.Size([50, 1])
accuracy:0.7
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: EigenWorms Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/EigenWorms
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 360
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 4
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
128
131
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_EigenWorms_MambaSingleLayer_UEA_ftM_sl17984_ll0_pl0_dm32_ds1_expand1_dc4_nk360_tvdt1_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
131
loading model
test shape: torch.Size([131, 5]) torch.Size([131, 1])
accuracy:0.8396946564885496
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: Epilepsy Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/Epilepsy
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 5
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
137
138
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_Epilepsy_MambaSingleLayer_UEA_ftM_sl206_ll0_pl0_dm32_ds1_expand1_dc4_nk5_tvdt1_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
138
loading model
test shape: torch.Size([138, 4]) torch.Size([138, 1])
accuracy:0.9782608695652174
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: ERing Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/ERing
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 128
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
30
270
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_ERing_MambaSingleLayer_UEA_ftM_sl65_ll0_pl0_dm128_ds8_expand1_dc4_nk3_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
270
loading model
test shape: torch.Size([270, 6]) torch.Size([270, 1])
accuracy:0.937037037037037
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: EthanolConcentrationModel: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/EthanolConcentration
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 36
Enc In: 7 Dec In: 7
C Out: 7 d model: 512
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
261
263
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_EthanolConcentration_MambaSingleLayer_UEA_ftM_sl1751_ll0_pl0_dm512_ds4_expand1_dc4_nk36_tvdt0_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
263
loading model
test shape: torch.Size([263, 4]) torch.Size([263, 1])
accuracy:0.42585551330798477
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: FaceDetection Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/FaceDetection
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 256
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
5890
3524
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_FaceDetection_MambaSingleLayer_UEA_ftM_sl62_ll0_pl0_dm256_ds16_expand1_dc4_nk3_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
3524
loading model
test shape: torch.Size([3524, 2]) torch.Size([3524, 1])
accuracy:0.6929625425652668
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: FingerMovements Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/FingerMovements
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
316
100
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_FingerMovements_MambaSingleLayer_UEA_ftM_sl50_ll0_pl0_dm32_ds1_expand1_dc4_nk3_tvdt0_tvB1_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
100
loading model
test shape: torch.Size([100, 2]) torch.Size([100, 1])
accuracy:0.71
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: HandMovementDirectionModel: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/HandMovementDirection
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 8
Enc In: 7 Dec In: 7
C Out: 7 d model: 256
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
160
74
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_HandMovementDirection_MambaSingleLayer_UEA_ftM_sl400_ll0_pl0_dm256_ds16_expand1_dc4_nk8_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
74
loading model
test shape: torch.Size([74, 4]) torch.Size([74, 1])
accuracy:0.7027027027027027
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: Handwriting Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/Handwriting
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 4
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
150
850
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_Handwriting_MambaSingleLayer_UEA_ftM_sl152_ll0_pl0_dm1024_ds4_expand1_dc4_nk4_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
850
loading model
test shape: torch.Size([850, 26]) torch.Size([850, 1])
accuracy:0.6082352941176471
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: Heartbeat Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/Heartbeat
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 9
Enc In: 7 Dec In: 7
C Out: 7 d model: 64
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
204
205
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_Heartbeat_MambaSingleLayer_UEA_ftM_sl405_ll0_pl0_dm64_ds16_expand1_dc4_nk9_tvdt0_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
205
loading model
test shape: torch.Size([205, 2]) torch.Size([205, 1])
accuracy:0.8048780487804879
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: InsectWingbeat Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/InsectWingbeat
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
25000
25000
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_InsectWingbeat_MambaSingleLayer_UEA_ftM_sl22_ll0_pl0_dm1024_ds8_expand1_dc4_nk3_tvdt0_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
25000
loading model
test shape: torch.Size([25000, 10]) torch.Size([25000, 1])
accuracy:0.66304
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: JapaneseVowels Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/JapaneseVowels
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 128
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
270
370
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_JapaneseVowels_MambaSingleLayer_UEA_ftM_sl29_ll0_pl0_dm128_ds8_expand1_dc4_nk3_tvdt1_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
370
loading model
test shape: torch.Size([370, 9]) torch.Size([370, 1])
accuracy:0.9864864864864865
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: Libras Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/Libras
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
180
180
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_Libras_MambaSingleLayer_UEA_ftM_sl45_ll0_pl0_dm1024_ds4_expand1_dc4_nk3_tvdt1_tvB1_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
180
loading model
test shape: torch.Size([180, 15]) torch.Size([180, 1])
accuracy:0.9166666666666666
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: LSST Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/LSST
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
2459
2466
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_LSST_MambaSingleLayer_UEA_ftM_sl36_ll0_pl0_dm1024_ds4_expand1_dc4_nk3_tvdt1_tvB1_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2466
loading model
test shape: torch.Size([2466, 14]) torch.Size([2466, 1])
accuracy:0.4557988645579886
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: MotorImagery Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/MotorImagery
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 60
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
278
100
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_MotorImagery_MambaSingleLayer_UEA_ftM_sl3000_ll0_pl0_dm32_ds8_expand1_dc4_nk60_tvdt0_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
100
loading model
test shape: torch.Size([100, 2]) torch.Size([100, 1])
accuracy:0.69
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: NATOPS Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/NATOPS
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 512
n heads: 8 e layers: 2
d layers: 1 d FF: 2
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
180
180
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_NATOPS_MambaSingleLayer_UEA_ftM_sl51_ll0_pl0_dm512_ds2_expand1_dc4_nk3_tvdt0_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
180
loading model
test shape: torch.Size([180, 6]) torch.Size([180, 1])
accuracy:0.9888888888888889
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: PEMS-SF Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/PEMS-SF
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 512
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
267
173
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_PEMS-SF_MambaSingleLayer_UEA_ftM_sl144_ll0_pl0_dm512_ds1_expand1_dc4_nk3_tvdt1_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
173
loading model
test shape: torch.Size([173, 7]) torch.Size([173, 1])
accuracy:0.8554913294797688
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: PenDigits Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/PenDigits
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 64
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
7494
3498
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_PenDigits_MambaSingleLayer_UEA_ftM_sl8_ll0_pl0_dm64_ds1_expand1_dc4_nk3_tvdt0_tvB1_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
3498
loading model
test shape: torch.Size([3498, 10]) torch.Size([3498, 1])
accuracy:0.9925671812464265
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: PhonemeSpectra Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/PhonemeSpectra
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 5
Enc In: 7 Dec In: 7
C Out: 7 d model: 256
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
3315
3353
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_PhonemeSpectra_MambaSingleLayer_UEA_ftM_sl217_ll0_pl0_dm256_ds4_expand1_dc4_nk5_tvdt1_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
3353
loading model
test shape: torch.Size([3353, 39]) torch.Size([3353, 1])
accuracy:0.3033104682373993
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: RacketSports Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/RacketSports
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 4
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
151
152
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_RacketSports_MambaSingleLayer_UEA_ftM_sl30_ll0_pl0_dm1024_ds4_expand1_dc4_nk3_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
152
loading model
test shape: torch.Size([152, 4]) torch.Size([152, 1])
accuracy:0.9276315789473685
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: SelfRegulationSCP1 Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/SelfRegulationSCP1
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 18
Enc In: 7 Dec In: 7
C Out: 7 d model: 256
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
268
293
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_SelfRegulationSCP1_MambaSingleLayer_UEA_ftM_sl896_ll0_pl0_dm256_ds16_expand1_dc4_nk18_tvdt1_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
293
loading model
test shape: torch.Size([293, 2]) torch.Size([293, 1])
accuracy:0.9249146757679181
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: SelfRegulationSCP2 Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/SelfRegulationSCP2
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 24
Enc In: 7 Dec In: 7
C Out: 7 d model: 256
n heads: 8 e layers: 2
d layers: 1 d FF: 16
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
200
180
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_SelfRegulationSCP2_MambaSingleLayer_UEA_ftM_sl1152_ll0_pl0_dm256_ds16_expand1_dc4_nk24_tvdt1_tvB1_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
180
loading model
test shape: torch.Size([180, 2]) torch.Size([180, 1])
accuracy:0.65
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: SpokenArabicDigits Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/SpokenArabicDigits
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 3
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 8
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
6599
2199
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_SpokenArabicDigits_MambaSingleLayer_UEA_ftM_sl93_ll0_pl0_dm1024_ds8_expand1_dc4_nk3_tvdt0_tvB1_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2199
loading model
test shape: torch.Size([2199, 10]) torch.Size([2199, 1])
accuracy:0.9995452478399273
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: StandWalkJump Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/StandWalkJump
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 50
Enc In: 7 Dec In: 7
C Out: 7 d model: 32
n heads: 8 e layers: 2
d layers: 1 d FF: 1
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
12
15
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_StandWalkJump_MambaSingleLayer_UEA_ftM_sl2500_ll0_pl0_dm32_ds1_expand1_dc4_nk50_tvdt1_tvB0_tvC0_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
15
loading model
test shape: torch.Size([15, 3]) torch.Size([15, 1])
accuracy:0.7333333333333333
Using GPU
Args in experiment:
[1mBasic Config[0m
Task Name: classification Is Training: 0
Model ID: UWaveGestureLibrary Model: MambaSingleLayer
[1mData Loader[0m
Data: UEA Root Path: ./dataset/UWaveGestureLibrary
Data Path: ETTh1.csv Features: M
Target: OT Freq: h
Checkpoints: ./checkpoints_best/MambaSL
[1mModel Parameters[0m
Top k: 5 Num Kernels: 7
Enc In: 7 Dec In: 7
C Out: 7 d model: 1024
n heads: 8 e layers: 2
d layers: 1 d FF: 2
Moving Avg: 25 Factor: 1
Distil: 1 Dropout: 0.1
Embed: timeF Activation: gelu
[1mRun Parameters[0m
Num Workers: 10 Itr: 1
Train Epochs: 100 Batch Size: 16
Patience: 10 Learning Rate: 0.001
Des: gating4proposed Loss: MSE
Lradj: type1 Use Amp: 0
[1mGPU[0m
Use GPU: 1 GPU: 0
Use Multi GPU: 0 Devices: 0,1,2,3
[1mDe-stationary Projector Params[0m
P Hidden Dims: 128, 128 P Hidden Layers: 2
Use GPU: cuda:0
120
320
🚀 Lazy Loading: MambaSingleLayer ...
>>>>>>>testing : classification_CLS_UWaveGestureLibrary_MambaSingleLayer_UEA_ftM_sl315_ll0_pl0_dm1024_ds2_expand1_dc4_nk7_tvdt0_tvB0_tvC1_useD0_gating4proposed_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
320
loading model
test shape: torch.Size([320, 8]) torch.Size([320, 1])
accuracy:0.934375