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cnn1depoch12.out
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nohup: 忽略输入
2022-01-11 22:40:28:INFO:Finish setting logger...
2022-01-11 22:40:28:INFO:==> Training/Evaluation parameters are:
2022-01-11 22:40:28:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42'
2022-01-11 22:40:28:INFO: data_fn=1
2022-01-11 22:40:28:INFO: datatest_fn=1
2022-01-11 22:40:28:INFO: filter_kernel_size=1
2022-01-11 22:40:28:INFO: override_data_cache=False
2022-01-11 22:40:28:INFO: maxRUL=125
2022-01-11 22:40:28:INFO: low_ratio=0.1
2022-01-11 22:40:28:INFO: high_ratio=0.99
2022-01-11 22:40:28:INFO: aug_ratio=150
2022-01-11 22:40:28:INFO: noise_amplitude=0.01
2022-01-11 22:40:28:INFO: modeltype='cnn1d'
2022-01-11 22:40:28:INFO: max_seq_len=550
2022-01-11 22:40:28:INFO: d_model=128
2022-01-11 22:40:28:INFO: p_dropout=0.1
2022-01-11 22:40:28:INFO: n_head=4
2022-01-11 22:40:28:INFO: n_layer=2
2022-01-11 22:40:28:INFO: dim_feedforward=512
2022-01-11 22:40:28:INFO: e_dropout=0.1
2022-01-11 22:40:28:INFO: activation='relu'
2022-01-11 22:40:28:INFO: layer_norm=False
2022-01-11 22:40:28:INFO: support_size=0
2022-01-11 22:40:28:INFO: inner_steps=1
2022-01-11 22:40:28:INFO: lr_inner=0.0001
2022-01-11 22:40:28:INFO: lr_meta=0.001
2022-01-11 22:40:28:INFO: n_epochs=12
2022-01-11 22:40:28:INFO: train_batch_size=20
2022-01-11 22:40:28:INFO: eval_batch_size=1
2022-01-11 22:40:28:INFO: lr=0.001
2022-01-11 22:40:28:INFO: weight_decay=0.01
2022-01-11 22:40:28:INFO: warmup_ratio=0.0
2022-01-11 22:40:28:INFO: max_grad_norm=5.0
2022-01-11 22:40:28:INFO: logging_steps=50
2022-01-11 22:40:28:INFO: seed=42
2022-01-11 22:40:28:INFO: gpu_id=0
2022-01-11 22:40:28:INFO: do_train=True
2022-01-11 22:40:28:INFO: do_eval=False
2022-01-11 22:40:28:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 22:40:28:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 22:40:28:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 22:40:28:INFO: device=device(type='cuda'))
2022-01-11 22:40:28:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 22:40:28:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 22:40:28:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 22:40:28:INFO:==> Min_max normalization...
2022-01-11 22:40:28:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 22:40:28:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 22:40:28:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 22:40:28:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 22:40:28:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 22:40:28:INFO: min_rul: 7, max_rul: 145
2022-01-11 22:40:28:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 22:40:28:INFO: min_ratio = 0.2067
2022-01-11 22:40:28:INFO: max_ratio = 0.9667
2022-01-11 22:40:28:INFO:==> Min_max normalization...
2022-01-11 22:40:28:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 22:40:28:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 22:40:32:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 22:40:32:INFO:NumExpr defaulting to 8 threads.
2022-01-11 22:40:32:INFO:=============== Scheme: Normal Learning ===============
2022-01-11 22:40:32:INFO: Num examples = 15000
2022-01-11 22:40:32:INFO: Num epochs = 12
2022-01-11 22:40:32:INFO: Batch size = 20
2022-01-11 22:40:32:INFO: Total optimization steps = 9000
2022-01-11 22:40:41:INFO:==> Group parameters for optimization...
2022-01-11 22:40:41:INFO: Parameters to update are:
2022-01-11 22:40:41:INFO: conv1.0.weight
2022-01-11 22:40:41:INFO: conv2.0.weight
2022-01-11 22:40:41:INFO: conv3.0.weight
2022-01-11 22:40:41:INFO: conv4.0.weight
2022-01-11 22:40:41:INFO: conv5.0.weight
2022-01-11 22:40:41:INFO: fc_1.0.weight
2022-01-11 22:40:41:INFO: fc_1.0.bias
2022-01-11 22:40:41:INFO: fc_2.weight
2022-01-11 22:40:41:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 22:40:43:INFO:Epoch: 0 global_step: 0/9000 lr: 0.00100 loss: 0.0017
2022-01-11 22:41:01:INFO:Epoch: 0 global_step: 50/9000 lr: 0.00099 loss: 0.0245
2022-01-11 22:41:20:INFO:Epoch: 0 global_step: 100/9000 lr: 0.00099 loss: 0.0077
2022-01-11 22:41:39:INFO:Epoch: 0 global_step: 150/9000 lr: 0.00098 loss: 0.0070
2022-01-11 22:41:57:INFO:Epoch: 0 global_step: 200/9000 lr: 0.00098 loss: 0.0062
2022-01-11 22:42:16:INFO:Epoch: 0 global_step: 250/9000 lr: 0.00097 loss: 0.0056
2022-01-11 22:42:34:INFO:Epoch: 0 global_step: 300/9000 lr: 0.00097 loss: 0.0048
2022-01-11 22:42:53:INFO:Epoch: 0 global_step: 350/9000 lr: 0.00096 loss: 0.0039
2022-01-11 22:43:12:INFO:Epoch: 0 global_step: 400/9000 lr: 0.00096 loss: 0.0036
2022-01-11 22:43:30:INFO:Epoch: 0 global_step: 450/9000 lr: 0.00095 loss: 0.0037
2022-01-11 22:43:49:INFO:Epoch: 0 global_step: 500/9000 lr: 0.00094 loss: 0.0035
2022-01-11 22:44:07:INFO:Epoch: 0 global_step: 550/9000 lr: 0.00094 loss: 0.0032
2022-01-11 22:44:26:INFO:Epoch: 0 global_step: 600/9000 lr: 0.00093 loss: 0.0028
2022-01-11 22:44:44:INFO:Epoch: 0 global_step: 650/9000 lr: 0.00093 loss: 0.0030
2022-01-11 22:45:03:INFO:Epoch: 0 global_step: 700/9000 lr: 0.00092 loss: 0.0029
2022-01-11 22:45:23:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 22:45:23:INFO: Num examples = 100
2022-01-11 22:45:23:INFO: RMSE = 34.0395
2022-01-11 22:45:25:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 22:45:25:INFO: Num examples = 100
2022-01-11 22:45:25:INFO: RMSE = 33.3675
2022-01-11 22:45:25:INFO:==> Minimal valid RMSE!
2022-01-11 22:45:25:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 22:45:26:INFO:Epoch: 1 global_step: 750/9000 lr: 0.00092 loss: 0.0030
2022-01-11 22:45:44:INFO:Epoch: 1 global_step: 800/9000 lr: 0.00091 loss: 0.0027
2022-01-11 22:46:03:INFO:Epoch: 1 global_step: 850/9000 lr: 0.00091 loss: 0.0028
2022-01-11 22:46:21:INFO:Epoch: 1 global_step: 900/9000 lr: 0.00090 loss: 0.0022
2022-01-11 22:46:40:INFO:Epoch: 1 global_step: 950/9000 lr: 0.00089 loss: 0.0026
2022-01-11 22:46:58:INFO:Epoch: 1 global_step: 1000/9000 lr: 0.00089 loss: 0.0023
2022-01-11 22:47:17:INFO:Epoch: 1 global_step: 1050/9000 lr: 0.00088 loss: 0.0025
2022-01-11 22:47:36:INFO:Epoch: 1 global_step: 1100/9000 lr: 0.00088 loss: 0.0023
2022-01-11 22:47:54:INFO:Epoch: 1 global_step: 1150/9000 lr: 0.00087 loss: 0.0024
2022-01-11 22:48:13:INFO:Epoch: 1 global_step: 1200/9000 lr: 0.00087 loss: 0.0021
2022-01-11 22:48:31:INFO:Epoch: 1 global_step: 1250/9000 lr: 0.00086 loss: 0.0026
2022-01-11 22:48:50:INFO:Epoch: 1 global_step: 1300/9000 lr: 0.00086 loss: 0.0020
2022-01-11 22:49:09:INFO:Epoch: 1 global_step: 1350/9000 lr: 0.00085 loss: 0.0020
2022-01-11 22:49:27:INFO:Epoch: 1 global_step: 1400/9000 lr: 0.00084 loss: 0.0020
2022-01-11 22:49:46:INFO:Epoch: 1 global_step: 1450/9000 lr: 0.00084 loss: 0.0017
2022-01-11 22:50:06:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 22:50:06:INFO: Num examples = 100
2022-01-11 22:50:06:INFO: RMSE = 31.1376
2022-01-11 22:50:08:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 22:50:08:INFO: Num examples = 100
2022-01-11 22:50:08:INFO: RMSE = 24.7693
2022-01-11 22:50:08:INFO:==> Minimal valid RMSE!
2022-01-11 22:50:08:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 22:50:08:INFO:Epoch: 2 global_step: 1500/9000 lr: 0.00083 loss: 0.0018
2022-01-11 22:50:27:INFO:Epoch: 2 global_step: 1550/9000 lr: 0.00083 loss: 0.0019
2022-01-11 22:50:46:INFO:Epoch: 2 global_step: 1600/9000 lr: 0.00082 loss: 0.0017
2022-01-11 22:51:04:INFO:Epoch: 2 global_step: 1650/9000 lr: 0.00082 loss: 0.0019
2022-01-11 22:51:23:INFO:Epoch: 2 global_step: 1700/9000 lr: 0.00081 loss: 0.0019
2022-01-11 22:51:41:INFO:Epoch: 2 global_step: 1750/9000 lr: 0.00081 loss: 0.0017
2022-01-11 22:52:00:INFO:Epoch: 2 global_step: 1800/9000 lr: 0.00080 loss: 0.0017
2022-01-11 22:52:19:INFO:Epoch: 2 global_step: 1850/9000 lr: 0.00079 loss: 0.0017
2022-01-11 22:52:37:INFO:Epoch: 2 global_step: 1900/9000 lr: 0.00079 loss: 0.0015
2022-01-11 22:52:56:INFO:Epoch: 2 global_step: 1950/9000 lr: 0.00078 loss: 0.0015
2022-01-11 22:53:14:INFO:Epoch: 2 global_step: 2000/9000 lr: 0.00078 loss: 0.0016
2022-01-11 22:53:33:INFO:Epoch: 2 global_step: 2050/9000 lr: 0.00077 loss: 0.0016
2022-01-11 22:53:51:INFO:Epoch: 2 global_step: 2100/9000 lr: 0.00077 loss: 0.0015
2022-01-11 22:54:10:INFO:Epoch: 2 global_step: 2150/9000 lr: 0.00076 loss: 0.0014
2022-01-11 22:54:29:INFO:Epoch: 2 global_step: 2200/9000 lr: 0.00076 loss: 0.0014
2022-01-11 22:54:49:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 22:54:49:INFO: Num examples = 100
2022-01-11 22:54:49:INFO: RMSE = 30.8675
2022-01-11 22:54:51:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 22:54:51:INFO: Num examples = 100
2022-01-11 22:54:51:INFO: RMSE = 24.0344
2022-01-11 22:54:51:INFO:==> Minimal valid RMSE!
2022-01-11 22:54:51:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 22:54:52:INFO:Epoch: 3 global_step: 2250/9000 lr: 0.00075 loss: 0.0014
2022-01-11 22:55:10:INFO:Epoch: 3 global_step: 2300/9000 lr: 0.00074 loss: 0.0013
2022-01-11 22:55:29:INFO:Epoch: 3 global_step: 2350/9000 lr: 0.00074 loss: 0.0013
2022-01-11 22:55:47:INFO:Epoch: 3 global_step: 2400/9000 lr: 0.00073 loss: 0.0014
2022-01-11 22:56:06:INFO:Epoch: 3 global_step: 2450/9000 lr: 0.00073 loss: 0.0012
2022-01-11 22:56:24:INFO:Epoch: 3 global_step: 2500/9000 lr: 0.00072 loss: 0.0012
2022-01-11 22:56:43:INFO:Epoch: 3 global_step: 2550/9000 lr: 0.00072 loss: 0.0012
2022-01-11 22:57:02:INFO:Epoch: 3 global_step: 2600/9000 lr: 0.00071 loss: 0.0012
2022-01-11 22:57:20:INFO:Epoch: 3 global_step: 2650/9000 lr: 0.00071 loss: 0.0014
2022-01-11 22:57:39:INFO:Epoch: 3 global_step: 2700/9000 lr: 0.00070 loss: 0.0012
2022-01-11 22:57:57:INFO:Epoch: 3 global_step: 2750/9000 lr: 0.00069 loss: 0.0012
2022-01-11 22:58:16:INFO:Epoch: 3 global_step: 2800/9000 lr: 0.00069 loss: 0.0012
2022-01-11 22:58:34:INFO:Epoch: 3 global_step: 2850/9000 lr: 0.00068 loss: 0.0013
2022-01-11 22:58:53:INFO:Epoch: 3 global_step: 2900/9000 lr: 0.00068 loss: 0.0011
2022-01-11 22:59:11:INFO:Epoch: 3 global_step: 2950/9000 lr: 0.00067 loss: 0.0010
2022-01-11 22:59:32:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 22:59:32:INFO: Num examples = 100
2022-01-11 22:59:32:INFO: RMSE = 31.2568
2022-01-11 22:59:34:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 22:59:34:INFO: Num examples = 100
2022-01-11 22:59:34:INFO: RMSE = 26.1756
2022-01-11 22:59:34:INFO:Epoch: 4 global_step: 3000/9000 lr: 0.00067 loss: 0.0011
2022-01-11 22:59:53:INFO:Epoch: 4 global_step: 3050/9000 lr: 0.00066 loss: 0.0010
2022-01-11 23:00:11:INFO:Epoch: 4 global_step: 3100/9000 lr: 0.00066 loss: 0.0010
2022-01-11 23:00:30:INFO:Epoch: 4 global_step: 3150/9000 lr: 0.00065 loss: 0.0009
2022-01-11 23:00:48:INFO:Epoch: 4 global_step: 3200/9000 lr: 0.00064 loss: 0.0011
2022-01-11 23:01:07:INFO:Epoch: 4 global_step: 3250/9000 lr: 0.00064 loss: 0.0011
2022-01-11 23:01:26:INFO:Epoch: 4 global_step: 3300/9000 lr: 0.00063 loss: 0.0010
2022-01-11 23:01:44:INFO:Epoch: 4 global_step: 3350/9000 lr: 0.00063 loss: 0.0010
2022-01-11 23:02:03:INFO:Epoch: 4 global_step: 3400/9000 lr: 0.00062 loss: 0.0011
2022-01-11 23:02:21:INFO:Epoch: 4 global_step: 3450/9000 lr: 0.00062 loss: 0.0010
2022-01-11 23:02:40:INFO:Epoch: 4 global_step: 3500/9000 lr: 0.00061 loss: 0.0010
2022-01-11 23:02:58:INFO:Epoch: 4 global_step: 3550/9000 lr: 0.00061 loss: 0.0010
2022-01-11 23:03:17:INFO:Epoch: 4 global_step: 3600/9000 lr: 0.00060 loss: 0.0010
2022-01-11 23:03:36:INFO:Epoch: 4 global_step: 3650/9000 lr: 0.00059 loss: 0.0011
2022-01-11 23:03:54:INFO:Epoch: 4 global_step: 3700/9000 lr: 0.00059 loss: 0.0009
2022-01-11 23:04:14:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:04:14:INFO: Num examples = 100
2022-01-11 23:04:14:INFO: RMSE = 29.9128
2022-01-11 23:04:17:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:04:17:INFO: Num examples = 100
2022-01-11 23:04:17:INFO: RMSE = 25.4110
2022-01-11 23:04:17:INFO:Epoch: 5 global_step: 3750/9000 lr: 0.00058 loss: 0.0009
2022-01-11 23:04:36:INFO:Epoch: 5 global_step: 3800/9000 lr: 0.00058 loss: 0.0010
2022-01-11 23:04:54:INFO:Epoch: 5 global_step: 3850/9000 lr: 0.00057 loss: 0.0009
2022-01-11 23:05:13:INFO:Epoch: 5 global_step: 3900/9000 lr: 0.00057 loss: 0.0008
2022-01-11 23:05:31:INFO:Epoch: 5 global_step: 3950/9000 lr: 0.00056 loss: 0.0010
2022-01-11 23:05:50:INFO:Epoch: 5 global_step: 4000/9000 lr: 0.00056 loss: 0.0009
2022-01-11 23:06:08:INFO:Epoch: 5 global_step: 4050/9000 lr: 0.00055 loss: 0.0009
2022-01-11 23:06:27:INFO:Epoch: 5 global_step: 4100/9000 lr: 0.00054 loss: 0.0010
2022-01-11 23:06:46:INFO:Epoch: 5 global_step: 4150/9000 lr: 0.00054 loss: 0.0008
2022-01-11 23:07:04:INFO:Epoch: 5 global_step: 4200/9000 lr: 0.00053 loss: 0.0008
2022-01-11 23:07:23:INFO:Epoch: 5 global_step: 4250/9000 lr: 0.00053 loss: 0.0009
2022-01-11 23:07:41:INFO:Epoch: 5 global_step: 4300/9000 lr: 0.00052 loss: 0.0007
2022-01-11 23:08:00:INFO:Epoch: 5 global_step: 4350/9000 lr: 0.00052 loss: 0.0008
2022-01-11 23:08:18:INFO:Epoch: 5 global_step: 4400/9000 lr: 0.00051 loss: 0.0008
2022-01-11 23:08:37:INFO:Epoch: 5 global_step: 4450/9000 lr: 0.00051 loss: 0.0009
2022-01-11 23:08:57:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:08:57:INFO: Num examples = 100
2022-01-11 23:08:57:INFO: RMSE = 29.8746
2022-01-11 23:08:59:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:08:59:INFO: Num examples = 100
2022-01-11 23:08:59:INFO: RMSE = 24.5785
2022-01-11 23:09:00:INFO:Epoch: 6 global_step: 4500/9000 lr: 0.00050 loss: 0.0007
2022-01-11 23:09:18:INFO:Epoch: 6 global_step: 4550/9000 lr: 0.00049 loss: 0.0008
2022-01-11 23:09:37:INFO:Epoch: 6 global_step: 4600/9000 lr: 0.00049 loss: 0.0007
2022-01-11 23:09:56:INFO:Epoch: 6 global_step: 4650/9000 lr: 0.00048 loss: 0.0007
2022-01-11 23:10:14:INFO:Epoch: 6 global_step: 4700/9000 lr: 0.00048 loss: 0.0007
2022-01-11 23:10:33:INFO:Epoch: 6 global_step: 4750/9000 lr: 0.00047 loss: 0.0007
2022-01-11 23:10:51:INFO:Epoch: 6 global_step: 4800/9000 lr: 0.00047 loss: 0.0007
2022-01-11 23:11:10:INFO:Epoch: 6 global_step: 4850/9000 lr: 0.00046 loss: 0.0007
2022-01-11 23:11:28:INFO:Epoch: 6 global_step: 4900/9000 lr: 0.00046 loss: 0.0006
2022-01-11 23:11:47:INFO:Epoch: 6 global_step: 4950/9000 lr: 0.00045 loss: 0.0007
2022-01-11 23:12:06:INFO:Epoch: 6 global_step: 5000/9000 lr: 0.00044 loss: 0.0007
2022-01-11 23:12:24:INFO:Epoch: 6 global_step: 5050/9000 lr: 0.00044 loss: 0.0007
2022-01-11 23:12:43:INFO:Epoch: 6 global_step: 5100/9000 lr: 0.00043 loss: 0.0007
2022-01-11 23:13:01:INFO:Epoch: 6 global_step: 5150/9000 lr: 0.00043 loss: 0.0007
2022-01-11 23:13:20:INFO:Epoch: 6 global_step: 5200/9000 lr: 0.00042 loss: 0.0006
2022-01-11 23:13:40:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:13:40:INFO: Num examples = 100
2022-01-11 23:13:40:INFO: RMSE = 31.0820
2022-01-11 23:13:43:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:13:43:INFO: Num examples = 100
2022-01-11 23:13:43:INFO: RMSE = 24.0309
2022-01-11 23:13:43:INFO:==> Minimal valid RMSE!
2022-01-11 23:13:43:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 23:13:43:INFO:Epoch: 7 global_step: 5250/9000 lr: 0.00042 loss: 0.0007
2022-01-11 23:14:02:INFO:Epoch: 7 global_step: 5300/9000 lr: 0.00041 loss: 0.0007
2022-01-11 23:14:20:INFO:Epoch: 7 global_step: 5350/9000 lr: 0.00041 loss: 0.0006
2022-01-11 23:14:39:INFO:Epoch: 7 global_step: 5400/9000 lr: 0.00040 loss: 0.0006
2022-01-11 23:14:57:INFO:Epoch: 7 global_step: 5450/9000 lr: 0.00039 loss: 0.0006
2022-01-11 23:15:16:INFO:Epoch: 7 global_step: 5500/9000 lr: 0.00039 loss: 0.0005
2022-01-11 23:15:34:INFO:Epoch: 7 global_step: 5550/9000 lr: 0.00038 loss: 0.0006
2022-01-11 23:15:53:INFO:Epoch: 7 global_step: 5600/9000 lr: 0.00038 loss: 0.0006
2022-01-11 23:16:12:INFO:Epoch: 7 global_step: 5650/9000 lr: 0.00037 loss: 0.0005
2022-01-11 23:16:30:INFO:Epoch: 7 global_step: 5700/9000 lr: 0.00037 loss: 0.0006
2022-01-11 23:16:49:INFO:Epoch: 7 global_step: 5750/9000 lr: 0.00036 loss: 0.0006
2022-01-11 23:17:07:INFO:Epoch: 7 global_step: 5800/9000 lr: 0.00036 loss: 0.0005
2022-01-11 23:17:26:INFO:Epoch: 7 global_step: 5850/9000 lr: 0.00035 loss: 0.0005
2022-01-11 23:17:44:INFO:Epoch: 7 global_step: 5900/9000 lr: 0.00034 loss: 0.0006
2022-01-11 23:18:03:INFO:Epoch: 7 global_step: 5950/9000 lr: 0.00034 loss: 0.0005
2022-01-11 23:18:24:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:18:24:INFO: Num examples = 100
2022-01-11 23:18:24:INFO: RMSE = 31.5319
2022-01-11 23:18:26:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:18:26:INFO: Num examples = 100
2022-01-11 23:18:26:INFO: RMSE = 24.6488
2022-01-11 23:18:26:INFO:Epoch: 8 global_step: 6000/9000 lr: 0.00033 loss: 0.0005
2022-01-11 23:18:45:INFO:Epoch: 8 global_step: 6050/9000 lr: 0.00033 loss: 0.0005
2022-01-11 23:19:03:INFO:Epoch: 8 global_step: 6100/9000 lr: 0.00032 loss: 0.0005
2022-01-11 23:19:22:INFO:Epoch: 8 global_step: 6150/9000 lr: 0.00032 loss: 0.0005
2022-01-11 23:19:40:INFO:Epoch: 8 global_step: 6200/9000 lr: 0.00031 loss: 0.0005
2022-01-11 23:19:59:INFO:Epoch: 8 global_step: 6250/9000 lr: 0.00031 loss: 0.0005
2022-01-11 23:20:18:INFO:Epoch: 8 global_step: 6300/9000 lr: 0.00030 loss: 0.0005
2022-01-11 23:20:36:INFO:Epoch: 8 global_step: 6350/9000 lr: 0.00029 loss: 0.0004
2022-01-11 23:20:55:INFO:Epoch: 8 global_step: 6400/9000 lr: 0.00029 loss: 0.0004
2022-01-11 23:21:13:INFO:Epoch: 8 global_step: 6450/9000 lr: 0.00028 loss: 0.0004
2022-01-11 23:21:32:INFO:Epoch: 8 global_step: 6500/9000 lr: 0.00028 loss: 0.0005
2022-01-11 23:21:51:INFO:Epoch: 8 global_step: 6550/9000 lr: 0.00027 loss: 0.0004
2022-01-11 23:22:09:INFO:Epoch: 8 global_step: 6600/9000 lr: 0.00027 loss: 0.0005
2022-01-11 23:22:28:INFO:Epoch: 8 global_step: 6650/9000 lr: 0.00026 loss: 0.0004
2022-01-11 23:22:46:INFO:Epoch: 8 global_step: 6700/9000 lr: 0.00026 loss: 0.0004
2022-01-11 23:23:07:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:23:07:INFO: Num examples = 100
2022-01-11 23:23:07:INFO: RMSE = 31.8778
2022-01-11 23:23:09:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:23:09:INFO: Num examples = 100
2022-01-11 23:23:09:INFO: RMSE = 22.7859
2022-01-11 23:23:09:INFO:==> Minimal valid RMSE!
2022-01-11 23:23:09:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-42...
2022-01-11 23:23:09:INFO:Epoch: 9 global_step: 6750/9000 lr: 0.00025 loss: 0.0004
2022-01-11 23:23:28:INFO:Epoch: 9 global_step: 6800/9000 lr: 0.00024 loss: 0.0004
2022-01-11 23:23:46:INFO:Epoch: 9 global_step: 6850/9000 lr: 0.00024 loss: 0.0004
2022-01-11 23:24:05:INFO:Epoch: 9 global_step: 6900/9000 lr: 0.00023 loss: 0.0004
2022-01-11 23:24:23:INFO:Epoch: 9 global_step: 6950/9000 lr: 0.00023 loss: 0.0004
2022-01-11 23:24:42:INFO:Epoch: 9 global_step: 7000/9000 lr: 0.00022 loss: 0.0004
2022-01-11 23:25:00:INFO:Epoch: 9 global_step: 7050/9000 lr: 0.00022 loss: 0.0004
2022-01-11 23:25:19:INFO:Epoch: 9 global_step: 7100/9000 lr: 0.00021 loss: 0.0004
2022-01-11 23:25:38:INFO:Epoch: 9 global_step: 7150/9000 lr: 0.00021 loss: 0.0004
2022-01-11 23:25:56:INFO:Epoch: 9 global_step: 7200/9000 lr: 0.00020 loss: 0.0004
2022-01-11 23:26:15:INFO:Epoch: 9 global_step: 7250/9000 lr: 0.00019 loss: 0.0004
2022-01-11 23:26:33:INFO:Epoch: 9 global_step: 7300/9000 lr: 0.00019 loss: 0.0004
2022-01-11 23:26:52:INFO:Epoch: 9 global_step: 7350/9000 lr: 0.00018 loss: 0.0004
2022-01-11 23:27:10:INFO:Epoch: 9 global_step: 7400/9000 lr: 0.00018 loss: 0.0004
2022-01-11 23:27:29:INFO:Epoch: 9 global_step: 7450/9000 lr: 0.00017 loss: 0.0003
2022-01-11 23:27:49:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:27:49:INFO: Num examples = 100
2022-01-11 23:27:49:INFO: RMSE = 31.8400
2022-01-11 23:27:52:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:27:52:INFO: Num examples = 100
2022-01-11 23:27:52:INFO: RMSE = 25.1788
2022-01-11 23:27:52:INFO:Epoch: 10 global_step: 7500/9000 lr: 0.00017 loss: 0.0003
2022-01-11 23:28:10:INFO:Epoch: 10 global_step: 7550/9000 lr: 0.00016 loss: 0.0003
2022-01-11 23:28:29:INFO:Epoch: 10 global_step: 7600/9000 lr: 0.00016 loss: 0.0003
2022-01-11 23:28:48:INFO:Epoch: 10 global_step: 7650/9000 lr: 0.00015 loss: 0.0003
2022-01-11 23:29:06:INFO:Epoch: 10 global_step: 7700/9000 lr: 0.00014 loss: 0.0003
2022-01-11 23:29:25:INFO:Epoch: 10 global_step: 7750/9000 lr: 0.00014 loss: 0.0003
2022-01-11 23:29:43:INFO:Epoch: 10 global_step: 7800/9000 lr: 0.00013 loss: 0.0003
2022-01-11 23:30:02:INFO:Epoch: 10 global_step: 7850/9000 lr: 0.00013 loss: 0.0003
2022-01-11 23:30:21:INFO:Epoch: 10 global_step: 7900/9000 lr: 0.00012 loss: 0.0003
2022-01-11 23:30:39:INFO:Epoch: 10 global_step: 7950/9000 lr: 0.00012 loss: 0.0003
2022-01-11 23:30:58:INFO:Epoch: 10 global_step: 8000/9000 lr: 0.00011 loss: 0.0003
2022-01-11 23:31:16:INFO:Epoch: 10 global_step: 8050/9000 lr: 0.00011 loss: 0.0003
2022-01-11 23:31:35:INFO:Epoch: 10 global_step: 8100/9000 lr: 0.00010 loss: 0.0003
2022-01-11 23:31:54:INFO:Epoch: 10 global_step: 8150/9000 lr: 0.00009 loss: 0.0004
2022-01-11 23:32:12:INFO:Epoch: 10 global_step: 8200/9000 lr: 0.00009 loss: 0.0003
2022-01-11 23:32:32:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:32:32:INFO: Num examples = 100
2022-01-11 23:32:32:INFO: RMSE = 31.7549
2022-01-11 23:32:35:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:32:35:INFO: Num examples = 100
2022-01-11 23:32:35:INFO: RMSE = 23.6598
2022-01-11 23:32:35:INFO:Epoch: 11 global_step: 8250/9000 lr: 0.00008 loss: 0.0003
2022-01-11 23:32:53:INFO:Epoch: 11 global_step: 8300/9000 lr: 0.00008 loss: 0.0003
2022-01-11 23:33:12:INFO:Epoch: 11 global_step: 8350/9000 lr: 0.00007 loss: 0.0003
2022-01-11 23:33:31:INFO:Epoch: 11 global_step: 8400/9000 lr: 0.00007 loss: 0.0003
2022-01-11 23:33:49:INFO:Epoch: 11 global_step: 8450/9000 lr: 0.00006 loss: 0.0003
2022-01-11 23:34:08:INFO:Epoch: 11 global_step: 8500/9000 lr: 0.00006 loss: 0.0003
2022-01-11 23:34:26:INFO:Epoch: 11 global_step: 8550/9000 lr: 0.00005 loss: 0.0003
2022-01-11 23:34:45:INFO:Epoch: 11 global_step: 8600/9000 lr: 0.00004 loss: 0.0003
2022-01-11 23:35:03:INFO:Epoch: 11 global_step: 8650/9000 lr: 0.00004 loss: 0.0003
2022-01-11 23:35:22:INFO:Epoch: 11 global_step: 8700/9000 lr: 0.00003 loss: 0.0003
2022-01-11 23:35:40:INFO:Epoch: 11 global_step: 8750/9000 lr: 0.00003 loss: 0.0003
2022-01-11 23:35:59:INFO:Epoch: 11 global_step: 8800/9000 lr: 0.00002 loss: 0.0003
2022-01-11 23:36:17:INFO:Epoch: 11 global_step: 8850/9000 lr: 0.00002 loss: 0.0003
2022-01-11 23:36:36:INFO:Epoch: 11 global_step: 8900/9000 lr: 0.00001 loss: 0.0003
2022-01-11 23:36:54:INFO:Epoch: 11 global_step: 8950/9000 lr: 0.00001 loss: 0.0003
2022-01-11 23:37:15:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:37:15:INFO: Num examples = 100
2022-01-11 23:37:15:INFO: RMSE = 32.2523
2022-01-11 23:37:17:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:37:17:INFO: Num examples = 100
2022-01-11 23:37:17:INFO: RMSE = 23.6336
2022-01-11 23:37:17:INFO: Output TEST RMSE: 31.8778
2022-01-11 23:37:17:INFO: VALID RMSEs: 33.3675 24.7693 24.0344 26.1756 25.4110 24.5785 24.0309 24.6488 22.7859 25.1788 23.6598 23.6336
2022-01-11 23:37:17:INFO: TEST RMSEs: 34.0395 31.1376 30.8675 31.2568 29.9128 29.8746 31.0820 31.5319 31.8778 31.8400 31.7549 32.2523
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
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!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
2022-01-11 23:37:20:INFO:Finish setting logger...
2022-01-11 23:37:20:INFO:==> Training/Evaluation parameters are:
2022-01-11 23:37:20:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667'
2022-01-11 23:37:20:INFO: data_fn=1
2022-01-11 23:37:20:INFO: datatest_fn=1
2022-01-11 23:37:20:INFO: filter_kernel_size=1
2022-01-11 23:37:20:INFO: override_data_cache=False
2022-01-11 23:37:20:INFO: maxRUL=125
2022-01-11 23:37:20:INFO: low_ratio=0.1
2022-01-11 23:37:20:INFO: high_ratio=0.99
2022-01-11 23:37:20:INFO: aug_ratio=150
2022-01-11 23:37:20:INFO: noise_amplitude=0.01
2022-01-11 23:37:20:INFO: modeltype='cnn1d'
2022-01-11 23:37:20:INFO: max_seq_len=550
2022-01-11 23:37:20:INFO: d_model=128
2022-01-11 23:37:20:INFO: p_dropout=0.1
2022-01-11 23:37:20:INFO: n_head=4
2022-01-11 23:37:20:INFO: n_layer=2
2022-01-11 23:37:20:INFO: dim_feedforward=512
2022-01-11 23:37:20:INFO: e_dropout=0.1
2022-01-11 23:37:20:INFO: activation='relu'
2022-01-11 23:37:20:INFO: layer_norm=False
2022-01-11 23:37:20:INFO: support_size=0
2022-01-11 23:37:20:INFO: inner_steps=1
2022-01-11 23:37:20:INFO: lr_inner=0.0001
2022-01-11 23:37:20:INFO: lr_meta=0.001
2022-01-11 23:37:20:INFO: n_epochs=12
2022-01-11 23:37:20:INFO: train_batch_size=20
2022-01-11 23:37:20:INFO: eval_batch_size=1
2022-01-11 23:37:20:INFO: lr=0.001
2022-01-11 23:37:20:INFO: weight_decay=0.01
2022-01-11 23:37:20:INFO: warmup_ratio=0.0
2022-01-11 23:37:20:INFO: max_grad_norm=5.0
2022-01-11 23:37:20:INFO: logging_steps=50
2022-01-11 23:37:20:INFO: seed=667
2022-01-11 23:37:20:INFO: gpu_id=0
2022-01-11 23:37:20:INFO: do_train=True
2022-01-11 23:37:20:INFO: do_eval=False
2022-01-11 23:37:20:INFO: train_data_fn='data/train_FD001.txt'
2022-01-11 23:37:20:INFO: test_data_fn='data/test_FD001.txt'
2022-01-11 23:37:20:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-11 23:37:20:INFO: device=device(type='cuda'))
2022-01-11 23:37:20:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 23:37:20:INFO:==> Read data from data/train_FD001.txt...
2022-01-11 23:37:20:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 23:37:20:INFO:==> Min_max normalization...
2022-01-11 23:37:20:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 23:37:20:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 23:37:20:INFO:==> Read data from data/test_FD001.txt...
2022-01-11 23:37:20:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-11 23:37:20:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-11 23:37:20:INFO: min_rul: 7, max_rul: 145
2022-01-11 23:37:20:INFO:==> Input length ratio of the [TEST] data:
2022-01-11 23:37:20:INFO: min_ratio = 0.2067
2022-01-11 23:37:20:INFO: max_ratio = 0.9667
2022-01-11 23:37:20:INFO:==> Min_max normalization...
2022-01-11 23:37:20:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-11 23:37:20:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-11 23:37:24:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-11 23:37:24:INFO:NumExpr defaulting to 8 threads.
2022-01-11 23:37:24:INFO:=============== Scheme: Normal Learning ===============
2022-01-11 23:37:24:INFO: Num examples = 15000
2022-01-11 23:37:24:INFO: Num epochs = 12
2022-01-11 23:37:24:INFO: Batch size = 20
2022-01-11 23:37:24:INFO: Total optimization steps = 9000
2022-01-11 23:37:32:INFO:==> Group parameters for optimization...
2022-01-11 23:37:32:INFO: Parameters to update are:
2022-01-11 23:37:32:INFO: conv1.0.weight
2022-01-11 23:37:32:INFO: conv2.0.weight
2022-01-11 23:37:32:INFO: conv3.0.weight
2022-01-11 23:37:32:INFO: conv4.0.weight
2022-01-11 23:37:32:INFO: conv5.0.weight
2022-01-11 23:37:32:INFO: fc_1.0.weight
2022-01-11 23:37:32:INFO: fc_1.0.bias
2022-01-11 23:37:32:INFO: fc_2.weight
2022-01-11 23:37:32:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-11 23:37:35:INFO:Epoch: 0 global_step: 0/9000 lr: 0.00100 loss: 0.0011
2022-01-11 23:37:53:INFO:Epoch: 0 global_step: 50/9000 lr: 0.00099 loss: 0.0172
2022-01-11 23:38:12:INFO:Epoch: 0 global_step: 100/9000 lr: 0.00099 loss: 0.0073
2022-01-11 23:38:30:INFO:Epoch: 0 global_step: 150/9000 lr: 0.00098 loss: 0.0064
2022-01-11 23:38:49:INFO:Epoch: 0 global_step: 200/9000 lr: 0.00098 loss: 0.0053
2022-01-11 23:39:07:INFO:Epoch: 0 global_step: 250/9000 lr: 0.00097 loss: 0.0049
2022-01-11 23:39:26:INFO:Epoch: 0 global_step: 300/9000 lr: 0.00097 loss: 0.0036
2022-01-11 23:39:44:INFO:Epoch: 0 global_step: 350/9000 lr: 0.00096 loss: 0.0036
2022-01-11 23:40:02:INFO:Epoch: 0 global_step: 400/9000 lr: 0.00096 loss: 0.0031
2022-01-11 23:40:21:INFO:Epoch: 0 global_step: 450/9000 lr: 0.00095 loss: 0.0029
2022-01-11 23:40:40:INFO:Epoch: 0 global_step: 500/9000 lr: 0.00094 loss: 0.0029
2022-01-11 23:40:58:INFO:Epoch: 0 global_step: 550/9000 lr: 0.00094 loss: 0.0029
2022-01-11 23:41:17:INFO:Epoch: 0 global_step: 600/9000 lr: 0.00093 loss: 0.0031
2022-01-11 23:41:35:INFO:Epoch: 0 global_step: 650/9000 lr: 0.00093 loss: 0.0032
2022-01-11 23:41:54:INFO:Epoch: 0 global_step: 700/9000 lr: 0.00092 loss: 0.0026
2022-01-11 23:42:14:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:42:14:INFO: Num examples = 100
2022-01-11 23:42:14:INFO: RMSE = 32.1515
2022-01-11 23:42:16:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:42:16:INFO: Num examples = 100
2022-01-11 23:42:16:INFO: RMSE = 34.3315
2022-01-11 23:42:16:INFO:==> Minimal valid RMSE!
2022-01-11 23:42:16:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 23:42:16:INFO:Epoch: 1 global_step: 750/9000 lr: 0.00092 loss: 0.0026
2022-01-11 23:42:35:INFO:Epoch: 1 global_step: 800/9000 lr: 0.00091 loss: 0.0024
2022-01-11 23:42:54:INFO:Epoch: 1 global_step: 850/9000 lr: 0.00091 loss: 0.0023
2022-01-11 23:43:12:INFO:Epoch: 1 global_step: 900/9000 lr: 0.00090 loss: 0.0024
2022-01-11 23:43:31:INFO:Epoch: 1 global_step: 950/9000 lr: 0.00089 loss: 0.0023
2022-01-11 23:43:49:INFO:Epoch: 1 global_step: 1000/9000 lr: 0.00089 loss: 0.0022
2022-01-11 23:44:08:INFO:Epoch: 1 global_step: 1050/9000 lr: 0.00088 loss: 0.0021
2022-01-11 23:44:26:INFO:Epoch: 1 global_step: 1100/9000 lr: 0.00088 loss: 0.0025
2022-01-11 23:44:45:INFO:Epoch: 1 global_step: 1150/9000 lr: 0.00087 loss: 0.0022
2022-01-11 23:45:03:INFO:Epoch: 1 global_step: 1200/9000 lr: 0.00087 loss: 0.0030
2022-01-11 23:45:22:INFO:Epoch: 1 global_step: 1250/9000 lr: 0.00086 loss: 0.0024
2022-01-11 23:45:40:INFO:Epoch: 1 global_step: 1300/9000 lr: 0.00086 loss: 0.0021
2022-01-11 23:45:59:INFO:Epoch: 1 global_step: 1350/9000 lr: 0.00085 loss: 0.0026
2022-01-11 23:46:18:INFO:Epoch: 1 global_step: 1400/9000 lr: 0.00084 loss: 0.0019
2022-01-11 23:46:36:INFO:Epoch: 1 global_step: 1450/9000 lr: 0.00084 loss: 0.0021
2022-01-11 23:46:56:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:46:56:INFO: Num examples = 100
2022-01-11 23:46:56:INFO: RMSE = 32.9730
2022-01-11 23:46:58:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:46:58:INFO: Num examples = 100
2022-01-11 23:46:58:INFO: RMSE = 26.4313
2022-01-11 23:46:58:INFO:==> Minimal valid RMSE!
2022-01-11 23:46:58:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 23:46:59:INFO:Epoch: 2 global_step: 1500/9000 lr: 0.00083 loss: 0.0019
2022-01-11 23:47:17:INFO:Epoch: 2 global_step: 1550/9000 lr: 0.00083 loss: 0.0023
2022-01-11 23:47:36:INFO:Epoch: 2 global_step: 1600/9000 lr: 0.00082 loss: 0.0021
2022-01-11 23:47:55:INFO:Epoch: 2 global_step: 1650/9000 lr: 0.00082 loss: 0.0021
2022-01-11 23:48:13:INFO:Epoch: 2 global_step: 1700/9000 lr: 0.00081 loss: 0.0022
2022-01-11 23:48:32:INFO:Epoch: 2 global_step: 1750/9000 lr: 0.00081 loss: 0.0020
2022-01-11 23:48:50:INFO:Epoch: 2 global_step: 1800/9000 lr: 0.00080 loss: 0.0018
2022-01-11 23:49:09:INFO:Epoch: 2 global_step: 1850/9000 lr: 0.00079 loss: 0.0020
2022-01-11 23:49:27:INFO:Epoch: 2 global_step: 1900/9000 lr: 0.00079 loss: 0.0019
2022-01-11 23:49:46:INFO:Epoch: 2 global_step: 1950/9000 lr: 0.00078 loss: 0.0019
2022-01-11 23:50:04:INFO:Epoch: 2 global_step: 2000/9000 lr: 0.00078 loss: 0.0021
2022-01-11 23:50:23:INFO:Epoch: 2 global_step: 2050/9000 lr: 0.00077 loss: 0.0019
2022-01-11 23:50:41:INFO:Epoch: 2 global_step: 2100/9000 lr: 0.00077 loss: 0.0017
2022-01-11 23:51:00:INFO:Epoch: 2 global_step: 2150/9000 lr: 0.00076 loss: 0.0022
2022-01-11 23:51:18:INFO:Epoch: 2 global_step: 2200/9000 lr: 0.00076 loss: 0.0019
2022-01-11 23:51:39:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:51:39:INFO: Num examples = 100
2022-01-11 23:51:39:INFO: RMSE = 33.2890
2022-01-11 23:51:41:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:51:41:INFO: Num examples = 100
2022-01-11 23:51:41:INFO: RMSE = 30.8470
2022-01-11 23:51:41:INFO:Epoch: 3 global_step: 2250/9000 lr: 0.00075 loss: 0.0020
2022-01-11 23:52:00:INFO:Epoch: 3 global_step: 2300/9000 lr: 0.00074 loss: 0.0018
2022-01-11 23:52:18:INFO:Epoch: 3 global_step: 2350/9000 lr: 0.00074 loss: 0.0017
2022-01-11 23:52:37:INFO:Epoch: 3 global_step: 2400/9000 lr: 0.00073 loss: 0.0016
2022-01-11 23:52:56:INFO:Epoch: 3 global_step: 2450/9000 lr: 0.00073 loss: 0.0016
2022-01-11 23:53:14:INFO:Epoch: 3 global_step: 2500/9000 lr: 0.00072 loss: 0.0017
2022-01-11 23:53:33:INFO:Epoch: 3 global_step: 2550/9000 lr: 0.00072 loss: 0.0018
2022-01-11 23:53:51:INFO:Epoch: 3 global_step: 2600/9000 lr: 0.00071 loss: 0.0017
2022-01-11 23:54:09:INFO:Epoch: 3 global_step: 2650/9000 lr: 0.00071 loss: 0.0016
2022-01-11 23:54:28:INFO:Epoch: 3 global_step: 2700/9000 lr: 0.00070 loss: 0.0015
2022-01-11 23:54:46:INFO:Epoch: 3 global_step: 2750/9000 lr: 0.00069 loss: 0.0016
2022-01-11 23:55:05:INFO:Epoch: 3 global_step: 2800/9000 lr: 0.00069 loss: 0.0016
2022-01-11 23:55:24:INFO:Epoch: 3 global_step: 2850/9000 lr: 0.00068 loss: 0.0015
2022-01-11 23:55:42:INFO:Epoch: 3 global_step: 2900/9000 lr: 0.00068 loss: 0.0016
2022-01-11 23:56:01:INFO:Epoch: 3 global_step: 2950/9000 lr: 0.00067 loss: 0.0014
2022-01-11 23:56:21:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-11 23:56:21:INFO: Num examples = 100
2022-01-11 23:56:21:INFO: RMSE = 32.5811
2022-01-11 23:56:23:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-11 23:56:23:INFO: Num examples = 100
2022-01-11 23:56:23:INFO: RMSE = 25.0415
2022-01-11 23:56:23:INFO:==> Minimal valid RMSE!
2022-01-11 23:56:23:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-667...
2022-01-11 23:56:24:INFO:Epoch: 4 global_step: 3000/9000 lr: 0.00067 loss: 0.0015
2022-01-11 23:56:42:INFO:Epoch: 4 global_step: 3050/9000 lr: 0.00066 loss: 0.0014
2022-01-11 23:57:01:INFO:Epoch: 4 global_step: 3100/9000 lr: 0.00066 loss: 0.0013
2022-01-11 23:57:19:INFO:Epoch: 4 global_step: 3150/9000 lr: 0.00065 loss: 0.0014
2022-01-11 23:57:38:INFO:Epoch: 4 global_step: 3200/9000 lr: 0.00064 loss: 0.0013
2022-01-11 23:57:56:INFO:Epoch: 4 global_step: 3250/9000 lr: 0.00064 loss: 0.0013
2022-01-11 23:58:15:INFO:Epoch: 4 global_step: 3300/9000 lr: 0.00063 loss: 0.0013
2022-01-11 23:58:33:INFO:Epoch: 4 global_step: 3350/9000 lr: 0.00063 loss: 0.0014
2022-01-11 23:58:52:INFO:Epoch: 4 global_step: 3400/9000 lr: 0.00062 loss: 0.0013
2022-01-11 23:59:10:INFO:Epoch: 4 global_step: 3450/9000 lr: 0.00062 loss: 0.0013
2022-01-11 23:59:29:INFO:Epoch: 4 global_step: 3500/9000 lr: 0.00061 loss: 0.0013
2022-01-11 23:59:48:INFO:Epoch: 4 global_step: 3550/9000 lr: 0.00061 loss: 0.0012
2022-01-12 00:00:06:INFO:Epoch: 4 global_step: 3600/9000 lr: 0.00060 loss: 0.0012
2022-01-12 00:00:25:INFO:Epoch: 4 global_step: 3650/9000 lr: 0.00059 loss: 0.0011
2022-01-12 00:00:43:INFO:Epoch: 4 global_step: 3700/9000 lr: 0.00059 loss: 0.0011
2022-01-12 00:01:04:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:01:04:INFO: Num examples = 100
2022-01-12 00:01:04:INFO: RMSE = 31.2504
2022-01-12 00:01:06:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:01:06:INFO: Num examples = 100
2022-01-12 00:01:06:INFO: RMSE = 26.1662
2022-01-12 00:01:06:INFO:Epoch: 5 global_step: 3750/9000 lr: 0.00058 loss: 0.0013
2022-01-12 00:01:25:INFO:Epoch: 5 global_step: 3800/9000 lr: 0.00058 loss: 0.0011
2022-01-12 00:01:43:INFO:Epoch: 5 global_step: 3850/9000 lr: 0.00057 loss: 0.0011
2022-01-12 00:02:02:INFO:Epoch: 5 global_step: 3900/9000 lr: 0.00057 loss: 0.0011
2022-01-12 00:02:20:INFO:Epoch: 5 global_step: 3950/9000 lr: 0.00056 loss: 0.0012
2022-01-12 00:02:39:INFO:Epoch: 5 global_step: 4000/9000 lr: 0.00056 loss: 0.0011
2022-01-12 00:02:58:INFO:Epoch: 5 global_step: 4050/9000 lr: 0.00055 loss: 0.0011
2022-01-12 00:03:16:INFO:Epoch: 5 global_step: 4100/9000 lr: 0.00054 loss: 0.0011
2022-01-12 00:03:35:INFO:Epoch: 5 global_step: 4150/9000 lr: 0.00054 loss: 0.0011
2022-01-12 00:03:53:INFO:Epoch: 5 global_step: 4200/9000 lr: 0.00053 loss: 0.0010
2022-01-12 00:04:12:INFO:Epoch: 5 global_step: 4250/9000 lr: 0.00053 loss: 0.0013
2022-01-12 00:04:30:INFO:Epoch: 5 global_step: 4300/9000 lr: 0.00052 loss: 0.0011
2022-01-12 00:04:49:INFO:Epoch: 5 global_step: 4350/9000 lr: 0.00052 loss: 0.0010
2022-01-12 00:05:07:INFO:Epoch: 5 global_step: 4400/9000 lr: 0.00051 loss: 0.0011
2022-01-12 00:05:25:INFO:Epoch: 5 global_step: 4450/9000 lr: 0.00051 loss: 0.0010
2022-01-12 00:05:46:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:05:46:INFO: Num examples = 100
2022-01-12 00:05:46:INFO: RMSE = 31.3420
2022-01-12 00:05:48:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:05:48:INFO: Num examples = 100
2022-01-12 00:05:48:INFO: RMSE = 27.6826
2022-01-12 00:05:48:INFO:Epoch: 6 global_step: 4500/9000 lr: 0.00050 loss: 0.0010
2022-01-12 00:06:07:INFO:Epoch: 6 global_step: 4550/9000 lr: 0.00049 loss: 0.0010
2022-01-12 00:06:25:INFO:Epoch: 6 global_step: 4600/9000 lr: 0.00049 loss: 0.0010
2022-01-12 00:06:44:INFO:Epoch: 6 global_step: 4650/9000 lr: 0.00048 loss: 0.0009
2022-01-12 00:07:02:INFO:Epoch: 6 global_step: 4700/9000 lr: 0.00048 loss: 0.0010
2022-01-12 00:07:21:INFO:Epoch: 6 global_step: 4750/9000 lr: 0.00047 loss: 0.0010
2022-01-12 00:07:40:INFO:Epoch: 6 global_step: 4800/9000 lr: 0.00047 loss: 0.0010
2022-01-12 00:07:58:INFO:Epoch: 6 global_step: 4850/9000 lr: 0.00046 loss: 0.0009
2022-01-12 00:08:17:INFO:Epoch: 6 global_step: 4900/9000 lr: 0.00046 loss: 0.0010
2022-01-12 00:08:35:INFO:Epoch: 6 global_step: 4950/9000 lr: 0.00045 loss: 0.0010
2022-01-12 00:08:54:INFO:Epoch: 6 global_step: 5000/9000 lr: 0.00044 loss: 0.0009
2022-01-12 00:09:12:INFO:Epoch: 6 global_step: 5050/9000 lr: 0.00044 loss: 0.0009
2022-01-12 00:09:31:INFO:Epoch: 6 global_step: 5100/9000 lr: 0.00043 loss: 0.0009
2022-01-12 00:09:49:INFO:Epoch: 6 global_step: 5150/9000 lr: 0.00043 loss: 0.0008
2022-01-12 00:10:08:INFO:Epoch: 6 global_step: 5200/9000 lr: 0.00042 loss: 0.0009
2022-01-12 00:10:28:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:10:28:INFO: Num examples = 100
2022-01-12 00:10:28:INFO: RMSE = 31.3335
2022-01-12 00:10:30:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:10:30:INFO: Num examples = 100
2022-01-12 00:10:30:INFO: RMSE = 29.1480
2022-01-12 00:10:31:INFO:Epoch: 7 global_step: 5250/9000 lr: 0.00042 loss: 0.0009
2022-01-12 00:10:49:INFO:Epoch: 7 global_step: 5300/9000 lr: 0.00041 loss: 0.0009
2022-01-12 00:11:08:INFO:Epoch: 7 global_step: 5350/9000 lr: 0.00041 loss: 0.0009
2022-01-12 00:11:26:INFO:Epoch: 7 global_step: 5400/9000 lr: 0.00040 loss: 0.0009
2022-01-12 00:11:45:INFO:Epoch: 7 global_step: 5450/9000 lr: 0.00039 loss: 0.0009
2022-01-12 00:12:04:INFO:Epoch: 7 global_step: 5500/9000 lr: 0.00039 loss: 0.0009
2022-01-12 00:12:22:INFO:Epoch: 7 global_step: 5550/9000 lr: 0.00038 loss: 0.0008
2022-01-12 00:12:41:INFO:Epoch: 7 global_step: 5600/9000 lr: 0.00038 loss: 0.0009
2022-01-12 00:12:59:INFO:Epoch: 7 global_step: 5650/9000 lr: 0.00037 loss: 0.0008
2022-01-12 00:13:18:INFO:Epoch: 7 global_step: 5700/9000 lr: 0.00037 loss: 0.0009
2022-01-12 00:13:36:INFO:Epoch: 7 global_step: 5750/9000 lr: 0.00036 loss: 0.0009
2022-01-12 00:13:55:INFO:Epoch: 7 global_step: 5800/9000 lr: 0.00036 loss: 0.0009
2022-01-12 00:14:13:INFO:Epoch: 7 global_step: 5850/9000 lr: 0.00035 loss: 0.0009
2022-01-12 00:14:32:INFO:Epoch: 7 global_step: 5900/9000 lr: 0.00034 loss: 0.0009
2022-01-12 00:14:50:INFO:Epoch: 7 global_step: 5950/9000 lr: 0.00034 loss: 0.0009
2022-01-12 00:15:11:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:15:11:INFO: Num examples = 100
2022-01-12 00:15:11:INFO: RMSE = 30.7786
2022-01-12 00:15:13:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:15:13:INFO: Num examples = 100
2022-01-12 00:15:13:INFO: RMSE = 28.5608
2022-01-12 00:15:13:INFO:Epoch: 8 global_step: 6000/9000 lr: 0.00033 loss: 0.0008
2022-01-12 00:15:32:INFO:Epoch: 8 global_step: 6050/9000 lr: 0.00033 loss: 0.0008
2022-01-12 00:15:50:INFO:Epoch: 8 global_step: 6100/9000 lr: 0.00032 loss: 0.0008
2022-01-12 00:16:09:INFO:Epoch: 8 global_step: 6150/9000 lr: 0.00032 loss: 0.0008
2022-01-12 00:16:28:INFO:Epoch: 8 global_step: 6200/9000 lr: 0.00031 loss: 0.0008
2022-01-12 00:16:46:INFO:Epoch: 8 global_step: 6250/9000 lr: 0.00031 loss: 0.0008
2022-01-12 00:17:05:INFO:Epoch: 8 global_step: 6300/9000 lr: 0.00030 loss: 0.0008
2022-01-12 00:17:23:INFO:Epoch: 8 global_step: 6350/9000 lr: 0.00029 loss: 0.0007
2022-01-12 00:17:42:INFO:Epoch: 8 global_step: 6400/9000 lr: 0.00029 loss: 0.0007
2022-01-12 00:18:00:INFO:Epoch: 8 global_step: 6450/9000 lr: 0.00028 loss: 0.0008
2022-01-12 00:18:19:INFO:Epoch: 8 global_step: 6500/9000 lr: 0.00028 loss: 0.0008
2022-01-12 00:18:38:INFO:Epoch: 8 global_step: 6550/9000 lr: 0.00027 loss: 0.0008
2022-01-12 00:18:56:INFO:Epoch: 8 global_step: 6600/9000 lr: 0.00027 loss: 0.0007
2022-01-12 00:19:15:INFO:Epoch: 8 global_step: 6650/9000 lr: 0.00026 loss: 0.0008
2022-01-12 00:19:33:INFO:Epoch: 8 global_step: 6700/9000 lr: 0.00026 loss: 0.0007
2022-01-12 00:19:53:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:19:53:INFO: Num examples = 100
2022-01-12 00:19:53:INFO: RMSE = 32.4080
2022-01-12 00:19:56:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:19:56:INFO: Num examples = 100
2022-01-12 00:19:56:INFO: RMSE = 28.6378
2022-01-12 00:19:56:INFO:Epoch: 9 global_step: 6750/9000 lr: 0.00025 loss: 0.0008
2022-01-12 00:20:15:INFO:Epoch: 9 global_step: 6800/9000 lr: 0.00024 loss: 0.0007
2022-01-12 00:20:33:INFO:Epoch: 9 global_step: 6850/9000 lr: 0.00024 loss: 0.0007
2022-01-12 00:20:52:INFO:Epoch: 9 global_step: 6900/9000 lr: 0.00023 loss: 0.0006
2022-01-12 00:21:10:INFO:Epoch: 9 global_step: 6950/9000 lr: 0.00023 loss: 0.0007
2022-01-12 00:21:29:INFO:Epoch: 9 global_step: 7000/9000 lr: 0.00022 loss: 0.0008
2022-01-12 00:21:47:INFO:Epoch: 9 global_step: 7050/9000 lr: 0.00022 loss: 0.0007
2022-01-12 00:22:06:INFO:Epoch: 9 global_step: 7100/9000 lr: 0.00021 loss: 0.0007
2022-01-12 00:22:24:INFO:Epoch: 9 global_step: 7150/9000 lr: 0.00021 loss: 0.0007
2022-01-12 00:22:43:INFO:Epoch: 9 global_step: 7200/9000 lr: 0.00020 loss: 0.0008
2022-01-12 00:23:01:INFO:Epoch: 9 global_step: 7250/9000 lr: 0.00019 loss: 0.0006
2022-01-12 00:23:20:INFO:Epoch: 9 global_step: 7300/9000 lr: 0.00019 loss: 0.0008
2022-01-12 00:23:39:INFO:Epoch: 9 global_step: 7350/9000 lr: 0.00018 loss: 0.0007
2022-01-12 00:23:57:INFO:Epoch: 9 global_step: 7400/9000 lr: 0.00018 loss: 0.0007
2022-01-12 00:24:16:INFO:Epoch: 9 global_step: 7450/9000 lr: 0.00017 loss: 0.0007
2022-01-12 00:24:36:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:24:36:INFO: Num examples = 100
2022-01-12 00:24:36:INFO: RMSE = 32.0662
2022-01-12 00:24:38:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:24:38:INFO: Num examples = 100
2022-01-12 00:24:38:INFO: RMSE = 27.9830
2022-01-12 00:24:39:INFO:Epoch: 10 global_step: 7500/9000 lr: 0.00017 loss: 0.0007
2022-01-12 00:24:57:INFO:Epoch: 10 global_step: 7550/9000 lr: 0.00016 loss: 0.0007
2022-01-12 00:25:16:INFO:Epoch: 10 global_step: 7600/9000 lr: 0.00016 loss: 0.0007
2022-01-12 00:25:34:INFO:Epoch: 10 global_step: 7650/9000 lr: 0.00015 loss: 0.0007
2022-01-12 00:25:53:INFO:Epoch: 10 global_step: 7700/9000 lr: 0.00014 loss: 0.0007
2022-01-12 00:26:11:INFO:Epoch: 10 global_step: 7750/9000 lr: 0.00014 loss: 0.0007
2022-01-12 00:26:30:INFO:Epoch: 10 global_step: 7800/9000 lr: 0.00013 loss: 0.0007
2022-01-12 00:26:48:INFO:Epoch: 10 global_step: 7850/9000 lr: 0.00013 loss: 0.0006
2022-01-12 00:27:07:INFO:Epoch: 10 global_step: 7900/9000 lr: 0.00012 loss: 0.0006
2022-01-12 00:27:25:INFO:Epoch: 10 global_step: 7950/9000 lr: 0.00012 loss: 0.0007
2022-01-12 00:27:44:INFO:Epoch: 10 global_step: 8000/9000 lr: 0.00011 loss: 0.0007
2022-01-12 00:28:03:INFO:Epoch: 10 global_step: 8050/9000 lr: 0.00011 loss: 0.0006
2022-01-12 00:28:21:INFO:Epoch: 10 global_step: 8100/9000 lr: 0.00010 loss: 0.0007
2022-01-12 00:28:40:INFO:Epoch: 10 global_step: 8150/9000 lr: 0.00009 loss: 0.0006
2022-01-12 00:28:58:INFO:Epoch: 10 global_step: 8200/9000 lr: 0.00009 loss: 0.0007
2022-01-12 00:29:19:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:29:19:INFO: Num examples = 100
2022-01-12 00:29:19:INFO: RMSE = 31.7852
2022-01-12 00:29:21:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:29:21:INFO: Num examples = 100
2022-01-12 00:29:21:INFO: RMSE = 28.4003
2022-01-12 00:29:21:INFO:Epoch: 11 global_step: 8250/9000 lr: 0.00008 loss: 0.0006
2022-01-12 00:29:40:INFO:Epoch: 11 global_step: 8300/9000 lr: 0.00008 loss: 0.0007
2022-01-12 00:29:59:INFO:Epoch: 11 global_step: 8350/9000 lr: 0.00007 loss: 0.0007
2022-01-12 00:30:17:INFO:Epoch: 11 global_step: 8400/9000 lr: 0.00007 loss: 0.0007
2022-01-12 00:30:36:INFO:Epoch: 11 global_step: 8450/9000 lr: 0.00006 loss: 0.0006
2022-01-12 00:30:54:INFO:Epoch: 11 global_step: 8500/9000 lr: 0.00006 loss: 0.0007
2022-01-12 00:31:12:INFO:Epoch: 11 global_step: 8550/9000 lr: 0.00005 loss: 0.0006
2022-01-12 00:31:31:INFO:Epoch: 11 global_step: 8600/9000 lr: 0.00004 loss: 0.0007
2022-01-12 00:31:50:INFO:Epoch: 11 global_step: 8650/9000 lr: 0.00004 loss: 0.0006
2022-01-12 00:32:08:INFO:Epoch: 11 global_step: 8700/9000 lr: 0.00003 loss: 0.0006
2022-01-12 00:32:27:INFO:Epoch: 11 global_step: 8750/9000 lr: 0.00003 loss: 0.0006
2022-01-12 00:32:45:INFO:Epoch: 11 global_step: 8800/9000 lr: 0.00002 loss: 0.0006
2022-01-12 00:33:04:INFO:Epoch: 11 global_step: 8850/9000 lr: 0.00002 loss: 0.0006
2022-01-12 00:33:22:INFO:Epoch: 11 global_step: 8900/9000 lr: 0.00001 loss: 0.0006
2022-01-12 00:33:41:INFO:Epoch: 11 global_step: 8950/9000 lr: 0.00001 loss: 0.0006
2022-01-12 00:34:01:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:34:01:INFO: Num examples = 100
2022-01-12 00:34:01:INFO: RMSE = 31.6681
2022-01-12 00:34:03:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:34:03:INFO: Num examples = 100
2022-01-12 00:34:03:INFO: RMSE = 28.1340
2022-01-12 00:34:03:INFO: Output TEST RMSE: 32.5811
2022-01-12 00:34:03:INFO: VALID RMSEs: 34.3315 26.4313 30.8470 25.0415 26.1662 27.6826 29.1480 28.5608 28.6378 27.9830 28.4003 28.1340
2022-01-12 00:34:03:INFO: TEST RMSEs: 32.1515 32.9730 33.2890 32.5811 31.2504 31.3420 31.3335 30.7786 32.4080 32.0662 31.7852 31.6681
!!! Reset batch info !!! mode: [TRAIN]
!!! Reset batch info !!! mode: [TEST]
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!!! Reset batch info !!! mode: [TEST]
!!! Reset batch info !!! mode: [VALID]
!!! Reset batch info !!! mode: [TRAIN]
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!!! Reset batch info !!! mode: [VALID]
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!!! Reset batch info !!! mode: [VALID]
2022-01-12 00:34:06:INFO:Finish setting logger...
2022-01-12 00:34:06:INFO:==> Training/Evaluation parameters are:
2022-01-12 00:34:06:INFO: Namespace(model_dir='cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128'
2022-01-12 00:34:06:INFO: data_fn=1
2022-01-12 00:34:06:INFO: datatest_fn=1
2022-01-12 00:34:06:INFO: filter_kernel_size=1
2022-01-12 00:34:06:INFO: override_data_cache=False
2022-01-12 00:34:06:INFO: maxRUL=125
2022-01-12 00:34:06:INFO: low_ratio=0.1
2022-01-12 00:34:06:INFO: high_ratio=0.99
2022-01-12 00:34:06:INFO: aug_ratio=150
2022-01-12 00:34:06:INFO: noise_amplitude=0.01
2022-01-12 00:34:06:INFO: modeltype='cnn1d'
2022-01-12 00:34:06:INFO: max_seq_len=550
2022-01-12 00:34:06:INFO: d_model=128
2022-01-12 00:34:06:INFO: p_dropout=0.1
2022-01-12 00:34:06:INFO: n_head=4
2022-01-12 00:34:06:INFO: n_layer=2
2022-01-12 00:34:06:INFO: dim_feedforward=512
2022-01-12 00:34:06:INFO: e_dropout=0.1
2022-01-12 00:34:06:INFO: activation='relu'
2022-01-12 00:34:06:INFO: layer_norm=False
2022-01-12 00:34:06:INFO: support_size=0
2022-01-12 00:34:06:INFO: inner_steps=1
2022-01-12 00:34:06:INFO: lr_inner=0.0001
2022-01-12 00:34:06:INFO: lr_meta=0.001
2022-01-12 00:34:06:INFO: n_epochs=12
2022-01-12 00:34:06:INFO: train_batch_size=20
2022-01-12 00:34:06:INFO: eval_batch_size=1
2022-01-12 00:34:06:INFO: lr=0.001
2022-01-12 00:34:06:INFO: weight_decay=0.01
2022-01-12 00:34:06:INFO: warmup_ratio=0.0
2022-01-12 00:34:06:INFO: max_grad_norm=5.0
2022-01-12 00:34:06:INFO: logging_steps=50
2022-01-12 00:34:06:INFO: seed=128
2022-01-12 00:34:06:INFO: gpu_id=0
2022-01-12 00:34:06:INFO: do_train=True
2022-01-12 00:34:06:INFO: do_eval=False
2022-01-12 00:34:06:INFO: train_data_fn='data/train_FD001.txt'
2022-01-12 00:34:06:INFO: test_data_fn='data/test_FD001.txt'
2022-01-12 00:34:06:INFO: target_ruls_fn='data/RUL_FD001.txt'
2022-01-12 00:34:06:INFO: device=device(type='cuda'))
2022-01-12 00:34:06:INFO:Dump arguments to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-12 00:34:06:INFO:==> Read data from data/train_FD001.txt...
2022-01-12 00:34:06:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-12 00:34:06:INFO:==> Min_max normalization...
2022-01-12 00:34:07:INFO: The min value is [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-12 00:34:07:INFO: The max value is [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-12 00:34:07:INFO:==> Read data from data/test_FD001.txt...
2022-01-12 00:34:07:INFO: The selected feature idxs are: 0, 1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23
2022-01-12 00:34:07:INFO:==> Read RULsfrom data/RUL_FD001.txt...
2022-01-12 00:34:07:INFO: min_rul: 7, max_rul: 145
2022-01-12 00:34:07:INFO:==> Input length ratio of the [TEST] data:
2022-01-12 00:34:07:INFO: min_ratio = 0.2067
2022-01-12 00:34:07:INFO: max_ratio = 0.9667
2022-01-12 00:34:07:INFO:==> Min_max normalization...
2022-01-12 00:34:07:INFO: With given min value [-0.008700000122189522, -0.0006000000284984708, 641.2100219726562, 1571.0400390625, 1382.25, 549.8499755859375, 2387.89990234375, 9021.73046875, 46.849998474121094, 518.6900024414062, 2387.8798828125, 8099.93994140625, 8.324899673461914, 388.0, 38.13999938964844, 22.89419937133789]
2022-01-12 00:34:07:INFO: With given max value [0.008700000122189522, 0.0006000000284984708, 644.530029296875, 1616.9100341796875, 1441.489990234375, 556.0599975585938, 2388.56005859375, 9244.58984375, 48.529998779296875, 523.3800048828125, 2388.56005859375, 8293.7197265625, 8.584799766540527, 400.0, 39.43000030517578, 23.61840057373047]
2022-01-12 00:34:10:INFO:Note: NumExpr detected 40 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2022-01-12 00:34:10:INFO:NumExpr defaulting to 8 threads.
2022-01-12 00:34:10:INFO:=============== Scheme: Normal Learning ===============
2022-01-12 00:34:10:INFO: Num examples = 15000
2022-01-12 00:34:10:INFO: Num epochs = 12
2022-01-12 00:34:10:INFO: Batch size = 20
2022-01-12 00:34:10:INFO: Total optimization steps = 9000
2022-01-12 00:34:19:INFO:==> Group parameters for optimization...
2022-01-12 00:34:19:INFO: Parameters to update are:
2022-01-12 00:34:19:INFO: conv1.0.weight
2022-01-12 00:34:19:INFO: conv2.0.weight
2022-01-12 00:34:19:INFO: conv3.0.weight
2022-01-12 00:34:19:INFO: conv4.0.weight
2022-01-12 00:34:19:INFO: conv5.0.weight
2022-01-12 00:34:19:INFO: fc_1.0.weight
2022-01-12 00:34:19:INFO: fc_1.0.bias
2022-01-12 00:34:19:INFO: fc_2.weight
2022-01-12 00:34:19:INFO: fc_2.bias
/data/moy20/Meta-Learning/Meta-prognosis-main/optimizer.py:78: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1623448255797/work/torch/csrc/utils/python_arg_parser.cpp:1025.)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/data/moy20/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
warnings.warn("To get the last learning rate computed by the scheduler, "
2022-01-12 00:34:21:INFO:Epoch: 0 global_step: 0/9000 lr: 0.00100 loss: 0.0013
2022-01-12 00:34:39:INFO:Epoch: 0 global_step: 50/9000 lr: 0.00099 loss: 0.0252
2022-01-12 00:34:58:INFO:Epoch: 0 global_step: 100/9000 lr: 0.00099 loss: 0.0093
2022-01-12 00:35:16:INFO:Epoch: 0 global_step: 150/9000 lr: 0.00098 loss: 0.0050
2022-01-12 00:35:35:INFO:Epoch: 0 global_step: 200/9000 lr: 0.00098 loss: 0.0038
2022-01-12 00:35:53:INFO:Epoch: 0 global_step: 250/9000 lr: 0.00097 loss: 0.0040
2022-01-12 00:36:12:INFO:Epoch: 0 global_step: 300/9000 lr: 0.00097 loss: 0.0033
2022-01-12 00:36:31:INFO:Epoch: 0 global_step: 350/9000 lr: 0.00096 loss: 0.0032
2022-01-12 00:36:49:INFO:Epoch: 0 global_step: 400/9000 lr: 0.00096 loss: 0.0027
2022-01-12 00:37:08:INFO:Epoch: 0 global_step: 450/9000 lr: 0.00095 loss: 0.0027
2022-01-12 00:37:26:INFO:Epoch: 0 global_step: 500/9000 lr: 0.00094 loss: 0.0024
2022-01-12 00:37:45:INFO:Epoch: 0 global_step: 550/9000 lr: 0.00094 loss: 0.0025
2022-01-12 00:38:03:INFO:Epoch: 0 global_step: 600/9000 lr: 0.00093 loss: 0.0024
2022-01-12 00:38:22:INFO:Epoch: 0 global_step: 650/9000 lr: 0.00093 loss: 0.0026
2022-01-12 00:38:40:INFO:Epoch: 0 global_step: 700/9000 lr: 0.00092 loss: 0.0027
2022-01-12 00:39:01:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:39:01:INFO: Num examples = 100
2022-01-12 00:39:01:INFO: RMSE = 31.9210
2022-01-12 00:39:03:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:39:03:INFO: Num examples = 100
2022-01-12 00:39:03:INFO: RMSE = 30.7162
2022-01-12 00:39:03:INFO:==> Minimal valid RMSE!
2022-01-12 00:39:03:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-12 00:39:03:INFO:Epoch: 1 global_step: 750/9000 lr: 0.00092 loss: 0.0026
2022-01-12 00:39:22:INFO:Epoch: 1 global_step: 800/9000 lr: 0.00091 loss: 0.0026
2022-01-12 00:39:40:INFO:Epoch: 1 global_step: 850/9000 lr: 0.00091 loss: 0.0023
2022-01-12 00:39:59:INFO:Epoch: 1 global_step: 900/9000 lr: 0.00090 loss: 0.0023
2022-01-12 00:40:17:INFO:Epoch: 1 global_step: 950/9000 lr: 0.00089 loss: 0.0022
2022-01-12 00:40:36:INFO:Epoch: 1 global_step: 1000/9000 lr: 0.00089 loss: 0.0024
2022-01-12 00:40:54:INFO:Epoch: 1 global_step: 1050/9000 lr: 0.00088 loss: 0.0025
2022-01-12 00:41:13:INFO:Epoch: 1 global_step: 1100/9000 lr: 0.00088 loss: 0.0023
2022-01-12 00:41:32:INFO:Epoch: 1 global_step: 1150/9000 lr: 0.00087 loss: 0.0021
2022-01-12 00:41:50:INFO:Epoch: 1 global_step: 1200/9000 lr: 0.00087 loss: 0.0020
2022-01-12 00:42:09:INFO:Epoch: 1 global_step: 1250/9000 lr: 0.00086 loss: 0.0021
2022-01-12 00:42:27:INFO:Epoch: 1 global_step: 1300/9000 lr: 0.00086 loss: 0.0021
2022-01-12 00:42:46:INFO:Epoch: 1 global_step: 1350/9000 lr: 0.00085 loss: 0.0020
2022-01-12 00:43:04:INFO:Epoch: 1 global_step: 1400/9000 lr: 0.00084 loss: 0.0020
2022-01-12 00:43:23:INFO:Epoch: 1 global_step: 1450/9000 lr: 0.00084 loss: 0.0021
2022-01-12 00:43:43:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:43:43:INFO: Num examples = 100
2022-01-12 00:43:43:INFO: RMSE = 32.6403
2022-01-12 00:43:45:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:43:45:INFO: Num examples = 100
2022-01-12 00:43:45:INFO: RMSE = 31.2958
2022-01-12 00:43:46:INFO:Epoch: 2 global_step: 1500/9000 lr: 0.00083 loss: 0.0020
2022-01-12 00:44:04:INFO:Epoch: 2 global_step: 1550/9000 lr: 0.00083 loss: 0.0023
2022-01-12 00:44:23:INFO:Epoch: 2 global_step: 1600/9000 lr: 0.00082 loss: 0.0020
2022-01-12 00:44:41:INFO:Epoch: 2 global_step: 1650/9000 lr: 0.00082 loss: 0.0021
2022-01-12 00:45:00:INFO:Epoch: 2 global_step: 1700/9000 lr: 0.00081 loss: 0.0019
2022-01-12 00:45:18:INFO:Epoch: 2 global_step: 1750/9000 lr: 0.00081 loss: 0.0018
2022-01-12 00:45:37:INFO:Epoch: 2 global_step: 1800/9000 lr: 0.00080 loss: 0.0020
2022-01-12 00:45:55:INFO:Epoch: 2 global_step: 1850/9000 lr: 0.00079 loss: 0.0026
2022-01-12 00:46:14:INFO:Epoch: 2 global_step: 1900/9000 lr: 0.00079 loss: 0.0019
2022-01-12 00:46:33:INFO:Epoch: 2 global_step: 1950/9000 lr: 0.00078 loss: 0.0018
2022-01-12 00:46:51:INFO:Epoch: 2 global_step: 2000/9000 lr: 0.00078 loss: 0.0019
2022-01-12 00:47:10:INFO:Epoch: 2 global_step: 2050/9000 lr: 0.00077 loss: 0.0019
2022-01-12 00:47:28:INFO:Epoch: 2 global_step: 2100/9000 lr: 0.00077 loss: 0.0017
2022-01-12 00:47:47:INFO:Epoch: 2 global_step: 2150/9000 lr: 0.00076 loss: 0.0019
2022-01-12 00:48:05:INFO:Epoch: 2 global_step: 2200/9000 lr: 0.00076 loss: 0.0017
2022-01-12 00:48:26:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:48:26:INFO: Num examples = 100
2022-01-12 00:48:26:INFO: RMSE = 30.7177
2022-01-12 00:48:28:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:48:28:INFO: Num examples = 100
2022-01-12 00:48:28:INFO: RMSE = 33.2470
2022-01-12 00:48:28:INFO:Epoch: 3 global_step: 2250/9000 lr: 0.00075 loss: 0.0016
2022-01-12 00:48:47:INFO:Epoch: 3 global_step: 2300/9000 lr: 0.00074 loss: 0.0020
2022-01-12 00:49:06:INFO:Epoch: 3 global_step: 2350/9000 lr: 0.00074 loss: 0.0015
2022-01-12 00:49:24:INFO:Epoch: 3 global_step: 2400/9000 lr: 0.00073 loss: 0.0016
2022-01-12 00:49:43:INFO:Epoch: 3 global_step: 2450/9000 lr: 0.00073 loss: 0.0017
2022-01-12 00:50:01:INFO:Epoch: 3 global_step: 2500/9000 lr: 0.00072 loss: 0.0016
2022-01-12 00:50:20:INFO:Epoch: 3 global_step: 2550/9000 lr: 0.00072 loss: 0.0018
2022-01-12 00:50:38:INFO:Epoch: 3 global_step: 2600/9000 lr: 0.00071 loss: 0.0016
2022-01-12 00:50:57:INFO:Epoch: 3 global_step: 2650/9000 lr: 0.00071 loss: 0.0016
2022-01-12 00:51:15:INFO:Epoch: 3 global_step: 2700/9000 lr: 0.00070 loss: 0.0017
2022-01-12 00:51:34:INFO:Epoch: 3 global_step: 2750/9000 lr: 0.00069 loss: 0.0015
2022-01-12 00:51:52:INFO:Epoch: 3 global_step: 2800/9000 lr: 0.00069 loss: 0.0014
2022-01-12 00:52:11:INFO:Epoch: 3 global_step: 2850/9000 lr: 0.00068 loss: 0.0015
2022-01-12 00:52:29:INFO:Epoch: 3 global_step: 2900/9000 lr: 0.00068 loss: 0.0013
2022-01-12 00:52:48:INFO:Epoch: 3 global_step: 2950/9000 lr: 0.00067 loss: 0.0015
2022-01-12 00:53:08:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:53:08:INFO: Num examples = 100
2022-01-12 00:53:08:INFO: RMSE = 30.9322
2022-01-12 00:53:10:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:53:10:INFO: Num examples = 100
2022-01-12 00:53:10:INFO: RMSE = 31.8127
2022-01-12 00:53:11:INFO:Epoch: 4 global_step: 3000/9000 lr: 0.00067 loss: 0.0014
2022-01-12 00:53:29:INFO:Epoch: 4 global_step: 3050/9000 lr: 0.00066 loss: 0.0013
2022-01-12 00:53:48:INFO:Epoch: 4 global_step: 3100/9000 lr: 0.00066 loss: 0.0013
2022-01-12 00:54:06:INFO:Epoch: 4 global_step: 3150/9000 lr: 0.00065 loss: 0.0012
2022-01-12 00:54:25:INFO:Epoch: 4 global_step: 3200/9000 lr: 0.00064 loss: 0.0012
2022-01-12 00:54:43:INFO:Epoch: 4 global_step: 3250/9000 lr: 0.00064 loss: 0.0012
2022-01-12 00:55:02:INFO:Epoch: 4 global_step: 3300/9000 lr: 0.00063 loss: 0.0013
2022-01-12 00:55:20:INFO:Epoch: 4 global_step: 3350/9000 lr: 0.00063 loss: 0.0013
2022-01-12 00:55:39:INFO:Epoch: 4 global_step: 3400/9000 lr: 0.00062 loss: 0.0014
2022-01-12 00:55:58:INFO:Epoch: 4 global_step: 3450/9000 lr: 0.00062 loss: 0.0012
2022-01-12 00:56:16:INFO:Epoch: 4 global_step: 3500/9000 lr: 0.00061 loss: 0.0012
2022-01-12 00:56:35:INFO:Epoch: 4 global_step: 3550/9000 lr: 0.00061 loss: 0.0011
2022-01-12 00:56:53:INFO:Epoch: 4 global_step: 3600/9000 lr: 0.00060 loss: 0.0012
2022-01-12 00:57:12:INFO:Epoch: 4 global_step: 3650/9000 lr: 0.00059 loss: 0.0011
2022-01-12 00:57:30:INFO:Epoch: 4 global_step: 3700/9000 lr: 0.00059 loss: 0.0012
2022-01-12 00:57:51:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 00:57:51:INFO: Num examples = 100
2022-01-12 00:57:51:INFO: RMSE = 31.8077
2022-01-12 00:57:53:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 00:57:53:INFO: Num examples = 100
2022-01-12 00:57:53:INFO: RMSE = 31.4109
2022-01-12 00:57:53:INFO:Epoch: 5 global_step: 3750/9000 lr: 0.00058 loss: 0.0011
2022-01-12 00:58:12:INFO:Epoch: 5 global_step: 3800/9000 lr: 0.00058 loss: 0.0011
2022-01-12 00:58:30:INFO:Epoch: 5 global_step: 3850/9000 lr: 0.00057 loss: 0.0010
2022-01-12 00:58:49:INFO:Epoch: 5 global_step: 3900/9000 lr: 0.00057 loss: 0.0011
2022-01-12 00:59:07:INFO:Epoch: 5 global_step: 3950/9000 lr: 0.00056 loss: 0.0010
2022-01-12 00:59:26:INFO:Epoch: 5 global_step: 4000/9000 lr: 0.00056 loss: 0.0011
2022-01-12 00:59:44:INFO:Epoch: 5 global_step: 4050/9000 lr: 0.00055 loss: 0.0011
2022-01-12 01:00:03:INFO:Epoch: 5 global_step: 4100/9000 lr: 0.00054 loss: 0.0009
2022-01-12 01:00:21:INFO:Epoch: 5 global_step: 4150/9000 lr: 0.00054 loss: 0.0009
2022-01-12 01:00:40:INFO:Epoch: 5 global_step: 4200/9000 lr: 0.00053 loss: 0.0010
2022-01-12 01:00:58:INFO:Epoch: 5 global_step: 4250/9000 lr: 0.00053 loss: 0.0010
2022-01-12 01:01:17:INFO:Epoch: 5 global_step: 4300/9000 lr: 0.00052 loss: 0.0010
2022-01-12 01:01:35:INFO:Epoch: 5 global_step: 4350/9000 lr: 0.00052 loss: 0.0011
2022-01-12 01:01:54:INFO:Epoch: 5 global_step: 4400/9000 lr: 0.00051 loss: 0.0009
2022-01-12 01:02:13:INFO:Epoch: 5 global_step: 4450/9000 lr: 0.00051 loss: 0.0010
2022-01-12 01:02:33:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 01:02:33:INFO: Num examples = 100
2022-01-12 01:02:33:INFO: RMSE = 30.7844
2022-01-12 01:02:35:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 01:02:35:INFO: Num examples = 100
2022-01-12 01:02:35:INFO: RMSE = 28.7352
2022-01-12 01:02:35:INFO:==> Minimal valid RMSE!
2022-01-12 01:02:35:INFO:Save model to cnn1dmodels/data-1_n_epochs-12_aug-150_noise-0.01_supportSize-0_innerSteps-1_lrMeta-0.001_lrInner-0.0001_warmUp-0.0_seed-128...
2022-01-12 01:02:35:INFO:Epoch: 6 global_step: 4500/9000 lr: 0.00050 loss: 0.0010
2022-01-12 01:02:54:INFO:Epoch: 6 global_step: 4550/9000 lr: 0.00049 loss: 0.0009
2022-01-12 01:03:12:INFO:Epoch: 6 global_step: 4600/9000 lr: 0.00049 loss: 0.0009
2022-01-12 01:03:31:INFO:Epoch: 6 global_step: 4650/9000 lr: 0.00048 loss: 0.0010
2022-01-12 01:03:50:INFO:Epoch: 6 global_step: 4700/9000 lr: 0.00048 loss: 0.0009
2022-01-12 01:04:08:INFO:Epoch: 6 global_step: 4750/9000 lr: 0.00047 loss: 0.0009
2022-01-12 01:04:27:INFO:Epoch: 6 global_step: 4800/9000 lr: 0.00047 loss: 0.0009
2022-01-12 01:04:45:INFO:Epoch: 6 global_step: 4850/9000 lr: 0.00046 loss: 0.0009
2022-01-12 01:05:04:INFO:Epoch: 6 global_step: 4900/9000 lr: 0.00046 loss: 0.0010
2022-01-12 01:05:22:INFO:Epoch: 6 global_step: 4950/9000 lr: 0.00045 loss: 0.0010
2022-01-12 01:05:41:INFO:Epoch: 6 global_step: 5000/9000 lr: 0.00044 loss: 0.0009
2022-01-12 01:05:59:INFO:Epoch: 6 global_step: 5050/9000 lr: 0.00044 loss: 0.0009
2022-01-12 01:06:18:INFO:Epoch: 6 global_step: 5100/9000 lr: 0.00043 loss: 0.0008
2022-01-12 01:06:36:INFO:Epoch: 6 global_step: 5150/9000 lr: 0.00043 loss: 0.0009
2022-01-12 01:06:55:INFO:Epoch: 6 global_step: 5200/9000 lr: 0.00042 loss: 0.0008
2022-01-12 01:07:15:INFO:############### Compute RMSEs @ mode [TEST] ###############
2022-01-12 01:07:15:INFO: Num examples = 100
2022-01-12 01:07:15:INFO: RMSE = 31.0135
2022-01-12 01:07:17:INFO:############### Compute RMSEs @ mode [VALID] ###############
2022-01-12 01:07:17:INFO: Num examples = 100
2022-01-12 01:07:17:INFO: RMSE = 31.3105
2022-01-12 01:07:18:INFO:Epoch: 7 global_step: 5250/9000 lr: 0.00042 loss: 0.0008
2022-01-12 01:07:36:INFO:Epoch: 7 global_step: 5300/9000 lr: 0.00041 loss: 0.0009
2022-01-12 01:07:55:INFO:Epoch: 7 global_step: 5350/9000 lr: 0.00041 loss: 0.0008
2022-01-12 01:08:13:INFO:Epoch: 7 global_step: 5400/9000 lr: 0.00040 loss: 0.0009
2022-01-12 01:08:32:INFO:Epoch: 7 global_step: 5450/9000 lr: 0.00039 loss: 0.0008
2022-01-12 01:08:50:INFO:Epoch: 7 global_step: 5500/9000 lr: 0.00039 loss: 0.0009
2022-01-12 01:09:09:INFO:Epoch: 7 global_step: 5550/9000 lr: 0.00038 loss: 0.0008