Note
Go to the end to download the full example code.
Deep Evidential Regression on a Toy Example¶
This tutorial provides an introduction to probabilistic regression in TorchUncertainty.
More specifically, we present Deep Evidential Regression (DER) using a practical example. We demonstrate an application of DER by tackling the toy-problem of fitting \(y=x^3\) using a Multi-Layer Perceptron (MLP) neural network model. The output layer of the MLP provides a NormalInverseGamma distribution which is used to optimize the model, through its negative log-likelihood.
DER represents an evidential approach to quantifying epistemic and aleatoric uncertainty in neural network regression models. This method involves introducing prior distributions over the parameters of the Gaussian likelihood function. Then, the MLP model estimates the parameters of this evidential distribution.
Training a MLP with DER using TorchUncertainty models and PyTorch Lightning¶
In this part, we train a neural network, based on the model and routines already implemented in TU.
1. Loading the utilities¶
To train a MLP with the DER loss function using TorchUncertainty, we have to load the following modules:
our TUTrainer
the model: mlp from torch_uncertainty.models.mlp
the regression training routine from torch_uncertainty.routines
the evidential objective: the DERLoss from torch_uncertainty.losses. This loss contains the classic NLL loss and a regularization term.
a dataset that generates samples from a noisy cubic function: Cubic from torch_uncertainty.datasets.regression
We also need to define an optimizer using torch.optim and the neural network utils within torch.nn.
import torch
from lightning import LightningDataModule
from torch import nn, optim
from torch_uncertainty import TUTrainer
from torch_uncertainty.models.mlp import mlp
from torch_uncertainty.datasets.regression.toy import Cubic
from torch_uncertainty.losses import DERLoss
from torch_uncertainty.routines import RegressionRoutine
from torch_uncertainty.layers.distributions import NormalInverseGammaLayer
2. The Optimization Recipe¶
We use the Adam optimizer with a rate of 5e-4.
def optim_regression(
model: nn.Module,
learning_rate: float = 5e-4,
):
optimizer = optim.Adam(
model.parameters(),
lr=learning_rate,
weight_decay=0,
)
return optimizer
3. Creating the necessary variables¶
In the following, we create a trainer to train the model, the same synthetic regression datasets as in the original DER paper and the model, a simple MLP with 2 hidden layers of 64 neurons each. Please note that this MLP finishes with a NormalInverseGammaLayer that interpret the outputs of the model as the parameters of a Normal Inverse Gamma distribution.
trainer = TUTrainer(accelerator="cpu", max_epochs=50) #, enable_progress_bar=False)
# dataset
train_ds = Cubic(num_samples=1000)
val_ds = Cubic(num_samples=300)
# datamodule
datamodule = LightningDataModule.from_datasets(
train_ds, val_dataset=val_ds, test_dataset=val_ds, batch_size=32
)
datamodule.training_task = "regression"
# model
model = mlp(
in_features=1,
num_outputs=4,
hidden_dims=[64, 64],
final_layer=NormalInverseGammaLayer,
final_layer_args={"dim": 1},
)
4. The Loss and the Training Routine¶
Next, we need to define the loss to be used during training. To do this, we set the weight of the regularizer of the DER Loss. After that, we define the training routine using the probabilistic regression training routine from torch_uncertainty.routines. In this routine, we provide the model, the DER loss, and the optimization recipe.
loss = DERLoss(reg_weight=1e-2)
routine = RegressionRoutine(
probabilistic=True,
output_dim=1,
model=model,
loss=loss,
optim_recipe=optim_regression(model),
)
5. Gathering Everything and Training the Model¶
Finally, we train the model using the trainer and the regression routine. We also test the model using the same trainer
trainer.fit(model=routine, datamodule=datamodule)
trainer.test(model=routine, datamodule=datamodule)
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/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (32) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
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Epoch 1: 84%|████████▍ | 27/32 [00:00<00:00, 324.38it/s, v_num=2, train_loss=5.200, RMSE=24.60]
Epoch 1: 88%|████████▊ | 28/32 [00:00<00:00, 325.42it/s, v_num=2, train_loss=5.200, RMSE=24.60]
Epoch 1: 88%|████████▊ | 28/32 [00:00<00:00, 324.62it/s, v_num=2, train_loss=5.440, RMSE=24.60]
Epoch 1: 91%|█████████ | 29/32 [00:00<00:00, 325.58it/s, v_num=2, train_loss=5.440, RMSE=24.60]
Epoch 1: 91%|█████████ | 29/32 [00:00<00:00, 324.82it/s, v_num=2, train_loss=5.370, RMSE=24.60]
Epoch 1: 94%|█████████▍| 30/32 [00:00<00:00, 325.86it/s, v_num=2, train_loss=5.370, RMSE=24.60]
Epoch 1: 94%|█████████▍| 30/32 [00:00<00:00, 325.05it/s, v_num=2, train_loss=5.220, RMSE=24.60]
Epoch 1: 97%|█████████▋| 31/32 [00:00<00:00, 325.98it/s, v_num=2, train_loss=5.220, RMSE=24.60]
Epoch 1: 97%|█████████▋| 31/32 [00:00<00:00, 325.27it/s, v_num=2, train_loss=5.480, RMSE=24.60]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 326.26it/s, v_num=2, train_loss=5.480, RMSE=24.60]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 325.57it/s, v_num=2, train_loss=5.880, RMSE=24.60]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 619.33it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 617.04it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 620.20it/s]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 266.93it/s, v_num=2, train_loss=5.880, RMSE=24.30]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 265.74it/s, v_num=2, train_loss=5.880, RMSE=24.30]
Epoch 1: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=5.880, RMSE=24.30]
Epoch 2: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=5.880, RMSE=24.30]
Epoch 2: 3%|▎ | 1/32 [00:00<00:00, 308.95it/s, v_num=2, train_loss=5.880, RMSE=24.30]
Epoch 2: 3%|▎ | 1/32 [00:00<00:00, 289.88it/s, v_num=2, train_loss=5.500, RMSE=24.30]
Epoch 2: 6%|▋ | 2/32 [00:00<00:00, 315.54it/s, v_num=2, train_loss=5.500, RMSE=24.30]
Epoch 2: 6%|▋ | 2/32 [00:00<00:00, 305.43it/s, v_num=2, train_loss=4.710, RMSE=24.30]
Epoch 2: 9%|▉ | 3/32 [00:00<00:00, 317.80it/s, v_num=2, train_loss=4.710, RMSE=24.30]
Epoch 2: 9%|▉ | 3/32 [00:00<00:00, 310.92it/s, v_num=2, train_loss=5.830, RMSE=24.30]
Epoch 2: 12%|█▎ | 4/32 [00:00<00:00, 321.34it/s, v_num=2, train_loss=5.830, RMSE=24.30]
Epoch 2: 12%|█▎ | 4/32 [00:00<00:00, 316.05it/s, v_num=2, train_loss=5.200, RMSE=24.30]
Epoch 2: 16%|█▌ | 5/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=5.200, RMSE=24.30]
Epoch 2: 16%|█▌ | 5/32 [00:00<00:00, 318.15it/s, v_num=2, train_loss=6.260, RMSE=24.30]
Epoch 2: 19%|█▉ | 6/32 [00:00<00:00, 322.90it/s, v_num=2, train_loss=6.260, RMSE=24.30]
Epoch 2: 19%|█▉ | 6/32 [00:00<00:00, 319.31it/s, v_num=2, train_loss=4.840, RMSE=24.30]
Epoch 2: 22%|██▏ | 7/32 [00:00<00:00, 323.85it/s, v_num=2, train_loss=4.840, RMSE=24.30]
Epoch 2: 22%|██▏ | 7/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=5.290, RMSE=24.30]
Epoch 2: 25%|██▌ | 8/32 [00:00<00:00, 323.53it/s, v_num=2, train_loss=5.290, RMSE=24.30]
Epoch 2: 25%|██▌ | 8/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=5.200, RMSE=24.30]
Epoch 2: 28%|██▊ | 9/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=5.200, RMSE=24.30]
Epoch 2: 28%|██▊ | 9/32 [00:00<00:00, 321.97it/s, v_num=2, train_loss=5.050, RMSE=24.30]
Epoch 2: 31%|███▏ | 10/32 [00:00<00:00, 325.05it/s, v_num=2, train_loss=5.050, RMSE=24.30]
Epoch 2: 31%|███▏ | 10/32 [00:00<00:00, 322.87it/s, v_num=2, train_loss=4.390, RMSE=24.30]
Epoch 2: 34%|███▍ | 11/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=4.390, RMSE=24.30]
Epoch 2: 34%|███▍ | 11/32 [00:00<00:00, 323.53it/s, v_num=2, train_loss=4.610, RMSE=24.30]
Epoch 2: 38%|███▊ | 12/32 [00:00<00:00, 325.94it/s, v_num=2, train_loss=4.610, RMSE=24.30]
Epoch 2: 38%|███▊ | 12/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=4.910, RMSE=24.30]
Epoch 2: 41%|████ | 13/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=4.910, RMSE=24.30]
Epoch 2: 41%|████ | 13/32 [00:00<00:00, 324.66it/s, v_num=2, train_loss=4.590, RMSE=24.30]
Epoch 2: 44%|████▍ | 14/32 [00:00<00:00, 326.72it/s, v_num=2, train_loss=4.590, RMSE=24.30]
Epoch 2: 44%|████▍ | 14/32 [00:00<00:00, 325.05it/s, v_num=2, train_loss=4.940, RMSE=24.30]
Epoch 2: 47%|████▋ | 15/32 [00:00<00:00, 326.85it/s, v_num=2, train_loss=4.940, RMSE=24.30]
Epoch 2: 47%|████▋ | 15/32 [00:00<00:00, 325.38it/s, v_num=2, train_loss=4.740, RMSE=24.30]
Epoch 2: 50%|█████ | 16/32 [00:00<00:00, 326.99it/s, v_num=2, train_loss=4.740, RMSE=24.30]
Epoch 2: 50%|█████ | 16/32 [00:00<00:00, 325.60it/s, v_num=2, train_loss=4.140, RMSE=24.30]
Epoch 2: 53%|█████▎ | 17/32 [00:00<00:00, 327.39it/s, v_num=2, train_loss=4.140, RMSE=24.30]
Epoch 2: 53%|█████▎ | 17/32 [00:00<00:00, 326.08it/s, v_num=2, train_loss=5.260, RMSE=24.30]
Epoch 2: 56%|█████▋ | 18/32 [00:00<00:00, 327.42it/s, v_num=2, train_loss=5.260, RMSE=24.30]
Epoch 2: 56%|█████▋ | 18/32 [00:00<00:00, 326.19it/s, v_num=2, train_loss=5.140, RMSE=24.30]
Epoch 2: 59%|█████▉ | 19/32 [00:00<00:00, 327.32it/s, v_num=2, train_loss=5.140, RMSE=24.30]
Epoch 2: 59%|█████▉ | 19/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=4.320, RMSE=24.30]
Epoch 2: 62%|██████▎ | 20/32 [00:00<00:00, 327.29it/s, v_num=2, train_loss=4.320, RMSE=24.30]
Epoch 2: 62%|██████▎ | 20/32 [00:00<00:00, 326.17it/s, v_num=2, train_loss=4.810, RMSE=24.30]
Epoch 2: 66%|██████▌ | 21/32 [00:00<00:00, 327.54it/s, v_num=2, train_loss=4.810, RMSE=24.30]
Epoch 2: 66%|██████▌ | 21/32 [00:00<00:00, 326.48it/s, v_num=2, train_loss=3.780, RMSE=24.30]
Epoch 2: 69%|██████▉ | 22/32 [00:00<00:00, 327.67it/s, v_num=2, train_loss=3.780, RMSE=24.30]
Epoch 2: 69%|██████▉ | 22/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=4.240, RMSE=24.30]
Epoch 2: 72%|███████▏ | 23/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=4.240, RMSE=24.30]
Epoch 2: 72%|███████▏ | 23/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=4.170, RMSE=24.30]
Epoch 2: 75%|███████▌ | 24/32 [00:00<00:00, 327.73it/s, v_num=2, train_loss=4.170, RMSE=24.30]
Epoch 2: 75%|███████▌ | 24/32 [00:00<00:00, 326.79it/s, v_num=2, train_loss=4.430, RMSE=24.30]
Epoch 2: 78%|███████▊ | 25/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=4.430, RMSE=24.30]
Epoch 2: 78%|███████▊ | 25/32 [00:00<00:00, 327.05it/s, v_num=2, train_loss=4.950, RMSE=24.30]
Epoch 2: 81%|████████▏ | 26/32 [00:00<00:00, 327.93it/s, v_num=2, train_loss=4.950, RMSE=24.30]
Epoch 2: 81%|████████▏ | 26/32 [00:00<00:00, 327.07it/s, v_num=2, train_loss=3.940, RMSE=24.30]
Epoch 2: 84%|████████▍ | 27/32 [00:00<00:00, 328.04it/s, v_num=2, train_loss=3.940, RMSE=24.30]
Epoch 2: 84%|████████▍ | 27/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 88%|████████▊ | 28/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 88%|████████▊ | 28/32 [00:00<00:00, 327.10it/s, v_num=2, train_loss=3.940, RMSE=24.30]
Epoch 2: 91%|█████████ | 29/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=3.940, RMSE=24.30]
Epoch 2: 91%|█████████ | 29/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=3.440, RMSE=24.30]
Epoch 2: 94%|█████████▍| 30/32 [00:00<00:00, 328.01it/s, v_num=2, train_loss=3.440, RMSE=24.30]
Epoch 2: 94%|█████████▍| 30/32 [00:00<00:00, 327.26it/s, v_num=2, train_loss=4.440, RMSE=24.30]
Epoch 2: 97%|█████████▋| 31/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=4.440, RMSE=24.30]
Epoch 2: 97%|█████████▋| 31/32 [00:00<00:00, 327.40it/s, v_num=2, train_loss=3.810, RMSE=24.30]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 328.24it/s, v_num=2, train_loss=3.810, RMSE=24.30]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 327.54it/s, v_num=2, train_loss=3.620, RMSE=24.30]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 630.49it/s]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 268.72it/s, v_num=2, train_loss=3.620, RMSE=24.10]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 267.61it/s, v_num=2, train_loss=3.620, RMSE=24.10]
Epoch 2: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.620, RMSE=24.10]
Epoch 3: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.620, RMSE=24.10]
Epoch 3: 3%|▎ | 1/32 [00:00<00:00, 316.36it/s, v_num=2, train_loss=3.620, RMSE=24.10]
Epoch 3: 3%|▎ | 1/32 [00:00<00:00, 296.54it/s, v_num=2, train_loss=4.420, RMSE=24.10]
Epoch 3: 6%|▋ | 2/32 [00:00<00:00, 319.24it/s, v_num=2, train_loss=4.420, RMSE=24.10]
Epoch 3: 6%|▋ | 2/32 [00:00<00:00, 308.81it/s, v_num=2, train_loss=4.200, RMSE=24.10]
Epoch 3: 9%|▉ | 3/32 [00:00<00:00, 322.72it/s, v_num=2, train_loss=4.200, RMSE=24.10]
Epoch 3: 9%|▉ | 3/32 [00:00<00:00, 315.66it/s, v_num=2, train_loss=4.370, RMSE=24.10]
Epoch 3: 12%|█▎ | 4/32 [00:00<00:00, 324.53it/s, v_num=2, train_loss=4.370, RMSE=24.10]
Epoch 3: 12%|█▎ | 4/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=4.280, RMSE=24.10]
Epoch 3: 16%|█▌ | 5/32 [00:00<00:00, 318.17it/s, v_num=2, train_loss=4.280, RMSE=24.10]
Epoch 3: 16%|█▌ | 5/32 [00:00<00:00, 313.98it/s, v_num=2, train_loss=3.890, RMSE=24.10]
Epoch 3: 19%|█▉ | 6/32 [00:00<00:00, 319.71it/s, v_num=2, train_loss=3.890, RMSE=24.10]
Epoch 3: 19%|█▉ | 6/32 [00:00<00:00, 316.21it/s, v_num=2, train_loss=4.320, RMSE=24.10]
Epoch 3: 22%|██▏ | 7/32 [00:00<00:00, 320.94it/s, v_num=2, train_loss=4.320, RMSE=24.10]
Epoch 3: 22%|██▏ | 7/32 [00:00<00:00, 317.92it/s, v_num=2, train_loss=4.090, RMSE=24.10]
Epoch 3: 25%|██▌ | 8/32 [00:00<00:00, 321.86it/s, v_num=2, train_loss=4.090, RMSE=24.10]
Epoch 3: 25%|██▌ | 8/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.610, RMSE=24.10]
Epoch 3: 28%|██▊ | 9/32 [00:00<00:00, 322.53it/s, v_num=2, train_loss=3.610, RMSE=24.10]
Epoch 3: 28%|██▊ | 9/32 [00:00<00:00, 320.14it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 31%|███▏ | 10/32 [00:00<00:00, 323.34it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 31%|███▏ | 10/32 [00:00<00:00, 321.17it/s, v_num=2, train_loss=3.890, RMSE=24.10]
Epoch 3: 34%|███▍ | 11/32 [00:00<00:00, 324.23it/s, v_num=2, train_loss=3.890, RMSE=24.10]
Epoch 3: 34%|███▍ | 11/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=4.230, RMSE=24.10]
Epoch 3: 38%|███▊ | 12/32 [00:00<00:00, 324.46it/s, v_num=2, train_loss=4.230, RMSE=24.10]
Epoch 3: 38%|███▊ | 12/32 [00:00<00:00, 322.65it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 41%|████ | 13/32 [00:00<00:00, 324.03it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 41%|████ | 13/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=4.120, RMSE=24.10]
Epoch 3: 44%|████▍ | 14/32 [00:00<00:00, 324.37it/s, v_num=2, train_loss=4.120, RMSE=24.10]
Epoch 3: 44%|████▍ | 14/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=4.140, RMSE=24.10]
Epoch 3: 47%|████▋ | 15/32 [00:00<00:00, 324.95it/s, v_num=2, train_loss=4.140, RMSE=24.10]
Epoch 3: 47%|████▋ | 15/32 [00:00<00:00, 323.35it/s, v_num=2, train_loss=3.610, RMSE=24.10]
Epoch 3: 50%|█████ | 16/32 [00:00<00:00, 325.10it/s, v_num=2, train_loss=3.610, RMSE=24.10]
Epoch 3: 50%|█████ | 16/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=4.070, RMSE=24.10]
Epoch 3: 53%|█████▎ | 17/32 [00:00<00:00, 325.19it/s, v_num=2, train_loss=4.070, RMSE=24.10]
Epoch 3: 53%|█████▎ | 17/32 [00:00<00:00, 323.89it/s, v_num=2, train_loss=3.870, RMSE=24.10]
Epoch 3: 56%|█████▋ | 18/32 [00:00<00:00, 325.36it/s, v_num=2, train_loss=3.870, RMSE=24.10]
Epoch 3: 56%|█████▋ | 18/32 [00:00<00:00, 324.07it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 59%|█████▉ | 19/32 [00:00<00:00, 325.47it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 59%|█████▉ | 19/32 [00:00<00:00, 324.30it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 62%|██████▎ | 20/32 [00:00<00:00, 325.37it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 62%|██████▎ | 20/32 [00:00<00:00, 324.27it/s, v_num=2, train_loss=3.910, RMSE=24.10]
Epoch 3: 66%|██████▌ | 21/32 [00:00<00:00, 325.65it/s, v_num=2, train_loss=3.910, RMSE=24.10]
Epoch 3: 66%|██████▌ | 21/32 [00:00<00:00, 324.61it/s, v_num=2, train_loss=3.930, RMSE=24.10]
Epoch 3: 69%|██████▉ | 22/32 [00:00<00:00, 325.74it/s, v_num=2, train_loss=3.930, RMSE=24.10]
Epoch 3: 69%|██████▉ | 22/32 [00:00<00:00, 324.73it/s, v_num=2, train_loss=4.000, RMSE=24.10]
Epoch 3: 72%|███████▏ | 23/32 [00:00<00:00, 325.77it/s, v_num=2, train_loss=4.000, RMSE=24.10]
Epoch 3: 72%|███████▏ | 23/32 [00:00<00:00, 324.81it/s, v_num=2, train_loss=4.180, RMSE=24.10]
Epoch 3: 75%|███████▌ | 24/32 [00:00<00:00, 325.93it/s, v_num=2, train_loss=4.180, RMSE=24.10]
Epoch 3: 75%|███████▌ | 24/32 [00:00<00:00, 325.01it/s, v_num=2, train_loss=3.930, RMSE=24.10]
Epoch 3: 78%|███████▊ | 25/32 [00:00<00:00, 326.10it/s, v_num=2, train_loss=3.930, RMSE=24.10]
Epoch 3: 78%|███████▊ | 25/32 [00:00<00:00, 325.22it/s, v_num=2, train_loss=4.300, RMSE=24.10]
Epoch 3: 81%|████████▏ | 26/32 [00:00<00:00, 326.12it/s, v_num=2, train_loss=4.300, RMSE=24.10]
Epoch 3: 81%|████████▏ | 26/32 [00:00<00:00, 325.27it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 84%|████████▍ | 27/32 [00:00<00:00, 326.18it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 84%|████████▍ | 27/32 [00:00<00:00, 325.36it/s, v_num=2, train_loss=3.780, RMSE=24.10]
Epoch 3: 88%|████████▊ | 28/32 [00:00<00:00, 326.41it/s, v_num=2, train_loss=3.780, RMSE=24.10]
Epoch 3: 88%|████████▊ | 28/32 [00:00<00:00, 325.61it/s, v_num=2, train_loss=4.280, RMSE=24.10]
Epoch 3: 91%|█████████ | 29/32 [00:00<00:00, 326.47it/s, v_num=2, train_loss=4.280, RMSE=24.10]
Epoch 3: 91%|█████████ | 29/32 [00:00<00:00, 325.71it/s, v_num=2, train_loss=4.060, RMSE=24.10]
Epoch 3: 94%|█████████▍| 30/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=4.060, RMSE=24.10]
Epoch 3: 94%|█████████▍| 30/32 [00:00<00:00, 325.83it/s, v_num=2, train_loss=3.860, RMSE=24.10]
Epoch 3: 97%|█████████▋| 31/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=3.860, RMSE=24.10]
Epoch 3: 97%|█████████▋| 31/32 [00:00<00:00, 325.95it/s, v_num=2, train_loss=3.940, RMSE=24.10]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 326.96it/s, v_num=2, train_loss=3.940, RMSE=24.10]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 326.23it/s, v_num=2, train_loss=4.380, RMSE=24.10]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 542.94it/s]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 259.15it/s, v_num=2, train_loss=4.380, RMSE=23.80]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 257.78it/s, v_num=2, train_loss=4.380, RMSE=23.80]
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Epoch 4: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.380, RMSE=23.80]
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Epoch 4: 3%|▎ | 1/32 [00:00<00:00, 244.72it/s, v_num=2, train_loss=3.710, RMSE=23.80]
Epoch 4: 6%|▋ | 2/32 [00:00<00:00, 258.57it/s, v_num=2, train_loss=3.710, RMSE=23.80]
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Epoch 4: 12%|█▎ | 4/32 [00:00<00:00, 258.16it/s, v_num=2, train_loss=4.430, RMSE=23.80]
Epoch 4: 12%|█▎ | 4/32 [00:00<00:00, 254.01it/s, v_num=2, train_loss=4.120, RMSE=23.80]
Epoch 4: 16%|█▌ | 5/32 [00:00<00:00, 258.43it/s, v_num=2, train_loss=4.120, RMSE=23.80]
Epoch 4: 16%|█▌ | 5/32 [00:00<00:00, 255.06it/s, v_num=2, train_loss=3.830, RMSE=23.80]
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Epoch 4: 22%|██▏ | 7/32 [00:00<00:00, 255.78it/s, v_num=2, train_loss=4.640, RMSE=23.80]
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Epoch 4: 25%|██▌ | 8/32 [00:00<00:00, 256.19it/s, v_num=2, train_loss=3.960, RMSE=23.80]
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Epoch 4: 31%|███▏ | 10/32 [00:00<00:00, 256.58it/s, v_num=2, train_loss=4.200, RMSE=23.80]
Epoch 4: 34%|███▍ | 11/32 [00:00<00:00, 258.54it/s, v_num=2, train_loss=4.200, RMSE=23.80]
Epoch 4: 34%|███▍ | 11/32 [00:00<00:00, 257.01it/s, v_num=2, train_loss=3.730, RMSE=23.80]
Epoch 4: 38%|███▊ | 12/32 [00:00<00:00, 262.71it/s, v_num=2, train_loss=3.730, RMSE=23.80]
Epoch 4: 38%|███▊ | 12/32 [00:00<00:00, 261.31it/s, v_num=2, train_loss=4.070, RMSE=23.80]
Epoch 4: 41%|████ | 13/32 [00:00<00:00, 266.22it/s, v_num=2, train_loss=4.070, RMSE=23.80]
Epoch 4: 41%|████ | 13/32 [00:00<00:00, 265.08it/s, v_num=2, train_loss=3.760, RMSE=23.80]
Epoch 4: 44%|████▍ | 14/32 [00:00<00:00, 269.76it/s, v_num=2, train_loss=3.760, RMSE=23.80]
Epoch 4: 44%|████▍ | 14/32 [00:00<00:00, 268.65it/s, v_num=2, train_loss=4.190, RMSE=23.80]
Epoch 4: 47%|████▋ | 15/32 [00:00<00:00, 272.72it/s, v_num=2, train_loss=4.190, RMSE=23.80]
Epoch 4: 47%|████▋ | 15/32 [00:00<00:00, 271.66it/s, v_num=2, train_loss=4.050, RMSE=23.80]
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Epoch 4: 50%|█████ | 16/32 [00:00<00:00, 274.61it/s, v_num=2, train_loss=4.010, RMSE=23.80]
Epoch 4: 53%|█████▎ | 17/32 [00:00<00:00, 278.05it/s, v_num=2, train_loss=4.010, RMSE=23.80]
Epoch 4: 53%|█████▎ | 17/32 [00:00<00:00, 277.10it/s, v_num=2, train_loss=3.940, RMSE=23.80]
Epoch 4: 56%|█████▋ | 18/32 [00:00<00:00, 280.22it/s, v_num=2, train_loss=3.940, RMSE=23.80]
Epoch 4: 56%|█████▋ | 18/32 [00:00<00:00, 279.31it/s, v_num=2, train_loss=4.370, RMSE=23.80]
Epoch 4: 59%|█████▉ | 19/32 [00:00<00:00, 282.24it/s, v_num=2, train_loss=4.370, RMSE=23.80]
Epoch 4: 59%|█████▉ | 19/32 [00:00<00:00, 281.34it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 62%|██████▎ | 20/32 [00:00<00:00, 284.00it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 62%|██████▎ | 20/32 [00:00<00:00, 283.14it/s, v_num=2, train_loss=3.840, RMSE=23.80]
Epoch 4: 66%|██████▌ | 21/32 [00:00<00:00, 285.79it/s, v_num=2, train_loss=3.840, RMSE=23.80]
Epoch 4: 66%|██████▌ | 21/32 [00:00<00:00, 284.97it/s, v_num=2, train_loss=4.120, RMSE=23.80]
Epoch 4: 69%|██████▉ | 22/32 [00:00<00:00, 287.45it/s, v_num=2, train_loss=4.120, RMSE=23.80]
Epoch 4: 69%|██████▉ | 22/32 [00:00<00:00, 286.66it/s, v_num=2, train_loss=3.910, RMSE=23.80]
Epoch 4: 72%|███████▏ | 23/32 [00:00<00:00, 287.19it/s, v_num=2, train_loss=3.910, RMSE=23.80]
Epoch 4: 72%|███████▏ | 23/32 [00:00<00:00, 286.43it/s, v_num=2, train_loss=4.060, RMSE=23.80]
Epoch 4: 75%|███████▌ | 24/32 [00:00<00:00, 288.72it/s, v_num=2, train_loss=4.060, RMSE=23.80]
Epoch 4: 75%|███████▌ | 24/32 [00:00<00:00, 288.00it/s, v_num=2, train_loss=3.890, RMSE=23.80]
Epoch 4: 78%|███████▊ | 25/32 [00:00<00:00, 290.13it/s, v_num=2, train_loss=3.890, RMSE=23.80]
Epoch 4: 78%|███████▊ | 25/32 [00:00<00:00, 289.41it/s, v_num=2, train_loss=3.880, RMSE=23.80]
Epoch 4: 81%|████████▏ | 26/32 [00:00<00:00, 291.31it/s, v_num=2, train_loss=3.880, RMSE=23.80]
Epoch 4: 81%|████████▏ | 26/32 [00:00<00:00, 290.54it/s, v_num=2, train_loss=4.280, RMSE=23.80]
Epoch 4: 84%|████████▍ | 27/32 [00:00<00:00, 292.34it/s, v_num=2, train_loss=4.280, RMSE=23.80]
Epoch 4: 84%|████████▍ | 27/32 [00:00<00:00, 291.68it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 88%|████████▊ | 28/32 [00:00<00:00, 293.50it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 88%|████████▊ | 28/32 [00:00<00:00, 292.85it/s, v_num=2, train_loss=3.720, RMSE=23.80]
Epoch 4: 91%|█████████ | 29/32 [00:00<00:00, 294.68it/s, v_num=2, train_loss=3.720, RMSE=23.80]
Epoch 4: 91%|█████████ | 29/32 [00:00<00:00, 294.06it/s, v_num=2, train_loss=3.800, RMSE=23.80]
Epoch 4: 94%|█████████▍| 30/32 [00:00<00:00, 295.67it/s, v_num=2, train_loss=3.800, RMSE=23.80]
Epoch 4: 94%|█████████▍| 30/32 [00:00<00:00, 295.06it/s, v_num=2, train_loss=4.080, RMSE=23.80]
Epoch 4: 97%|█████████▋| 31/32 [00:00<00:00, 296.69it/s, v_num=2, train_loss=4.080, RMSE=23.80]
Epoch 4: 97%|█████████▋| 31/32 [00:00<00:00, 296.10it/s, v_num=2, train_loss=3.870, RMSE=23.80]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 297.70it/s, v_num=2, train_loss=3.870, RMSE=23.80]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 297.11it/s, v_num=2, train_loss=3.520, RMSE=23.80]
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Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 247.51it/s, v_num=2, train_loss=3.520, RMSE=23.50]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 246.59it/s, v_num=2, train_loss=3.520, RMSE=23.50]
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Epoch 5: 12%|█▎ | 4/32 [00:00<00:00, 325.22it/s, v_num=2, train_loss=3.870, RMSE=23.50]
Epoch 5: 12%|█▎ | 4/32 [00:00<00:00, 319.49it/s, v_num=2, train_loss=3.850, RMSE=23.50]
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Epoch 5: 16%|█▌ | 5/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=3.840, RMSE=23.50]
Epoch 5: 19%|█▉ | 6/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=3.840, RMSE=23.50]
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Epoch 5: 38%|███▊ | 12/32 [00:00<00:00, 324.83it/s, v_num=2, train_loss=3.740, RMSE=23.50]
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Epoch 5: 59%|█████▉ | 19/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=4.130, RMSE=23.50]
Epoch 5: 62%|██████▎ | 20/32 [00:00<00:00, 327.98it/s, v_num=2, train_loss=4.130, RMSE=23.50]
Epoch 5: 62%|██████▎ | 20/32 [00:00<00:00, 326.81it/s, v_num=2, train_loss=4.090, RMSE=23.50]
Epoch 5: 66%|██████▌ | 21/32 [00:00<00:00, 327.96it/s, v_num=2, train_loss=4.090, RMSE=23.50]
Epoch 5: 66%|██████▌ | 21/32 [00:00<00:00, 326.89it/s, v_num=2, train_loss=3.950, RMSE=23.50]
Epoch 5: 69%|██████▉ | 22/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=3.950, RMSE=23.50]
Epoch 5: 69%|██████▉ | 22/32 [00:00<00:00, 326.94it/s, v_num=2, train_loss=4.020, RMSE=23.50]
Epoch 5: 72%|███████▏ | 23/32 [00:00<00:00, 328.08it/s, v_num=2, train_loss=4.020, RMSE=23.50]
Epoch 5: 72%|███████▏ | 23/32 [00:00<00:00, 326.98it/s, v_num=2, train_loss=3.710, RMSE=23.50]
Epoch 5: 75%|███████▌ | 24/32 [00:00<00:00, 328.07it/s, v_num=2, train_loss=3.710, RMSE=23.50]
Epoch 5: 75%|███████▌ | 24/32 [00:00<00:00, 327.14it/s, v_num=2, train_loss=4.160, RMSE=23.50]
Epoch 5: 78%|███████▊ | 25/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=4.160, RMSE=23.50]
Epoch 5: 78%|███████▊ | 25/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=3.920, RMSE=23.50]
Epoch 5: 81%|████████▏ | 26/32 [00:00<00:00, 327.99it/s, v_num=2, train_loss=3.920, RMSE=23.50]
Epoch 5: 81%|████████▏ | 26/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=4.330, RMSE=23.50]
Epoch 5: 84%|████████▍ | 27/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=4.330, RMSE=23.50]
Epoch 5: 84%|████████▍ | 27/32 [00:00<00:00, 327.07it/s, v_num=2, train_loss=4.190, RMSE=23.50]
Epoch 5: 88%|████████▊ | 28/32 [00:00<00:00, 328.01it/s, v_num=2, train_loss=4.190, RMSE=23.50]
Epoch 5: 88%|████████▊ | 28/32 [00:00<00:00, 327.20it/s, v_num=2, train_loss=4.360, RMSE=23.50]
Epoch 5: 91%|█████████ | 29/32 [00:00<00:00, 327.72it/s, v_num=2, train_loss=4.360, RMSE=23.50]
Epoch 5: 91%|█████████ | 29/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=3.950, RMSE=23.50]
Epoch 5: 94%|█████████▍| 30/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=3.950, RMSE=23.50]
Epoch 5: 94%|█████████▍| 30/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=4.460, RMSE=23.50]
Epoch 5: 97%|█████████▋| 31/32 [00:00<00:00, 327.38it/s, v_num=2, train_loss=4.460, RMSE=23.50]
Epoch 5: 97%|█████████▋| 31/32 [00:00<00:00, 326.59it/s, v_num=2, train_loss=4.100, RMSE=23.50]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 327.25it/s, v_num=2, train_loss=4.100, RMSE=23.50]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 326.53it/s, v_num=2, train_loss=3.230, RMSE=23.50]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 614.10it/s]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 266.32it/s, v_num=2, train_loss=3.230, RMSE=23.00]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 265.16it/s, v_num=2, train_loss=3.230, RMSE=23.00]
Epoch 5: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.230, RMSE=23.00]
Epoch 6: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.230, RMSE=23.00]
Epoch 6: 3%|▎ | 1/32 [00:00<00:00, 289.74it/s, v_num=2, train_loss=3.230, RMSE=23.00]
Epoch 6: 3%|▎ | 1/32 [00:00<00:00, 272.89it/s, v_num=2, train_loss=3.660, RMSE=23.00]
Epoch 6: 6%|▋ | 2/32 [00:00<00:00, 302.00it/s, v_num=2, train_loss=3.660, RMSE=23.00]
Epoch 6: 6%|▋ | 2/32 [00:00<00:00, 292.67it/s, v_num=2, train_loss=3.900, RMSE=23.00]
Epoch 6: 9%|▉ | 3/32 [00:00<00:00, 309.32it/s, v_num=2, train_loss=3.900, RMSE=23.00]
Epoch 6: 9%|▉ | 3/32 [00:00<00:00, 302.74it/s, v_num=2, train_loss=4.230, RMSE=23.00]
Epoch 6: 12%|█▎ | 4/32 [00:00<00:00, 312.85it/s, v_num=2, train_loss=4.230, RMSE=23.00]
Epoch 6: 12%|█▎ | 4/32 [00:00<00:00, 307.80it/s, v_num=2, train_loss=3.700, RMSE=23.00]
Epoch 6: 16%|█▌ | 5/32 [00:00<00:00, 315.27it/s, v_num=2, train_loss=3.700, RMSE=23.00]
Epoch 6: 16%|█▌ | 5/32 [00:00<00:00, 311.17it/s, v_num=2, train_loss=3.690, RMSE=23.00]
Epoch 6: 19%|█▉ | 6/32 [00:00<00:00, 317.16it/s, v_num=2, train_loss=3.690, RMSE=23.00]
Epoch 6: 19%|█▉ | 6/32 [00:00<00:00, 313.60it/s, v_num=2, train_loss=3.510, RMSE=23.00]
Epoch 6: 22%|██▏ | 7/32 [00:00<00:00, 318.97it/s, v_num=2, train_loss=3.510, RMSE=23.00]
Epoch 6: 22%|██▏ | 7/32 [00:00<00:00, 315.96it/s, v_num=2, train_loss=4.520, RMSE=23.00]
Epoch 6: 25%|██▌ | 8/32 [00:00<00:00, 320.13it/s, v_num=2, train_loss=4.520, RMSE=23.00]
Epoch 6: 25%|██▌ | 8/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 28%|██▊ | 9/32 [00:00<00:00, 315.88it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 28%|██▊ | 9/32 [00:00<00:00, 313.57it/s, v_num=2, train_loss=4.370, RMSE=23.00]
Epoch 6: 31%|███▏ | 10/32 [00:00<00:00, 316.97it/s, v_num=2, train_loss=4.370, RMSE=23.00]
Epoch 6: 31%|███▏ | 10/32 [00:00<00:00, 314.86it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 34%|███▍ | 11/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 34%|███▍ | 11/32 [00:00<00:00, 315.99it/s, v_num=2, train_loss=4.190, RMSE=23.00]
Epoch 6: 38%|███▊ | 12/32 [00:00<00:00, 319.11it/s, v_num=2, train_loss=4.190, RMSE=23.00]
Epoch 6: 38%|███▊ | 12/32 [00:00<00:00, 317.32it/s, v_num=2, train_loss=4.160, RMSE=23.00]
Epoch 6: 41%|████ | 13/32 [00:00<00:00, 319.60it/s, v_num=2, train_loss=4.160, RMSE=23.00]
Epoch 6: 41%|████ | 13/32 [00:00<00:00, 317.95it/s, v_num=2, train_loss=3.610, RMSE=23.00]
Epoch 6: 44%|████▍ | 14/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.610, RMSE=23.00]
Epoch 6: 44%|████▍ | 14/32 [00:00<00:00, 318.41it/s, v_num=2, train_loss=3.700, RMSE=23.00]
Epoch 6: 47%|████▋ | 15/32 [00:00<00:00, 320.29it/s, v_num=2, train_loss=3.700, RMSE=23.00]
Epoch 6: 47%|████▋ | 15/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.290, RMSE=23.00]
Epoch 6: 50%|█████ | 16/32 [00:00<00:00, 320.95it/s, v_num=2, train_loss=3.290, RMSE=23.00]
Epoch 6: 50%|█████ | 16/32 [00:00<00:00, 319.60it/s, v_num=2, train_loss=4.210, RMSE=23.00]
Epoch 6: 53%|█████▎ | 17/32 [00:00<00:00, 321.31it/s, v_num=2, train_loss=4.210, RMSE=23.00]
Epoch 6: 53%|█████▎ | 17/32 [00:00<00:00, 320.05it/s, v_num=2, train_loss=3.630, RMSE=23.00]
Epoch 6: 56%|█████▋ | 18/32 [00:00<00:00, 321.70it/s, v_num=2, train_loss=3.630, RMSE=23.00]
Epoch 6: 56%|█████▋ | 18/32 [00:00<00:00, 320.49it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 59%|█████▉ | 19/32 [00:00<00:00, 321.87it/s, v_num=2, train_loss=4.080, RMSE=23.00]
Epoch 6: 59%|█████▉ | 19/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=4.430, RMSE=23.00]
Epoch 6: 62%|██████▎ | 20/32 [00:00<00:00, 322.35it/s, v_num=2, train_loss=4.430, RMSE=23.00]
Epoch 6: 62%|██████▎ | 20/32 [00:00<00:00, 321.26it/s, v_num=2, train_loss=4.030, RMSE=23.00]
Epoch 6: 66%|██████▌ | 21/32 [00:00<00:00, 322.23it/s, v_num=2, train_loss=4.030, RMSE=23.00]
Epoch 6: 66%|██████▌ | 21/32 [00:00<00:00, 321.18it/s, v_num=2, train_loss=4.100, RMSE=23.00]
Epoch 6: 69%|██████▉ | 22/32 [00:00<00:00, 322.30it/s, v_num=2, train_loss=4.100, RMSE=23.00]
Epoch 6: 69%|██████▉ | 22/32 [00:00<00:00, 321.27it/s, v_num=2, train_loss=3.930, RMSE=23.00]
Epoch 6: 72%|███████▏ | 23/32 [00:00<00:00, 321.69it/s, v_num=2, train_loss=3.930, RMSE=23.00]
Epoch 6: 72%|███████▏ | 23/32 [00:00<00:00, 320.72it/s, v_num=2, train_loss=3.830, RMSE=23.00]
Epoch 6: 75%|███████▌ | 24/32 [00:00<00:00, 321.56it/s, v_num=2, train_loss=3.830, RMSE=23.00]
Epoch 6: 75%|███████▌ | 24/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.770, RMSE=23.00]
Epoch 6: 78%|███████▊ | 25/32 [00:00<00:00, 321.61it/s, v_num=2, train_loss=3.770, RMSE=23.00]
Epoch 6: 78%|███████▊ | 25/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=3.870, RMSE=23.00]
Epoch 6: 81%|████████▏ | 26/32 [00:00<00:00, 321.69it/s, v_num=2, train_loss=3.870, RMSE=23.00]
Epoch 6: 81%|████████▏ | 26/32 [00:00<00:00, 320.86it/s, v_num=2, train_loss=4.290, RMSE=23.00]
Epoch 6: 84%|████████▍ | 27/32 [00:00<00:00, 321.81it/s, v_num=2, train_loss=4.290, RMSE=23.00]
Epoch 6: 84%|████████▍ | 27/32 [00:00<00:00, 321.01it/s, v_num=2, train_loss=4.020, RMSE=23.00]
Epoch 6: 88%|████████▊ | 28/32 [00:00<00:00, 322.01it/s, v_num=2, train_loss=4.020, RMSE=23.00]
Epoch 6: 88%|████████▊ | 28/32 [00:00<00:00, 321.23it/s, v_num=2, train_loss=3.760, RMSE=23.00]
Epoch 6: 91%|█████████ | 29/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=3.760, RMSE=23.00]
Epoch 6: 91%|█████████ | 29/32 [00:00<00:00, 321.49it/s, v_num=2, train_loss=3.840, RMSE=23.00]
Epoch 6: 94%|█████████▍| 30/32 [00:00<00:00, 322.37it/s, v_num=2, train_loss=3.840, RMSE=23.00]
Epoch 6: 94%|█████████▍| 30/32 [00:00<00:00, 321.64it/s, v_num=2, train_loss=3.830, RMSE=23.00]
Epoch 6: 97%|█████████▋| 31/32 [00:00<00:00, 322.49it/s, v_num=2, train_loss=3.830, RMSE=23.00]
Epoch 6: 97%|█████████▋| 31/32 [00:00<00:00, 321.79it/s, v_num=2, train_loss=3.750, RMSE=23.00]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 322.71it/s, v_num=2, train_loss=3.750, RMSE=23.00]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 322.01it/s, v_num=2, train_loss=4.320, RMSE=23.00]
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Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 264.05it/s, v_num=2, train_loss=4.320, RMSE=22.50]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 262.96it/s, v_num=2, train_loss=4.320, RMSE=22.50]
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Epoch 7: 3%|▎ | 1/32 [00:00<00:00, 295.29it/s, v_num=2, train_loss=4.320, RMSE=22.50]
Epoch 7: 3%|▎ | 1/32 [00:00<00:00, 277.82it/s, v_num=2, train_loss=4.180, RMSE=22.50]
Epoch 7: 6%|▋ | 2/32 [00:00<00:00, 307.76it/s, v_num=2, train_loss=4.180, RMSE=22.50]
Epoch 7: 6%|▋ | 2/32 [00:00<00:00, 298.10it/s, v_num=2, train_loss=4.020, RMSE=22.50]
Epoch 7: 9%|▉ | 3/32 [00:00<00:00, 315.01it/s, v_num=2, train_loss=4.020, RMSE=22.50]
Epoch 7: 9%|▉ | 3/32 [00:00<00:00, 307.12it/s, v_num=2, train_loss=3.640, RMSE=22.50]
Epoch 7: 12%|█▎ | 4/32 [00:00<00:00, 317.86it/s, v_num=2, train_loss=3.640, RMSE=22.50]
Epoch 7: 12%|█▎ | 4/32 [00:00<00:00, 312.64it/s, v_num=2, train_loss=4.160, RMSE=22.50]
Epoch 7: 16%|█▌ | 5/32 [00:00<00:00, 319.21it/s, v_num=2, train_loss=4.160, RMSE=22.50]
Epoch 7: 16%|█▌ | 5/32 [00:00<00:00, 314.89it/s, v_num=2, train_loss=3.860, RMSE=22.50]
Epoch 7: 19%|█▉ | 6/32 [00:00<00:00, 320.60it/s, v_num=2, train_loss=3.860, RMSE=22.50]
Epoch 7: 19%|█▉ | 6/32 [00:00<00:00, 317.07it/s, v_num=2, train_loss=3.760, RMSE=22.50]
Epoch 7: 22%|██▏ | 7/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=3.760, RMSE=22.50]
Epoch 7: 22%|██▏ | 7/32 [00:00<00:00, 317.61it/s, v_num=2, train_loss=3.760, RMSE=22.50]
Epoch 7: 25%|██▌ | 8/32 [00:00<00:00, 322.14it/s, v_num=2, train_loss=3.760, RMSE=22.50]
Epoch 7: 25%|██▌ | 8/32 [00:00<00:00, 319.38it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 7: 28%|██▊ | 9/32 [00:00<00:00, 322.61it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 7: 28%|██▊ | 9/32 [00:00<00:00, 320.21it/s, v_num=2, train_loss=4.170, RMSE=22.50]
Epoch 7: 31%|███▏ | 10/32 [00:00<00:00, 323.07it/s, v_num=2, train_loss=4.170, RMSE=22.50]
Epoch 7: 31%|███▏ | 10/32 [00:00<00:00, 320.91it/s, v_num=2, train_loss=3.970, RMSE=22.50]
Epoch 7: 34%|███▍ | 11/32 [00:00<00:00, 323.05it/s, v_num=2, train_loss=3.970, RMSE=22.50]
Epoch 7: 34%|███▍ | 11/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.770, RMSE=22.50]
Epoch 7: 38%|███▊ | 12/32 [00:00<00:00, 323.76it/s, v_num=2, train_loss=3.770, RMSE=22.50]
Epoch 7: 38%|███▊ | 12/32 [00:00<00:00, 321.95it/s, v_num=2, train_loss=3.670, RMSE=22.50]
Epoch 7: 41%|████ | 13/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=3.670, RMSE=22.50]
Epoch 7: 41%|████ | 13/32 [00:00<00:00, 322.42it/s, v_num=2, train_loss=3.850, RMSE=22.50]
Epoch 7: 44%|████▍ | 14/32 [00:00<00:00, 324.33it/s, v_num=2, train_loss=3.850, RMSE=22.50]
Epoch 7: 44%|████▍ | 14/32 [00:00<00:00, 322.76it/s, v_num=2, train_loss=3.870, RMSE=22.50]
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Epoch 7: 53%|█████▎ | 17/32 [00:00<00:00, 325.16it/s, v_num=2, train_loss=3.760, RMSE=22.50]
Epoch 7: 53%|█████▎ | 17/32 [00:00<00:00, 323.88it/s, v_num=2, train_loss=3.520, RMSE=22.50]
Epoch 7: 56%|█████▋ | 18/32 [00:00<00:00, 325.18it/s, v_num=2, train_loss=3.520, RMSE=22.50]
Epoch 7: 56%|█████▋ | 18/32 [00:00<00:00, 323.93it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 7: 59%|█████▉ | 19/32 [00:00<00:00, 325.21it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 7: 59%|█████▉ | 19/32 [00:00<00:00, 324.05it/s, v_num=2, train_loss=3.790, RMSE=22.50]
Epoch 7: 62%|██████▎ | 20/32 [00:00<00:00, 325.64it/s, v_num=2, train_loss=3.790, RMSE=22.50]
Epoch 7: 62%|██████▎ | 20/32 [00:00<00:00, 324.33it/s, v_num=2, train_loss=4.280, RMSE=22.50]
Epoch 7: 66%|██████▌ | 21/32 [00:00<00:00, 325.69it/s, v_num=2, train_loss=4.280, RMSE=22.50]
Epoch 7: 66%|██████▌ | 21/32 [00:00<00:00, 324.64it/s, v_num=2, train_loss=3.700, RMSE=22.50]
Epoch 7: 69%|██████▉ | 22/32 [00:00<00:00, 325.81it/s, v_num=2, train_loss=3.700, RMSE=22.50]
Epoch 7: 69%|██████▉ | 22/32 [00:00<00:00, 324.81it/s, v_num=2, train_loss=4.060, RMSE=22.50]
Epoch 7: 72%|███████▏ | 23/32 [00:00<00:00, 326.01it/s, v_num=2, train_loss=4.060, RMSE=22.50]
Epoch 7: 72%|███████▏ | 23/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=3.830, RMSE=22.50]
Epoch 7: 75%|███████▌ | 24/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=3.830, RMSE=22.50]
Epoch 7: 75%|███████▌ | 24/32 [00:00<00:00, 325.29it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 7: 78%|███████▊ | 25/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 7: 78%|███████▊ | 25/32 [00:00<00:00, 325.50it/s, v_num=2, train_loss=3.890, RMSE=22.50]
Epoch 7: 81%|████████▏ | 26/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=3.890, RMSE=22.50]
Epoch 7: 81%|████████▏ | 26/32 [00:00<00:00, 325.66it/s, v_num=2, train_loss=3.800, RMSE=22.50]
Epoch 7: 84%|████████▍ | 27/32 [00:00<00:00, 324.92it/s, v_num=2, train_loss=3.800, RMSE=22.50]
Epoch 7: 84%|████████▍ | 27/32 [00:00<00:00, 324.10it/s, v_num=2, train_loss=3.810, RMSE=22.50]
Epoch 7: 88%|████████▊ | 28/32 [00:00<00:00, 325.04it/s, v_num=2, train_loss=3.810, RMSE=22.50]
Epoch 7: 88%|████████▊ | 28/32 [00:00<00:00, 324.25it/s, v_num=2, train_loss=4.200, RMSE=22.50]
Epoch 7: 91%|█████████ | 29/32 [00:00<00:00, 325.31it/s, v_num=2, train_loss=4.200, RMSE=22.50]
Epoch 7: 91%|█████████ | 29/32 [00:00<00:00, 324.55it/s, v_num=2, train_loss=3.790, RMSE=22.50]
Epoch 7: 94%|█████████▍| 30/32 [00:00<00:00, 325.35it/s, v_num=2, train_loss=3.790, RMSE=22.50]
Epoch 7: 94%|█████████▍| 30/32 [00:00<00:00, 324.61it/s, v_num=2, train_loss=3.830, RMSE=22.50]
Epoch 7: 97%|█████████▋| 31/32 [00:00<00:00, 325.37it/s, v_num=2, train_loss=3.830, RMSE=22.50]
Epoch 7: 97%|█████████▋| 31/32 [00:00<00:00, 324.66it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 325.64it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 324.96it/s, v_num=2, train_loss=4.130, RMSE=22.50]
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Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 618.07it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 619.41it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 619.11it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 623.24it/s]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 266.70it/s, v_num=2, train_loss=4.130, RMSE=21.80]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 265.61it/s, v_num=2, train_loss=4.130, RMSE=21.80]
Epoch 7: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.130, RMSE=21.80]
Epoch 8: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.130, RMSE=21.80]
Epoch 8: 3%|▎ | 1/32 [00:00<00:00, 310.55it/s, v_num=2, train_loss=4.130, RMSE=21.80]
Epoch 8: 3%|▎ | 1/32 [00:00<00:00, 291.47it/s, v_num=2, train_loss=3.990, RMSE=21.80]
Epoch 8: 6%|▋ | 2/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.990, RMSE=21.80]
Epoch 8: 6%|▋ | 2/32 [00:00<00:00, 308.16it/s, v_num=2, train_loss=4.370, RMSE=21.80]
Epoch 8: 9%|▉ | 3/32 [00:00<00:00, 322.86it/s, v_num=2, train_loss=4.370, RMSE=21.80]
Epoch 8: 9%|▉ | 3/32 [00:00<00:00, 315.82it/s, v_num=2, train_loss=3.920, RMSE=21.80]
Epoch 8: 12%|█▎ | 4/32 [00:00<00:00, 324.33it/s, v_num=2, train_loss=3.920, RMSE=21.80]
Epoch 8: 12%|█▎ | 4/32 [00:00<00:00, 318.91it/s, v_num=2, train_loss=3.950, RMSE=21.80]
Epoch 8: 16%|█▌ | 5/32 [00:00<00:00, 325.02it/s, v_num=2, train_loss=3.950, RMSE=21.80]
Epoch 8: 16%|█▌ | 5/32 [00:00<00:00, 320.70it/s, v_num=2, train_loss=3.460, RMSE=21.80]
Epoch 8: 19%|█▉ | 6/32 [00:00<00:00, 325.51it/s, v_num=2, train_loss=3.460, RMSE=21.80]
Epoch 8: 19%|█▉ | 6/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=3.650, RMSE=21.80]
Epoch 8: 22%|██▏ | 7/32 [00:00<00:00, 326.47it/s, v_num=2, train_loss=3.650, RMSE=21.80]
Epoch 8: 22%|██▏ | 7/32 [00:00<00:00, 323.34it/s, v_num=2, train_loss=3.770, RMSE=21.80]
Epoch 8: 25%|██▌ | 8/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.770, RMSE=21.80]
Epoch 8: 25%|██▌ | 8/32 [00:00<00:00, 323.84it/s, v_num=2, train_loss=3.830, RMSE=21.80]
Epoch 8: 28%|██▊ | 9/32 [00:00<00:00, 326.65it/s, v_num=2, train_loss=3.830, RMSE=21.80]
Epoch 8: 28%|██▊ | 9/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.810, RMSE=21.80]
Epoch 8: 31%|███▏ | 10/32 [00:00<00:00, 325.70it/s, v_num=2, train_loss=3.810, RMSE=21.80]
Epoch 8: 31%|███▏ | 10/32 [00:00<00:00, 323.51it/s, v_num=2, train_loss=3.810, RMSE=21.80]
Epoch 8: 34%|███▍ | 11/32 [00:00<00:00, 326.18it/s, v_num=2, train_loss=3.810, RMSE=21.80]
Epoch 8: 34%|███▍ | 11/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=4.140, RMSE=21.80]
Epoch 8: 38%|███▊ | 12/32 [00:00<00:00, 326.52it/s, v_num=2, train_loss=4.140, RMSE=21.80]
Epoch 8: 38%|███▊ | 12/32 [00:00<00:00, 324.69it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 41%|████ | 13/32 [00:00<00:00, 326.74it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 41%|████ | 13/32 [00:00<00:00, 325.04it/s, v_num=2, train_loss=3.840, RMSE=21.80]
Epoch 8: 44%|████▍ | 14/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=3.840, RMSE=21.80]
Epoch 8: 44%|████▍ | 14/32 [00:00<00:00, 325.20it/s, v_num=2, train_loss=4.030, RMSE=21.80]
Epoch 8: 47%|████▋ | 15/32 [00:00<00:00, 327.11it/s, v_num=2, train_loss=4.030, RMSE=21.80]
Epoch 8: 47%|████▋ | 15/32 [00:00<00:00, 325.64it/s, v_num=2, train_loss=3.920, RMSE=21.80]
Epoch 8: 50%|█████ | 16/32 [00:00<00:00, 327.16it/s, v_num=2, train_loss=3.920, RMSE=21.80]
Epoch 8: 50%|█████ | 16/32 [00:00<00:00, 325.77it/s, v_num=2, train_loss=3.990, RMSE=21.80]
Epoch 8: 53%|█████▎ | 17/32 [00:00<00:00, 327.30it/s, v_num=2, train_loss=3.990, RMSE=21.80]
Epoch 8: 53%|█████▎ | 17/32 [00:00<00:00, 325.95it/s, v_num=2, train_loss=4.370, RMSE=21.80]
Epoch 8: 56%|█████▋ | 18/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=4.370, RMSE=21.80]
Epoch 8: 56%|█████▋ | 18/32 [00:00<00:00, 326.19it/s, v_num=2, train_loss=3.650, RMSE=21.80]
Epoch 8: 59%|█████▉ | 19/32 [00:00<00:00, 327.51it/s, v_num=2, train_loss=3.650, RMSE=21.80]
Epoch 8: 59%|█████▉ | 19/32 [00:00<00:00, 326.18it/s, v_num=2, train_loss=3.530, RMSE=21.80]
Epoch 8: 62%|██████▎ | 20/32 [00:00<00:00, 327.37it/s, v_num=2, train_loss=3.530, RMSE=21.80]
Epoch 8: 62%|██████▎ | 20/32 [00:00<00:00, 326.22it/s, v_num=2, train_loss=3.460, RMSE=21.80]
Epoch 8: 66%|██████▌ | 21/32 [00:00<00:00, 327.35it/s, v_num=2, train_loss=3.460, RMSE=21.80]
Epoch 8: 66%|██████▌ | 21/32 [00:00<00:00, 326.19it/s, v_num=2, train_loss=3.800, RMSE=21.80]
Epoch 8: 69%|██████▉ | 22/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=3.800, RMSE=21.80]
Epoch 8: 69%|██████▉ | 22/32 [00:00<00:00, 326.30it/s, v_num=2, train_loss=4.110, RMSE=21.80]
Epoch 8: 72%|███████▏ | 23/32 [00:00<00:00, 327.43it/s, v_num=2, train_loss=4.110, RMSE=21.80]
Epoch 8: 72%|███████▏ | 23/32 [00:00<00:00, 326.46it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 75%|███████▌ | 24/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 75%|███████▌ | 24/32 [00:00<00:00, 326.38it/s, v_num=2, train_loss=4.290, RMSE=21.80]
Epoch 8: 78%|███████▊ | 25/32 [00:00<00:00, 327.36it/s, v_num=2, train_loss=4.290, RMSE=21.80]
Epoch 8: 78%|███████▊ | 25/32 [00:00<00:00, 326.48it/s, v_num=2, train_loss=4.100, RMSE=21.80]
Epoch 8: 81%|████████▏ | 26/32 [00:00<00:00, 327.46it/s, v_num=2, train_loss=4.100, RMSE=21.80]
Epoch 8: 81%|████████▏ | 26/32 [00:00<00:00, 326.61it/s, v_num=2, train_loss=3.580, RMSE=21.80]
Epoch 8: 84%|████████▍ | 27/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=3.580, RMSE=21.80]
Epoch 8: 84%|████████▍ | 27/32 [00:00<00:00, 326.59it/s, v_num=2, train_loss=3.840, RMSE=21.80]
Epoch 8: 88%|████████▊ | 28/32 [00:00<00:00, 327.29it/s, v_num=2, train_loss=3.840, RMSE=21.80]
Epoch 8: 88%|████████▊ | 28/32 [00:00<00:00, 326.50it/s, v_num=2, train_loss=3.660, RMSE=21.80]
Epoch 8: 91%|█████████ | 29/32 [00:00<00:00, 327.26it/s, v_num=2, train_loss=3.660, RMSE=21.80]
Epoch 8: 91%|█████████ | 29/32 [00:00<00:00, 326.50it/s, v_num=2, train_loss=4.140, RMSE=21.80]
Epoch 8: 94%|█████████▍| 30/32 [00:00<00:00, 327.33it/s, v_num=2, train_loss=4.140, RMSE=21.80]
Epoch 8: 94%|█████████▍| 30/32 [00:00<00:00, 326.60it/s, v_num=2, train_loss=3.690, RMSE=21.80]
Epoch 8: 97%|█████████▋| 31/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=3.690, RMSE=21.80]
Epoch 8: 97%|█████████▋| 31/32 [00:00<00:00, 326.69it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 327.55it/s, v_num=2, train_loss=3.730, RMSE=21.80]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=3.430, RMSE=21.80]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 627.19it/s]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 267.89it/s, v_num=2, train_loss=3.430, RMSE=21.10]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 266.81it/s, v_num=2, train_loss=3.430, RMSE=21.10]
Epoch 8: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.430, RMSE=21.10]
Epoch 9: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.430, RMSE=21.10]
Epoch 9: 3%|▎ | 1/32 [00:00<00:00, 316.98it/s, v_num=2, train_loss=3.430, RMSE=21.10]
Epoch 9: 3%|▎ | 1/32 [00:00<00:00, 294.25it/s, v_num=2, train_loss=3.570, RMSE=21.10]
Epoch 9: 6%|▋ | 2/32 [00:00<00:00, 321.35it/s, v_num=2, train_loss=3.570, RMSE=21.10]
Epoch 9: 6%|▋ | 2/32 [00:00<00:00, 311.02it/s, v_num=2, train_loss=4.060, RMSE=21.10]
Epoch 9: 9%|▉ | 3/32 [00:00<00:00, 323.58it/s, v_num=2, train_loss=4.060, RMSE=21.10]
Epoch 9: 9%|▉ | 3/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=4.000, RMSE=21.10]
Epoch 9: 12%|█▎ | 4/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=4.000, RMSE=21.10]
Epoch 9: 12%|█▎ | 4/32 [00:00<00:00, 318.14it/s, v_num=2, train_loss=4.210, RMSE=21.10]
Epoch 9: 16%|█▌ | 5/32 [00:00<00:00, 324.17it/s, v_num=2, train_loss=4.210, RMSE=21.10]
Epoch 9: 16%|█▌ | 5/32 [00:00<00:00, 319.83it/s, v_num=2, train_loss=4.040, RMSE=21.10]
Epoch 9: 19%|█▉ | 6/32 [00:00<00:00, 325.64it/s, v_num=2, train_loss=4.040, RMSE=21.10]
Epoch 9: 19%|█▉ | 6/32 [00:00<00:00, 322.01it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 22%|██▏ | 7/32 [00:00<00:00, 325.45it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 22%|██▏ | 7/32 [00:00<00:00, 322.34it/s, v_num=2, train_loss=4.110, RMSE=21.10]
Epoch 9: 25%|██▌ | 8/32 [00:00<00:00, 325.72it/s, v_num=2, train_loss=4.110, RMSE=21.10]
Epoch 9: 25%|██▌ | 8/32 [00:00<00:00, 322.87it/s, v_num=2, train_loss=3.860, RMSE=21.10]
Epoch 9: 28%|██▊ | 9/32 [00:00<00:00, 325.98it/s, v_num=2, train_loss=3.860, RMSE=21.10]
Epoch 9: 28%|██▊ | 9/32 [00:00<00:00, 323.54it/s, v_num=2, train_loss=3.880, RMSE=21.10]
Epoch 9: 31%|███▏ | 10/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.880, RMSE=21.10]
Epoch 9: 31%|███▏ | 10/32 [00:00<00:00, 324.39it/s, v_num=2, train_loss=4.260, RMSE=21.10]
Epoch 9: 34%|███▍ | 11/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=4.260, RMSE=21.10]
Epoch 9: 34%|███▍ | 11/32 [00:00<00:00, 324.57it/s, v_num=2, train_loss=3.160, RMSE=21.10]
Epoch 9: 38%|███▊ | 12/32 [00:00<00:00, 326.75it/s, v_num=2, train_loss=3.160, RMSE=21.10]
Epoch 9: 38%|███▊ | 12/32 [00:00<00:00, 324.91it/s, v_num=2, train_loss=4.030, RMSE=21.10]
Epoch 9: 41%|████ | 13/32 [00:00<00:00, 323.77it/s, v_num=2, train_loss=4.030, RMSE=21.10]
Epoch 9: 41%|████ | 13/32 [00:00<00:00, 322.09it/s, v_num=2, train_loss=3.770, RMSE=21.10]
Epoch 9: 44%|████▍ | 14/32 [00:00<00:00, 324.12it/s, v_num=2, train_loss=3.770, RMSE=21.10]
Epoch 9: 44%|████▍ | 14/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=4.140, RMSE=21.10]
Epoch 9: 47%|████▋ | 15/32 [00:00<00:00, 324.48it/s, v_num=2, train_loss=4.140, RMSE=21.10]
Epoch 9: 47%|████▋ | 15/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 50%|█████ | 16/32 [00:00<00:00, 324.77it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 50%|█████ | 16/32 [00:00<00:00, 323.42it/s, v_num=2, train_loss=3.950, RMSE=21.10]
Epoch 9: 53%|█████▎ | 17/32 [00:00<00:00, 325.03it/s, v_num=2, train_loss=3.950, RMSE=21.10]
Epoch 9: 53%|█████▎ | 17/32 [00:00<00:00, 323.74it/s, v_num=2, train_loss=3.850, RMSE=21.10]
Epoch 9: 56%|█████▋ | 18/32 [00:00<00:00, 325.21it/s, v_num=2, train_loss=3.850, RMSE=21.10]
Epoch 9: 56%|█████▋ | 18/32 [00:00<00:00, 323.97it/s, v_num=2, train_loss=3.770, RMSE=21.10]
Epoch 9: 59%|█████▉ | 19/32 [00:00<00:00, 325.67it/s, v_num=2, train_loss=3.770, RMSE=21.10]
Epoch 9: 59%|█████▉ | 19/32 [00:00<00:00, 324.51it/s, v_num=2, train_loss=4.070, RMSE=21.10]
Epoch 9: 62%|██████▎ | 20/32 [00:00<00:00, 325.85it/s, v_num=2, train_loss=4.070, RMSE=21.10]
Epoch 9: 62%|██████▎ | 20/32 [00:00<00:00, 324.74it/s, v_num=2, train_loss=4.100, RMSE=21.10]
Epoch 9: 66%|██████▌ | 21/32 [00:00<00:00, 326.01it/s, v_num=2, train_loss=4.100, RMSE=21.10]
Epoch 9: 66%|██████▌ | 21/32 [00:00<00:00, 324.96it/s, v_num=2, train_loss=3.800, RMSE=21.10]
Epoch 9: 69%|██████▉ | 22/32 [00:00<00:00, 326.07it/s, v_num=2, train_loss=3.800, RMSE=21.10]
Epoch 9: 69%|██████▉ | 22/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=3.640, RMSE=21.10]
Epoch 9: 72%|███████▏ | 23/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=3.640, RMSE=21.10]
Epoch 9: 72%|███████▏ | 23/32 [00:00<00:00, 325.39it/s, v_num=2, train_loss=3.460, RMSE=21.10]
Epoch 9: 75%|███████▌ | 24/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=3.460, RMSE=21.10]
Epoch 9: 75%|███████▌ | 24/32 [00:00<00:00, 325.60it/s, v_num=2, train_loss=4.080, RMSE=21.10]
Epoch 9: 78%|███████▊ | 25/32 [00:00<00:00, 326.70it/s, v_num=2, train_loss=4.080, RMSE=21.10]
Epoch 9: 78%|███████▊ | 25/32 [00:00<00:00, 325.82it/s, v_num=2, train_loss=3.630, RMSE=21.10]
Epoch 9: 81%|████████▏ | 26/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=3.630, RMSE=21.10]
Epoch 9: 81%|████████▏ | 26/32 [00:00<00:00, 325.84it/s, v_num=2, train_loss=3.820, RMSE=21.10]
Epoch 9: 84%|████████▍ | 27/32 [00:00<00:00, 326.85it/s, v_num=2, train_loss=3.820, RMSE=21.10]
Epoch 9: 84%|████████▍ | 27/32 [00:00<00:00, 326.03it/s, v_num=2, train_loss=3.630, RMSE=21.10]
Epoch 9: 88%|████████▊ | 28/32 [00:00<00:00, 326.99it/s, v_num=2, train_loss=3.630, RMSE=21.10]
Epoch 9: 88%|████████▊ | 28/32 [00:00<00:00, 326.19it/s, v_num=2, train_loss=3.270, RMSE=21.10]
Epoch 9: 91%|█████████ | 29/32 [00:00<00:00, 327.11it/s, v_num=2, train_loss=3.270, RMSE=21.10]
Epoch 9: 91%|█████████ | 29/32 [00:00<00:00, 326.34it/s, v_num=2, train_loss=3.820, RMSE=21.10]
Epoch 9: 94%|█████████▍| 30/32 [00:00<00:00, 327.18it/s, v_num=2, train_loss=3.820, RMSE=21.10]
Epoch 9: 94%|█████████▍| 30/32 [00:00<00:00, 326.43it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 97%|█████████▋| 31/32 [00:00<00:00, 327.30it/s, v_num=2, train_loss=3.680, RMSE=21.10]
Epoch 9: 97%|█████████▋| 31/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.500, RMSE=21.10]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 327.42it/s, v_num=2, train_loss=3.500, RMSE=21.10]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 326.73it/s, v_num=2, train_loss=3.470, RMSE=21.10]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 640.55it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 630.00it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 627.96it/s]
Validation DataLoader 0: 50%|█████ | 5/10 [00:00<00:00, 618.65it/s]
Validation DataLoader 0: 60%|██████ | 6/10 [00:00<00:00, 618.96it/s]
Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 619.57it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 618.48it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 618.54it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 621.31it/s]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 267.54it/s, v_num=2, train_loss=3.470, RMSE=20.30]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 266.44it/s, v_num=2, train_loss=3.470, RMSE=20.30]
Epoch 9: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.470, RMSE=20.30]
Epoch 10: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.470, RMSE=20.30]
Epoch 10: 3%|▎ | 1/32 [00:00<00:00, 313.71it/s, v_num=2, train_loss=3.470, RMSE=20.30]
Epoch 10: 3%|▎ | 1/32 [00:00<00:00, 294.01it/s, v_num=2, train_loss=3.740, RMSE=20.30]
Epoch 10: 6%|▋ | 2/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.740, RMSE=20.30]
Epoch 10: 6%|▋ | 2/32 [00:00<00:00, 307.97it/s, v_num=2, train_loss=3.700, RMSE=20.30]
Epoch 10: 9%|▉ | 3/32 [00:00<00:00, 321.50it/s, v_num=2, train_loss=3.700, RMSE=20.30]
Epoch 10: 9%|▉ | 3/32 [00:00<00:00, 314.46it/s, v_num=2, train_loss=4.050, RMSE=20.30]
Epoch 10: 12%|█▎ | 4/32 [00:00<00:00, 323.34it/s, v_num=2, train_loss=4.050, RMSE=20.30]
Epoch 10: 12%|█▎ | 4/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=3.770, RMSE=20.30]
Epoch 10: 16%|█▌ | 5/32 [00:00<00:00, 325.36it/s, v_num=2, train_loss=3.770, RMSE=20.30]
Epoch 10: 16%|█▌ | 5/32 [00:00<00:00, 321.02it/s, v_num=2, train_loss=3.820, RMSE=20.30]
Epoch 10: 19%|█▉ | 6/32 [00:00<00:00, 325.38it/s, v_num=2, train_loss=3.820, RMSE=20.30]
Epoch 10: 19%|█▉ | 6/32 [00:00<00:00, 321.57it/s, v_num=2, train_loss=3.970, RMSE=20.30]
Epoch 10: 22%|██▏ | 7/32 [00:00<00:00, 324.00it/s, v_num=2, train_loss=3.970, RMSE=20.30]
Epoch 10: 22%|██▏ | 7/32 [00:00<00:00, 320.76it/s, v_num=2, train_loss=3.940, RMSE=20.30]
Epoch 10: 25%|██▌ | 8/32 [00:00<00:00, 324.51it/s, v_num=2, train_loss=3.940, RMSE=20.30]
Epoch 10: 25%|██▌ | 8/32 [00:00<00:00, 321.79it/s, v_num=2, train_loss=4.050, RMSE=20.30]
Epoch 10: 28%|██▊ | 9/32 [00:00<00:00, 325.38it/s, v_num=2, train_loss=4.050, RMSE=20.30]
Epoch 10: 28%|██▊ | 9/32 [00:00<00:00, 322.98it/s, v_num=2, train_loss=3.840, RMSE=20.30]
Epoch 10: 31%|███▏ | 10/32 [00:00<00:00, 325.86it/s, v_num=2, train_loss=3.840, RMSE=20.30]
Epoch 10: 31%|███▏ | 10/32 [00:00<00:00, 323.68it/s, v_num=2, train_loss=3.390, RMSE=20.30]
Epoch 10: 34%|███▍ | 11/32 [00:00<00:00, 326.28it/s, v_num=2, train_loss=3.390, RMSE=20.30]
Epoch 10: 34%|███▍ | 11/32 [00:00<00:00, 324.28it/s, v_num=2, train_loss=3.750, RMSE=20.30]
Epoch 10: 38%|███▊ | 12/32 [00:00<00:00, 326.61it/s, v_num=2, train_loss=3.750, RMSE=20.30]
Epoch 10: 38%|███▊ | 12/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=3.950, RMSE=20.30]
Epoch 10: 41%|████ | 13/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=3.950, RMSE=20.30]
Epoch 10: 41%|████ | 13/32 [00:00<00:00, 325.26it/s, v_num=2, train_loss=3.700, RMSE=20.30]
Epoch 10: 44%|████▍ | 14/32 [00:00<00:00, 327.20it/s, v_num=2, train_loss=3.700, RMSE=20.30]
Epoch 10: 44%|████▍ | 14/32 [00:00<00:00, 325.62it/s, v_num=2, train_loss=3.630, RMSE=20.30]
Epoch 10: 47%|████▋ | 15/32 [00:00<00:00, 327.34it/s, v_num=2, train_loss=3.630, RMSE=20.30]
Epoch 10: 47%|████▋ | 15/32 [00:00<00:00, 325.86it/s, v_num=2, train_loss=3.720, RMSE=20.30]
Epoch 10: 50%|█████ | 16/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=3.720, RMSE=20.30]
Epoch 10: 50%|█████ | 16/32 [00:00<00:00, 326.10it/s, v_num=2, train_loss=3.940, RMSE=20.30]
Epoch 10: 53%|█████▎ | 17/32 [00:00<00:00, 327.87it/s, v_num=2, train_loss=3.940, RMSE=20.30]
Epoch 10: 53%|█████▎ | 17/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=3.440, RMSE=20.30]
Epoch 10: 56%|█████▋ | 18/32 [00:00<00:00, 327.92it/s, v_num=2, train_loss=3.440, RMSE=20.30]
Epoch 10: 56%|█████▋ | 18/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=4.000, RMSE=20.30]
Epoch 10: 59%|█████▉ | 19/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=4.000, RMSE=20.30]
Epoch 10: 59%|█████▉ | 19/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=3.500, RMSE=20.30]
Epoch 10: 62%|██████▎ | 20/32 [00:00<00:00, 328.06it/s, v_num=2, train_loss=3.500, RMSE=20.30]
Epoch 10: 62%|██████▎ | 20/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=3.810, RMSE=20.30]
Epoch 10: 66%|██████▌ | 21/32 [00:00<00:00, 328.23it/s, v_num=2, train_loss=3.810, RMSE=20.30]
Epoch 10: 66%|██████▌ | 21/32 [00:00<00:00, 327.17it/s, v_num=2, train_loss=3.810, RMSE=20.30]
Epoch 10: 69%|██████▉ | 22/32 [00:00<00:00, 328.17it/s, v_num=2, train_loss=3.810, RMSE=20.30]
Epoch 10: 69%|██████▉ | 22/32 [00:00<00:00, 327.16it/s, v_num=2, train_loss=3.570, RMSE=20.30]
Epoch 10: 72%|███████▏ | 23/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.570, RMSE=20.30]
Epoch 10: 72%|███████▏ | 23/32 [00:00<00:00, 327.18it/s, v_num=2, train_loss=3.360, RMSE=20.30]
Epoch 10: 75%|███████▌ | 24/32 [00:00<00:00, 328.22it/s, v_num=2, train_loss=3.360, RMSE=20.30]
Epoch 10: 75%|███████▌ | 24/32 [00:00<00:00, 327.29it/s, v_num=2, train_loss=4.020, RMSE=20.30]
Epoch 10: 78%|███████▊ | 25/32 [00:00<00:00, 328.42it/s, v_num=2, train_loss=4.020, RMSE=20.30]
Epoch 10: 78%|███████▊ | 25/32 [00:00<00:00, 327.39it/s, v_num=2, train_loss=3.790, RMSE=20.30]
Epoch 10: 81%|████████▏ | 26/32 [00:00<00:00, 327.86it/s, v_num=2, train_loss=3.790, RMSE=20.30]
Epoch 10: 81%|████████▏ | 26/32 [00:00<00:00, 327.00it/s, v_num=2, train_loss=4.140, RMSE=20.30]
Epoch 10: 84%|████████▍ | 27/32 [00:00<00:00, 327.91it/s, v_num=2, train_loss=4.140, RMSE=20.30]
Epoch 10: 84%|████████▍ | 27/32 [00:00<00:00, 327.09it/s, v_num=2, train_loss=3.840, RMSE=20.30]
Epoch 10: 88%|████████▊ | 28/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=3.840, RMSE=20.30]
Epoch 10: 88%|████████▊ | 28/32 [00:00<00:00, 327.15it/s, v_num=2, train_loss=3.830, RMSE=20.30]
Epoch 10: 91%|█████████ | 29/32 [00:00<00:00, 327.97it/s, v_num=2, train_loss=3.830, RMSE=20.30]
Epoch 10: 91%|█████████ | 29/32 [00:00<00:00, 327.20it/s, v_num=2, train_loss=3.820, RMSE=20.30]
Epoch 10: 94%|█████████▍| 30/32 [00:00<00:00, 328.16it/s, v_num=2, train_loss=3.820, RMSE=20.30]
Epoch 10: 94%|█████████▍| 30/32 [00:00<00:00, 327.42it/s, v_num=2, train_loss=3.520, RMSE=20.30]
Epoch 10: 97%|█████████▋| 31/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=3.520, RMSE=20.30]
Epoch 10: 97%|█████████▋| 31/32 [00:00<00:00, 325.96it/s, v_num=2, train_loss=3.670, RMSE=20.30]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 326.80it/s, v_num=2, train_loss=3.670, RMSE=20.30]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=3.560, RMSE=20.30]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 638.11it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 629.37it/s]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 622.39it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 626.33it/s]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 267.41it/s, v_num=2, train_loss=3.560, RMSE=19.40]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 266.29it/s, v_num=2, train_loss=3.560, RMSE=19.40]
Epoch 10: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.560, RMSE=19.40]
Epoch 11: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.560, RMSE=19.40]
Epoch 11: 3%|▎ | 1/32 [00:00<00:00, 292.65it/s, v_num=2, train_loss=3.560, RMSE=19.40]
Epoch 11: 3%|▎ | 1/32 [00:00<00:00, 273.80it/s, v_num=2, train_loss=4.260, RMSE=19.40]
Epoch 11: 6%|▋ | 2/32 [00:00<00:00, 299.96it/s, v_num=2, train_loss=4.260, RMSE=19.40]
Epoch 11: 6%|▋ | 2/32 [00:00<00:00, 290.36it/s, v_num=2, train_loss=4.010, RMSE=19.40]
Epoch 11: 9%|▉ | 3/32 [00:00<00:00, 305.03it/s, v_num=2, train_loss=4.010, RMSE=19.40]
Epoch 11: 9%|▉ | 3/32 [00:00<00:00, 298.55it/s, v_num=2, train_loss=3.320, RMSE=19.40]
Epoch 11: 12%|█▎ | 4/32 [00:00<00:00, 307.83it/s, v_num=2, train_loss=3.320, RMSE=19.40]
Epoch 11: 12%|█▎ | 4/32 [00:00<00:00, 302.20it/s, v_num=2, train_loss=3.240, RMSE=19.40]
Epoch 11: 16%|█▌ | 5/32 [00:00<00:00, 311.15it/s, v_num=2, train_loss=3.240, RMSE=19.40]
Epoch 11: 16%|█▌ | 5/32 [00:00<00:00, 307.19it/s, v_num=2, train_loss=3.580, RMSE=19.40]
Epoch 11: 19%|█▉ | 6/32 [00:00<00:00, 313.00it/s, v_num=2, train_loss=3.580, RMSE=19.40]
Epoch 11: 19%|█▉ | 6/32 [00:00<00:00, 309.65it/s, v_num=2, train_loss=3.970, RMSE=19.40]
Epoch 11: 22%|██▏ | 7/32 [00:00<00:00, 315.20it/s, v_num=2, train_loss=3.970, RMSE=19.40]
Epoch 11: 22%|██▏ | 7/32 [00:00<00:00, 312.28it/s, v_num=2, train_loss=3.830, RMSE=19.40]
Epoch 11: 25%|██▌ | 8/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=3.830, RMSE=19.40]
Epoch 11: 25%|██▌ | 8/32 [00:00<00:00, 313.94it/s, v_num=2, train_loss=4.170, RMSE=19.40]
Epoch 11: 28%|██▊ | 9/32 [00:00<00:00, 317.76it/s, v_num=2, train_loss=4.170, RMSE=19.40]
Epoch 11: 28%|██▊ | 9/32 [00:00<00:00, 315.47it/s, v_num=2, train_loss=3.650, RMSE=19.40]
Epoch 11: 31%|███▏ | 10/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=3.650, RMSE=19.40]
Epoch 11: 31%|███▏ | 10/32 [00:00<00:00, 316.90it/s, v_num=2, train_loss=3.740, RMSE=19.40]
Epoch 11: 34%|███▍ | 11/32 [00:00<00:00, 319.81it/s, v_num=2, train_loss=3.740, RMSE=19.40]
Epoch 11: 34%|███▍ | 11/32 [00:00<00:00, 317.75it/s, v_num=2, train_loss=3.990, RMSE=19.40]
Epoch 11: 38%|███▊ | 12/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=3.990, RMSE=19.40]
Epoch 11: 38%|███▊ | 12/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=3.500, RMSE=19.40]
Epoch 11: 41%|████ | 13/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=3.500, RMSE=19.40]
Epoch 11: 41%|████ | 13/32 [00:00<00:00, 319.41it/s, v_num=2, train_loss=4.000, RMSE=19.40]
Epoch 11: 44%|████▍ | 14/32 [00:00<00:00, 321.62it/s, v_num=2, train_loss=4.000, RMSE=19.40]
Epoch 11: 44%|████▍ | 14/32 [00:00<00:00, 320.11it/s, v_num=2, train_loss=3.530, RMSE=19.40]
Epoch 11: 47%|████▋ | 15/32 [00:00<00:00, 322.14it/s, v_num=2, train_loss=3.530, RMSE=19.40]
Epoch 11: 47%|████▋ | 15/32 [00:00<00:00, 320.70it/s, v_num=2, train_loss=3.440, RMSE=19.40]
Epoch 11: 50%|█████ | 16/32 [00:00<00:00, 322.55it/s, v_num=2, train_loss=3.440, RMSE=19.40]
Epoch 11: 50%|█████ | 16/32 [00:00<00:00, 321.18it/s, v_num=2, train_loss=3.650, RMSE=19.40]
Epoch 11: 53%|█████▎ | 17/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=3.650, RMSE=19.40]
Epoch 11: 53%|█████▎ | 17/32 [00:00<00:00, 321.76it/s, v_num=2, train_loss=3.600, RMSE=19.40]
Epoch 11: 56%|█████▋ | 18/32 [00:00<00:00, 323.23it/s, v_num=2, train_loss=3.600, RMSE=19.40]
Epoch 11: 56%|█████▋ | 18/32 [00:00<00:00, 321.95it/s, v_num=2, train_loss=3.380, RMSE=19.40]
Epoch 11: 59%|█████▉ | 19/32 [00:00<00:00, 323.26it/s, v_num=2, train_loss=3.380, RMSE=19.40]
Epoch 11: 59%|█████▉ | 19/32 [00:00<00:00, 322.13it/s, v_num=2, train_loss=4.180, RMSE=19.40]
Epoch 11: 62%|██████▎ | 20/32 [00:00<00:00, 322.97it/s, v_num=2, train_loss=4.180, RMSE=19.40]
Epoch 11: 62%|██████▎ | 20/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=3.990, RMSE=19.40]
Epoch 11: 66%|██████▌ | 21/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=3.990, RMSE=19.40]
Epoch 11: 66%|██████▌ | 21/32 [00:00<00:00, 322.14it/s, v_num=2, train_loss=3.940, RMSE=19.40]
Epoch 11: 69%|██████▉ | 22/32 [00:00<00:00, 323.31it/s, v_num=2, train_loss=3.940, RMSE=19.40]
Epoch 11: 69%|██████▉ | 22/32 [00:00<00:00, 322.32it/s, v_num=2, train_loss=4.020, RMSE=19.40]
Epoch 11: 72%|███████▏ | 23/32 [00:00<00:00, 323.53it/s, v_num=2, train_loss=4.020, RMSE=19.40]
Epoch 11: 72%|███████▏ | 23/32 [00:00<00:00, 322.60it/s, v_num=2, train_loss=3.610, RMSE=19.40]
Epoch 11: 75%|███████▌ | 24/32 [00:00<00:00, 323.74it/s, v_num=2, train_loss=3.610, RMSE=19.40]
Epoch 11: 75%|███████▌ | 24/32 [00:00<00:00, 322.84it/s, v_num=2, train_loss=3.730, RMSE=19.40]
Epoch 11: 78%|███████▊ | 25/32 [00:00<00:00, 323.86it/s, v_num=2, train_loss=3.730, RMSE=19.40]
Epoch 11: 78%|███████▊ | 25/32 [00:00<00:00, 322.93it/s, v_num=2, train_loss=3.770, RMSE=19.40]
Epoch 11: 81%|████████▏ | 26/32 [00:00<00:00, 323.86it/s, v_num=2, train_loss=3.770, RMSE=19.40]
Epoch 11: 81%|████████▏ | 26/32 [00:00<00:00, 323.02it/s, v_num=2, train_loss=3.540, RMSE=19.40]
Epoch 11: 84%|████████▍ | 27/32 [00:00<00:00, 323.82it/s, v_num=2, train_loss=3.540, RMSE=19.40]
Epoch 11: 84%|████████▍ | 27/32 [00:00<00:00, 323.00it/s, v_num=2, train_loss=3.940, RMSE=19.40]
Epoch 11: 88%|████████▊ | 28/32 [00:00<00:00, 323.43it/s, v_num=2, train_loss=3.940, RMSE=19.40]
Epoch 11: 88%|████████▊ | 28/32 [00:00<00:00, 322.64it/s, v_num=2, train_loss=3.620, RMSE=19.40]
Epoch 11: 91%|█████████ | 29/32 [00:00<00:00, 323.51it/s, v_num=2, train_loss=3.620, RMSE=19.40]
Epoch 11: 91%|█████████ | 29/32 [00:00<00:00, 322.76it/s, v_num=2, train_loss=3.610, RMSE=19.40]
Epoch 11: 94%|█████████▍| 30/32 [00:00<00:00, 323.79it/s, v_num=2, train_loss=3.610, RMSE=19.40]
Epoch 11: 94%|█████████▍| 30/32 [00:00<00:00, 323.06it/s, v_num=2, train_loss=3.400, RMSE=19.40]
Epoch 11: 97%|█████████▋| 31/32 [00:00<00:00, 323.92it/s, v_num=2, train_loss=3.400, RMSE=19.40]
Epoch 11: 97%|█████████▋| 31/32 [00:00<00:00, 323.22it/s, v_num=2, train_loss=3.640, RMSE=19.40]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 324.19it/s, v_num=2, train_loss=3.640, RMSE=19.40]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 323.52it/s, v_num=2, train_loss=2.820, RMSE=19.40]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 621.21it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 625.74it/s]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 265.30it/s, v_num=2, train_loss=2.820, RMSE=18.50]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 264.12it/s, v_num=2, train_loss=2.820, RMSE=18.50]
Epoch 11: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.820, RMSE=18.50]
Epoch 12: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.820, RMSE=18.50]
Epoch 12: 3%|▎ | 1/32 [00:00<00:00, 292.29it/s, v_num=2, train_loss=2.820, RMSE=18.50]
Epoch 12: 3%|▎ | 1/32 [00:00<00:00, 270.41it/s, v_num=2, train_loss=4.040, RMSE=18.50]
Epoch 12: 6%|▋ | 2/32 [00:00<00:00, 284.55it/s, v_num=2, train_loss=4.040, RMSE=18.50]
Epoch 12: 6%|▋ | 2/32 [00:00<00:00, 276.23it/s, v_num=2, train_loss=3.200, RMSE=18.50]
Epoch 12: 9%|▉ | 3/32 [00:00<00:00, 295.43it/s, v_num=2, train_loss=3.200, RMSE=18.50]
Epoch 12: 9%|▉ | 3/32 [00:00<00:00, 289.54it/s, v_num=2, train_loss=3.400, RMSE=18.50]
Epoch 12: 12%|█▎ | 4/32 [00:00<00:00, 301.99it/s, v_num=2, train_loss=3.400, RMSE=18.50]
Epoch 12: 12%|█▎ | 4/32 [00:00<00:00, 297.34it/s, v_num=2, train_loss=3.890, RMSE=18.50]
Epoch 12: 16%|█▌ | 5/32 [00:00<00:00, 306.73it/s, v_num=2, train_loss=3.890, RMSE=18.50]
Epoch 12: 16%|█▌ | 5/32 [00:00<00:00, 302.86it/s, v_num=2, train_loss=3.300, RMSE=18.50]
Epoch 12: 19%|█▉ | 6/32 [00:00<00:00, 309.76it/s, v_num=2, train_loss=3.300, RMSE=18.50]
Epoch 12: 19%|█▉ | 6/32 [00:00<00:00, 306.49it/s, v_num=2, train_loss=3.620, RMSE=18.50]
Epoch 12: 22%|██▏ | 7/32 [00:00<00:00, 309.81it/s, v_num=2, train_loss=3.620, RMSE=18.50]
Epoch 12: 22%|██▏ | 7/32 [00:00<00:00, 306.63it/s, v_num=2, train_loss=4.160, RMSE=18.50]
Epoch 12: 25%|██▌ | 8/32 [00:00<00:00, 307.72it/s, v_num=2, train_loss=4.160, RMSE=18.50]
Epoch 12: 25%|██▌ | 8/32 [00:00<00:00, 305.30it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 12: 28%|██▊ | 9/32 [00:00<00:00, 310.05it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 12: 28%|██▊ | 9/32 [00:00<00:00, 307.81it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 12: 31%|███▏ | 10/32 [00:00<00:00, 312.07it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 12: 31%|███▏ | 10/32 [00:00<00:00, 310.08it/s, v_num=2, train_loss=3.510, RMSE=18.50]
Epoch 12: 34%|███▍ | 11/32 [00:00<00:00, 313.62it/s, v_num=2, train_loss=3.510, RMSE=18.50]
Epoch 12: 34%|███▍ | 11/32 [00:00<00:00, 311.80it/s, v_num=2, train_loss=3.640, RMSE=18.50]
Epoch 12: 38%|███▊ | 12/32 [00:00<00:00, 314.78it/s, v_num=2, train_loss=3.640, RMSE=18.50]
Epoch 12: 38%|███▊ | 12/32 [00:00<00:00, 313.07it/s, v_num=2, train_loss=3.290, RMSE=18.50]
Epoch 12: 41%|████ | 13/32 [00:00<00:00, 315.79it/s, v_num=2, train_loss=3.290, RMSE=18.50]
Epoch 12: 41%|████ | 13/32 [00:00<00:00, 314.22it/s, v_num=2, train_loss=3.880, RMSE=18.50]
Epoch 12: 44%|████▍ | 14/32 [00:00<00:00, 316.98it/s, v_num=2, train_loss=3.880, RMSE=18.50]
Epoch 12: 44%|████▍ | 14/32 [00:00<00:00, 315.51it/s, v_num=2, train_loss=3.670, RMSE=18.50]
Epoch 12: 47%|████▋ | 15/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.670, RMSE=18.50]
Epoch 12: 47%|████▋ | 15/32 [00:00<00:00, 316.51it/s, v_num=2, train_loss=4.110, RMSE=18.50]
Epoch 12: 50%|█████ | 16/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=4.110, RMSE=18.50]
Epoch 12: 50%|█████ | 16/32 [00:00<00:00, 317.39it/s, v_num=2, train_loss=3.950, RMSE=18.50]
Epoch 12: 53%|█████▎ | 17/32 [00:00<00:00, 316.44it/s, v_num=2, train_loss=3.950, RMSE=18.50]
Epoch 12: 53%|█████▎ | 17/32 [00:00<00:00, 315.22it/s, v_num=2, train_loss=3.440, RMSE=18.50]
Epoch 12: 56%|█████▋ | 18/32 [00:00<00:00, 317.32it/s, v_num=2, train_loss=3.440, RMSE=18.50]
Epoch 12: 56%|█████▋ | 18/32 [00:00<00:00, 316.03it/s, v_num=2, train_loss=3.550, RMSE=18.50]
Epoch 12: 59%|█████▉ | 19/32 [00:00<00:00, 318.01it/s, v_num=2, train_loss=3.550, RMSE=18.50]
Epoch 12: 59%|█████▉ | 19/32 [00:00<00:00, 316.92it/s, v_num=2, train_loss=4.080, RMSE=18.50]
Epoch 12: 62%|██████▎ | 20/32 [00:00<00:00, 318.56it/s, v_num=2, train_loss=4.080, RMSE=18.50]
Epoch 12: 62%|██████▎ | 20/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=3.920, RMSE=18.50]
Epoch 12: 66%|██████▌ | 21/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.920, RMSE=18.50]
Epoch 12: 66%|██████▌ | 21/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.760, RMSE=18.50]
Epoch 12: 69%|██████▉ | 22/32 [00:00<00:00, 319.68it/s, v_num=2, train_loss=3.760, RMSE=18.50]
Epoch 12: 69%|██████▉ | 22/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=3.580, RMSE=18.50]
Epoch 12: 72%|███████▏ | 23/32 [00:00<00:00, 320.05it/s, v_num=2, train_loss=3.580, RMSE=18.50]
Epoch 12: 72%|███████▏ | 23/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=3.560, RMSE=18.50]
Epoch 12: 75%|███████▌ | 24/32 [00:00<00:00, 320.50it/s, v_num=2, train_loss=3.560, RMSE=18.50]
Epoch 12: 75%|███████▌ | 24/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.630, RMSE=18.50]
Epoch 12: 78%|███████▊ | 25/32 [00:00<00:00, 320.90it/s, v_num=2, train_loss=3.630, RMSE=18.50]
Epoch 12: 78%|███████▊ | 25/32 [00:00<00:00, 320.06it/s, v_num=2, train_loss=4.020, RMSE=18.50]
Epoch 12: 81%|████████▏ | 26/32 [00:00<00:00, 321.39it/s, v_num=2, train_loss=4.020, RMSE=18.50]
Epoch 12: 81%|████████▏ | 26/32 [00:00<00:00, 320.47it/s, v_num=2, train_loss=4.160, RMSE=18.50]
Epoch 12: 84%|████████▍ | 27/32 [00:00<00:00, 321.68it/s, v_num=2, train_loss=4.160, RMSE=18.50]
Epoch 12: 84%|████████▍ | 27/32 [00:00<00:00, 320.89it/s, v_num=2, train_loss=3.470, RMSE=18.50]
Epoch 12: 88%|████████▊ | 28/32 [00:00<00:00, 321.91it/s, v_num=2, train_loss=3.470, RMSE=18.50]
Epoch 12: 88%|████████▊ | 28/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.500, RMSE=18.50]
Epoch 12: 91%|█████████ | 29/32 [00:00<00:00, 322.21it/s, v_num=2, train_loss=3.500, RMSE=18.50]
Epoch 12: 91%|█████████ | 29/32 [00:00<00:00, 321.48it/s, v_num=2, train_loss=3.330, RMSE=18.50]
Epoch 12: 94%|█████████▍| 30/32 [00:00<00:00, 322.13it/s, v_num=2, train_loss=3.330, RMSE=18.50]
Epoch 12: 94%|█████████▍| 30/32 [00:00<00:00, 321.43it/s, v_num=2, train_loss=3.220, RMSE=18.50]
Epoch 12: 97%|█████████▋| 31/32 [00:00<00:00, 322.50it/s, v_num=2, train_loss=3.220, RMSE=18.50]
Epoch 12: 97%|█████████▋| 31/32 [00:00<00:00, 321.81it/s, v_num=2, train_loss=3.760, RMSE=18.50]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 322.82it/s, v_num=2, train_loss=3.760, RMSE=18.50]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 322.15it/s, v_num=2, train_loss=3.410, RMSE=18.50]
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Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 264.18it/s, v_num=2, train_loss=3.410, RMSE=17.60]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 263.05it/s, v_num=2, train_loss=3.410, RMSE=17.60]
Epoch 12: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.410, RMSE=17.60]
Epoch 13: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.410, RMSE=17.60]
Epoch 13: 3%|▎ | 1/32 [00:00<00:00, 294.38it/s, v_num=2, train_loss=3.410, RMSE=17.60]
Epoch 13: 3%|▎ | 1/32 [00:00<00:00, 277.40it/s, v_num=2, train_loss=3.420, RMSE=17.60]
Epoch 13: 6%|▋ | 2/32 [00:00<00:00, 304.82it/s, v_num=2, train_loss=3.420, RMSE=17.60]
Epoch 13: 6%|▋ | 2/32 [00:00<00:00, 295.43it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 9%|▉ | 3/32 [00:00<00:00, 310.65it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 9%|▉ | 3/32 [00:00<00:00, 304.17it/s, v_num=2, train_loss=3.520, RMSE=17.60]
Epoch 13: 12%|█▎ | 4/32 [00:00<00:00, 310.93it/s, v_num=2, train_loss=3.520, RMSE=17.60]
Epoch 13: 12%|█▎ | 4/32 [00:00<00:00, 306.00it/s, v_num=2, train_loss=3.710, RMSE=17.60]
Epoch 13: 16%|█▌ | 5/32 [00:00<00:00, 313.32it/s, v_num=2, train_loss=3.710, RMSE=17.60]
Epoch 13: 16%|█▌ | 5/32 [00:00<00:00, 309.32it/s, v_num=2, train_loss=3.500, RMSE=17.60]
Epoch 13: 19%|█▉ | 6/32 [00:00<00:00, 315.57it/s, v_num=2, train_loss=3.500, RMSE=17.60]
Epoch 13: 19%|█▉ | 6/32 [00:00<00:00, 312.13it/s, v_num=2, train_loss=3.870, RMSE=17.60]
Epoch 13: 22%|██▏ | 7/32 [00:00<00:00, 315.78it/s, v_num=2, train_loss=3.870, RMSE=17.60]
Epoch 13: 22%|██▏ | 7/32 [00:00<00:00, 312.87it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 25%|██▌ | 8/32 [00:00<00:00, 316.62it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 25%|██▌ | 8/32 [00:00<00:00, 314.03it/s, v_num=2, train_loss=3.600, RMSE=17.60]
Epoch 13: 28%|██▊ | 9/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.600, RMSE=17.60]
Epoch 13: 28%|██▊ | 9/32 [00:00<00:00, 315.52it/s, v_num=2, train_loss=3.360, RMSE=17.60]
Epoch 13: 31%|███▏ | 10/32 [00:00<00:00, 319.24it/s, v_num=2, train_loss=3.360, RMSE=17.60]
Epoch 13: 31%|███▏ | 10/32 [00:00<00:00, 317.17it/s, v_num=2, train_loss=3.500, RMSE=17.60]
Epoch 13: 34%|███▍ | 11/32 [00:00<00:00, 320.13it/s, v_num=2, train_loss=3.500, RMSE=17.60]
Epoch 13: 34%|███▍ | 11/32 [00:00<00:00, 318.23it/s, v_num=2, train_loss=3.640, RMSE=17.60]
Epoch 13: 38%|███▊ | 12/32 [00:00<00:00, 320.66it/s, v_num=2, train_loss=3.640, RMSE=17.60]
Epoch 13: 38%|███▊ | 12/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.240, RMSE=17.60]
Epoch 13: 41%|████ | 13/32 [00:00<00:00, 321.19it/s, v_num=2, train_loss=3.240, RMSE=17.60]
Epoch 13: 41%|████ | 13/32 [00:00<00:00, 319.56it/s, v_num=2, train_loss=3.480, RMSE=17.60]
Epoch 13: 44%|████▍ | 14/32 [00:00<00:00, 321.96it/s, v_num=2, train_loss=3.480, RMSE=17.60]
Epoch 13: 44%|████▍ | 14/32 [00:00<00:00, 320.44it/s, v_num=2, train_loss=3.720, RMSE=17.60]
Epoch 13: 47%|████▋ | 15/32 [00:00<00:00, 322.35it/s, v_num=2, train_loss=3.720, RMSE=17.60]
Epoch 13: 47%|████▋ | 15/32 [00:00<00:00, 320.88it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 50%|█████ | 16/32 [00:00<00:00, 322.73it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 50%|█████ | 16/32 [00:00<00:00, 321.39it/s, v_num=2, train_loss=3.470, RMSE=17.60]
Epoch 13: 53%|█████▎ | 17/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=3.470, RMSE=17.60]
Epoch 13: 53%|█████▎ | 17/32 [00:00<00:00, 321.77it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 56%|█████▋ | 18/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 56%|█████▋ | 18/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=3.820, RMSE=17.60]
Epoch 13: 59%|█████▉ | 19/32 [00:00<00:00, 323.67it/s, v_num=2, train_loss=3.820, RMSE=17.60]
Epoch 13: 59%|█████▉ | 19/32 [00:00<00:00, 322.53it/s, v_num=2, train_loss=3.800, RMSE=17.60]
Epoch 13: 62%|██████▎ | 20/32 [00:00<00:00, 323.91it/s, v_num=2, train_loss=3.800, RMSE=17.60]
Epoch 13: 62%|██████▎ | 20/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.590, RMSE=17.60]
Epoch 13: 66%|██████▌ | 21/32 [00:00<00:00, 324.13it/s, v_num=2, train_loss=3.590, RMSE=17.60]
Epoch 13: 66%|██████▌ | 21/32 [00:00<00:00, 323.11it/s, v_num=2, train_loss=3.720, RMSE=17.60]
Epoch 13: 69%|██████▉ | 22/32 [00:00<00:00, 324.37it/s, v_num=2, train_loss=3.720, RMSE=17.60]
Epoch 13: 69%|██████▉ | 22/32 [00:00<00:00, 323.37it/s, v_num=2, train_loss=3.700, RMSE=17.60]
Epoch 13: 72%|███████▏ | 23/32 [00:00<00:00, 324.16it/s, v_num=2, train_loss=3.700, RMSE=17.60]
Epoch 13: 72%|███████▏ | 23/32 [00:00<00:00, 323.19it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 75%|███████▌ | 24/32 [00:00<00:00, 323.63it/s, v_num=2, train_loss=3.740, RMSE=17.60]
Epoch 13: 75%|███████▌ | 24/32 [00:00<00:00, 322.64it/s, v_num=2, train_loss=3.380, RMSE=17.60]
Epoch 13: 78%|███████▊ | 25/32 [00:00<00:00, 322.98it/s, v_num=2, train_loss=3.380, RMSE=17.60]
Epoch 13: 78%|███████▊ | 25/32 [00:00<00:00, 322.10it/s, v_num=2, train_loss=3.870, RMSE=17.60]
Epoch 13: 81%|████████▏ | 26/32 [00:00<00:00, 322.86it/s, v_num=2, train_loss=3.870, RMSE=17.60]
Epoch 13: 81%|████████▏ | 26/32 [00:00<00:00, 322.02it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 84%|████████▍ | 27/32 [00:00<00:00, 322.94it/s, v_num=2, train_loss=3.880, RMSE=17.60]
Epoch 13: 84%|████████▍ | 27/32 [00:00<00:00, 322.13it/s, v_num=2, train_loss=3.690, RMSE=17.60]
Epoch 13: 88%|████████▊ | 28/32 [00:00<00:00, 322.97it/s, v_num=2, train_loss=3.690, RMSE=17.60]
Epoch 13: 88%|████████▊ | 28/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=3.800, RMSE=17.60]
Epoch 13: 91%|█████████ | 29/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.800, RMSE=17.60]
Epoch 13: 91%|█████████ | 29/32 [00:00<00:00, 322.02it/s, v_num=2, train_loss=3.810, RMSE=17.60]
Epoch 13: 94%|█████████▍| 30/32 [00:00<00:00, 322.54it/s, v_num=2, train_loss=3.810, RMSE=17.60]
Epoch 13: 94%|█████████▍| 30/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.770, RMSE=17.60]
Epoch 13: 97%|█████████▋| 31/32 [00:00<00:00, 322.51it/s, v_num=2, train_loss=3.770, RMSE=17.60]
Epoch 13: 97%|█████████▋| 31/32 [00:00<00:00, 321.71it/s, v_num=2, train_loss=3.080, RMSE=17.60]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 322.59it/s, v_num=2, train_loss=3.080, RMSE=17.60]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 321.92it/s, v_num=2, train_loss=3.170, RMSE=17.60]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 619.73it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 622.44it/s]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 263.71it/s, v_num=2, train_loss=3.170, RMSE=16.80]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 262.58it/s, v_num=2, train_loss=3.170, RMSE=16.80]
Epoch 13: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.170, RMSE=16.80]
Epoch 14: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.170, RMSE=16.80]
Epoch 14: 3%|▎ | 1/32 [00:00<00:00, 286.75it/s, v_num=2, train_loss=3.170, RMSE=16.80]
Epoch 14: 3%|▎ | 1/32 [00:00<00:00, 270.32it/s, v_num=2, train_loss=3.710, RMSE=16.80]
Epoch 14: 6%|▋ | 2/32 [00:00<00:00, 300.75it/s, v_num=2, train_loss=3.710, RMSE=16.80]
Epoch 14: 6%|▋ | 2/32 [00:00<00:00, 290.46it/s, v_num=2, train_loss=3.510, RMSE=16.80]
Epoch 14: 9%|▉ | 3/32 [00:00<00:00, 295.46it/s, v_num=2, train_loss=3.510, RMSE=16.80]
Epoch 14: 9%|▉ | 3/32 [00:00<00:00, 289.54it/s, v_num=2, train_loss=3.390, RMSE=16.80]
Epoch 14: 12%|█▎ | 4/32 [00:00<00:00, 301.81it/s, v_num=2, train_loss=3.390, RMSE=16.80]
Epoch 14: 12%|█▎ | 4/32 [00:00<00:00, 297.17it/s, v_num=2, train_loss=3.370, RMSE=16.80]
Epoch 14: 16%|█▌ | 5/32 [00:00<00:00, 306.12it/s, v_num=2, train_loss=3.370, RMSE=16.80]
Epoch 14: 16%|█▌ | 5/32 [00:00<00:00, 302.33it/s, v_num=2, train_loss=3.220, RMSE=16.80]
Epoch 14: 19%|█▉ | 6/32 [00:00<00:00, 309.55it/s, v_num=2, train_loss=3.220, RMSE=16.80]
Epoch 14: 19%|█▉ | 6/32 [00:00<00:00, 306.30it/s, v_num=2, train_loss=3.420, RMSE=16.80]
Epoch 14: 22%|██▏ | 7/32 [00:00<00:00, 312.07it/s, v_num=2, train_loss=3.420, RMSE=16.80]
Epoch 14: 22%|██▏ | 7/32 [00:00<00:00, 309.25it/s, v_num=2, train_loss=3.650, RMSE=16.80]
Epoch 14: 25%|██▌ | 8/32 [00:00<00:00, 313.82it/s, v_num=2, train_loss=3.650, RMSE=16.80]
Epoch 14: 25%|██▌ | 8/32 [00:00<00:00, 311.30it/s, v_num=2, train_loss=3.800, RMSE=16.80]
Epoch 14: 28%|██▊ | 9/32 [00:00<00:00, 315.36it/s, v_num=2, train_loss=3.800, RMSE=16.80]
Epoch 14: 28%|██▊ | 9/32 [00:00<00:00, 313.09it/s, v_num=2, train_loss=3.480, RMSE=16.80]
Epoch 14: 31%|███▏ | 10/32 [00:00<00:00, 316.65it/s, v_num=2, train_loss=3.480, RMSE=16.80]
Epoch 14: 31%|███▏ | 10/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=3.820, RMSE=16.80]
Epoch 14: 34%|███▍ | 11/32 [00:00<00:00, 317.82it/s, v_num=2, train_loss=3.820, RMSE=16.80]
Epoch 14: 34%|███▍ | 11/32 [00:00<00:00, 315.94it/s, v_num=2, train_loss=3.160, RMSE=16.80]
Epoch 14: 38%|███▊ | 12/32 [00:00<00:00, 318.20it/s, v_num=2, train_loss=3.160, RMSE=16.80]
Epoch 14: 38%|███▊ | 12/32 [00:00<00:00, 316.48it/s, v_num=2, train_loss=3.850, RMSE=16.80]
Epoch 14: 41%|████ | 13/32 [00:00<00:00, 318.78it/s, v_num=2, train_loss=3.850, RMSE=16.80]
Epoch 14: 41%|████ | 13/32 [00:00<00:00, 317.17it/s, v_num=2, train_loss=3.750, RMSE=16.80]
Epoch 14: 44%|████▍ | 14/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.750, RMSE=16.80]
Epoch 14: 44%|████▍ | 14/32 [00:00<00:00, 317.85it/s, v_num=2, train_loss=3.670, RMSE=16.80]
Epoch 14: 47%|████▋ | 15/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.670, RMSE=16.80]
Epoch 14: 47%|████▋ | 15/32 [00:00<00:00, 318.51it/s, v_num=2, train_loss=4.020, RMSE=16.80]
Epoch 14: 50%|█████ | 16/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=4.020, RMSE=16.80]
Epoch 14: 50%|█████ | 16/32 [00:00<00:00, 319.26it/s, v_num=2, train_loss=3.350, RMSE=16.80]
Epoch 14: 53%|█████▎ | 17/32 [00:00<00:00, 320.95it/s, v_num=2, train_loss=3.350, RMSE=16.80]
Epoch 14: 53%|█████▎ | 17/32 [00:00<00:00, 319.71it/s, v_num=2, train_loss=3.580, RMSE=16.80]
Epoch 14: 56%|█████▋ | 18/32 [00:00<00:00, 321.28it/s, v_num=2, train_loss=3.580, RMSE=16.80]
Epoch 14: 56%|█████▋ | 18/32 [00:00<00:00, 320.11it/s, v_num=2, train_loss=3.460, RMSE=16.80]
Epoch 14: 59%|█████▉ | 19/32 [00:00<00:00, 321.64it/s, v_num=2, train_loss=3.460, RMSE=16.80]
Epoch 14: 59%|█████▉ | 19/32 [00:00<00:00, 320.54it/s, v_num=2, train_loss=3.990, RMSE=16.80]
Epoch 14: 62%|██████▎ | 20/32 [00:00<00:00, 322.16it/s, v_num=2, train_loss=3.990, RMSE=16.80]
Epoch 14: 62%|██████▎ | 20/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=3.860, RMSE=16.80]
Epoch 14: 66%|██████▌ | 21/32 [00:00<00:00, 322.55it/s, v_num=2, train_loss=3.860, RMSE=16.80]
Epoch 14: 66%|██████▌ | 21/32 [00:00<00:00, 321.54it/s, v_num=2, train_loss=3.970, RMSE=16.80]
Epoch 14: 69%|██████▉ | 22/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.970, RMSE=16.80]
Epoch 14: 69%|██████▉ | 22/32 [00:00<00:00, 321.85it/s, v_num=2, train_loss=3.380, RMSE=16.80]
Epoch 14: 72%|███████▏ | 23/32 [00:00<00:00, 323.13it/s, v_num=2, train_loss=3.380, RMSE=16.80]
Epoch 14: 72%|███████▏ | 23/32 [00:00<00:00, 322.12it/s, v_num=2, train_loss=3.520, RMSE=16.80]
Epoch 14: 75%|███████▌ | 24/32 [00:00<00:00, 323.48it/s, v_num=2, train_loss=3.520, RMSE=16.80]
Epoch 14: 75%|███████▌ | 24/32 [00:00<00:00, 322.59it/s, v_num=2, train_loss=3.570, RMSE=16.80]
Epoch 14: 78%|███████▊ | 25/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=3.570, RMSE=16.80]
Epoch 14: 78%|███████▊ | 25/32 [00:00<00:00, 322.86it/s, v_num=2, train_loss=3.650, RMSE=16.80]
Epoch 14: 81%|████████▏ | 26/32 [00:00<00:00, 323.82it/s, v_num=2, train_loss=3.650, RMSE=16.80]
Epoch 14: 81%|████████▏ | 26/32 [00:00<00:00, 323.00it/s, v_num=2, train_loss=3.400, RMSE=16.80]
Epoch 14: 84%|████████▍ | 27/32 [00:00<00:00, 323.63it/s, v_num=2, train_loss=3.400, RMSE=16.80]
Epoch 14: 84%|████████▍ | 27/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.540, RMSE=16.80]
Epoch 14: 88%|████████▊ | 28/32 [00:00<00:00, 323.93it/s, v_num=2, train_loss=3.540, RMSE=16.80]
Epoch 14: 88%|████████▊ | 28/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=3.770, RMSE=16.80]
Epoch 14: 91%|█████████ | 29/32 [00:00<00:00, 323.66it/s, v_num=2, train_loss=3.770, RMSE=16.80]
Epoch 14: 91%|█████████ | 29/32 [00:00<00:00, 322.92it/s, v_num=2, train_loss=3.350, RMSE=16.80]
Epoch 14: 94%|█████████▍| 30/32 [00:00<00:00, 323.72it/s, v_num=2, train_loss=3.350, RMSE=16.80]
Epoch 14: 94%|█████████▍| 30/32 [00:00<00:00, 323.00it/s, v_num=2, train_loss=3.600, RMSE=16.80]
Epoch 14: 97%|█████████▋| 31/32 [00:00<00:00, 323.86it/s, v_num=2, train_loss=3.600, RMSE=16.80]
Epoch 14: 97%|█████████▋| 31/32 [00:00<00:00, 323.17it/s, v_num=2, train_loss=3.820, RMSE=16.80]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 324.15it/s, v_num=2, train_loss=3.820, RMSE=16.80]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 323.38it/s, v_num=2, train_loss=3.210, RMSE=16.80]
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Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 265.04it/s, v_num=2, train_loss=3.210, RMSE=16.00]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 263.91it/s, v_num=2, train_loss=3.210, RMSE=16.00]
Epoch 14: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.210, RMSE=16.00]
Epoch 15: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.210, RMSE=16.00]
Epoch 15: 3%|▎ | 1/32 [00:00<00:00, 289.04it/s, v_num=2, train_loss=3.210, RMSE=16.00]
Epoch 15: 3%|▎ | 1/32 [00:00<00:00, 272.25it/s, v_num=2, train_loss=3.810, RMSE=16.00]
Epoch 15: 6%|▋ | 2/32 [00:00<00:00, 299.99it/s, v_num=2, train_loss=3.810, RMSE=16.00]
Epoch 15: 6%|▋ | 2/32 [00:00<00:00, 290.92it/s, v_num=2, train_loss=3.360, RMSE=16.00]
Epoch 15: 9%|▉ | 3/32 [00:00<00:00, 307.38it/s, v_num=2, train_loss=3.360, RMSE=16.00]
Epoch 15: 9%|▉ | 3/32 [00:00<00:00, 301.03it/s, v_num=2, train_loss=3.240, RMSE=16.00]
Epoch 15: 12%|█▎ | 4/32 [00:00<00:00, 310.85it/s, v_num=2, train_loss=3.240, RMSE=16.00]
Epoch 15: 12%|█▎ | 4/32 [00:00<00:00, 305.94it/s, v_num=2, train_loss=3.630, RMSE=16.00]
Epoch 15: 16%|█▌ | 5/32 [00:00<00:00, 313.94it/s, v_num=2, train_loss=3.630, RMSE=16.00]
Epoch 15: 16%|█▌ | 5/32 [00:00<00:00, 309.91it/s, v_num=2, train_loss=3.630, RMSE=16.00]
Epoch 15: 19%|█▉ | 6/32 [00:00<00:00, 316.31it/s, v_num=2, train_loss=3.630, RMSE=16.00]
Epoch 15: 19%|█▉ | 6/32 [00:00<00:00, 312.93it/s, v_num=2, train_loss=3.230, RMSE=16.00]
Epoch 15: 22%|██▏ | 7/32 [00:00<00:00, 318.51it/s, v_num=2, train_loss=3.230, RMSE=16.00]
Epoch 15: 22%|██▏ | 7/32 [00:00<00:00, 315.54it/s, v_num=2, train_loss=3.530, RMSE=16.00]
Epoch 15: 25%|██▌ | 8/32 [00:00<00:00, 319.83it/s, v_num=2, train_loss=3.530, RMSE=16.00]
Epoch 15: 25%|██▌ | 8/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=3.900, RMSE=16.00]
Epoch 15: 28%|██▊ | 9/32 [00:00<00:00, 320.91it/s, v_num=2, train_loss=3.900, RMSE=16.00]
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Epoch 15: 31%|███▏ | 10/32 [00:00<00:00, 321.74it/s, v_num=2, train_loss=3.470, RMSE=16.00]
Epoch 15: 31%|███▏ | 10/32 [00:00<00:00, 319.61it/s, v_num=2, train_loss=3.940, RMSE=16.00]
Epoch 15: 34%|███▍ | 11/32 [00:00<00:00, 322.77it/s, v_num=2, train_loss=3.940, RMSE=16.00]
Epoch 15: 34%|███▍ | 11/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.500, RMSE=16.00]
Epoch 15: 38%|███▊ | 12/32 [00:00<00:00, 323.16it/s, v_num=2, train_loss=3.500, RMSE=16.00]
Epoch 15: 38%|███▊ | 12/32 [00:00<00:00, 321.30it/s, v_num=2, train_loss=3.410, RMSE=16.00]
Epoch 15: 41%|████ | 13/32 [00:00<00:00, 323.04it/s, v_num=2, train_loss=3.410, RMSE=16.00]
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Epoch 15: 47%|████▋ | 15/32 [00:00<00:00, 323.95it/s, v_num=2, train_loss=3.570, RMSE=16.00]
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Epoch 15: 50%|█████ | 16/32 [00:00<00:00, 324.15it/s, v_num=2, train_loss=3.490, RMSE=16.00]
Epoch 15: 50%|█████ | 16/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.660, RMSE=16.00]
Epoch 15: 53%|█████▎ | 17/32 [00:00<00:00, 324.41it/s, v_num=2, train_loss=3.660, RMSE=16.00]
Epoch 15: 53%|█████▎ | 17/32 [00:00<00:00, 323.15it/s, v_num=2, train_loss=3.600, RMSE=16.00]
Epoch 15: 56%|█████▋ | 18/32 [00:00<00:00, 324.74it/s, v_num=2, train_loss=3.600, RMSE=16.00]
Epoch 15: 56%|█████▋ | 18/32 [00:00<00:00, 323.54it/s, v_num=2, train_loss=3.680, RMSE=16.00]
Epoch 15: 59%|█████▉ | 19/32 [00:00<00:00, 325.11it/s, v_num=2, train_loss=3.680, RMSE=16.00]
Epoch 15: 59%|█████▉ | 19/32 [00:00<00:00, 323.81it/s, v_num=2, train_loss=4.000, RMSE=16.00]
Epoch 15: 62%|██████▎ | 20/32 [00:00<00:00, 325.37it/s, v_num=2, train_loss=4.000, RMSE=16.00]
Epoch 15: 62%|██████▎ | 20/32 [00:00<00:00, 324.30it/s, v_num=2, train_loss=3.390, RMSE=16.00]
Epoch 15: 66%|██████▌ | 21/32 [00:00<00:00, 323.17it/s, v_num=2, train_loss=3.390, RMSE=16.00]
Epoch 15: 66%|██████▌ | 21/32 [00:00<00:00, 322.14it/s, v_num=2, train_loss=3.710, RMSE=16.00]
Epoch 15: 69%|██████▉ | 22/32 [00:00<00:00, 323.35it/s, v_num=2, train_loss=3.710, RMSE=16.00]
Epoch 15: 69%|██████▉ | 22/32 [00:00<00:00, 322.37it/s, v_num=2, train_loss=3.320, RMSE=16.00]
Epoch 15: 72%|███████▏ | 23/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=3.320, RMSE=16.00]
Epoch 15: 72%|███████▏ | 23/32 [00:00<00:00, 322.56it/s, v_num=2, train_loss=3.400, RMSE=16.00]
Epoch 15: 75%|███████▌ | 24/32 [00:00<00:00, 323.74it/s, v_num=2, train_loss=3.400, RMSE=16.00]
Epoch 15: 75%|███████▌ | 24/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.720, RMSE=16.00]
Epoch 15: 78%|███████▊ | 25/32 [00:00<00:00, 323.92it/s, v_num=2, train_loss=3.720, RMSE=16.00]
Epoch 15: 78%|███████▊ | 25/32 [00:00<00:00, 323.06it/s, v_num=2, train_loss=3.310, RMSE=16.00]
Epoch 15: 81%|████████▏ | 26/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=3.310, RMSE=16.00]
Epoch 15: 81%|████████▏ | 26/32 [00:00<00:00, 323.26it/s, v_num=2, train_loss=3.700, RMSE=16.00]
Epoch 15: 84%|████████▍ | 27/32 [00:00<00:00, 324.24it/s, v_num=2, train_loss=3.700, RMSE=16.00]
Epoch 15: 84%|████████▍ | 27/32 [00:00<00:00, 323.44it/s, v_num=2, train_loss=3.370, RMSE=16.00]
Epoch 15: 88%|████████▊ | 28/32 [00:00<00:00, 324.60it/s, v_num=2, train_loss=3.370, RMSE=16.00]
Epoch 15: 88%|████████▊ | 28/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=3.600, RMSE=16.00]
Epoch 15: 91%|█████████ | 29/32 [00:00<00:00, 324.73it/s, v_num=2, train_loss=3.600, RMSE=16.00]
Epoch 15: 91%|█████████ | 29/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=3.410, RMSE=16.00]
Epoch 15: 94%|█████████▍| 30/32 [00:00<00:00, 324.89it/s, v_num=2, train_loss=3.410, RMSE=16.00]
Epoch 15: 94%|█████████▍| 30/32 [00:00<00:00, 324.16it/s, v_num=2, train_loss=3.900, RMSE=16.00]
Epoch 15: 97%|█████████▋| 31/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=3.900, RMSE=16.00]
Epoch 15: 97%|█████████▋| 31/32 [00:00<00:00, 324.36it/s, v_num=2, train_loss=3.070, RMSE=16.00]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 325.41it/s, v_num=2, train_loss=3.070, RMSE=16.00]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=3.970, RMSE=16.00]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 624.34it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 627.74it/s]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 266.47it/s, v_num=2, train_loss=3.970, RMSE=15.20]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 265.30it/s, v_num=2, train_loss=3.970, RMSE=15.20]
Epoch 15: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.970, RMSE=15.20]
Epoch 16: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.970, RMSE=15.20]
Epoch 16: 3%|▎ | 1/32 [00:00<00:00, 316.46it/s, v_num=2, train_loss=3.970, RMSE=15.20]
Epoch 16: 3%|▎ | 1/32 [00:00<00:00, 296.84it/s, v_num=2, train_loss=3.260, RMSE=15.20]
Epoch 16: 6%|▋ | 2/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=3.260, RMSE=15.20]
Epoch 16: 6%|▋ | 2/32 [00:00<00:00, 312.13it/s, v_num=2, train_loss=3.450, RMSE=15.20]
Epoch 16: 9%|▉ | 3/32 [00:00<00:00, 324.29it/s, v_num=2, train_loss=3.450, RMSE=15.20]
Epoch 16: 9%|▉ | 3/32 [00:00<00:00, 317.27it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 12%|█▎ | 4/32 [00:00<00:00, 325.31it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 12%|█▎ | 4/32 [00:00<00:00, 319.97it/s, v_num=2, train_loss=3.380, RMSE=15.20]
Epoch 16: 16%|█▌ | 5/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=3.380, RMSE=15.20]
Epoch 16: 16%|█▌ | 5/32 [00:00<00:00, 321.84it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 19%|█▉ | 6/32 [00:00<00:00, 327.46it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 19%|█▉ | 6/32 [00:00<00:00, 323.82it/s, v_num=2, train_loss=3.460, RMSE=15.20]
Epoch 16: 22%|██▏ | 7/32 [00:00<00:00, 327.84it/s, v_num=2, train_loss=3.460, RMSE=15.20]
Epoch 16: 22%|██▏ | 7/32 [00:00<00:00, 324.74it/s, v_num=2, train_loss=3.480, RMSE=15.20]
Epoch 16: 25%|██▌ | 8/32 [00:00<00:00, 328.16it/s, v_num=2, train_loss=3.480, RMSE=15.20]
Epoch 16: 25%|██▌ | 8/32 [00:00<00:00, 325.35it/s, v_num=2, train_loss=3.530, RMSE=15.20]
Epoch 16: 28%|██▊ | 9/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=3.530, RMSE=15.20]
Epoch 16: 28%|██▊ | 9/32 [00:00<00:00, 325.68it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 31%|███▏ | 10/32 [00:00<00:00, 328.62it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 31%|███▏ | 10/32 [00:00<00:00, 326.44it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 34%|███▍ | 11/32 [00:00<00:00, 328.84it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 34%|███▍ | 11/32 [00:00<00:00, 326.83it/s, v_num=2, train_loss=3.790, RMSE=15.20]
Epoch 16: 38%|███▊ | 12/32 [00:00<00:00, 328.87it/s, v_num=2, train_loss=3.790, RMSE=15.20]
Epoch 16: 38%|███▊ | 12/32 [00:00<00:00, 327.03it/s, v_num=2, train_loss=3.160, RMSE=15.20]
Epoch 16: 41%|████ | 13/32 [00:00<00:00, 328.13it/s, v_num=2, train_loss=3.160, RMSE=15.20]
Epoch 16: 41%|████ | 13/32 [00:00<00:00, 326.42it/s, v_num=2, train_loss=3.500, RMSE=15.20]
Epoch 16: 44%|████▍ | 14/32 [00:00<00:00, 328.50it/s, v_num=2, train_loss=3.500, RMSE=15.20]
Epoch 16: 44%|████▍ | 14/32 [00:00<00:00, 326.72it/s, v_num=2, train_loss=4.110, RMSE=15.20]
Epoch 16: 47%|████▋ | 15/32 [00:00<00:00, 328.27it/s, v_num=2, train_loss=4.110, RMSE=15.20]
Epoch 16: 47%|████▋ | 15/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=3.230, RMSE=15.20]
Epoch 16: 50%|█████ | 16/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.230, RMSE=15.20]
Epoch 16: 50%|█████ | 16/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=3.770, RMSE=15.20]
Epoch 16: 53%|█████▎ | 17/32 [00:00<00:00, 328.25it/s, v_num=2, train_loss=3.770, RMSE=15.20]
Epoch 16: 53%|█████▎ | 17/32 [00:00<00:00, 326.96it/s, v_num=2, train_loss=3.820, RMSE=15.20]
Epoch 16: 56%|█████▋ | 18/32 [00:00<00:00, 328.24it/s, v_num=2, train_loss=3.820, RMSE=15.20]
Epoch 16: 56%|█████▋ | 18/32 [00:00<00:00, 326.99it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 59%|█████▉ | 19/32 [00:00<00:00, 328.38it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 59%|█████▉ | 19/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=3.340, RMSE=15.20]
Epoch 16: 62%|██████▎ | 20/32 [00:00<00:00, 328.39it/s, v_num=2, train_loss=3.340, RMSE=15.20]
Epoch 16: 62%|██████▎ | 20/32 [00:00<00:00, 327.26it/s, v_num=2, train_loss=3.480, RMSE=15.20]
Epoch 16: 66%|██████▌ | 21/32 [00:00<00:00, 328.36it/s, v_num=2, train_loss=3.480, RMSE=15.20]
Epoch 16: 66%|██████▌ | 21/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=3.630, RMSE=15.20]
Epoch 16: 69%|██████▉ | 22/32 [00:00<00:00, 328.41it/s, v_num=2, train_loss=3.630, RMSE=15.20]
Epoch 16: 69%|██████▉ | 22/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 72%|███████▏ | 23/32 [00:00<00:00, 328.62it/s, v_num=2, train_loss=3.920, RMSE=15.20]
Epoch 16: 72%|███████▏ | 23/32 [00:00<00:00, 327.66it/s, v_num=2, train_loss=3.430, RMSE=15.20]
Epoch 16: 75%|███████▌ | 24/32 [00:00<00:00, 328.67it/s, v_num=2, train_loss=3.430, RMSE=15.20]
Epoch 16: 75%|███████▌ | 24/32 [00:00<00:00, 327.75it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 78%|███████▊ | 25/32 [00:00<00:00, 328.69it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 78%|███████▊ | 25/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 81%|████████▏ | 26/32 [00:00<00:00, 328.73it/s, v_num=2, train_loss=3.570, RMSE=15.20]
Epoch 16: 81%|████████▏ | 26/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=3.720, RMSE=15.20]
Epoch 16: 84%|████████▍ | 27/32 [00:00<00:00, 328.95it/s, v_num=2, train_loss=3.720, RMSE=15.20]
Epoch 16: 84%|████████▍ | 27/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=3.150, RMSE=15.20]
Epoch 16: 88%|████████▊ | 28/32 [00:00<00:00, 328.85it/s, v_num=2, train_loss=3.150, RMSE=15.20]
Epoch 16: 88%|████████▊ | 28/32 [00:00<00:00, 328.06it/s, v_num=2, train_loss=3.340, RMSE=15.20]
Epoch 16: 91%|█████████ | 29/32 [00:00<00:00, 328.71it/s, v_num=2, train_loss=3.340, RMSE=15.20]
Epoch 16: 91%|█████████ | 29/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=3.770, RMSE=15.20]
Epoch 16: 94%|█████████▍| 30/32 [00:00<00:00, 328.76it/s, v_num=2, train_loss=3.770, RMSE=15.20]
Epoch 16: 94%|█████████▍| 30/32 [00:00<00:00, 328.02it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 97%|█████████▋| 31/32 [00:00<00:00, 328.62it/s, v_num=2, train_loss=3.300, RMSE=15.20]
Epoch 16: 97%|█████████▋| 31/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=2.900, RMSE=15.20]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 328.79it/s, v_num=2, train_loss=2.900, RMSE=15.20]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 328.10it/s, v_num=2, train_loss=3.400, RMSE=15.20]
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Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 268.38it/s, v_num=2, train_loss=3.400, RMSE=14.50]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 267.24it/s, v_num=2, train_loss=3.400, RMSE=14.50]
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Epoch 17: 3%|▎ | 1/32 [00:00<00:00, 300.88it/s, v_num=2, train_loss=3.400, RMSE=14.50]
Epoch 17: 3%|▎ | 1/32 [00:00<00:00, 283.04it/s, v_num=2, train_loss=3.670, RMSE=14.50]
Epoch 17: 6%|▋ | 2/32 [00:00<00:00, 310.20it/s, v_num=2, train_loss=3.670, RMSE=14.50]
Epoch 17: 6%|▋ | 2/32 [00:00<00:00, 300.59it/s, v_num=2, train_loss=3.410, RMSE=14.50]
Epoch 17: 9%|▉ | 3/32 [00:00<00:00, 314.90it/s, v_num=2, train_loss=3.410, RMSE=14.50]
Epoch 17: 9%|▉ | 3/32 [00:00<00:00, 308.25it/s, v_num=2, train_loss=3.350, RMSE=14.50]
Epoch 17: 12%|█▎ | 4/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=3.350, RMSE=14.50]
Epoch 17: 12%|█▎ | 4/32 [00:00<00:00, 312.09it/s, v_num=2, train_loss=3.260, RMSE=14.50]
Epoch 17: 16%|█▌ | 5/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=3.260, RMSE=14.50]
Epoch 17: 16%|█▌ | 5/32 [00:00<00:00, 316.10it/s, v_num=2, train_loss=3.570, RMSE=14.50]
Epoch 17: 19%|█▉ | 6/32 [00:00<00:00, 321.60it/s, v_num=2, train_loss=3.570, RMSE=14.50]
Epoch 17: 19%|█▉ | 6/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.370, RMSE=14.50]
Epoch 17: 22%|██▏ | 7/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.370, RMSE=14.50]
Epoch 17: 22%|██▏ | 7/32 [00:00<00:00, 314.51it/s, v_num=2, train_loss=3.550, RMSE=14.50]
Epoch 17: 25%|██▌ | 8/32 [00:00<00:00, 318.51it/s, v_num=2, train_loss=3.550, RMSE=14.50]
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Epoch 17: 28%|██▊ | 9/32 [00:00<00:00, 319.71it/s, v_num=2, train_loss=3.410, RMSE=14.50]
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Epoch 17: 31%|███▏ | 10/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=3.870, RMSE=14.50]
Epoch 17: 34%|███▍ | 11/32 [00:00<00:00, 321.77it/s, v_num=2, train_loss=3.870, RMSE=14.50]
Epoch 17: 34%|███▍ | 11/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.740, RMSE=14.50]
Epoch 17: 38%|███▊ | 12/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=3.740, RMSE=14.50]
Epoch 17: 38%|███▊ | 12/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.570, RMSE=14.50]
Epoch 17: 41%|████ | 13/32 [00:00<00:00, 322.80it/s, v_num=2, train_loss=3.570, RMSE=14.50]
Epoch 17: 41%|████ | 13/32 [00:00<00:00, 321.17it/s, v_num=2, train_loss=3.550, RMSE=14.50]
Epoch 17: 44%|████▍ | 14/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=3.550, RMSE=14.50]
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Epoch 17: 47%|████▋ | 15/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=3.550, RMSE=14.50]
Epoch 17: 47%|████▋ | 15/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 50%|█████ | 16/32 [00:00<00:00, 323.69it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 50%|█████ | 16/32 [00:00<00:00, 322.35it/s, v_num=2, train_loss=3.420, RMSE=14.50]
Epoch 17: 53%|█████▎ | 17/32 [00:00<00:00, 324.02it/s, v_num=2, train_loss=3.420, RMSE=14.50]
Epoch 17: 53%|█████▎ | 17/32 [00:00<00:00, 322.75it/s, v_num=2, train_loss=3.460, RMSE=14.50]
Epoch 17: 56%|█████▋ | 18/32 [00:00<00:00, 324.38it/s, v_num=2, train_loss=3.460, RMSE=14.50]
Epoch 17: 56%|█████▋ | 18/32 [00:00<00:00, 323.19it/s, v_num=2, train_loss=3.320, RMSE=14.50]
Epoch 17: 59%|█████▉ | 19/32 [00:00<00:00, 324.66it/s, v_num=2, train_loss=3.320, RMSE=14.50]
Epoch 17: 59%|█████▉ | 19/32 [00:00<00:00, 323.53it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 62%|██████▎ | 20/32 [00:00<00:00, 324.87it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 62%|██████▎ | 20/32 [00:00<00:00, 323.79it/s, v_num=2, train_loss=3.470, RMSE=14.50]
Epoch 17: 66%|██████▌ | 21/32 [00:00<00:00, 325.03it/s, v_num=2, train_loss=3.470, RMSE=14.50]
Epoch 17: 66%|██████▌ | 21/32 [00:00<00:00, 324.00it/s, v_num=2, train_loss=3.480, RMSE=14.50]
Epoch 17: 69%|██████▉ | 22/32 [00:00<00:00, 325.40it/s, v_num=2, train_loss=3.480, RMSE=14.50]
Epoch 17: 69%|██████▉ | 22/32 [00:00<00:00, 324.41it/s, v_num=2, train_loss=3.460, RMSE=14.50]
Epoch 17: 72%|███████▏ | 23/32 [00:00<00:00, 325.61it/s, v_num=2, train_loss=3.460, RMSE=14.50]
Epoch 17: 72%|███████▏ | 23/32 [00:00<00:00, 324.66it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 75%|███████▌ | 24/32 [00:00<00:00, 325.70it/s, v_num=2, train_loss=3.440, RMSE=14.50]
Epoch 17: 75%|███████▌ | 24/32 [00:00<00:00, 324.78it/s, v_num=2, train_loss=3.510, RMSE=14.50]
Epoch 17: 78%|███████▊ | 25/32 [00:00<00:00, 325.78it/s, v_num=2, train_loss=3.510, RMSE=14.50]
Epoch 17: 78%|███████▊ | 25/32 [00:00<00:00, 324.92it/s, v_num=2, train_loss=3.530, RMSE=14.50]
Epoch 17: 81%|████████▏ | 26/32 [00:00<00:00, 325.97it/s, v_num=2, train_loss=3.530, RMSE=14.50]
Epoch 17: 81%|████████▏ | 26/32 [00:00<00:00, 325.12it/s, v_num=2, train_loss=3.450, RMSE=14.50]
Epoch 17: 84%|████████▍ | 27/32 [00:00<00:00, 325.96it/s, v_num=2, train_loss=3.450, RMSE=14.50]
Epoch 17: 84%|████████▍ | 27/32 [00:00<00:00, 325.16it/s, v_num=2, train_loss=3.310, RMSE=14.50]
Epoch 17: 88%|████████▊ | 28/32 [00:00<00:00, 326.05it/s, v_num=2, train_loss=3.310, RMSE=14.50]
Epoch 17: 88%|████████▊ | 28/32 [00:00<00:00, 325.27it/s, v_num=2, train_loss=3.710, RMSE=14.50]
Epoch 17: 91%|█████████ | 29/32 [00:00<00:00, 326.10it/s, v_num=2, train_loss=3.710, RMSE=14.50]
Epoch 17: 91%|█████████ | 29/32 [00:00<00:00, 325.35it/s, v_num=2, train_loss=3.260, RMSE=14.50]
Epoch 17: 94%|█████████▍| 30/32 [00:00<00:00, 326.33it/s, v_num=2, train_loss=3.260, RMSE=14.50]
Epoch 17: 94%|█████████▍| 30/32 [00:00<00:00, 325.51it/s, v_num=2, train_loss=3.520, RMSE=14.50]
Epoch 17: 97%|█████████▋| 31/32 [00:00<00:00, 326.42it/s, v_num=2, train_loss=3.520, RMSE=14.50]
Epoch 17: 97%|█████████▋| 31/32 [00:00<00:00, 325.73it/s, v_num=2, train_loss=3.360, RMSE=14.50]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=3.360, RMSE=14.50]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 326.01it/s, v_num=2, train_loss=3.570, RMSE=14.50]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 625.22it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 625.48it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 629.20it/s]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 267.16it/s, v_num=2, train_loss=3.570, RMSE=13.90]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 265.97it/s, v_num=2, train_loss=3.570, RMSE=13.90]
Epoch 17: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.570, RMSE=13.90]
Epoch 18: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.570, RMSE=13.90]
Epoch 18: 3%|▎ | 1/32 [00:00<00:00, 292.49it/s, v_num=2, train_loss=3.570, RMSE=13.90]
Epoch 18: 3%|▎ | 1/32 [00:00<00:00, 275.42it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 6%|▋ | 2/32 [00:00<00:00, 304.92it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 6%|▋ | 2/32 [00:00<00:00, 295.63it/s, v_num=2, train_loss=3.450, RMSE=13.90]
Epoch 18: 9%|▉ | 3/32 [00:00<00:00, 311.01it/s, v_num=2, train_loss=3.450, RMSE=13.90]
Epoch 18: 9%|▉ | 3/32 [00:00<00:00, 304.55it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 12%|█▎ | 4/32 [00:00<00:00, 315.68it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 12%|█▎ | 4/32 [00:00<00:00, 309.90it/s, v_num=2, train_loss=3.650, RMSE=13.90]
Epoch 18: 16%|█▌ | 5/32 [00:00<00:00, 317.86it/s, v_num=2, train_loss=3.650, RMSE=13.90]
Epoch 18: 16%|█▌ | 5/32 [00:00<00:00, 313.76it/s, v_num=2, train_loss=3.300, RMSE=13.90]
Epoch 18: 19%|█▉ | 6/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.300, RMSE=13.90]
Epoch 18: 19%|█▉ | 6/32 [00:00<00:00, 316.18it/s, v_num=2, train_loss=3.430, RMSE=13.90]
Epoch 18: 22%|██▏ | 7/32 [00:00<00:00, 320.61it/s, v_num=2, train_loss=3.430, RMSE=13.90]
Epoch 18: 22%|██▏ | 7/32 [00:00<00:00, 317.60it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 25%|██▌ | 8/32 [00:00<00:00, 321.61it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 25%|██▌ | 8/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=3.270, RMSE=13.90]
Epoch 18: 28%|██▊ | 9/32 [00:00<00:00, 322.74it/s, v_num=2, train_loss=3.270, RMSE=13.90]
Epoch 18: 28%|██▊ | 9/32 [00:00<00:00, 320.39it/s, v_num=2, train_loss=3.780, RMSE=13.90]
Epoch 18: 31%|███▏ | 10/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=3.780, RMSE=13.90]
Epoch 18: 31%|███▏ | 10/32 [00:00<00:00, 320.23it/s, v_num=2, train_loss=3.290, RMSE=13.90]
Epoch 18: 34%|███▍ | 11/32 [00:00<00:00, 323.00it/s, v_num=2, train_loss=3.290, RMSE=13.90]
Epoch 18: 34%|███▍ | 11/32 [00:00<00:00, 321.07it/s, v_num=2, train_loss=3.640, RMSE=13.90]
Epoch 18: 38%|███▊ | 12/32 [00:00<00:00, 323.66it/s, v_num=2, train_loss=3.640, RMSE=13.90]
Epoch 18: 38%|███▊ | 12/32 [00:00<00:00, 321.86it/s, v_num=2, train_loss=3.790, RMSE=13.90]
Epoch 18: 41%|████ | 13/32 [00:00<00:00, 324.42it/s, v_num=2, train_loss=3.790, RMSE=13.90]
Epoch 18: 41%|████ | 13/32 [00:00<00:00, 322.77it/s, v_num=2, train_loss=3.110, RMSE=13.90]
Epoch 18: 44%|████▍ | 14/32 [00:00<00:00, 324.68it/s, v_num=2, train_loss=3.110, RMSE=13.90]
Epoch 18: 44%|████▍ | 14/32 [00:00<00:00, 323.14it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 47%|████▋ | 15/32 [00:00<00:00, 324.84it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 47%|████▋ | 15/32 [00:00<00:00, 323.41it/s, v_num=2, train_loss=3.430, RMSE=13.90]
Epoch 18: 50%|█████ | 16/32 [00:00<00:00, 325.10it/s, v_num=2, train_loss=3.430, RMSE=13.90]
Epoch 18: 50%|█████ | 16/32 [00:00<00:00, 323.70it/s, v_num=2, train_loss=3.440, RMSE=13.90]
Epoch 18: 53%|█████▎ | 17/32 [00:00<00:00, 325.47it/s, v_num=2, train_loss=3.440, RMSE=13.90]
Epoch 18: 53%|█████▎ | 17/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=3.370, RMSE=13.90]
Epoch 18: 56%|█████▋ | 18/32 [00:00<00:00, 325.67it/s, v_num=2, train_loss=3.370, RMSE=13.90]
Epoch 18: 56%|█████▋ | 18/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=3.580, RMSE=13.90]
Epoch 18: 59%|█████▉ | 19/32 [00:00<00:00, 325.79it/s, v_num=2, train_loss=3.580, RMSE=13.90]
Epoch 18: 59%|█████▉ | 19/32 [00:00<00:00, 324.65it/s, v_num=2, train_loss=3.370, RMSE=13.90]
Epoch 18: 62%|██████▎ | 20/32 [00:00<00:00, 325.78it/s, v_num=2, train_loss=3.370, RMSE=13.90]
Epoch 18: 62%|██████▎ | 20/32 [00:00<00:00, 324.68it/s, v_num=2, train_loss=3.270, RMSE=13.90]
Epoch 18: 66%|██████▌ | 21/32 [00:00<00:00, 326.14it/s, v_num=2, train_loss=3.270, RMSE=13.90]
Epoch 18: 66%|██████▌ | 21/32 [00:00<00:00, 325.07it/s, v_num=2, train_loss=2.610, RMSE=13.90]
Epoch 18: 69%|██████▉ | 22/32 [00:00<00:00, 326.29it/s, v_num=2, train_loss=2.610, RMSE=13.90]
Epoch 18: 69%|██████▉ | 22/32 [00:00<00:00, 325.30it/s, v_num=2, train_loss=3.160, RMSE=13.90]
Epoch 18: 72%|███████▏ | 23/32 [00:00<00:00, 326.44it/s, v_num=2, train_loss=3.160, RMSE=13.90]
Epoch 18: 72%|███████▏ | 23/32 [00:00<00:00, 325.49it/s, v_num=2, train_loss=3.760, RMSE=13.90]
Epoch 18: 75%|███████▌ | 24/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=3.760, RMSE=13.90]
Epoch 18: 75%|███████▌ | 24/32 [00:00<00:00, 325.65it/s, v_num=2, train_loss=3.520, RMSE=13.90]
Epoch 18: 78%|███████▊ | 25/32 [00:00<00:00, 324.59it/s, v_num=2, train_loss=3.520, RMSE=13.90]
Epoch 18: 78%|███████▊ | 25/32 [00:00<00:00, 323.72it/s, v_num=2, train_loss=3.450, RMSE=13.90]
Epoch 18: 81%|████████▏ | 26/32 [00:00<00:00, 324.86it/s, v_num=2, train_loss=3.450, RMSE=13.90]
Epoch 18: 81%|████████▏ | 26/32 [00:00<00:00, 324.02it/s, v_num=2, train_loss=3.440, RMSE=13.90]
Epoch 18: 84%|████████▍ | 27/32 [00:00<00:00, 325.04it/s, v_num=2, train_loss=3.440, RMSE=13.90]
Epoch 18: 84%|████████▍ | 27/32 [00:00<00:00, 324.24it/s, v_num=2, train_loss=3.420, RMSE=13.90]
Epoch 18: 88%|████████▊ | 28/32 [00:00<00:00, 325.15it/s, v_num=2, train_loss=3.420, RMSE=13.90]
Epoch 18: 88%|████████▊ | 28/32 [00:00<00:00, 324.39it/s, v_num=2, train_loss=3.310, RMSE=13.90]
Epoch 18: 91%|█████████ | 29/32 [00:00<00:00, 325.28it/s, v_num=2, train_loss=3.310, RMSE=13.90]
Epoch 18: 91%|█████████ | 29/32 [00:00<00:00, 324.53it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 94%|█████████▍| 30/32 [00:00<00:00, 325.54it/s, v_num=2, train_loss=3.770, RMSE=13.90]
Epoch 18: 94%|█████████▍| 30/32 [00:00<00:00, 324.76it/s, v_num=2, train_loss=3.560, RMSE=13.90]
Epoch 18: 97%|█████████▋| 31/32 [00:00<00:00, 325.67it/s, v_num=2, train_loss=3.560, RMSE=13.90]
Epoch 18: 97%|█████████▋| 31/32 [00:00<00:00, 324.97it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 325.93it/s, v_num=2, train_loss=3.400, RMSE=13.90]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 325.26it/s, v_num=2, train_loss=2.790, RMSE=13.90]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 630.04it/s]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 267.10it/s, v_num=2, train_loss=2.790, RMSE=13.30]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 265.99it/s, v_num=2, train_loss=2.790, RMSE=13.30]
Epoch 18: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.790, RMSE=13.30]
Epoch 19: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.790, RMSE=13.30]
Epoch 19: 3%|▎ | 1/32 [00:00<00:00, 295.58it/s, v_num=2, train_loss=2.790, RMSE=13.30]
Epoch 19: 3%|▎ | 1/32 [00:00<00:00, 278.16it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 6%|▋ | 2/32 [00:00<00:00, 306.47it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 6%|▋ | 2/32 [00:00<00:00, 296.66it/s, v_num=2, train_loss=3.540, RMSE=13.30]
Epoch 19: 9%|▉ | 3/32 [00:00<00:00, 311.26it/s, v_num=2, train_loss=3.540, RMSE=13.30]
Epoch 19: 9%|▉ | 3/32 [00:00<00:00, 304.72it/s, v_num=2, train_loss=3.220, RMSE=13.30]
Epoch 19: 12%|█▎ | 4/32 [00:00<00:00, 314.40it/s, v_num=2, train_loss=3.220, RMSE=13.30]
Epoch 19: 12%|█▎ | 4/32 [00:00<00:00, 309.33it/s, v_num=2, train_loss=3.330, RMSE=13.30]
Epoch 19: 16%|█▌ | 5/32 [00:00<00:00, 316.16it/s, v_num=2, train_loss=3.330, RMSE=13.30]
Epoch 19: 16%|█▌ | 5/32 [00:00<00:00, 311.93it/s, v_num=2, train_loss=3.370, RMSE=13.30]
Epoch 19: 19%|█▉ | 6/32 [00:00<00:00, 317.38it/s, v_num=2, train_loss=3.370, RMSE=13.30]
Epoch 19: 19%|█▉ | 6/32 [00:00<00:00, 313.96it/s, v_num=2, train_loss=3.460, RMSE=13.30]
Epoch 19: 22%|██▏ | 7/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=3.460, RMSE=13.30]
Epoch 19: 22%|██▏ | 7/32 [00:00<00:00, 316.02it/s, v_num=2, train_loss=3.500, RMSE=13.30]
Epoch 19: 25%|██▌ | 8/32 [00:00<00:00, 320.72it/s, v_num=2, train_loss=3.500, RMSE=13.30]
Epoch 19: 25%|██▌ | 8/32 [00:00<00:00, 318.11it/s, v_num=2, train_loss=3.250, RMSE=13.30]
Epoch 19: 28%|██▊ | 9/32 [00:00<00:00, 321.58it/s, v_num=2, train_loss=3.250, RMSE=13.30]
Epoch 19: 28%|██▊ | 9/32 [00:00<00:00, 319.23it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 31%|███▏ | 10/32 [00:00<00:00, 322.32it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 31%|███▏ | 10/32 [00:00<00:00, 320.20it/s, v_num=2, train_loss=3.870, RMSE=13.30]
Epoch 19: 34%|███▍ | 11/32 [00:00<00:00, 322.89it/s, v_num=2, train_loss=3.870, RMSE=13.30]
Epoch 19: 34%|███▍ | 11/32 [00:00<00:00, 320.96it/s, v_num=2, train_loss=3.670, RMSE=13.30]
Epoch 19: 38%|███▊ | 12/32 [00:00<00:00, 323.70it/s, v_num=2, train_loss=3.670, RMSE=13.30]
Epoch 19: 38%|███▊ | 12/32 [00:00<00:00, 321.91it/s, v_num=2, train_loss=3.500, RMSE=13.30]
Epoch 19: 41%|████ | 13/32 [00:00<00:00, 324.27it/s, v_num=2, train_loss=3.500, RMSE=13.30]
Epoch 19: 41%|████ | 13/32 [00:00<00:00, 322.62it/s, v_num=2, train_loss=3.450, RMSE=13.30]
Epoch 19: 44%|████▍ | 14/32 [00:00<00:00, 324.70it/s, v_num=2, train_loss=3.450, RMSE=13.30]
Epoch 19: 44%|████▍ | 14/32 [00:00<00:00, 323.06it/s, v_num=2, train_loss=3.350, RMSE=13.30]
Epoch 19: 47%|████▋ | 15/32 [00:00<00:00, 324.86it/s, v_num=2, train_loss=3.350, RMSE=13.30]
Epoch 19: 47%|████▋ | 15/32 [00:00<00:00, 323.42it/s, v_num=2, train_loss=3.220, RMSE=13.30]
Epoch 19: 50%|█████ | 16/32 [00:00<00:00, 325.05it/s, v_num=2, train_loss=3.220, RMSE=13.30]
Epoch 19: 50%|█████ | 16/32 [00:00<00:00, 323.55it/s, v_num=2, train_loss=3.680, RMSE=13.30]
Epoch 19: 53%|█████▎ | 17/32 [00:00<00:00, 325.33it/s, v_num=2, train_loss=3.680, RMSE=13.30]
Epoch 19: 53%|█████▎ | 17/32 [00:00<00:00, 324.05it/s, v_num=2, train_loss=3.410, RMSE=13.30]
Epoch 19: 56%|█████▋ | 18/32 [00:00<00:00, 325.66it/s, v_num=2, train_loss=3.410, RMSE=13.30]
Epoch 19: 56%|█████▋ | 18/32 [00:00<00:00, 324.43it/s, v_num=2, train_loss=3.020, RMSE=13.30]
Epoch 19: 59%|█████▉ | 19/32 [00:00<00:00, 325.85it/s, v_num=2, train_loss=3.020, RMSE=13.30]
Epoch 19: 59%|█████▉ | 19/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=3.240, RMSE=13.30]
Epoch 19: 62%|██████▎ | 20/32 [00:00<00:00, 326.20it/s, v_num=2, train_loss=3.240, RMSE=13.30]
Epoch 19: 62%|██████▎ | 20/32 [00:00<00:00, 324.95it/s, v_num=2, train_loss=4.000, RMSE=13.30]
Epoch 19: 66%|██████▌ | 21/32 [00:00<00:00, 326.42it/s, v_num=2, train_loss=4.000, RMSE=13.30]
Epoch 19: 66%|██████▌ | 21/32 [00:00<00:00, 325.38it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 69%|██████▉ | 22/32 [00:00<00:00, 326.62it/s, v_num=2, train_loss=3.430, RMSE=13.30]
Epoch 19: 69%|██████▉ | 22/32 [00:00<00:00, 325.63it/s, v_num=2, train_loss=3.290, RMSE=13.30]
Epoch 19: 72%|███████▏ | 23/32 [00:00<00:00, 326.34it/s, v_num=2, train_loss=3.290, RMSE=13.30]
Epoch 19: 72%|███████▏ | 23/32 [00:00<00:00, 325.38it/s, v_num=2, train_loss=3.390, RMSE=13.30]
Epoch 19: 75%|███████▌ | 24/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=3.390, RMSE=13.30]
Epoch 19: 75%|███████▌ | 24/32 [00:00<00:00, 325.59it/s, v_num=2, train_loss=3.480, RMSE=13.30]
Epoch 19: 78%|███████▊ | 25/32 [00:00<00:00, 326.74it/s, v_num=2, train_loss=3.480, RMSE=13.30]
Epoch 19: 78%|███████▊ | 25/32 [00:00<00:00, 325.83it/s, v_num=2, train_loss=3.290, RMSE=13.30]
Epoch 19: 81%|████████▏ | 26/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=3.290, RMSE=13.30]
Epoch 19: 81%|████████▏ | 26/32 [00:00<00:00, 325.93it/s, v_num=2, train_loss=3.310, RMSE=13.30]
Epoch 19: 84%|████████▍ | 27/32 [00:00<00:00, 326.83it/s, v_num=2, train_loss=3.310, RMSE=13.30]
Epoch 19: 84%|████████▍ | 27/32 [00:00<00:00, 326.02it/s, v_num=2, train_loss=3.160, RMSE=13.30]
Epoch 19: 88%|████████▊ | 28/32 [00:00<00:00, 326.96it/s, v_num=2, train_loss=3.160, RMSE=13.30]
Epoch 19: 88%|████████▊ | 28/32 [00:00<00:00, 326.17it/s, v_num=2, train_loss=3.300, RMSE=13.30]
Epoch 19: 91%|█████████ | 29/32 [00:00<00:00, 327.10it/s, v_num=2, train_loss=3.300, RMSE=13.30]
Epoch 19: 91%|█████████ | 29/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=3.180, RMSE=13.30]
Epoch 19: 94%|█████████▍| 30/32 [00:00<00:00, 327.17it/s, v_num=2, train_loss=3.180, RMSE=13.30]
Epoch 19: 94%|█████████▍| 30/32 [00:00<00:00, 326.45it/s, v_num=2, train_loss=3.780, RMSE=13.30]
Epoch 19: 97%|█████████▋| 31/32 [00:00<00:00, 327.21it/s, v_num=2, train_loss=3.780, RMSE=13.30]
Epoch 19: 97%|█████████▋| 31/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=3.160, RMSE=13.30]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=3.160, RMSE=13.30]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 326.73it/s, v_num=2, train_loss=3.350, RMSE=13.30]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 631.55it/s]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 267.99it/s, v_num=2, train_loss=3.350, RMSE=12.70]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 266.90it/s, v_num=2, train_loss=3.350, RMSE=12.70]
Epoch 19: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.350, RMSE=12.70]
Epoch 20: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.350, RMSE=12.70]
Epoch 20: 3%|▎ | 1/32 [00:00<00:00, 310.60it/s, v_num=2, train_loss=3.350, RMSE=12.70]
Epoch 20: 3%|▎ | 1/32 [00:00<00:00, 287.12it/s, v_num=2, train_loss=3.270, RMSE=12.70]
Epoch 20: 6%|▋ | 2/32 [00:00<00:00, 316.10it/s, v_num=2, train_loss=3.270, RMSE=12.70]
Epoch 20: 6%|▋ | 2/32 [00:00<00:00, 304.55it/s, v_num=2, train_loss=3.400, RMSE=12.70]
Epoch 20: 9%|▉ | 3/32 [00:00<00:00, 319.66it/s, v_num=2, train_loss=3.400, RMSE=12.70]
Epoch 20: 9%|▉ | 3/32 [00:00<00:00, 312.42it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 12%|█▎ | 4/32 [00:00<00:00, 320.59it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 12%|█▎ | 4/32 [00:00<00:00, 315.37it/s, v_num=2, train_loss=3.190, RMSE=12.70]
Epoch 20: 16%|█▌ | 5/32 [00:00<00:00, 321.88it/s, v_num=2, train_loss=3.190, RMSE=12.70]
Epoch 20: 16%|█▌ | 5/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=3.400, RMSE=12.70]
Epoch 20: 19%|█▉ | 6/32 [00:00<00:00, 323.34it/s, v_num=2, train_loss=3.400, RMSE=12.70]
Epoch 20: 19%|█▉ | 6/32 [00:00<00:00, 319.73it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 22%|██▏ | 7/32 [00:00<00:00, 324.70it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 22%|██▏ | 7/32 [00:00<00:00, 321.68it/s, v_num=2, train_loss=3.170, RMSE=12.70]
Epoch 20: 25%|██▌ | 8/32 [00:00<00:00, 325.52it/s, v_num=2, train_loss=3.170, RMSE=12.70]
Epoch 20: 25%|██▌ | 8/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.600, RMSE=12.70]
Epoch 20: 28%|██▊ | 9/32 [00:00<00:00, 326.07it/s, v_num=2, train_loss=3.600, RMSE=12.70]
Epoch 20: 28%|██▊ | 9/32 [00:00<00:00, 323.67it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 31%|███▏ | 10/32 [00:00<00:00, 326.46it/s, v_num=2, train_loss=3.560, RMSE=12.70]
Epoch 20: 31%|███▏ | 10/32 [00:00<00:00, 324.29it/s, v_num=2, train_loss=3.300, RMSE=12.70]
Epoch 20: 34%|███▍ | 11/32 [00:00<00:00, 323.57it/s, v_num=2, train_loss=3.300, RMSE=12.70]
Epoch 20: 34%|███▍ | 11/32 [00:00<00:00, 321.26it/s, v_num=2, train_loss=3.460, RMSE=12.70]
Epoch 20: 38%|███▊ | 12/32 [00:00<00:00, 317.12it/s, v_num=2, train_loss=3.460, RMSE=12.70]
Epoch 20: 38%|███▊ | 12/32 [00:00<00:00, 315.04it/s, v_num=2, train_loss=3.200, RMSE=12.70]
Epoch 20: 41%|████ | 13/32 [00:00<00:00, 314.80it/s, v_num=2, train_loss=3.200, RMSE=12.70]
Epoch 20: 41%|████ | 13/32 [00:00<00:00, 313.23it/s, v_num=2, train_loss=3.590, RMSE=12.70]
Epoch 20: 44%|████▍ | 14/32 [00:00<00:00, 315.54it/s, v_num=2, train_loss=3.590, RMSE=12.70]
Epoch 20: 44%|████▍ | 14/32 [00:00<00:00, 314.08it/s, v_num=2, train_loss=3.300, RMSE=12.70]
Epoch 20: 47%|████▋ | 15/32 [00:00<00:00, 316.17it/s, v_num=2, train_loss=3.300, RMSE=12.70]
Epoch 20: 47%|████▋ | 15/32 [00:00<00:00, 314.77it/s, v_num=2, train_loss=3.690, RMSE=12.70]
Epoch 20: 50%|█████ | 16/32 [00:00<00:00, 316.84it/s, v_num=2, train_loss=3.690, RMSE=12.70]
Epoch 20: 50%|█████ | 16/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=3.360, RMSE=12.70]
Epoch 20: 53%|█████▎ | 17/32 [00:00<00:00, 317.59it/s, v_num=2, train_loss=3.360, RMSE=12.70]
Epoch 20: 53%|█████▎ | 17/32 [00:00<00:00, 316.38it/s, v_num=2, train_loss=3.510, RMSE=12.70]
Epoch 20: 56%|█████▋ | 18/32 [00:00<00:00, 318.02it/s, v_num=2, train_loss=3.510, RMSE=12.70]
Epoch 20: 56%|█████▋ | 18/32 [00:00<00:00, 316.88it/s, v_num=2, train_loss=3.330, RMSE=12.70]
Epoch 20: 59%|█████▉ | 19/32 [00:00<00:00, 318.70it/s, v_num=2, train_loss=3.330, RMSE=12.70]
Epoch 20: 59%|█████▉ | 19/32 [00:00<00:00, 317.61it/s, v_num=2, train_loss=3.550, RMSE=12.70]
Epoch 20: 62%|██████▎ | 20/32 [00:00<00:00, 319.19it/s, v_num=2, train_loss=3.550, RMSE=12.70]
Epoch 20: 62%|██████▎ | 20/32 [00:00<00:00, 318.15it/s, v_num=2, train_loss=3.410, RMSE=12.70]
Epoch 20: 66%|██████▌ | 21/32 [00:00<00:00, 319.81it/s, v_num=2, train_loss=3.410, RMSE=12.70]
Epoch 20: 66%|██████▌ | 21/32 [00:00<00:00, 318.82it/s, v_num=2, train_loss=3.290, RMSE=12.70]
Epoch 20: 69%|██████▉ | 22/32 [00:00<00:00, 320.29it/s, v_num=2, train_loss=3.290, RMSE=12.70]
Epoch 20: 69%|██████▉ | 22/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.460, RMSE=12.70]
Epoch 20: 72%|███████▏ | 23/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=3.460, RMSE=12.70]
Epoch 20: 72%|███████▏ | 23/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=3.200, RMSE=12.70]
Epoch 20: 75%|███████▌ | 24/32 [00:00<00:00, 321.12it/s, v_num=2, train_loss=3.200, RMSE=12.70]
Epoch 20: 75%|███████▌ | 24/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=3.240, RMSE=12.70]
Epoch 20: 78%|███████▊ | 25/32 [00:00<00:00, 321.59it/s, v_num=2, train_loss=3.240, RMSE=12.70]
Epoch 20: 78%|███████▊ | 25/32 [00:00<00:00, 320.75it/s, v_num=2, train_loss=3.380, RMSE=12.70]
Epoch 20: 81%|████████▏ | 26/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=3.380, RMSE=12.70]
Epoch 20: 81%|████████▏ | 26/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.250, RMSE=12.70]
Epoch 20: 84%|████████▍ | 27/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=3.250, RMSE=12.70]
Epoch 20: 84%|████████▍ | 27/32 [00:00<00:00, 321.41it/s, v_num=2, train_loss=3.130, RMSE=12.70]
Epoch 20: 88%|████████▊ | 28/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=3.130, RMSE=12.70]
Epoch 20: 88%|████████▊ | 28/32 [00:00<00:00, 321.60it/s, v_num=2, train_loss=3.440, RMSE=12.70]
Epoch 20: 91%|█████████ | 29/32 [00:00<00:00, 322.72it/s, v_num=2, train_loss=3.440, RMSE=12.70]
Epoch 20: 91%|█████████ | 29/32 [00:00<00:00, 321.98it/s, v_num=2, train_loss=3.030, RMSE=12.70]
Epoch 20: 94%|█████████▍| 30/32 [00:00<00:00, 322.89it/s, v_num=2, train_loss=3.030, RMSE=12.70]
Epoch 20: 94%|█████████▍| 30/32 [00:00<00:00, 322.17it/s, v_num=2, train_loss=3.380, RMSE=12.70]
Epoch 20: 97%|█████████▋| 31/32 [00:00<00:00, 323.07it/s, v_num=2, train_loss=3.380, RMSE=12.70]
Epoch 20: 97%|█████████▋| 31/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=3.450, RMSE=12.70]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 323.35it/s, v_num=2, train_loss=3.450, RMSE=12.70]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 322.68it/s, v_num=2, train_loss=3.160, RMSE=12.70]
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Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 264.90it/s, v_num=2, train_loss=3.160, RMSE=12.20]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 263.79it/s, v_num=2, train_loss=3.160, RMSE=12.20]
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Epoch 21: 3%|▎ | 1/32 [00:00<00:00, 299.76it/s, v_num=2, train_loss=3.160, RMSE=12.20]
Epoch 21: 3%|▎ | 1/32 [00:00<00:00, 280.74it/s, v_num=2, train_loss=3.530, RMSE=12.20]
Epoch 21: 6%|▋ | 2/32 [00:00<00:00, 308.34it/s, v_num=2, train_loss=3.530, RMSE=12.20]
Epoch 21: 6%|▋ | 2/32 [00:00<00:00, 298.01it/s, v_num=2, train_loss=3.130, RMSE=12.20]
Epoch 21: 9%|▉ | 3/32 [00:00<00:00, 314.90it/s, v_num=2, train_loss=3.130, RMSE=12.20]
Epoch 21: 9%|▉ | 3/32 [00:00<00:00, 307.91it/s, v_num=2, train_loss=3.330, RMSE=12.20]
Epoch 21: 12%|█▎ | 4/32 [00:00<00:00, 318.07it/s, v_num=2, train_loss=3.330, RMSE=12.20]
Epoch 21: 12%|█▎ | 4/32 [00:00<00:00, 312.95it/s, v_num=2, train_loss=3.580, RMSE=12.20]
Epoch 21: 16%|█▌ | 5/32 [00:00<00:00, 319.69it/s, v_num=2, train_loss=3.580, RMSE=12.20]
Epoch 21: 16%|█▌ | 5/32 [00:00<00:00, 315.52it/s, v_num=2, train_loss=3.640, RMSE=12.20]
Epoch 21: 19%|█▉ | 6/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=3.640, RMSE=12.20]
Epoch 21: 19%|█▉ | 6/32 [00:00<00:00, 317.42it/s, v_num=2, train_loss=3.050, RMSE=12.20]
Epoch 21: 22%|██▏ | 7/32 [00:00<00:00, 322.51it/s, v_num=2, train_loss=3.050, RMSE=12.20]
Epoch 21: 22%|██▏ | 7/32 [00:00<00:00, 319.49it/s, v_num=2, train_loss=3.550, RMSE=12.20]
Epoch 21: 25%|██▌ | 8/32 [00:00<00:00, 320.27it/s, v_num=2, train_loss=3.550, RMSE=12.20]
Epoch 21: 25%|██▌ | 8/32 [00:00<00:00, 317.64it/s, v_num=2, train_loss=3.220, RMSE=12.20]
Epoch 21: 28%|██▊ | 9/32 [00:00<00:00, 321.28it/s, v_num=2, train_loss=3.220, RMSE=12.20]
Epoch 21: 28%|██▊ | 9/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=3.470, RMSE=12.20]
Epoch 21: 31%|███▏ | 10/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=3.470, RMSE=12.20]
Epoch 21: 31%|███▏ | 10/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=3.270, RMSE=12.20]
Epoch 21: 34%|███▍ | 11/32 [00:00<00:00, 322.11it/s, v_num=2, train_loss=3.270, RMSE=12.20]
Epoch 21: 34%|███▍ | 11/32 [00:00<00:00, 320.13it/s, v_num=2, train_loss=3.390, RMSE=12.20]
Epoch 21: 38%|███▊ | 12/32 [00:00<00:00, 322.89it/s, v_num=2, train_loss=3.390, RMSE=12.20]
Epoch 21: 38%|███▊ | 12/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.200, RMSE=12.20]
Epoch 21: 41%|████ | 13/32 [00:00<00:00, 323.53it/s, v_num=2, train_loss=3.200, RMSE=12.20]
Epoch 21: 41%|████ | 13/32 [00:00<00:00, 321.83it/s, v_num=2, train_loss=3.060, RMSE=12.20]
Epoch 21: 44%|████▍ | 14/32 [00:00<00:00, 323.95it/s, v_num=2, train_loss=3.060, RMSE=12.20]
Epoch 21: 44%|████▍ | 14/32 [00:00<00:00, 322.42it/s, v_num=2, train_loss=3.420, RMSE=12.20]
Epoch 21: 47%|████▋ | 15/32 [00:00<00:00, 324.40it/s, v_num=2, train_loss=3.420, RMSE=12.20]
Epoch 21: 47%|████▋ | 15/32 [00:00<00:00, 322.97it/s, v_num=2, train_loss=3.350, RMSE=12.20]
Epoch 21: 50%|█████ | 16/32 [00:00<00:00, 325.02it/s, v_num=2, train_loss=3.350, RMSE=12.20]
Epoch 21: 50%|█████ | 16/32 [00:00<00:00, 323.68it/s, v_num=2, train_loss=3.860, RMSE=12.20]
Epoch 21: 53%|█████▎ | 17/32 [00:00<00:00, 325.28it/s, v_num=2, train_loss=3.860, RMSE=12.20]
Epoch 21: 53%|█████▎ | 17/32 [00:00<00:00, 324.01it/s, v_num=2, train_loss=3.620, RMSE=12.20]
Epoch 21: 56%|█████▋ | 18/32 [00:00<00:00, 325.60it/s, v_num=2, train_loss=3.620, RMSE=12.20]
Epoch 21: 56%|█████▋ | 18/32 [00:00<00:00, 324.39it/s, v_num=2, train_loss=3.400, RMSE=12.20]
Epoch 21: 59%|█████▉ | 19/32 [00:00<00:00, 325.88it/s, v_num=2, train_loss=3.400, RMSE=12.20]
Epoch 21: 59%|█████▉ | 19/32 [00:00<00:00, 324.75it/s, v_num=2, train_loss=3.260, RMSE=12.20]
Epoch 21: 62%|██████▎ | 20/32 [00:00<00:00, 326.15it/s, v_num=2, train_loss=3.260, RMSE=12.20]
Epoch 21: 62%|██████▎ | 20/32 [00:00<00:00, 325.03it/s, v_num=2, train_loss=3.230, RMSE=12.20]
Epoch 21: 66%|██████▌ | 21/32 [00:00<00:00, 326.27it/s, v_num=2, train_loss=3.230, RMSE=12.20]
Epoch 21: 66%|██████▌ | 21/32 [00:00<00:00, 325.23it/s, v_num=2, train_loss=2.890, RMSE=12.20]
Epoch 21: 69%|██████▉ | 22/32 [00:00<00:00, 326.26it/s, v_num=2, train_loss=2.890, RMSE=12.20]
Epoch 21: 69%|██████▉ | 22/32 [00:00<00:00, 325.27it/s, v_num=2, train_loss=3.470, RMSE=12.20]
Epoch 21: 72%|███████▏ | 23/32 [00:00<00:00, 326.41it/s, v_num=2, train_loss=3.470, RMSE=12.20]
Epoch 21: 72%|███████▏ | 23/32 [00:00<00:00, 325.47it/s, v_num=2, train_loss=3.160, RMSE=12.20]
Epoch 21: 75%|███████▌ | 24/32 [00:00<00:00, 326.73it/s, v_num=2, train_loss=3.160, RMSE=12.20]
Epoch 21: 75%|███████▌ | 24/32 [00:00<00:00, 325.82it/s, v_num=2, train_loss=3.180, RMSE=12.20]
Epoch 21: 78%|███████▊ | 25/32 [00:00<00:00, 326.93it/s, v_num=2, train_loss=3.180, RMSE=12.20]
Epoch 21: 78%|███████▊ | 25/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=3.380, RMSE=12.20]
Epoch 21: 81%|████████▏ | 26/32 [00:00<00:00, 327.05it/s, v_num=2, train_loss=3.380, RMSE=12.20]
Epoch 21: 81%|████████▏ | 26/32 [00:00<00:00, 326.21it/s, v_num=2, train_loss=3.280, RMSE=12.20]
Epoch 21: 84%|████████▍ | 27/32 [00:00<00:00, 327.08it/s, v_num=2, train_loss=3.280, RMSE=12.20]
Epoch 21: 84%|████████▍ | 27/32 [00:00<00:00, 326.28it/s, v_num=2, train_loss=3.310, RMSE=12.20]
Epoch 21: 88%|████████▊ | 28/32 [00:00<00:00, 327.20it/s, v_num=2, train_loss=3.310, RMSE=12.20]
Epoch 21: 88%|████████▊ | 28/32 [00:00<00:00, 326.41it/s, v_num=2, train_loss=3.410, RMSE=12.20]
Epoch 21: 91%|█████████ | 29/32 [00:00<00:00, 325.55it/s, v_num=2, train_loss=3.410, RMSE=12.20]
Epoch 21: 91%|█████████ | 29/32 [00:00<00:00, 324.80it/s, v_num=2, train_loss=3.150, RMSE=12.20]
Epoch 21: 94%|█████████▍| 30/32 [00:00<00:00, 325.59it/s, v_num=2, train_loss=3.150, RMSE=12.20]
Epoch 21: 94%|█████████▍| 30/32 [00:00<00:00, 324.86it/s, v_num=2, train_loss=3.340, RMSE=12.20]
Epoch 21: 97%|█████████▋| 31/32 [00:00<00:00, 325.66it/s, v_num=2, train_loss=3.340, RMSE=12.20]
Epoch 21: 97%|█████████▋| 31/32 [00:00<00:00, 324.96it/s, v_num=2, train_loss=3.240, RMSE=12.20]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 325.86it/s, v_num=2, train_loss=3.240, RMSE=12.20]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 325.16it/s, v_num=2, train_loss=4.200, RMSE=12.20]
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Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 623.85it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 624.11it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 623.22it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 627.35it/s]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 267.03it/s, v_num=2, train_loss=4.200, RMSE=11.70]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 265.96it/s, v_num=2, train_loss=4.200, RMSE=11.70]
Epoch 21: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.200, RMSE=11.70]
Epoch 22: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.200, RMSE=11.70]
Epoch 22: 3%|▎ | 1/32 [00:00<00:00, 312.03it/s, v_num=2, train_loss=4.200, RMSE=11.70]
Epoch 22: 3%|▎ | 1/32 [00:00<00:00, 293.06it/s, v_num=2, train_loss=3.140, RMSE=11.70]
Epoch 22: 6%|▋ | 2/32 [00:00<00:00, 320.07it/s, v_num=2, train_loss=3.140, RMSE=11.70]
Epoch 22: 6%|▋ | 2/32 [00:00<00:00, 309.91it/s, v_num=2, train_loss=3.200, RMSE=11.70]
Epoch 22: 9%|▉ | 3/32 [00:00<00:00, 322.51it/s, v_num=2, train_loss=3.200, RMSE=11.70]
Epoch 22: 9%|▉ | 3/32 [00:00<00:00, 315.57it/s, v_num=2, train_loss=3.660, RMSE=11.70]
Epoch 22: 12%|█▎ | 4/32 [00:00<00:00, 324.64it/s, v_num=2, train_loss=3.660, RMSE=11.70]
Epoch 22: 12%|█▎ | 4/32 [00:00<00:00, 319.27it/s, v_num=2, train_loss=3.400, RMSE=11.70]
Epoch 22: 16%|█▌ | 5/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=3.400, RMSE=11.70]
Epoch 22: 16%|█▌ | 5/32 [00:00<00:00, 321.24it/s, v_num=2, train_loss=3.400, RMSE=11.70]
Epoch 22: 19%|█▉ | 6/32 [00:00<00:00, 326.29it/s, v_num=2, train_loss=3.400, RMSE=11.70]
Epoch 22: 19%|█▉ | 6/32 [00:00<00:00, 322.68it/s, v_num=2, train_loss=3.570, RMSE=11.70]
Epoch 22: 22%|██▏ | 7/32 [00:00<00:00, 326.42it/s, v_num=2, train_loss=3.570, RMSE=11.70]
Epoch 22: 22%|██▏ | 7/32 [00:00<00:00, 323.29it/s, v_num=2, train_loss=3.310, RMSE=11.70]
Epoch 22: 25%|██▌ | 8/32 [00:00<00:00, 326.73it/s, v_num=2, train_loss=3.310, RMSE=11.70]
Epoch 22: 25%|██▌ | 8/32 [00:00<00:00, 324.01it/s, v_num=2, train_loss=3.220, RMSE=11.70]
Epoch 22: 28%|██▊ | 9/32 [00:00<00:00, 327.04it/s, v_num=2, train_loss=3.220, RMSE=11.70]
Epoch 22: 28%|██▊ | 9/32 [00:00<00:00, 324.57it/s, v_num=2, train_loss=3.550, RMSE=11.70]
Epoch 22: 31%|███▏ | 10/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=3.550, RMSE=11.70]
Epoch 22: 31%|███▏ | 10/32 [00:00<00:00, 325.58it/s, v_num=2, train_loss=3.430, RMSE=11.70]
Epoch 22: 34%|███▍ | 11/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=3.430, RMSE=11.70]
Epoch 22: 34%|███▍ | 11/32 [00:00<00:00, 326.14it/s, v_num=2, train_loss=3.350, RMSE=11.70]
Epoch 22: 38%|███▊ | 12/32 [00:00<00:00, 328.21it/s, v_num=2, train_loss=3.350, RMSE=11.70]
Epoch 22: 38%|███▊ | 12/32 [00:00<00:00, 326.36it/s, v_num=2, train_loss=3.000, RMSE=11.70]
Epoch 22: 41%|████ | 13/32 [00:00<00:00, 328.37it/s, v_num=2, train_loss=3.000, RMSE=11.70]
Epoch 22: 41%|████ | 13/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=3.300, RMSE=11.70]
Epoch 22: 44%|████▍ | 14/32 [00:00<00:00, 328.92it/s, v_num=2, train_loss=3.300, RMSE=11.70]
Epoch 22: 44%|████▍ | 14/32 [00:00<00:00, 327.14it/s, v_num=2, train_loss=3.610, RMSE=11.70]
Epoch 22: 47%|████▋ | 15/32 [00:00<00:00, 329.01it/s, v_num=2, train_loss=3.610, RMSE=11.70]
Epoch 22: 47%|████▋ | 15/32 [00:00<00:00, 327.54it/s, v_num=2, train_loss=2.910, RMSE=11.70]
Epoch 22: 50%|█████ | 16/32 [00:00<00:00, 329.07it/s, v_num=2, train_loss=2.910, RMSE=11.70]
Epoch 22: 50%|█████ | 16/32 [00:00<00:00, 327.69it/s, v_num=2, train_loss=3.160, RMSE=11.70]
Epoch 22: 53%|█████▎ | 17/32 [00:00<00:00, 329.06it/s, v_num=2, train_loss=3.160, RMSE=11.70]
Epoch 22: 53%|█████▎ | 17/32 [00:00<00:00, 327.76it/s, v_num=2, train_loss=3.500, RMSE=11.70]
Epoch 22: 56%|█████▋ | 18/32 [00:00<00:00, 329.35it/s, v_num=2, train_loss=3.500, RMSE=11.70]
Epoch 22: 56%|█████▋ | 18/32 [00:00<00:00, 327.93it/s, v_num=2, train_loss=3.190, RMSE=11.70]
Epoch 22: 59%|█████▉ | 19/32 [00:00<00:00, 329.30it/s, v_num=2, train_loss=3.190, RMSE=11.70]
Epoch 22: 59%|█████▉ | 19/32 [00:00<00:00, 328.13it/s, v_num=2, train_loss=3.130, RMSE=11.70]
Epoch 22: 62%|██████▎ | 20/32 [00:00<00:00, 329.42it/s, v_num=2, train_loss=3.130, RMSE=11.70]
Epoch 22: 62%|██████▎ | 20/32 [00:00<00:00, 328.31it/s, v_num=2, train_loss=3.540, RMSE=11.70]
Epoch 22: 66%|██████▌ | 21/32 [00:00<00:00, 329.43it/s, v_num=2, train_loss=3.540, RMSE=11.70]
Epoch 22: 66%|██████▌ | 21/32 [00:00<00:00, 328.38it/s, v_num=2, train_loss=3.270, RMSE=11.70]
Epoch 22: 69%|██████▉ | 22/32 [00:00<00:00, 329.72it/s, v_num=2, train_loss=3.270, RMSE=11.70]
Epoch 22: 69%|██████▉ | 22/32 [00:00<00:00, 328.56it/s, v_num=2, train_loss=3.080, RMSE=11.70]
Epoch 22: 72%|███████▏ | 23/32 [00:00<00:00, 329.67it/s, v_num=2, train_loss=3.080, RMSE=11.70]
Epoch 22: 72%|███████▏ | 23/32 [00:00<00:00, 328.71it/s, v_num=2, train_loss=3.300, RMSE=11.70]
Epoch 22: 75%|███████▌ | 24/32 [00:00<00:00, 329.79it/s, v_num=2, train_loss=3.300, RMSE=11.70]
Epoch 22: 75%|███████▌ | 24/32 [00:00<00:00, 328.86it/s, v_num=2, train_loss=3.290, RMSE=11.70]
Epoch 22: 78%|███████▊ | 25/32 [00:00<00:00, 329.74it/s, v_num=2, train_loss=3.290, RMSE=11.70]
Epoch 22: 78%|███████▊ | 25/32 [00:00<00:00, 328.84it/s, v_num=2, train_loss=3.370, RMSE=11.70]
Epoch 22: 81%|████████▏ | 26/32 [00:00<00:00, 329.64it/s, v_num=2, train_loss=3.370, RMSE=11.70]
Epoch 22: 81%|████████▏ | 26/32 [00:00<00:00, 328.77it/s, v_num=2, train_loss=3.640, RMSE=11.70]
Epoch 22: 84%|████████▍ | 27/32 [00:00<00:00, 329.69it/s, v_num=2, train_loss=3.640, RMSE=11.70]
Epoch 22: 84%|████████▍ | 27/32 [00:00<00:00, 328.86it/s, v_num=2, train_loss=3.180, RMSE=11.70]
Epoch 22: 88%|████████▊ | 28/32 [00:00<00:00, 329.22it/s, v_num=2, train_loss=3.180, RMSE=11.70]
Epoch 22: 88%|████████▊ | 28/32 [00:00<00:00, 328.35it/s, v_num=2, train_loss=3.290, RMSE=11.70]
Epoch 22: 91%|█████████ | 29/32 [00:00<00:00, 328.44it/s, v_num=2, train_loss=3.290, RMSE=11.70]
Epoch 22: 91%|█████████ | 29/32 [00:00<00:00, 327.61it/s, v_num=2, train_loss=3.190, RMSE=11.70]
Epoch 22: 94%|█████████▍| 30/32 [00:00<00:00, 327.74it/s, v_num=2, train_loss=3.190, RMSE=11.70]
Epoch 22: 94%|█████████▍| 30/32 [00:00<00:00, 326.92it/s, v_num=2, train_loss=3.160, RMSE=11.70]
Epoch 22: 97%|█████████▋| 31/32 [00:00<00:00, 327.02it/s, v_num=2, train_loss=3.160, RMSE=11.70]
Epoch 22: 97%|█████████▋| 31/32 [00:00<00:00, 326.27it/s, v_num=2, train_loss=3.090, RMSE=11.70]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 326.85it/s, v_num=2, train_loss=3.090, RMSE=11.70]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 326.15it/s, v_num=2, train_loss=3.210, RMSE=11.70]
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Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 265.89it/s, v_num=2, train_loss=3.210, RMSE=11.10]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 264.69it/s, v_num=2, train_loss=3.210, RMSE=11.10]
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Epoch 23: 3%|▎ | 1/32 [00:00<00:00, 249.88it/s, v_num=2, train_loss=3.490, RMSE=11.10]
Epoch 23: 6%|▋ | 2/32 [00:00<00:00, 282.46it/s, v_num=2, train_loss=3.490, RMSE=11.10]
Epoch 23: 6%|▋ | 2/32 [00:00<00:00, 274.28it/s, v_num=2, train_loss=3.270, RMSE=11.10]
Epoch 23: 9%|▉ | 3/32 [00:00<00:00, 293.45it/s, v_num=2, train_loss=3.270, RMSE=11.10]
Epoch 23: 9%|▉ | 3/32 [00:00<00:00, 287.62it/s, v_num=2, train_loss=3.530, RMSE=11.10]
Epoch 23: 12%|█▎ | 4/32 [00:00<00:00, 299.91it/s, v_num=2, train_loss=3.530, RMSE=11.10]
Epoch 23: 12%|█▎ | 4/32 [00:00<00:00, 295.31it/s, v_num=2, train_loss=3.360, RMSE=11.10]
Epoch 23: 16%|█▌ | 5/32 [00:00<00:00, 302.49it/s, v_num=2, train_loss=3.360, RMSE=11.10]
Epoch 23: 16%|█▌ | 5/32 [00:00<00:00, 298.78it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 19%|█▉ | 6/32 [00:00<00:00, 305.80it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 19%|█▉ | 6/32 [00:00<00:00, 302.52it/s, v_num=2, train_loss=3.790, RMSE=11.10]
Epoch 23: 22%|██▏ | 7/32 [00:00<00:00, 308.56it/s, v_num=2, train_loss=3.790, RMSE=11.10]
Epoch 23: 22%|██▏ | 7/32 [00:00<00:00, 305.75it/s, v_num=2, train_loss=3.070, RMSE=11.10]
Epoch 23: 25%|██▌ | 8/32 [00:00<00:00, 311.10it/s, v_num=2, train_loss=3.070, RMSE=11.10]
Epoch 23: 25%|██▌ | 8/32 [00:00<00:00, 308.63it/s, v_num=2, train_loss=3.230, RMSE=11.10]
Epoch 23: 28%|██▊ | 9/32 [00:00<00:00, 312.91it/s, v_num=2, train_loss=3.230, RMSE=11.10]
Epoch 23: 28%|██▊ | 9/32 [00:00<00:00, 310.69it/s, v_num=2, train_loss=3.940, RMSE=11.10]
Epoch 23: 31%|███▏ | 10/32 [00:00<00:00, 314.34it/s, v_num=2, train_loss=3.940, RMSE=11.10]
Epoch 23: 31%|███▏ | 10/32 [00:00<00:00, 312.33it/s, v_num=2, train_loss=3.140, RMSE=11.10]
Epoch 23: 34%|███▍ | 11/32 [00:00<00:00, 315.33it/s, v_num=2, train_loss=3.140, RMSE=11.10]
Epoch 23: 34%|███▍ | 11/32 [00:00<00:00, 313.48it/s, v_num=2, train_loss=2.920, RMSE=11.10]
Epoch 23: 38%|███▊ | 12/32 [00:00<00:00, 316.71it/s, v_num=2, train_loss=2.920, RMSE=11.10]
Epoch 23: 38%|███▊ | 12/32 [00:00<00:00, 315.00it/s, v_num=2, train_loss=2.990, RMSE=11.10]
Epoch 23: 41%|████ | 13/32 [00:00<00:00, 317.59it/s, v_num=2, train_loss=2.990, RMSE=11.10]
Epoch 23: 41%|████ | 13/32 [00:00<00:00, 316.01it/s, v_num=2, train_loss=3.180, RMSE=11.10]
Epoch 23: 44%|████▍ | 14/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.180, RMSE=11.10]
Epoch 23: 44%|████▍ | 14/32 [00:00<00:00, 316.79it/s, v_num=2, train_loss=3.300, RMSE=11.10]
Epoch 23: 47%|████▋ | 15/32 [00:00<00:00, 315.78it/s, v_num=2, train_loss=3.300, RMSE=11.10]
Epoch 23: 47%|████▋ | 15/32 [00:00<00:00, 314.41it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 50%|█████ | 16/32 [00:00<00:00, 316.69it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 50%|█████ | 16/32 [00:00<00:00, 315.19it/s, v_num=2, train_loss=3.400, RMSE=11.10]
Epoch 23: 53%|█████▎ | 17/32 [00:00<00:00, 317.38it/s, v_num=2, train_loss=3.400, RMSE=11.10]
Epoch 23: 53%|█████▎ | 17/32 [00:00<00:00, 316.18it/s, v_num=2, train_loss=3.310, RMSE=11.10]
Epoch 23: 56%|█████▋ | 18/32 [00:00<00:00, 317.93it/s, v_num=2, train_loss=3.310, RMSE=11.10]
Epoch 23: 56%|█████▋ | 18/32 [00:00<00:00, 316.78it/s, v_num=2, train_loss=3.180, RMSE=11.10]
Epoch 23: 59%|█████▉ | 19/32 [00:00<00:00, 318.50it/s, v_num=2, train_loss=3.180, RMSE=11.10]
Epoch 23: 59%|█████▉ | 19/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=3.200, RMSE=11.10]
Epoch 23: 62%|██████▎ | 20/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.200, RMSE=11.10]
Epoch 23: 62%|██████▎ | 20/32 [00:00<00:00, 318.07it/s, v_num=2, train_loss=3.490, RMSE=11.10]
Epoch 23: 66%|██████▌ | 21/32 [00:00<00:00, 319.46it/s, v_num=2, train_loss=3.490, RMSE=11.10]
Epoch 23: 66%|██████▌ | 21/32 [00:00<00:00, 318.43it/s, v_num=2, train_loss=3.320, RMSE=11.10]
Epoch 23: 69%|██████▉ | 22/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.320, RMSE=11.10]
Epoch 23: 69%|██████▉ | 22/32 [00:00<00:00, 318.68it/s, v_num=2, train_loss=3.130, RMSE=11.10]
Epoch 23: 72%|███████▏ | 23/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.130, RMSE=11.10]
Epoch 23: 72%|███████▏ | 23/32 [00:00<00:00, 319.02it/s, v_num=2, train_loss=3.050, RMSE=11.10]
Epoch 23: 75%|███████▌ | 24/32 [00:00<00:00, 320.25it/s, v_num=2, train_loss=3.050, RMSE=11.10]
Epoch 23: 75%|███████▌ | 24/32 [00:00<00:00, 319.37it/s, v_num=2, train_loss=3.160, RMSE=11.10]
Epoch 23: 78%|███████▊ | 25/32 [00:00<00:00, 320.69it/s, v_num=2, train_loss=3.160, RMSE=11.10]
Epoch 23: 78%|███████▊ | 25/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.350, RMSE=11.10]
Epoch 23: 81%|████████▏ | 26/32 [00:00<00:00, 320.97it/s, v_num=2, train_loss=3.350, RMSE=11.10]
Epoch 23: 81%|████████▏ | 26/32 [00:00<00:00, 320.15it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 84%|████████▍ | 27/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=3.110, RMSE=11.10]
Epoch 23: 84%|████████▍ | 27/32 [00:00<00:00, 320.43it/s, v_num=2, train_loss=3.370, RMSE=11.10]
Epoch 23: 88%|████████▊ | 28/32 [00:00<00:00, 321.43it/s, v_num=2, train_loss=3.370, RMSE=11.10]
Epoch 23: 88%|████████▊ | 28/32 [00:00<00:00, 320.67it/s, v_num=2, train_loss=3.160, RMSE=11.10]
Epoch 23: 91%|█████████ | 29/32 [00:00<00:00, 321.78it/s, v_num=2, train_loss=3.160, RMSE=11.10]
Epoch 23: 91%|█████████ | 29/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=3.370, RMSE=11.10]
Epoch 23: 94%|█████████▍| 30/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=3.370, RMSE=11.10]
Epoch 23: 94%|█████████▍| 30/32 [00:00<00:00, 321.19it/s, v_num=2, train_loss=2.990, RMSE=11.10]
Epoch 23: 97%|█████████▋| 31/32 [00:00<00:00, 322.04it/s, v_num=2, train_loss=2.990, RMSE=11.10]
Epoch 23: 97%|█████████▋| 31/32 [00:00<00:00, 321.34it/s, v_num=2, train_loss=3.410, RMSE=11.10]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 322.18it/s, v_num=2, train_loss=3.410, RMSE=11.10]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 321.52it/s, v_num=2, train_loss=2.840, RMSE=11.10]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 626.58it/s]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 264.05it/s, v_num=2, train_loss=2.840, RMSE=10.60]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 262.96it/s, v_num=2, train_loss=2.840, RMSE=10.60]
Epoch 23: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.840, RMSE=10.60]
Epoch 24: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.840, RMSE=10.60]
Epoch 24: 3%|▎ | 1/32 [00:00<00:00, 296.50it/s, v_num=2, train_loss=2.840, RMSE=10.60]
Epoch 24: 3%|▎ | 1/32 [00:00<00:00, 279.14it/s, v_num=2, train_loss=3.660, RMSE=10.60]
Epoch 24: 6%|▋ | 2/32 [00:00<00:00, 308.13it/s, v_num=2, train_loss=3.660, RMSE=10.60]
Epoch 24: 6%|▋ | 2/32 [00:00<00:00, 298.72it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 24: 9%|▉ | 3/32 [00:00<00:00, 311.97it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 24: 9%|▉ | 3/32 [00:00<00:00, 305.32it/s, v_num=2, train_loss=3.270, RMSE=10.60]
Epoch 24: 12%|█▎ | 4/32 [00:00<00:00, 315.43it/s, v_num=2, train_loss=3.270, RMSE=10.60]
Epoch 24: 12%|█▎ | 4/32 [00:00<00:00, 310.35it/s, v_num=2, train_loss=3.350, RMSE=10.60]
Epoch 24: 16%|█▌ | 5/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.350, RMSE=10.60]
Epoch 24: 16%|█▌ | 5/32 [00:00<00:00, 313.59it/s, v_num=2, train_loss=3.230, RMSE=10.60]
Epoch 24: 19%|█▉ | 6/32 [00:00<00:00, 319.27it/s, v_num=2, train_loss=3.230, RMSE=10.60]
Epoch 24: 19%|█▉ | 6/32 [00:00<00:00, 315.81it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 24: 22%|██▏ | 7/32 [00:00<00:00, 320.92it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 24: 22%|██▏ | 7/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 24: 25%|██▌ | 8/32 [00:00<00:00, 322.11it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 24: 25%|██▌ | 8/32 [00:00<00:00, 319.44it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 24: 28%|██▊ | 9/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 24: 28%|██▊ | 9/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=3.320, RMSE=10.60]
Epoch 24: 31%|███▏ | 10/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=3.320, RMSE=10.60]
Epoch 24: 31%|███▏ | 10/32 [00:00<00:00, 321.20it/s, v_num=2, train_loss=3.090, RMSE=10.60]
Epoch 24: 34%|███▍ | 11/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=3.090, RMSE=10.60]
Epoch 24: 34%|███▍ | 11/32 [00:00<00:00, 321.75it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 38%|███▊ | 12/32 [00:00<00:00, 324.43it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 38%|███▊ | 12/32 [00:00<00:00, 322.49it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 41%|████ | 13/32 [00:00<00:00, 324.72it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 41%|████ | 13/32 [00:00<00:00, 323.06it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 24: 44%|████▍ | 14/32 [00:00<00:00, 324.77it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 24: 44%|████▍ | 14/32 [00:00<00:00, 323.22it/s, v_num=2, train_loss=3.080, RMSE=10.60]
Epoch 24: 47%|████▋ | 15/32 [00:00<00:00, 325.12it/s, v_num=2, train_loss=3.080, RMSE=10.60]
Epoch 24: 47%|████▋ | 15/32 [00:00<00:00, 323.68it/s, v_num=2, train_loss=3.620, RMSE=10.60]
Epoch 24: 50%|█████ | 16/32 [00:00<00:00, 325.65it/s, v_num=2, train_loss=3.620, RMSE=10.60]
Epoch 24: 50%|█████ | 16/32 [00:00<00:00, 324.31it/s, v_num=2, train_loss=3.430, RMSE=10.60]
Epoch 24: 53%|█████▎ | 17/32 [00:00<00:00, 325.71it/s, v_num=2, train_loss=3.430, RMSE=10.60]
Epoch 24: 53%|█████▎ | 17/32 [00:00<00:00, 324.13it/s, v_num=2, train_loss=3.370, RMSE=10.60]
Epoch 24: 56%|█████▋ | 18/32 [00:00<00:00, 325.28it/s, v_num=2, train_loss=3.370, RMSE=10.60]
Epoch 24: 56%|█████▋ | 18/32 [00:00<00:00, 324.08it/s, v_num=2, train_loss=3.150, RMSE=10.60]
Epoch 24: 59%|█████▉ | 19/32 [00:00<00:00, 325.41it/s, v_num=2, train_loss=3.150, RMSE=10.60]
Epoch 24: 59%|█████▉ | 19/32 [00:00<00:00, 324.27it/s, v_num=2, train_loss=3.560, RMSE=10.60]
Epoch 24: 62%|██████▎ | 20/32 [00:00<00:00, 325.69it/s, v_num=2, train_loss=3.560, RMSE=10.60]
Epoch 24: 62%|██████▎ | 20/32 [00:00<00:00, 324.60it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 24: 66%|██████▌ | 21/32 [00:00<00:00, 325.90it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 24: 66%|██████▌ | 21/32 [00:00<00:00, 324.87it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 69%|██████▉ | 22/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 69%|██████▉ | 22/32 [00:00<00:00, 325.12it/s, v_num=2, train_loss=3.400, RMSE=10.60]
Epoch 24: 72%|███████▏ | 23/32 [00:00<00:00, 326.12it/s, v_num=2, train_loss=3.400, RMSE=10.60]
Epoch 24: 72%|███████▏ | 23/32 [00:00<00:00, 325.16it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 75%|███████▌ | 24/32 [00:00<00:00, 326.40it/s, v_num=2, train_loss=3.140, RMSE=10.60]
Epoch 24: 75%|███████▌ | 24/32 [00:00<00:00, 325.50it/s, v_num=2, train_loss=3.170, RMSE=10.60]
Epoch 24: 78%|███████▊ | 25/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=3.170, RMSE=10.60]
Epoch 24: 78%|███████▊ | 25/32 [00:00<00:00, 325.69it/s, v_num=2, train_loss=3.430, RMSE=10.60]
Epoch 24: 81%|████████▏ | 26/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.430, RMSE=10.60]
Epoch 24: 81%|████████▏ | 26/32 [00:00<00:00, 325.74it/s, v_num=2, train_loss=3.150, RMSE=10.60]
Epoch 24: 84%|████████▍ | 27/32 [00:00<00:00, 326.64it/s, v_num=2, train_loss=3.150, RMSE=10.60]
Epoch 24: 84%|████████▍ | 27/32 [00:00<00:00, 325.83it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 24: 88%|████████▊ | 28/32 [00:00<00:00, 326.79it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 24: 88%|████████▊ | 28/32 [00:00<00:00, 325.91it/s, v_num=2, train_loss=2.880, RMSE=10.60]
Epoch 24: 91%|█████████ | 29/32 [00:00<00:00, 326.93it/s, v_num=2, train_loss=2.880, RMSE=10.60]
Epoch 24: 91%|█████████ | 29/32 [00:00<00:00, 326.18it/s, v_num=2, train_loss=2.990, RMSE=10.60]
Epoch 24: 94%|█████████▍| 30/32 [00:00<00:00, 327.06it/s, v_num=2, train_loss=2.990, RMSE=10.60]
Epoch 24: 94%|█████████▍| 30/32 [00:00<00:00, 326.34it/s, v_num=2, train_loss=2.950, RMSE=10.60]
Epoch 24: 97%|█████████▋| 31/32 [00:00<00:00, 327.24it/s, v_num=2, train_loss=2.950, RMSE=10.60]
Epoch 24: 97%|█████████▋| 31/32 [00:00<00:00, 326.54it/s, v_num=2, train_loss=3.280, RMSE=10.60]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.280, RMSE=10.60]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 326.83it/s, v_num=2, train_loss=2.580, RMSE=10.60]
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Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 267.08it/s, v_num=2, train_loss=2.580, RMSE=10.00]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 265.97it/s, v_num=2, train_loss=2.580, RMSE=10.00]
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Epoch 25: 3%|▎ | 1/32 [00:00<00:00, 292.82it/s, v_num=2, train_loss=3.170, RMSE=10.00]
Epoch 25: 6%|▋ | 2/32 [00:00<00:00, 321.27it/s, v_num=2, train_loss=3.170, RMSE=10.00]
Epoch 25: 6%|▋ | 2/32 [00:00<00:00, 311.00it/s, v_num=2, train_loss=3.580, RMSE=10.00]
Epoch 25: 9%|▉ | 3/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.580, RMSE=10.00]
Epoch 25: 9%|▉ | 3/32 [00:00<00:00, 315.83it/s, v_num=2, train_loss=3.100, RMSE=10.00]
Epoch 25: 12%|█▎ | 4/32 [00:00<00:00, 324.69it/s, v_num=2, train_loss=3.100, RMSE=10.00]
Epoch 25: 12%|█▎ | 4/32 [00:00<00:00, 319.35it/s, v_num=2, train_loss=3.150, RMSE=10.00]
Epoch 25: 16%|█▌ | 5/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=3.150, RMSE=10.00]
Epoch 25: 16%|█▌ | 5/32 [00:00<00:00, 321.19it/s, v_num=2, train_loss=3.410, RMSE=10.00]
Epoch 25: 19%|█▉ | 6/32 [00:00<00:00, 326.74it/s, v_num=2, train_loss=3.410, RMSE=10.00]
Epoch 25: 19%|█▉ | 6/32 [00:00<00:00, 322.66it/s, v_num=2, train_loss=2.890, RMSE=10.00]
Epoch 25: 22%|██▏ | 7/32 [00:00<00:00, 327.11it/s, v_num=2, train_loss=2.890, RMSE=10.00]
Epoch 25: 22%|██▏ | 7/32 [00:00<00:00, 324.01it/s, v_num=2, train_loss=3.090, RMSE=10.00]
Epoch 25: 25%|██▌ | 8/32 [00:00<00:00, 327.46it/s, v_num=2, train_loss=3.090, RMSE=10.00]
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Epoch 25: 28%|██▊ | 9/32 [00:00<00:00, 327.65it/s, v_num=2, train_loss=3.260, RMSE=10.00]
Epoch 25: 28%|██▊ | 9/32 [00:00<00:00, 325.24it/s, v_num=2, train_loss=3.370, RMSE=10.00]
Epoch 25: 31%|███▏ | 10/32 [00:00<00:00, 328.50it/s, v_num=2, train_loss=3.370, RMSE=10.00]
Epoch 25: 31%|███▏ | 10/32 [00:00<00:00, 325.96it/s, v_num=2, train_loss=2.880, RMSE=10.00]
Epoch 25: 34%|███▍ | 11/32 [00:00<00:00, 328.70it/s, v_num=2, train_loss=2.880, RMSE=10.00]
Epoch 25: 34%|███▍ | 11/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=2.940, RMSE=10.00]
Epoch 25: 38%|███▊ | 12/32 [00:00<00:00, 328.69it/s, v_num=2, train_loss=2.940, RMSE=10.00]
Epoch 25: 38%|███▊ | 12/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=3.120, RMSE=10.00]
Epoch 25: 41%|████ | 13/32 [00:00<00:00, 328.68it/s, v_num=2, train_loss=3.120, RMSE=10.00]
Epoch 25: 41%|████ | 13/32 [00:00<00:00, 326.99it/s, v_num=2, train_loss=3.100, RMSE=10.00]
Epoch 25: 44%|████▍ | 14/32 [00:00<00:00, 329.08it/s, v_num=2, train_loss=3.100, RMSE=10.00]
Epoch 25: 44%|████▍ | 14/32 [00:00<00:00, 327.23it/s, v_num=2, train_loss=3.040, RMSE=10.00]
Epoch 25: 47%|████▋ | 15/32 [00:00<00:00, 328.96it/s, v_num=2, train_loss=3.040, RMSE=10.00]
Epoch 25: 47%|████▋ | 15/32 [00:00<00:00, 327.47it/s, v_num=2, train_loss=3.490, RMSE=10.00]
Epoch 25: 50%|█████ | 16/32 [00:00<00:00, 329.00it/s, v_num=2, train_loss=3.490, RMSE=10.00]
Epoch 25: 50%|█████ | 16/32 [00:00<00:00, 327.61it/s, v_num=2, train_loss=3.150, RMSE=10.00]
Epoch 25: 53%|█████▎ | 17/32 [00:00<00:00, 328.94it/s, v_num=2, train_loss=3.150, RMSE=10.00]
Epoch 25: 53%|█████▎ | 17/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.300, RMSE=10.00]
Epoch 25: 56%|█████▋ | 18/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=3.300, RMSE=10.00]
Epoch 25: 56%|█████▋ | 18/32 [00:00<00:00, 327.34it/s, v_num=2, train_loss=3.260, RMSE=10.00]
Epoch 25: 59%|█████▉ | 19/32 [00:00<00:00, 328.83it/s, v_num=2, train_loss=3.260, RMSE=10.00]
Epoch 25: 59%|█████▉ | 19/32 [00:00<00:00, 327.67it/s, v_num=2, train_loss=3.060, RMSE=10.00]
Epoch 25: 62%|██████▎ | 20/32 [00:00<00:00, 328.87it/s, v_num=2, train_loss=3.060, RMSE=10.00]
Epoch 25: 62%|██████▎ | 20/32 [00:00<00:00, 327.75it/s, v_num=2, train_loss=3.210, RMSE=10.00]
Epoch 25: 66%|██████▌ | 21/32 [00:00<00:00, 328.99it/s, v_num=2, train_loss=3.210, RMSE=10.00]
Epoch 25: 66%|██████▌ | 21/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=3.390, RMSE=10.00]
Epoch 25: 69%|██████▉ | 22/32 [00:00<00:00, 328.76it/s, v_num=2, train_loss=3.390, RMSE=10.00]
Epoch 25: 69%|██████▉ | 22/32 [00:00<00:00, 327.75it/s, v_num=2, train_loss=3.500, RMSE=10.00]
Epoch 25: 72%|███████▏ | 23/32 [00:00<00:00, 328.98it/s, v_num=2, train_loss=3.500, RMSE=10.00]
Epoch 25: 72%|███████▏ | 23/32 [00:00<00:00, 328.01it/s, v_num=2, train_loss=3.310, RMSE=10.00]
Epoch 25: 75%|███████▌ | 24/32 [00:00<00:00, 329.07it/s, v_num=2, train_loss=3.310, RMSE=10.00]
Epoch 25: 75%|███████▌ | 24/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.090, RMSE=10.00]
Epoch 25: 78%|███████▊ | 25/32 [00:00<00:00, 329.15it/s, v_num=2, train_loss=3.090, RMSE=10.00]
Epoch 25: 78%|███████▊ | 25/32 [00:00<00:00, 328.26it/s, v_num=2, train_loss=3.140, RMSE=10.00]
Epoch 25: 81%|████████▏ | 26/32 [00:00<00:00, 329.19it/s, v_num=2, train_loss=3.140, RMSE=10.00]
Epoch 25: 81%|████████▏ | 26/32 [00:00<00:00, 328.33it/s, v_num=2, train_loss=3.680, RMSE=10.00]
Epoch 25: 84%|████████▍ | 27/32 [00:00<00:00, 329.28it/s, v_num=2, train_loss=3.680, RMSE=10.00]
Epoch 25: 84%|████████▍ | 27/32 [00:00<00:00, 328.46it/s, v_num=2, train_loss=3.090, RMSE=10.00]
Epoch 25: 88%|████████▊ | 28/32 [00:00<00:00, 329.34it/s, v_num=2, train_loss=3.090, RMSE=10.00]
Epoch 25: 88%|████████▊ | 28/32 [00:00<00:00, 328.55it/s, v_num=2, train_loss=3.270, RMSE=10.00]
Epoch 25: 91%|█████████ | 29/32 [00:00<00:00, 329.29it/s, v_num=2, train_loss=3.270, RMSE=10.00]
Epoch 25: 91%|█████████ | 29/32 [00:00<00:00, 328.52it/s, v_num=2, train_loss=3.130, RMSE=10.00]
Epoch 25: 94%|█████████▍| 30/32 [00:00<00:00, 329.35it/s, v_num=2, train_loss=3.130, RMSE=10.00]
Epoch 25: 94%|█████████▍| 30/32 [00:00<00:00, 328.59it/s, v_num=2, train_loss=2.780, RMSE=10.00]
Epoch 25: 97%|█████████▋| 31/32 [00:00<00:00, 329.52it/s, v_num=2, train_loss=2.780, RMSE=10.00]
Epoch 25: 97%|█████████▋| 31/32 [00:00<00:00, 328.80it/s, v_num=2, train_loss=3.380, RMSE=10.00]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 329.67it/s, v_num=2, train_loss=3.380, RMSE=10.00]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 328.95it/s, v_num=2, train_loss=3.160, RMSE=10.00]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 632.73it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 633.00it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 636.13it/s]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 270.02it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 268.82it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 25: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 3%|▎ | 1/32 [00:00<00:00, 263.71it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 3%|▎ | 1/32 [00:00<00:00, 246.03it/s, v_num=2, train_loss=3.250, RMSE=9.500]
Epoch 26: 6%|▋ | 2/32 [00:00<00:00, 270.72it/s, v_num=2, train_loss=3.250, RMSE=9.500]
Epoch 26: 6%|▋ | 2/32 [00:00<00:00, 262.58it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 9%|▉ | 3/32 [00:00<00:00, 275.42it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 9%|▉ | 3/32 [00:00<00:00, 268.54it/s, v_num=2, train_loss=3.810, RMSE=9.500]
Epoch 26: 12%|█▎ | 4/32 [00:00<00:00, 272.22it/s, v_num=2, train_loss=3.810, RMSE=9.500]
Epoch 26: 12%|█▎ | 4/32 [00:00<00:00, 268.45it/s, v_num=2, train_loss=3.150, RMSE=9.500]
Epoch 26: 16%|█▌ | 5/32 [00:00<00:00, 281.44it/s, v_num=2, train_loss=3.150, RMSE=9.500]
Epoch 26: 16%|█▌ | 5/32 [00:00<00:00, 278.22it/s, v_num=2, train_loss=3.110, RMSE=9.500]
Epoch 26: 19%|█▉ | 6/32 [00:00<00:00, 288.29it/s, v_num=2, train_loss=3.110, RMSE=9.500]
Epoch 26: 19%|█▉ | 6/32 [00:00<00:00, 285.48it/s, v_num=2, train_loss=3.310, RMSE=9.500]
Epoch 26: 22%|██▏ | 7/32 [00:00<00:00, 293.39it/s, v_num=2, train_loss=3.310, RMSE=9.500]
Epoch 26: 22%|██▏ | 7/32 [00:00<00:00, 290.89it/s, v_num=2, train_loss=3.200, RMSE=9.500]
Epoch 26: 25%|██▌ | 8/32 [00:00<00:00, 297.28it/s, v_num=2, train_loss=3.200, RMSE=9.500]
Epoch 26: 25%|██▌ | 8/32 [00:00<00:00, 295.02it/s, v_num=2, train_loss=3.080, RMSE=9.500]
Epoch 26: 28%|██▊ | 9/32 [00:00<00:00, 300.45it/s, v_num=2, train_loss=3.080, RMSE=9.500]
Epoch 26: 28%|██▊ | 9/32 [00:00<00:00, 298.39it/s, v_num=2, train_loss=3.080, RMSE=9.500]
Epoch 26: 31%|███▏ | 10/32 [00:00<00:00, 303.11it/s, v_num=2, train_loss=3.080, RMSE=9.500]
Epoch 26: 31%|███▏ | 10/32 [00:00<00:00, 301.22it/s, v_num=2, train_loss=3.310, RMSE=9.500]
Epoch 26: 34%|███▍ | 11/32 [00:00<00:00, 305.67it/s, v_num=2, train_loss=3.310, RMSE=9.500]
Epoch 26: 34%|███▍ | 11/32 [00:00<00:00, 303.94it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 38%|███▊ | 12/32 [00:00<00:00, 307.28it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 38%|███▊ | 12/32 [00:00<00:00, 305.66it/s, v_num=2, train_loss=2.840, RMSE=9.500]
Epoch 26: 41%|████ | 13/32 [00:00<00:00, 308.82it/s, v_num=2, train_loss=2.840, RMSE=9.500]
Epoch 26: 41%|████ | 13/32 [00:00<00:00, 307.33it/s, v_num=2, train_loss=3.150, RMSE=9.500]
Epoch 26: 44%|████▍ | 14/32 [00:00<00:00, 309.81it/s, v_num=2, train_loss=3.150, RMSE=9.500]
Epoch 26: 44%|████▍ | 14/32 [00:00<00:00, 308.42it/s, v_num=2, train_loss=3.200, RMSE=9.500]
Epoch 26: 47%|████▋ | 15/32 [00:00<00:00, 311.36it/s, v_num=2, train_loss=3.200, RMSE=9.500]
Epoch 26: 47%|████▋ | 15/32 [00:00<00:00, 310.05it/s, v_num=2, train_loss=3.100, RMSE=9.500]
Epoch 26: 50%|█████ | 16/32 [00:00<00:00, 312.44it/s, v_num=2, train_loss=3.100, RMSE=9.500]
Epoch 26: 50%|█████ | 16/32 [00:00<00:00, 311.20it/s, v_num=2, train_loss=2.970, RMSE=9.500]
Epoch 26: 53%|█████▎ | 17/32 [00:00<00:00, 313.36it/s, v_num=2, train_loss=2.970, RMSE=9.500]
Epoch 26: 53%|█████▎ | 17/32 [00:00<00:00, 312.19it/s, v_num=2, train_loss=3.420, RMSE=9.500]
Epoch 26: 56%|█████▋ | 18/32 [00:00<00:00, 314.27it/s, v_num=2, train_loss=3.420, RMSE=9.500]
Epoch 26: 56%|█████▋ | 18/32 [00:00<00:00, 313.15it/s, v_num=2, train_loss=2.990, RMSE=9.500]
Epoch 26: 59%|█████▉ | 19/32 [00:00<00:00, 312.59it/s, v_num=2, train_loss=2.990, RMSE=9.500]
Epoch 26: 59%|█████▉ | 19/32 [00:00<00:00, 311.54it/s, v_num=2, train_loss=3.390, RMSE=9.500]
Epoch 26: 62%|██████▎ | 20/32 [00:00<00:00, 313.56it/s, v_num=2, train_loss=3.390, RMSE=9.500]
Epoch 26: 62%|██████▎ | 20/32 [00:00<00:00, 312.56it/s, v_num=2, train_loss=2.830, RMSE=9.500]
Epoch 26: 66%|██████▌ | 21/32 [00:00<00:00, 314.25it/s, v_num=2, train_loss=2.830, RMSE=9.500]
Epoch 26: 66%|██████▌ | 21/32 [00:00<00:00, 313.30it/s, v_num=2, train_loss=3.800, RMSE=9.500]
Epoch 26: 69%|██████▉ | 22/32 [00:00<00:00, 314.84it/s, v_num=2, train_loss=3.800, RMSE=9.500]
Epoch 26: 69%|██████▉ | 22/32 [00:00<00:00, 313.92it/s, v_num=2, train_loss=3.220, RMSE=9.500]
Epoch 26: 72%|███████▏ | 23/32 [00:00<00:00, 315.47it/s, v_num=2, train_loss=3.220, RMSE=9.500]
Epoch 26: 72%|███████▏ | 23/32 [00:00<00:00, 314.54it/s, v_num=2, train_loss=3.000, RMSE=9.500]
Epoch 26: 75%|███████▌ | 24/32 [00:00<00:00, 316.13it/s, v_num=2, train_loss=3.000, RMSE=9.500]
Epoch 26: 75%|███████▌ | 24/32 [00:00<00:00, 315.28it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 78%|███████▊ | 25/32 [00:00<00:00, 316.66it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 78%|███████▊ | 25/32 [00:00<00:00, 315.84it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 81%|████████▏ | 26/32 [00:00<00:00, 317.11it/s, v_num=2, train_loss=3.050, RMSE=9.500]
Epoch 26: 81%|████████▏ | 26/32 [00:00<00:00, 316.33it/s, v_num=2, train_loss=2.980, RMSE=9.500]
Epoch 26: 84%|████████▍ | 27/32 [00:00<00:00, 317.63it/s, v_num=2, train_loss=2.980, RMSE=9.500]
Epoch 26: 84%|████████▍ | 27/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=3.110, RMSE=9.500]
Epoch 26: 88%|████████▊ | 28/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.110, RMSE=9.500]
Epoch 26: 88%|████████▊ | 28/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=2.980, RMSE=9.500]
Epoch 26: 91%|█████████ | 29/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=2.980, RMSE=9.500]
Epoch 26: 91%|█████████ | 29/32 [00:00<00:00, 317.87it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 94%|█████████▍| 30/32 [00:00<00:00, 318.97it/s, v_num=2, train_loss=3.160, RMSE=9.500]
Epoch 26: 94%|█████████▍| 30/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.180, RMSE=9.500]
Epoch 26: 97%|█████████▋| 31/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.180, RMSE=9.500]
Epoch 26: 97%|█████████▋| 31/32 [00:00<00:00, 318.67it/s, v_num=2, train_loss=3.320, RMSE=9.500]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 319.85it/s, v_num=2, train_loss=3.320, RMSE=9.500]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 319.19it/s, v_num=2, train_loss=3.950, RMSE=9.500]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 628.79it/s]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 262.77it/s, v_num=2, train_loss=3.950, RMSE=9.130]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 261.71it/s, v_num=2, train_loss=3.950, RMSE=9.130]
Epoch 26: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.950, RMSE=9.130]
Epoch 27: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.950, RMSE=9.130]
Epoch 27: 3%|▎ | 1/32 [00:00<00:00, 308.72it/s, v_num=2, train_loss=3.950, RMSE=9.130]
Epoch 27: 3%|▎ | 1/32 [00:00<00:00, 287.62it/s, v_num=2, train_loss=2.810, RMSE=9.130]
Epoch 27: 6%|▋ | 2/32 [00:00<00:00, 316.15it/s, v_num=2, train_loss=2.810, RMSE=9.130]
Epoch 27: 6%|▋ | 2/32 [00:00<00:00, 306.30it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 9%|▉ | 3/32 [00:00<00:00, 320.42it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 9%|▉ | 3/32 [00:00<00:00, 313.59it/s, v_num=2, train_loss=3.030, RMSE=9.130]
Epoch 27: 12%|█▎ | 4/32 [00:00<00:00, 321.45it/s, v_num=2, train_loss=3.030, RMSE=9.130]
Epoch 27: 12%|█▎ | 4/32 [00:00<00:00, 316.22it/s, v_num=2, train_loss=2.750, RMSE=9.130]
Epoch 27: 16%|█▌ | 5/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=2.750, RMSE=9.130]
Epoch 27: 16%|█▌ | 5/32 [00:00<00:00, 318.82it/s, v_num=2, train_loss=3.110, RMSE=9.130]
Epoch 27: 19%|█▉ | 6/32 [00:00<00:00, 325.15it/s, v_num=2, train_loss=3.110, RMSE=9.130]
Epoch 27: 19%|█▉ | 6/32 [00:00<00:00, 321.62it/s, v_num=2, train_loss=3.060, RMSE=9.130]
Epoch 27: 22%|██▏ | 7/32 [00:00<00:00, 325.62it/s, v_num=2, train_loss=3.060, RMSE=9.130]
Epoch 27: 22%|██▏ | 7/32 [00:00<00:00, 322.31it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 25%|██▌ | 8/32 [00:00<00:00, 326.12it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 25%|██▌ | 8/32 [00:00<00:00, 323.45it/s, v_num=2, train_loss=2.990, RMSE=9.130]
Epoch 27: 28%|██▊ | 9/32 [00:00<00:00, 326.28it/s, v_num=2, train_loss=2.990, RMSE=9.130]
Epoch 27: 28%|██▊ | 9/32 [00:00<00:00, 323.83it/s, v_num=2, train_loss=3.090, RMSE=9.130]
Epoch 27: 31%|███▏ | 10/32 [00:00<00:00, 326.79it/s, v_num=2, train_loss=3.090, RMSE=9.130]
Epoch 27: 31%|███▏ | 10/32 [00:00<00:00, 324.62it/s, v_num=2, train_loss=2.830, RMSE=9.130]
Epoch 27: 34%|███▍ | 11/32 [00:00<00:00, 327.06it/s, v_num=2, train_loss=2.830, RMSE=9.130]
Epoch 27: 34%|███▍ | 11/32 [00:00<00:00, 325.08it/s, v_num=2, train_loss=3.510, RMSE=9.130]
Epoch 27: 38%|███▊ | 12/32 [00:00<00:00, 327.25it/s, v_num=2, train_loss=3.510, RMSE=9.130]
Epoch 27: 38%|███▊ | 12/32 [00:00<00:00, 325.44it/s, v_num=2, train_loss=3.370, RMSE=9.130]
Epoch 27: 41%|████ | 13/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=3.370, RMSE=9.130]
Epoch 27: 41%|████ | 13/32 [00:00<00:00, 325.72it/s, v_num=2, train_loss=3.230, RMSE=9.130]
Epoch 27: 44%|████▍ | 14/32 [00:00<00:00, 327.71it/s, v_num=2, train_loss=3.230, RMSE=9.130]
Epoch 27: 44%|████▍ | 14/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=3.390, RMSE=9.130]
Epoch 27: 47%|████▋ | 15/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=3.390, RMSE=9.130]
Epoch 27: 47%|████▋ | 15/32 [00:00<00:00, 326.31it/s, v_num=2, train_loss=3.220, RMSE=9.130]
Epoch 27: 50%|█████ | 16/32 [00:00<00:00, 327.83it/s, v_num=2, train_loss=3.220, RMSE=9.130]
Epoch 27: 50%|█████ | 16/32 [00:00<00:00, 326.46it/s, v_num=2, train_loss=3.210, RMSE=9.130]
Epoch 27: 53%|█████▎ | 17/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=3.210, RMSE=9.130]
Epoch 27: 53%|█████▎ | 17/32 [00:00<00:00, 326.61it/s, v_num=2, train_loss=3.190, RMSE=9.130]
Epoch 27: 56%|█████▋ | 18/32 [00:00<00:00, 328.11it/s, v_num=2, train_loss=3.190, RMSE=9.130]
Epoch 27: 56%|█████▋ | 18/32 [00:00<00:00, 326.89it/s, v_num=2, train_loss=3.430, RMSE=9.130]
Epoch 27: 59%|█████▉ | 19/32 [00:00<00:00, 328.13it/s, v_num=2, train_loss=3.430, RMSE=9.130]
Epoch 27: 59%|█████▉ | 19/32 [00:00<00:00, 326.98it/s, v_num=2, train_loss=3.390, RMSE=9.130]
Epoch 27: 62%|██████▎ | 20/32 [00:00<00:00, 328.24it/s, v_num=2, train_loss=3.390, RMSE=9.130]
Epoch 27: 62%|██████▎ | 20/32 [00:00<00:00, 327.14it/s, v_num=2, train_loss=2.890, RMSE=9.130]
Epoch 27: 66%|██████▌ | 21/32 [00:00<00:00, 328.28it/s, v_num=2, train_loss=2.890, RMSE=9.130]
Epoch 27: 66%|██████▌ | 21/32 [00:00<00:00, 327.23it/s, v_num=2, train_loss=3.070, RMSE=9.130]
Epoch 27: 69%|██████▉ | 22/32 [00:00<00:00, 328.52it/s, v_num=2, train_loss=3.070, RMSE=9.130]
Epoch 27: 69%|██████▉ | 22/32 [00:00<00:00, 327.52it/s, v_num=2, train_loss=3.180, RMSE=9.130]
Epoch 27: 72%|███████▏ | 23/32 [00:00<00:00, 328.56it/s, v_num=2, train_loss=3.180, RMSE=9.130]
Epoch 27: 72%|███████▏ | 23/32 [00:00<00:00, 327.60it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 75%|███████▌ | 24/32 [00:00<00:00, 328.58it/s, v_num=2, train_loss=3.250, RMSE=9.130]
Epoch 27: 75%|███████▌ | 24/32 [00:00<00:00, 327.64it/s, v_num=2, train_loss=3.360, RMSE=9.130]
Epoch 27: 78%|███████▊ | 25/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=3.360, RMSE=9.130]
Epoch 27: 78%|███████▊ | 25/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=3.270, RMSE=9.130]
Epoch 27: 81%|████████▏ | 26/32 [00:00<00:00, 328.90it/s, v_num=2, train_loss=3.270, RMSE=9.130]
Epoch 27: 81%|████████▏ | 26/32 [00:00<00:00, 328.05it/s, v_num=2, train_loss=3.200, RMSE=9.130]
Epoch 27: 84%|████████▍ | 27/32 [00:00<00:00, 328.79it/s, v_num=2, train_loss=3.200, RMSE=9.130]
Epoch 27: 84%|████████▍ | 27/32 [00:00<00:00, 327.89it/s, v_num=2, train_loss=3.070, RMSE=9.130]
Epoch 27: 88%|████████▊ | 28/32 [00:00<00:00, 328.34it/s, v_num=2, train_loss=3.070, RMSE=9.130]
Epoch 27: 88%|████████▊ | 28/32 [00:00<00:00, 327.55it/s, v_num=2, train_loss=3.200, RMSE=9.130]
Epoch 27: 91%|█████████ | 29/32 [00:00<00:00, 328.41it/s, v_num=2, train_loss=3.200, RMSE=9.130]
Epoch 27: 91%|█████████ | 29/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=3.220, RMSE=9.130]
Epoch 27: 94%|█████████▍| 30/32 [00:00<00:00, 328.36it/s, v_num=2, train_loss=3.220, RMSE=9.130]
Epoch 27: 94%|█████████▍| 30/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.080, RMSE=9.130]
Epoch 27: 97%|█████████▋| 31/32 [00:00<00:00, 328.43it/s, v_num=2, train_loss=3.080, RMSE=9.130]
Epoch 27: 97%|█████████▋| 31/32 [00:00<00:00, 327.72it/s, v_num=2, train_loss=2.780, RMSE=9.130]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 328.57it/s, v_num=2, train_loss=2.780, RMSE=9.130]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 327.89it/s, v_num=2, train_loss=2.780, RMSE=9.130]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 623.44it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 624.59it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 628.34it/s]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 268.64it/s, v_num=2, train_loss=2.780, RMSE=8.640]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 267.38it/s, v_num=2, train_loss=2.780, RMSE=8.640]
Epoch 27: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.780, RMSE=8.640]
Epoch 28: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.780, RMSE=8.640]
Epoch 28: 3%|▎ | 1/32 [00:00<00:00, 308.93it/s, v_num=2, train_loss=2.780, RMSE=8.640]
Epoch 28: 3%|▎ | 1/32 [00:00<00:00, 290.14it/s, v_num=2, train_loss=3.320, RMSE=8.640]
Epoch 28: 6%|▋ | 2/32 [00:00<00:00, 314.82it/s, v_num=2, train_loss=3.320, RMSE=8.640]
Epoch 28: 6%|▋ | 2/32 [00:00<00:00, 304.94it/s, v_num=2, train_loss=3.240, RMSE=8.640]
Epoch 28: 9%|▉ | 3/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=3.240, RMSE=8.640]
Epoch 28: 9%|▉ | 3/32 [00:00<00:00, 310.77it/s, v_num=2, train_loss=3.250, RMSE=8.640]
Epoch 28: 12%|█▎ | 4/32 [00:00<00:00, 321.20it/s, v_num=2, train_loss=3.250, RMSE=8.640]
Epoch 28: 12%|█▎ | 4/32 [00:00<00:00, 314.99it/s, v_num=2, train_loss=3.200, RMSE=8.640]
Epoch 28: 16%|█▌ | 5/32 [00:00<00:00, 313.25it/s, v_num=2, train_loss=3.200, RMSE=8.640]
Epoch 28: 16%|█▌ | 5/32 [00:00<00:00, 309.23it/s, v_num=2, train_loss=3.310, RMSE=8.640]
Epoch 28: 19%|█▉ | 6/32 [00:00<00:00, 315.67it/s, v_num=2, train_loss=3.310, RMSE=8.640]
Epoch 28: 19%|█▉ | 6/32 [00:00<00:00, 312.28it/s, v_num=2, train_loss=2.820, RMSE=8.640]
Epoch 28: 22%|██▏ | 7/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=2.820, RMSE=8.640]
Epoch 28: 22%|██▏ | 7/32 [00:00<00:00, 314.76it/s, v_num=2, train_loss=3.230, RMSE=8.640]
Epoch 28: 25%|██▌ | 8/32 [00:00<00:00, 318.87it/s, v_num=2, train_loss=3.230, RMSE=8.640]
Epoch 28: 25%|██▌ | 8/32 [00:00<00:00, 316.26it/s, v_num=2, train_loss=3.220, RMSE=8.640]
Epoch 28: 28%|██▊ | 9/32 [00:00<00:00, 320.47it/s, v_num=2, train_loss=3.220, RMSE=8.640]
Epoch 28: 28%|██▊ | 9/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.250, RMSE=8.640]
Epoch 28: 31%|███▏ | 10/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=3.250, RMSE=8.640]
Epoch 28: 31%|███▏ | 10/32 [00:00<00:00, 318.47it/s, v_num=2, train_loss=2.990, RMSE=8.640]
Epoch 28: 34%|███▍ | 11/32 [00:00<00:00, 321.38it/s, v_num=2, train_loss=2.990, RMSE=8.640]
Epoch 28: 34%|███▍ | 11/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=3.610, RMSE=8.640]
Epoch 28: 38%|███▊ | 12/32 [00:00<00:00, 322.11it/s, v_num=2, train_loss=3.610, RMSE=8.640]
Epoch 28: 38%|███▊ | 12/32 [00:00<00:00, 320.35it/s, v_num=2, train_loss=3.040, RMSE=8.640]
Epoch 28: 41%|████ | 13/32 [00:00<00:00, 323.05it/s, v_num=2, train_loss=3.040, RMSE=8.640]
Epoch 28: 41%|████ | 13/32 [00:00<00:00, 321.43it/s, v_num=2, train_loss=2.870, RMSE=8.640]
Epoch 28: 44%|████▍ | 14/32 [00:00<00:00, 323.68it/s, v_num=2, train_loss=2.870, RMSE=8.640]
Epoch 28: 44%|████▍ | 14/32 [00:00<00:00, 322.16it/s, v_num=2, train_loss=2.990, RMSE=8.640]
Epoch 28: 47%|████▋ | 15/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=2.990, RMSE=8.640]
Epoch 28: 47%|████▋ | 15/32 [00:00<00:00, 322.79it/s, v_num=2, train_loss=3.130, RMSE=8.640]
Epoch 28: 50%|█████ | 16/32 [00:00<00:00, 324.35it/s, v_num=2, train_loss=3.130, RMSE=8.640]
Epoch 28: 50%|█████ | 16/32 [00:00<00:00, 323.02it/s, v_num=2, train_loss=3.300, RMSE=8.640]
Epoch 28: 53%|█████▎ | 17/32 [00:00<00:00, 324.99it/s, v_num=2, train_loss=3.300, RMSE=8.640]
Epoch 28: 53%|█████▎ | 17/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=3.090, RMSE=8.640]
Epoch 28: 56%|█████▋ | 18/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.090, RMSE=8.640]
Epoch 28: 56%|█████▋ | 18/32 [00:00<00:00, 323.02it/s, v_num=2, train_loss=3.150, RMSE=8.640]
Epoch 28: 59%|█████▉ | 19/32 [00:00<00:00, 324.48it/s, v_num=2, train_loss=3.150, RMSE=8.640]
Epoch 28: 59%|█████▉ | 19/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=2.910, RMSE=8.640]
Epoch 28: 62%|██████▎ | 20/32 [00:00<00:00, 324.71it/s, v_num=2, train_loss=2.910, RMSE=8.640]
Epoch 28: 62%|██████▎ | 20/32 [00:00<00:00, 323.64it/s, v_num=2, train_loss=2.820, RMSE=8.640]
Epoch 28: 66%|██████▌ | 21/32 [00:00<00:00, 325.22it/s, v_num=2, train_loss=2.820, RMSE=8.640]
Epoch 28: 66%|██████▌ | 21/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=3.290, RMSE=8.640]
Epoch 28: 69%|██████▉ | 22/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=3.290, RMSE=8.640]
Epoch 28: 69%|██████▉ | 22/32 [00:00<00:00, 321.03it/s, v_num=2, train_loss=3.100, RMSE=8.640]
Epoch 28: 72%|███████▏ | 23/32 [00:00<00:00, 317.82it/s, v_num=2, train_loss=3.100, RMSE=8.640]
Epoch 28: 72%|███████▏ | 23/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=2.840, RMSE=8.640]
Epoch 28: 75%|███████▌ | 24/32 [00:00<00:00, 313.84it/s, v_num=2, train_loss=2.840, RMSE=8.640]
Epoch 28: 75%|███████▌ | 24/32 [00:00<00:00, 312.62it/s, v_num=2, train_loss=2.930, RMSE=8.640]
Epoch 28: 78%|███████▊ | 25/32 [00:00<00:00, 310.41it/s, v_num=2, train_loss=2.930, RMSE=8.640]
Epoch 28: 78%|███████▊ | 25/32 [00:00<00:00, 309.30it/s, v_num=2, train_loss=3.070, RMSE=8.640]
Epoch 28: 81%|████████▏ | 26/32 [00:00<00:00, 307.65it/s, v_num=2, train_loss=3.070, RMSE=8.640]
Epoch 28: 81%|████████▏ | 26/32 [00:00<00:00, 306.62it/s, v_num=2, train_loss=3.110, RMSE=8.640]
Epoch 28: 84%|████████▍ | 27/32 [00:00<00:00, 304.97it/s, v_num=2, train_loss=3.110, RMSE=8.640]
Epoch 28: 84%|████████▍ | 27/32 [00:00<00:00, 304.10it/s, v_num=2, train_loss=3.050, RMSE=8.640]
Epoch 28: 88%|████████▊ | 28/32 [00:00<00:00, 305.24it/s, v_num=2, train_loss=3.050, RMSE=8.640]
Epoch 28: 88%|████████▊ | 28/32 [00:00<00:00, 304.52it/s, v_num=2, train_loss=3.170, RMSE=8.640]
Epoch 28: 91%|█████████ | 29/32 [00:00<00:00, 305.36it/s, v_num=2, train_loss=3.170, RMSE=8.640]
Epoch 28: 91%|█████████ | 29/32 [00:00<00:00, 304.68it/s, v_num=2, train_loss=3.160, RMSE=8.640]
Epoch 28: 94%|█████████▍| 30/32 [00:00<00:00, 305.69it/s, v_num=2, train_loss=3.160, RMSE=8.640]
Epoch 28: 94%|█████████▍| 30/32 [00:00<00:00, 305.01it/s, v_num=2, train_loss=3.220, RMSE=8.640]
Epoch 28: 97%|█████████▋| 31/32 [00:00<00:00, 306.17it/s, v_num=2, train_loss=3.220, RMSE=8.640]
Epoch 28: 97%|█████████▋| 31/32 [00:00<00:00, 305.55it/s, v_num=2, train_loss=3.270, RMSE=8.640]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 306.55it/s, v_num=2, train_loss=3.270, RMSE=8.640]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 305.90it/s, v_num=2, train_loss=2.790, RMSE=8.640]
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Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 252.86it/s, v_num=2, train_loss=2.790, RMSE=8.230]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 251.87it/s, v_num=2, train_loss=2.790, RMSE=8.230]
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Epoch 29: 3%|▎ | 1/32 [00:00<00:00, 301.49it/s, v_num=2, train_loss=2.790, RMSE=8.230]
Epoch 29: 3%|▎ | 1/32 [00:00<00:00, 283.65it/s, v_num=2, train_loss=2.930, RMSE=8.230]
Epoch 29: 6%|▋ | 2/32 [00:00<00:00, 313.93it/s, v_num=2, train_loss=2.930, RMSE=8.230]
Epoch 29: 6%|▋ | 2/32 [00:00<00:00, 302.43it/s, v_num=2, train_loss=2.880, RMSE=8.230]
Epoch 29: 9%|▉ | 3/32 [00:00<00:00, 317.42it/s, v_num=2, train_loss=2.880, RMSE=8.230]
Epoch 29: 9%|▉ | 3/32 [00:00<00:00, 310.71it/s, v_num=2, train_loss=3.050, RMSE=8.230]
Epoch 29: 12%|█▎ | 4/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.050, RMSE=8.230]
Epoch 29: 12%|█▎ | 4/32 [00:00<00:00, 315.09it/s, v_num=2, train_loss=3.080, RMSE=8.230]
Epoch 29: 16%|█▌ | 5/32 [00:00<00:00, 322.22it/s, v_num=2, train_loss=3.080, RMSE=8.230]
Epoch 29: 16%|█▌ | 5/32 [00:00<00:00, 318.03it/s, v_num=2, train_loss=3.170, RMSE=8.230]
Epoch 29: 19%|█▉ | 6/32 [00:00<00:00, 323.72it/s, v_num=2, train_loss=3.170, RMSE=8.230]
Epoch 29: 19%|█▉ | 6/32 [00:00<00:00, 320.07it/s, v_num=2, train_loss=3.420, RMSE=8.230]
Epoch 29: 22%|██▏ | 7/32 [00:00<00:00, 325.24it/s, v_num=2, train_loss=3.420, RMSE=8.230]
Epoch 29: 22%|██▏ | 7/32 [00:00<00:00, 322.18it/s, v_num=2, train_loss=3.210, RMSE=8.230]
Epoch 29: 25%|██▌ | 8/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=3.210, RMSE=8.230]
Epoch 29: 25%|██▌ | 8/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.030, RMSE=8.230]
Epoch 29: 28%|██▊ | 9/32 [00:00<00:00, 326.12it/s, v_num=2, train_loss=3.030, RMSE=8.230]
Epoch 29: 28%|██▊ | 9/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=3.110, RMSE=8.230]
Epoch 29: 31%|███▏ | 10/32 [00:00<00:00, 326.36it/s, v_num=2, train_loss=3.110, RMSE=8.230]
Epoch 29: 31%|███▏ | 10/32 [00:00<00:00, 324.18it/s, v_num=2, train_loss=3.340, RMSE=8.230]
Epoch 29: 34%|███▍ | 11/32 [00:00<00:00, 326.97it/s, v_num=2, train_loss=3.340, RMSE=8.230]
Epoch 29: 34%|███▍ | 11/32 [00:00<00:00, 325.00it/s, v_num=2, train_loss=3.150, RMSE=8.230]
Epoch 29: 38%|███▊ | 12/32 [00:00<00:00, 326.64it/s, v_num=2, train_loss=3.150, RMSE=8.230]
Epoch 29: 38%|███▊ | 12/32 [00:00<00:00, 324.81it/s, v_num=2, train_loss=3.190, RMSE=8.230]
Epoch 29: 41%|████ | 13/32 [00:00<00:00, 326.74it/s, v_num=2, train_loss=3.190, RMSE=8.230]
Epoch 29: 41%|████ | 13/32 [00:00<00:00, 325.08it/s, v_num=2, train_loss=3.180, RMSE=8.230]
Epoch 29: 44%|████▍ | 14/32 [00:00<00:00, 327.02it/s, v_num=2, train_loss=3.180, RMSE=8.230]
Epoch 29: 44%|████▍ | 14/32 [00:00<00:00, 325.46it/s, v_num=2, train_loss=2.860, RMSE=8.230]
Epoch 29: 47%|████▋ | 15/32 [00:00<00:00, 327.53it/s, v_num=2, train_loss=2.860, RMSE=8.230]
Epoch 29: 47%|████▋ | 15/32 [00:00<00:00, 326.09it/s, v_num=2, train_loss=3.140, RMSE=8.230]
Epoch 29: 50%|█████ | 16/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=3.140, RMSE=8.230]
Epoch 29: 50%|█████ | 16/32 [00:00<00:00, 326.33it/s, v_num=2, train_loss=3.150, RMSE=8.230]
Epoch 29: 53%|█████▎ | 17/32 [00:00<00:00, 327.93it/s, v_num=2, train_loss=3.150, RMSE=8.230]
Epoch 29: 53%|█████▎ | 17/32 [00:00<00:00, 326.65it/s, v_num=2, train_loss=3.160, RMSE=8.230]
Epoch 29: 56%|█████▋ | 18/32 [00:00<00:00, 327.91it/s, v_num=2, train_loss=3.160, RMSE=8.230]
Epoch 29: 56%|█████▋ | 18/32 [00:00<00:00, 326.67it/s, v_num=2, train_loss=3.240, RMSE=8.230]
Epoch 29: 59%|█████▉ | 19/32 [00:00<00:00, 328.28it/s, v_num=2, train_loss=3.240, RMSE=8.230]
Epoch 29: 59%|█████▉ | 19/32 [00:00<00:00, 327.13it/s, v_num=2, train_loss=3.050, RMSE=8.230]
Epoch 29: 62%|██████▎ | 20/32 [00:00<00:00, 328.47it/s, v_num=2, train_loss=3.050, RMSE=8.230]
Epoch 29: 62%|██████▎ | 20/32 [00:00<00:00, 327.36it/s, v_num=2, train_loss=2.920, RMSE=8.230]
Epoch 29: 66%|██████▌ | 21/32 [00:00<00:00, 328.61it/s, v_num=2, train_loss=2.920, RMSE=8.230]
Epoch 29: 66%|██████▌ | 21/32 [00:00<00:00, 327.56it/s, v_num=2, train_loss=2.830, RMSE=8.230]
Epoch 29: 69%|██████▉ | 22/32 [00:00<00:00, 328.73it/s, v_num=2, train_loss=2.830, RMSE=8.230]
Epoch 29: 69%|██████▉ | 22/32 [00:00<00:00, 327.73it/s, v_num=2, train_loss=3.350, RMSE=8.230]
Epoch 29: 72%|███████▏ | 23/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=3.350, RMSE=8.230]
Epoch 29: 72%|███████▏ | 23/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=3.270, RMSE=8.230]
Epoch 29: 75%|███████▌ | 24/32 [00:00<00:00, 327.27it/s, v_num=2, train_loss=3.270, RMSE=8.230]
Epoch 29: 75%|███████▌ | 24/32 [00:00<00:00, 326.36it/s, v_num=2, train_loss=3.100, RMSE=8.230]
Epoch 29: 78%|███████▊ | 25/32 [00:00<00:00, 327.44it/s, v_num=2, train_loss=3.100, RMSE=8.230]
Epoch 29: 78%|███████▊ | 25/32 [00:00<00:00, 326.57it/s, v_num=2, train_loss=3.160, RMSE=8.230]
Epoch 29: 81%|████████▏ | 26/32 [00:00<00:00, 327.50it/s, v_num=2, train_loss=3.160, RMSE=8.230]
Epoch 29: 81%|████████▏ | 26/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=3.010, RMSE=8.230]
Epoch 29: 84%|████████▍ | 27/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=3.010, RMSE=8.230]
Epoch 29: 84%|████████▍ | 27/32 [00:00<00:00, 326.87it/s, v_num=2, train_loss=2.960, RMSE=8.230]
Epoch 29: 88%|████████▊ | 28/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=2.960, RMSE=8.230]
Epoch 29: 88%|████████▊ | 28/32 [00:00<00:00, 327.13it/s, v_num=2, train_loss=2.760, RMSE=8.230]
Epoch 29: 91%|█████████ | 29/32 [00:00<00:00, 327.99it/s, v_num=2, train_loss=2.760, RMSE=8.230]
Epoch 29: 91%|█████████ | 29/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=2.970, RMSE=8.230]
Epoch 29: 94%|█████████▍| 30/32 [00:00<00:00, 328.05it/s, v_num=2, train_loss=2.970, RMSE=8.230]
Epoch 29: 94%|█████████▍| 30/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=3.000, RMSE=8.230]
Epoch 29: 97%|█████████▋| 31/32 [00:00<00:00, 328.18it/s, v_num=2, train_loss=3.000, RMSE=8.230]
Epoch 29: 97%|█████████▋| 31/32 [00:00<00:00, 327.37it/s, v_num=2, train_loss=3.470, RMSE=8.230]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 328.26it/s, v_num=2, train_loss=3.470, RMSE=8.230]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 327.57it/s, v_num=2, train_loss=3.230, RMSE=8.230]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 626.95it/s]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 268.06it/s, v_num=2, train_loss=3.230, RMSE=7.990]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 266.94it/s, v_num=2, train_loss=3.230, RMSE=7.990]
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Epoch 30: 3%|▎ | 1/32 [00:00<00:00, 305.71it/s, v_num=2, train_loss=3.230, RMSE=7.990]
Epoch 30: 3%|▎ | 1/32 [00:00<00:00, 286.95it/s, v_num=2, train_loss=3.180, RMSE=7.990]
Epoch 30: 6%|▋ | 2/32 [00:00<00:00, 313.44it/s, v_num=2, train_loss=3.180, RMSE=7.990]
Epoch 30: 6%|▋ | 2/32 [00:00<00:00, 303.64it/s, v_num=2, train_loss=3.090, RMSE=7.990]
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Epoch 30: 12%|█▎ | 4/32 [00:00<00:00, 320.76it/s, v_num=2, train_loss=3.180, RMSE=7.990]
Epoch 30: 12%|█▎ | 4/32 [00:00<00:00, 315.56it/s, v_num=2, train_loss=2.950, RMSE=7.990]
Epoch 30: 16%|█▌ | 5/32 [00:00<00:00, 323.37it/s, v_num=2, train_loss=2.950, RMSE=7.990]
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Epoch 30: 19%|█▉ | 6/32 [00:00<00:00, 324.60it/s, v_num=2, train_loss=3.070, RMSE=7.990]
Epoch 30: 19%|█▉ | 6/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=3.430, RMSE=7.990]
Epoch 30: 22%|██▏ | 7/32 [00:00<00:00, 325.41it/s, v_num=2, train_loss=3.430, RMSE=7.990]
Epoch 30: 22%|██▏ | 7/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=2.950, RMSE=7.990]
Epoch 30: 25%|██▌ | 8/32 [00:00<00:00, 326.23it/s, v_num=2, train_loss=2.950, RMSE=7.990]
Epoch 30: 25%|██▌ | 8/32 [00:00<00:00, 323.54it/s, v_num=2, train_loss=3.200, RMSE=7.990]
Epoch 30: 28%|██▊ | 9/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=3.200, RMSE=7.990]
Epoch 30: 28%|██▊ | 9/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=3.290, RMSE=7.990]
Epoch 30: 31%|███▏ | 10/32 [00:00<00:00, 327.24it/s, v_num=2, train_loss=3.290, RMSE=7.990]
Epoch 30: 31%|███▏ | 10/32 [00:00<00:00, 325.07it/s, v_num=2, train_loss=3.100, RMSE=7.990]
Epoch 30: 34%|███▍ | 11/32 [00:00<00:00, 327.54it/s, v_num=2, train_loss=3.100, RMSE=7.990]
Epoch 30: 34%|███▍ | 11/32 [00:00<00:00, 325.57it/s, v_num=2, train_loss=3.180, RMSE=7.990]
Epoch 30: 38%|███▊ | 12/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=3.180, RMSE=7.990]
Epoch 30: 38%|███▊ | 12/32 [00:00<00:00, 325.88it/s, v_num=2, train_loss=3.240, RMSE=7.990]
Epoch 30: 41%|████ | 13/32 [00:00<00:00, 328.30it/s, v_num=2, train_loss=3.240, RMSE=7.990]
Epoch 30: 41%|████ | 13/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=3.050, RMSE=7.990]
Epoch 30: 44%|████▍ | 14/32 [00:00<00:00, 328.42it/s, v_num=2, train_loss=3.050, RMSE=7.990]
Epoch 30: 44%|████▍ | 14/32 [00:00<00:00, 326.85it/s, v_num=2, train_loss=2.910, RMSE=7.990]
Epoch 30: 47%|████▋ | 15/32 [00:00<00:00, 328.35it/s, v_num=2, train_loss=2.910, RMSE=7.990]
Epoch 30: 47%|████▋ | 15/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=3.010, RMSE=7.990]
Epoch 30: 50%|█████ | 16/32 [00:00<00:00, 328.49it/s, v_num=2, train_loss=3.010, RMSE=7.990]
Epoch 30: 50%|█████ | 16/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=2.960, RMSE=7.990]
Epoch 30: 53%|█████▎ | 17/32 [00:00<00:00, 328.66it/s, v_num=2, train_loss=2.960, RMSE=7.990]
Epoch 30: 53%|█████▎ | 17/32 [00:00<00:00, 327.34it/s, v_num=2, train_loss=3.400, RMSE=7.990]
Epoch 30: 56%|█████▋ | 18/32 [00:00<00:00, 328.80it/s, v_num=2, train_loss=3.400, RMSE=7.990]
Epoch 30: 56%|█████▋ | 18/32 [00:00<00:00, 327.58it/s, v_num=2, train_loss=3.200, RMSE=7.990]
Epoch 30: 59%|█████▉ | 19/32 [00:00<00:00, 328.79it/s, v_num=2, train_loss=3.200, RMSE=7.990]
Epoch 30: 59%|█████▉ | 19/32 [00:00<00:00, 327.63it/s, v_num=2, train_loss=2.810, RMSE=7.990]
Epoch 30: 62%|██████▎ | 20/32 [00:00<00:00, 328.93it/s, v_num=2, train_loss=2.810, RMSE=7.990]
Epoch 30: 62%|██████▎ | 20/32 [00:00<00:00, 327.84it/s, v_num=2, train_loss=2.870, RMSE=7.990]
Epoch 30: 66%|██████▌ | 21/32 [00:00<00:00, 329.04it/s, v_num=2, train_loss=2.870, RMSE=7.990]
Epoch 30: 66%|██████▌ | 21/32 [00:00<00:00, 328.00it/s, v_num=2, train_loss=2.960, RMSE=7.990]
Epoch 30: 69%|██████▉ | 22/32 [00:00<00:00, 329.33it/s, v_num=2, train_loss=2.960, RMSE=7.990]
Epoch 30: 69%|██████▉ | 22/32 [00:00<00:00, 328.32it/s, v_num=2, train_loss=3.250, RMSE=7.990]
Epoch 30: 72%|███████▏ | 23/32 [00:00<00:00, 329.31it/s, v_num=2, train_loss=3.250, RMSE=7.990]
Epoch 30: 72%|███████▏ | 23/32 [00:00<00:00, 328.36it/s, v_num=2, train_loss=3.250, RMSE=7.990]
Epoch 30: 75%|███████▌ | 24/32 [00:00<00:00, 329.33it/s, v_num=2, train_loss=3.250, RMSE=7.990]
Epoch 30: 75%|███████▌ | 24/32 [00:00<00:00, 328.42it/s, v_num=2, train_loss=2.990, RMSE=7.990]
Epoch 30: 78%|███████▊ | 25/32 [00:00<00:00, 329.41it/s, v_num=2, train_loss=2.990, RMSE=7.990]
Epoch 30: 78%|███████▊ | 25/32 [00:00<00:00, 328.50it/s, v_num=2, train_loss=2.950, RMSE=7.990]
Epoch 30: 81%|████████▏ | 26/32 [00:00<00:00, 329.53it/s, v_num=2, train_loss=2.950, RMSE=7.990]
Epoch 30: 81%|████████▏ | 26/32 [00:00<00:00, 328.68it/s, v_num=2, train_loss=3.030, RMSE=7.990]
Epoch 30: 84%|████████▍ | 27/32 [00:00<00:00, 329.43it/s, v_num=2, train_loss=3.030, RMSE=7.990]
Epoch 30: 84%|████████▍ | 27/32 [00:00<00:00, 328.60it/s, v_num=2, train_loss=3.150, RMSE=7.990]
Epoch 30: 88%|████████▊ | 28/32 [00:00<00:00, 329.29it/s, v_num=2, train_loss=3.150, RMSE=7.990]
Epoch 30: 88%|████████▊ | 28/32 [00:00<00:00, 328.49it/s, v_num=2, train_loss=3.210, RMSE=7.990]
Epoch 30: 91%|█████████ | 29/32 [00:00<00:00, 329.23it/s, v_num=2, train_loss=3.210, RMSE=7.990]
Epoch 30: 91%|█████████ | 29/32 [00:00<00:00, 328.46it/s, v_num=2, train_loss=2.800, RMSE=7.990]
Epoch 30: 94%|█████████▍| 30/32 [00:00<00:00, 329.32it/s, v_num=2, train_loss=2.800, RMSE=7.990]
Epoch 30: 94%|█████████▍| 30/32 [00:00<00:00, 328.58it/s, v_num=2, train_loss=2.760, RMSE=7.990]
Epoch 30: 97%|█████████▋| 31/32 [00:00<00:00, 329.24it/s, v_num=2, train_loss=2.760, RMSE=7.990]
Epoch 30: 97%|█████████▋| 31/32 [00:00<00:00, 328.52it/s, v_num=2, train_loss=3.260, RMSE=7.990]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 329.14it/s, v_num=2, train_loss=3.260, RMSE=7.990]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 328.45it/s, v_num=2, train_loss=2.680, RMSE=7.990]
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Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 267.80it/s, v_num=2, train_loss=2.680, RMSE=7.700]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 266.61it/s, v_num=2, train_loss=2.680, RMSE=7.700]
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Epoch 31: 12%|█▎ | 4/32 [00:00<00:00, 313.14it/s, v_num=2, train_loss=3.070, RMSE=7.700]
Epoch 31: 12%|█▎ | 4/32 [00:00<00:00, 308.14it/s, v_num=2, train_loss=2.930, RMSE=7.700]
Epoch 31: 16%|█▌ | 5/32 [00:00<00:00, 316.25it/s, v_num=2, train_loss=2.930, RMSE=7.700]
Epoch 31: 16%|█▌ | 5/32 [00:00<00:00, 312.16it/s, v_num=2, train_loss=3.190, RMSE=7.700]
Epoch 31: 19%|█▉ | 6/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.190, RMSE=7.700]
Epoch 31: 19%|█▉ | 6/32 [00:00<00:00, 314.45it/s, v_num=2, train_loss=2.850, RMSE=7.700]
Epoch 31: 22%|██▏ | 7/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=2.850, RMSE=7.700]
Epoch 31: 22%|██▏ | 7/32 [00:00<00:00, 316.17it/s, v_num=2, train_loss=2.950, RMSE=7.700]
Epoch 31: 25%|██▌ | 8/32 [00:00<00:00, 319.96it/s, v_num=2, train_loss=2.950, RMSE=7.700]
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Epoch 31: 31%|███▏ | 10/32 [00:00<00:00, 317.73it/s, v_num=2, train_loss=3.230, RMSE=7.700]
Epoch 31: 31%|███▏ | 10/32 [00:00<00:00, 315.67it/s, v_num=2, train_loss=2.970, RMSE=7.700]
Epoch 31: 34%|███▍ | 11/32 [00:00<00:00, 318.39it/s, v_num=2, train_loss=2.970, RMSE=7.700]
Epoch 31: 34%|███▍ | 11/32 [00:00<00:00, 316.50it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 38%|███▊ | 12/32 [00:00<00:00, 319.04it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 38%|███▊ | 12/32 [00:00<00:00, 317.25it/s, v_num=2, train_loss=2.950, RMSE=7.700]
Epoch 31: 41%|████ | 13/32 [00:00<00:00, 319.60it/s, v_num=2, train_loss=2.950, RMSE=7.700]
Epoch 31: 41%|████ | 13/32 [00:00<00:00, 317.99it/s, v_num=2, train_loss=3.050, RMSE=7.700]
Epoch 31: 44%|████▍ | 14/32 [00:00<00:00, 320.33it/s, v_num=2, train_loss=3.050, RMSE=7.700]
Epoch 31: 44%|████▍ | 14/32 [00:00<00:00, 318.82it/s, v_num=2, train_loss=2.880, RMSE=7.700]
Epoch 31: 47%|████▋ | 15/32 [00:00<00:00, 320.88it/s, v_num=2, train_loss=2.880, RMSE=7.700]
Epoch 31: 47%|████▋ | 15/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=2.930, RMSE=7.700]
Epoch 31: 50%|█████ | 16/32 [00:00<00:00, 321.35it/s, v_num=2, train_loss=2.930, RMSE=7.700]
Epoch 31: 50%|█████ | 16/32 [00:00<00:00, 320.03it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 53%|█████▎ | 17/32 [00:00<00:00, 321.71it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 53%|█████▎ | 17/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=3.250, RMSE=7.700]
Epoch 31: 56%|█████▋ | 18/32 [00:00<00:00, 322.10it/s, v_num=2, train_loss=3.250, RMSE=7.700]
Epoch 31: 56%|█████▋ | 18/32 [00:00<00:00, 320.92it/s, v_num=2, train_loss=2.960, RMSE=7.700]
Epoch 31: 59%|█████▉ | 19/32 [00:00<00:00, 322.34it/s, v_num=2, train_loss=2.960, RMSE=7.700]
Epoch 31: 59%|█████▉ | 19/32 [00:00<00:00, 321.22it/s, v_num=2, train_loss=3.070, RMSE=7.700]
Epoch 31: 62%|██████▎ | 20/32 [00:00<00:00, 322.62it/s, v_num=2, train_loss=3.070, RMSE=7.700]
Epoch 31: 62%|██████▎ | 20/32 [00:00<00:00, 321.54it/s, v_num=2, train_loss=3.010, RMSE=7.700]
Epoch 31: 66%|██████▌ | 21/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.010, RMSE=7.700]
Epoch 31: 66%|██████▌ | 21/32 [00:00<00:00, 321.78it/s, v_num=2, train_loss=3.110, RMSE=7.700]
Epoch 31: 69%|██████▉ | 22/32 [00:00<00:00, 323.24it/s, v_num=2, train_loss=3.110, RMSE=7.700]
Epoch 31: 69%|██████▉ | 22/32 [00:00<00:00, 322.26it/s, v_num=2, train_loss=3.190, RMSE=7.700]
Epoch 31: 72%|███████▏ | 23/32 [00:00<00:00, 323.44it/s, v_num=2, train_loss=3.190, RMSE=7.700]
Epoch 31: 72%|███████▏ | 23/32 [00:00<00:00, 322.50it/s, v_num=2, train_loss=2.940, RMSE=7.700]
Epoch 31: 75%|███████▌ | 24/32 [00:00<00:00, 323.55it/s, v_num=2, train_loss=2.940, RMSE=7.700]
Epoch 31: 75%|███████▌ | 24/32 [00:00<00:00, 322.65it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 78%|███████▊ | 25/32 [00:00<00:00, 323.70it/s, v_num=2, train_loss=3.220, RMSE=7.700]
Epoch 31: 78%|███████▊ | 25/32 [00:00<00:00, 322.84it/s, v_num=2, train_loss=2.960, RMSE=7.700]
Epoch 31: 81%|████████▏ | 26/32 [00:00<00:00, 324.03it/s, v_num=2, train_loss=2.960, RMSE=7.700]
Epoch 31: 81%|████████▏ | 26/32 [00:00<00:00, 323.21it/s, v_num=2, train_loss=3.320, RMSE=7.700]
Epoch 31: 84%|████████▍ | 27/32 [00:00<00:00, 324.19it/s, v_num=2, train_loss=3.320, RMSE=7.700]
Epoch 31: 84%|████████▍ | 27/32 [00:00<00:00, 323.39it/s, v_num=2, train_loss=3.370, RMSE=7.700]
Epoch 31: 88%|████████▊ | 28/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.370, RMSE=7.700]
Epoch 31: 88%|████████▊ | 28/32 [00:00<00:00, 323.43it/s, v_num=2, train_loss=2.760, RMSE=7.700]
Epoch 31: 91%|█████████ | 29/32 [00:00<00:00, 324.15it/s, v_num=2, train_loss=2.760, RMSE=7.700]
Epoch 31: 91%|█████████ | 29/32 [00:00<00:00, 323.40it/s, v_num=2, train_loss=2.820, RMSE=7.700]
Epoch 31: 94%|█████████▍| 30/32 [00:00<00:00, 324.26it/s, v_num=2, train_loss=2.820, RMSE=7.700]
Epoch 31: 94%|█████████▍| 30/32 [00:00<00:00, 323.43it/s, v_num=2, train_loss=3.100, RMSE=7.700]
Epoch 31: 97%|█████████▋| 31/32 [00:00<00:00, 324.35it/s, v_num=2, train_loss=3.100, RMSE=7.700]
Epoch 31: 97%|█████████▋| 31/32 [00:00<00:00, 323.65it/s, v_num=2, train_loss=3.080, RMSE=7.700]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 324.50it/s, v_num=2, train_loss=3.080, RMSE=7.700]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 323.82it/s, v_num=2, train_loss=3.610, RMSE=7.700]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 619.37it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 623.94it/s]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 265.91it/s, v_num=2, train_loss=3.610, RMSE=7.300]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 264.76it/s, v_num=2, train_loss=3.610, RMSE=7.300]
Epoch 31: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.610, RMSE=7.300]
Epoch 32: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.610, RMSE=7.300]
Epoch 32: 3%|▎ | 1/32 [00:00<00:00, 311.27it/s, v_num=2, train_loss=3.610, RMSE=7.300]
Epoch 32: 3%|▎ | 1/32 [00:00<00:00, 292.35it/s, v_num=2, train_loss=2.780, RMSE=7.300]
Epoch 32: 6%|▋ | 2/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=2.780, RMSE=7.300]
Epoch 32: 6%|▋ | 2/32 [00:00<00:00, 308.86it/s, v_num=2, train_loss=3.060, RMSE=7.300]
Epoch 32: 9%|▉ | 3/32 [00:00<00:00, 322.80it/s, v_num=2, train_loss=3.060, RMSE=7.300]
Epoch 32: 9%|▉ | 3/32 [00:00<00:00, 315.84it/s, v_num=2, train_loss=3.090, RMSE=7.300]
Epoch 32: 12%|█▎ | 4/32 [00:00<00:00, 325.44it/s, v_num=2, train_loss=3.090, RMSE=7.300]
Epoch 32: 12%|█▎ | 4/32 [00:00<00:00, 320.16it/s, v_num=2, train_loss=3.220, RMSE=7.300]
Epoch 32: 16%|█▌ | 5/32 [00:00<00:00, 325.17it/s, v_num=2, train_loss=3.220, RMSE=7.300]
Epoch 32: 16%|█▌ | 5/32 [00:00<00:00, 320.93it/s, v_num=2, train_loss=3.140, RMSE=7.300]
Epoch 32: 19%|█▉ | 6/32 [00:00<00:00, 325.99it/s, v_num=2, train_loss=3.140, RMSE=7.300]
Epoch 32: 19%|█▉ | 6/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=2.890, RMSE=7.300]
Epoch 32: 22%|██▏ | 7/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=2.890, RMSE=7.300]
Epoch 32: 22%|██▏ | 7/32 [00:00<00:00, 323.29it/s, v_num=2, train_loss=2.850, RMSE=7.300]
Epoch 32: 25%|██▌ | 8/32 [00:00<00:00, 326.94it/s, v_num=2, train_loss=2.850, RMSE=7.300]
Epoch 32: 25%|██▌ | 8/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=2.880, RMSE=7.300]
Epoch 32: 28%|██▊ | 9/32 [00:00<00:00, 327.21it/s, v_num=2, train_loss=2.880, RMSE=7.300]
Epoch 32: 28%|██▊ | 9/32 [00:00<00:00, 324.73it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 31%|███▏ | 10/32 [00:00<00:00, 327.08it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 31%|███▏ | 10/32 [00:00<00:00, 324.88it/s, v_num=2, train_loss=3.180, RMSE=7.300]
Epoch 32: 34%|███▍ | 11/32 [00:00<00:00, 327.17it/s, v_num=2, train_loss=3.180, RMSE=7.300]
Epoch 32: 34%|███▍ | 11/32 [00:00<00:00, 325.17it/s, v_num=2, train_loss=3.300, RMSE=7.300]
Epoch 32: 38%|███▊ | 12/32 [00:00<00:00, 327.60it/s, v_num=2, train_loss=3.300, RMSE=7.300]
Epoch 32: 38%|███▊ | 12/32 [00:00<00:00, 325.46it/s, v_num=2, train_loss=3.050, RMSE=7.300]
Epoch 32: 41%|████ | 13/32 [00:00<00:00, 327.75it/s, v_num=2, train_loss=3.050, RMSE=7.300]
Epoch 32: 41%|████ | 13/32 [00:00<00:00, 326.02it/s, v_num=2, train_loss=2.920, RMSE=7.300]
Epoch 32: 44%|████▍ | 14/32 [00:00<00:00, 327.73it/s, v_num=2, train_loss=2.920, RMSE=7.300]
Epoch 32: 44%|████▍ | 14/32 [00:00<00:00, 326.15it/s, v_num=2, train_loss=3.220, RMSE=7.300]
Epoch 32: 47%|████▋ | 15/32 [00:00<00:00, 327.56it/s, v_num=2, train_loss=3.220, RMSE=7.300]
Epoch 32: 47%|████▋ | 15/32 [00:00<00:00, 326.08it/s, v_num=2, train_loss=3.070, RMSE=7.300]
Epoch 32: 50%|█████ | 16/32 [00:00<00:00, 327.47it/s, v_num=2, train_loss=3.070, RMSE=7.300]
Epoch 32: 50%|█████ | 16/32 [00:00<00:00, 326.08it/s, v_num=2, train_loss=2.760, RMSE=7.300]
Epoch 32: 53%|█████▎ | 17/32 [00:00<00:00, 327.66it/s, v_num=2, train_loss=2.760, RMSE=7.300]
Epoch 32: 53%|█████▎ | 17/32 [00:00<00:00, 326.37it/s, v_num=2, train_loss=3.310, RMSE=7.300]
Epoch 32: 56%|█████▋ | 18/32 [00:00<00:00, 327.78it/s, v_num=2, train_loss=3.310, RMSE=7.300]
Epoch 32: 56%|█████▋ | 18/32 [00:00<00:00, 326.54it/s, v_num=2, train_loss=3.380, RMSE=7.300]
Epoch 32: 59%|█████▉ | 19/32 [00:00<00:00, 327.73it/s, v_num=2, train_loss=3.380, RMSE=7.300]
Epoch 32: 59%|█████▉ | 19/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.210, RMSE=7.300]
Epoch 32: 62%|██████▎ | 20/32 [00:00<00:00, 327.47it/s, v_num=2, train_loss=3.210, RMSE=7.300]
Epoch 32: 62%|██████▎ | 20/32 [00:00<00:00, 326.33it/s, v_num=2, train_loss=3.140, RMSE=7.300]
Epoch 32: 66%|██████▌ | 21/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=3.140, RMSE=7.300]
Epoch 32: 66%|██████▌ | 21/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 69%|██████▉ | 22/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 69%|██████▉ | 22/32 [00:00<00:00, 326.70it/s, v_num=2, train_loss=2.920, RMSE=7.300]
Epoch 32: 72%|███████▏ | 23/32 [00:00<00:00, 327.74it/s, v_num=2, train_loss=2.920, RMSE=7.300]
Epoch 32: 72%|███████▏ | 23/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=2.910, RMSE=7.300]
Epoch 32: 75%|███████▌ | 24/32 [00:00<00:00, 327.67it/s, v_num=2, train_loss=2.910, RMSE=7.300]
Epoch 32: 75%|███████▌ | 24/32 [00:00<00:00, 326.76it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 78%|███████▊ | 25/32 [00:00<00:00, 327.71it/s, v_num=2, train_loss=2.950, RMSE=7.300]
Epoch 32: 78%|███████▊ | 25/32 [00:00<00:00, 326.83it/s, v_num=2, train_loss=3.310, RMSE=7.300]
Epoch 32: 81%|████████▏ | 26/32 [00:00<00:00, 327.63it/s, v_num=2, train_loss=3.310, RMSE=7.300]
Epoch 32: 81%|████████▏ | 26/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=2.900, RMSE=7.300]
Epoch 32: 84%|████████▍ | 27/32 [00:00<00:00, 326.01it/s, v_num=2, train_loss=2.900, RMSE=7.300]
Epoch 32: 84%|████████▍ | 27/32 [00:00<00:00, 325.19it/s, v_num=2, train_loss=3.160, RMSE=7.300]
Epoch 32: 88%|████████▊ | 28/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=3.160, RMSE=7.300]
Epoch 32: 88%|████████▊ | 28/32 [00:00<00:00, 325.33it/s, v_num=2, train_loss=2.970, RMSE=7.300]
Epoch 32: 91%|█████████ | 29/32 [00:00<00:00, 326.16it/s, v_num=2, train_loss=2.970, RMSE=7.300]
Epoch 32: 91%|█████████ | 29/32 [00:00<00:00, 325.37it/s, v_num=2, train_loss=2.960, RMSE=7.300]
Epoch 32: 94%|█████████▍| 30/32 [00:00<00:00, 326.26it/s, v_num=2, train_loss=2.960, RMSE=7.300]
Epoch 32: 94%|█████████▍| 30/32 [00:00<00:00, 325.52it/s, v_num=2, train_loss=3.040, RMSE=7.300]
Epoch 32: 97%|█████████▋| 31/32 [00:00<00:00, 326.31it/s, v_num=2, train_loss=3.040, RMSE=7.300]
Epoch 32: 97%|█████████▋| 31/32 [00:00<00:00, 325.60it/s, v_num=2, train_loss=3.150, RMSE=7.300]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 326.48it/s, v_num=2, train_loss=3.150, RMSE=7.300]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 325.79it/s, v_num=2, train_loss=2.500, RMSE=7.300]
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Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 267.30it/s, v_num=2, train_loss=2.500, RMSE=7.070]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 266.23it/s, v_num=2, train_loss=2.500, RMSE=7.070]
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Epoch 33: 3%|▎ | 1/32 [00:00<00:00, 317.49it/s, v_num=2, train_loss=2.500, RMSE=7.070]
Epoch 33: 3%|▎ | 1/32 [00:00<00:00, 297.74it/s, v_num=2, train_loss=2.860, RMSE=7.070]
Epoch 33: 6%|▋ | 2/32 [00:00<00:00, 321.61it/s, v_num=2, train_loss=2.860, RMSE=7.070]
Epoch 33: 6%|▋ | 2/32 [00:00<00:00, 311.32it/s, v_num=2, train_loss=2.860, RMSE=7.070]
Epoch 33: 9%|▉ | 3/32 [00:00<00:00, 324.30it/s, v_num=2, train_loss=2.860, RMSE=7.070]
Epoch 33: 9%|▉ | 3/32 [00:00<00:00, 317.21it/s, v_num=2, train_loss=3.190, RMSE=7.070]
Epoch 33: 12%|█▎ | 4/32 [00:00<00:00, 324.79it/s, v_num=2, train_loss=3.190, RMSE=7.070]
Epoch 33: 12%|█▎ | 4/32 [00:00<00:00, 319.46it/s, v_num=2, train_loss=2.950, RMSE=7.070]
Epoch 33: 16%|█▌ | 5/32 [00:00<00:00, 325.13it/s, v_num=2, train_loss=2.950, RMSE=7.070]
Epoch 33: 16%|█▌ | 5/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.000, RMSE=7.070]
Epoch 33: 19%|█▉ | 6/32 [00:00<00:00, 325.56it/s, v_num=2, train_loss=3.000, RMSE=7.070]
Epoch 33: 19%|█▉ | 6/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=2.950, RMSE=7.070]
Epoch 33: 22%|██▏ | 7/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=2.950, RMSE=7.070]
Epoch 33: 22%|██▏ | 7/32 [00:00<00:00, 323.07it/s, v_num=2, train_loss=3.170, RMSE=7.070]
Epoch 33: 25%|██▌ | 8/32 [00:00<00:00, 326.90it/s, v_num=2, train_loss=3.170, RMSE=7.070]
Epoch 33: 25%|██▌ | 8/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.070, RMSE=7.070]
Epoch 33: 28%|██▊ | 9/32 [00:00<00:00, 326.91it/s, v_num=2, train_loss=3.070, RMSE=7.070]
Epoch 33: 28%|██▊ | 9/32 [00:00<00:00, 324.51it/s, v_num=2, train_loss=3.260, RMSE=7.070]
Epoch 33: 31%|███▏ | 10/32 [00:00<00:00, 327.17it/s, v_num=2, train_loss=3.260, RMSE=7.070]
Epoch 33: 31%|███▏ | 10/32 [00:00<00:00, 325.02it/s, v_num=2, train_loss=2.960, RMSE=7.070]
Epoch 33: 34%|███▍ | 11/32 [00:00<00:00, 327.86it/s, v_num=2, train_loss=2.960, RMSE=7.070]
Epoch 33: 34%|███▍ | 11/32 [00:00<00:00, 325.56it/s, v_num=2, train_loss=3.000, RMSE=7.070]
Epoch 33: 38%|███▊ | 12/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.000, RMSE=7.070]
Epoch 33: 38%|███▊ | 12/32 [00:00<00:00, 326.32it/s, v_num=2, train_loss=2.720, RMSE=7.070]
Epoch 33: 41%|████ | 13/32 [00:00<00:00, 328.27it/s, v_num=2, train_loss=2.720, RMSE=7.070]
Epoch 33: 41%|████ | 13/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=3.290, RMSE=7.070]
Epoch 33: 44%|████▍ | 14/32 [00:00<00:00, 327.97it/s, v_num=2, train_loss=3.290, RMSE=7.070]
Epoch 33: 44%|████▍ | 14/32 [00:00<00:00, 326.38it/s, v_num=2, train_loss=2.710, RMSE=7.070]
Epoch 33: 47%|████▋ | 15/32 [00:00<00:00, 327.84it/s, v_num=2, train_loss=2.710, RMSE=7.070]
Epoch 33: 47%|████▋ | 15/32 [00:00<00:00, 326.32it/s, v_num=2, train_loss=3.040, RMSE=7.070]
Epoch 33: 50%|█████ | 16/32 [00:00<00:00, 327.99it/s, v_num=2, train_loss=3.040, RMSE=7.070]
Epoch 33: 50%|█████ | 16/32 [00:00<00:00, 326.63it/s, v_num=2, train_loss=3.060, RMSE=7.070]
Epoch 33: 53%|█████▎ | 17/32 [00:00<00:00, 328.10it/s, v_num=2, train_loss=3.060, RMSE=7.070]
Epoch 33: 53%|█████▎ | 17/32 [00:00<00:00, 326.82it/s, v_num=2, train_loss=2.930, RMSE=7.070]
Epoch 33: 56%|█████▋ | 18/32 [00:00<00:00, 328.20it/s, v_num=2, train_loss=2.930, RMSE=7.070]
Epoch 33: 56%|█████▋ | 18/32 [00:00<00:00, 326.99it/s, v_num=2, train_loss=2.940, RMSE=7.070]
Epoch 33: 59%|█████▉ | 19/32 [00:00<00:00, 328.19it/s, v_num=2, train_loss=2.940, RMSE=7.070]
Epoch 33: 59%|█████▉ | 19/32 [00:00<00:00, 327.04it/s, v_num=2, train_loss=3.120, RMSE=7.070]
Epoch 33: 62%|██████▎ | 20/32 [00:00<00:00, 328.57it/s, v_num=2, train_loss=3.120, RMSE=7.070]
Epoch 33: 62%|██████▎ | 20/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=2.970, RMSE=7.070]
Epoch 33: 66%|██████▌ | 21/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=2.970, RMSE=7.070]
Epoch 33: 66%|██████▌ | 21/32 [00:00<00:00, 327.60it/s, v_num=2, train_loss=3.050, RMSE=7.070]
Epoch 33: 69%|██████▉ | 22/32 [00:00<00:00, 328.71it/s, v_num=2, train_loss=3.050, RMSE=7.070]
Epoch 33: 69%|██████▉ | 22/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=2.920, RMSE=7.070]
Epoch 33: 72%|███████▏ | 23/32 [00:00<00:00, 328.25it/s, v_num=2, train_loss=2.920, RMSE=7.070]
Epoch 33: 72%|███████▏ | 23/32 [00:00<00:00, 327.30it/s, v_num=2, train_loss=3.150, RMSE=7.070]
Epoch 33: 75%|███████▌ | 24/32 [00:00<00:00, 328.53it/s, v_num=2, train_loss=3.150, RMSE=7.070]
Epoch 33: 75%|███████▌ | 24/32 [00:00<00:00, 327.53it/s, v_num=2, train_loss=2.990, RMSE=7.070]
Epoch 33: 78%|███████▊ | 25/32 [00:00<00:00, 328.50it/s, v_num=2, train_loss=2.990, RMSE=7.070]
Epoch 33: 78%|███████▊ | 25/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=3.080, RMSE=7.070]
Epoch 33: 81%|████████▏ | 26/32 [00:00<00:00, 328.44it/s, v_num=2, train_loss=3.080, RMSE=7.070]
Epoch 33: 81%|████████▏ | 26/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.500, RMSE=7.070]
Epoch 33: 84%|████████▍ | 27/32 [00:00<00:00, 328.35it/s, v_num=2, train_loss=3.500, RMSE=7.070]
Epoch 33: 84%|████████▍ | 27/32 [00:00<00:00, 327.53it/s, v_num=2, train_loss=2.830, RMSE=7.070]
Epoch 33: 88%|████████▊ | 28/32 [00:00<00:00, 328.56it/s, v_num=2, train_loss=2.830, RMSE=7.070]
Epoch 33: 88%|████████▊ | 28/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=3.110, RMSE=7.070]
Epoch 33: 91%|█████████ | 29/32 [00:00<00:00, 328.66it/s, v_num=2, train_loss=3.110, RMSE=7.070]
Epoch 33: 91%|█████████ | 29/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=2.900, RMSE=7.070]
Epoch 33: 94%|█████████▍| 30/32 [00:00<00:00, 328.73it/s, v_num=2, train_loss=2.900, RMSE=7.070]
Epoch 33: 94%|█████████▍| 30/32 [00:00<00:00, 328.00it/s, v_num=2, train_loss=2.920, RMSE=7.070]
Epoch 33: 97%|█████████▋| 31/32 [00:00<00:00, 328.78it/s, v_num=2, train_loss=2.920, RMSE=7.070]
Epoch 33: 97%|█████████▋| 31/32 [00:00<00:00, 328.07it/s, v_num=2, train_loss=3.140, RMSE=7.070]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 329.09it/s, v_num=2, train_loss=3.140, RMSE=7.070]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 328.39it/s, v_num=2, train_loss=3.560, RMSE=7.070]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 634.07it/s]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 269.39it/s, v_num=2, train_loss=3.560, RMSE=6.850]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 268.29it/s, v_num=2, train_loss=3.560, RMSE=6.850]
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Epoch 34: 3%|▎ | 1/32 [00:00<00:00, 297.38it/s, v_num=2, train_loss=3.560, RMSE=6.850]
Epoch 34: 3%|▎ | 1/32 [00:00<00:00, 279.42it/s, v_num=2, train_loss=2.730, RMSE=6.850]
Epoch 34: 6%|▋ | 2/32 [00:00<00:00, 304.17it/s, v_num=2, train_loss=2.730, RMSE=6.850]
Epoch 34: 6%|▋ | 2/32 [00:00<00:00, 294.81it/s, v_num=2, train_loss=3.360, RMSE=6.850]
Epoch 34: 9%|▉ | 3/32 [00:00<00:00, 309.66it/s, v_num=2, train_loss=3.360, RMSE=6.850]
Epoch 34: 9%|▉ | 3/32 [00:00<00:00, 303.14it/s, v_num=2, train_loss=3.120, RMSE=6.850]
Epoch 34: 12%|█▎ | 4/32 [00:00<00:00, 313.76it/s, v_num=2, train_loss=3.120, RMSE=6.850]
Epoch 34: 12%|█▎ | 4/32 [00:00<00:00, 308.82it/s, v_num=2, train_loss=3.200, RMSE=6.850]
Epoch 34: 16%|█▌ | 5/32 [00:00<00:00, 315.93it/s, v_num=2, train_loss=3.200, RMSE=6.850]
Epoch 34: 16%|█▌ | 5/32 [00:00<00:00, 311.35it/s, v_num=2, train_loss=3.250, RMSE=6.850]
Epoch 34: 19%|█▉ | 6/32 [00:00<00:00, 317.94it/s, v_num=2, train_loss=3.250, RMSE=6.850]
Epoch 34: 19%|█▉ | 6/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=2.950, RMSE=6.850]
Epoch 34: 22%|██▏ | 7/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=2.950, RMSE=6.850]
Epoch 34: 22%|██▏ | 7/32 [00:00<00:00, 316.76it/s, v_num=2, train_loss=2.980, RMSE=6.850]
Epoch 34: 25%|██▌ | 8/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=2.980, RMSE=6.850]
Epoch 34: 25%|██▌ | 8/32 [00:00<00:00, 318.61it/s, v_num=2, train_loss=2.850, RMSE=6.850]
Epoch 34: 28%|██▊ | 9/32 [00:00<00:00, 322.11it/s, v_num=2, train_loss=2.850, RMSE=6.850]
Epoch 34: 28%|██▊ | 9/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=2.970, RMSE=6.850]
Epoch 34: 31%|███▏ | 10/32 [00:00<00:00, 323.29it/s, v_num=2, train_loss=2.970, RMSE=6.850]
Epoch 34: 31%|███▏ | 10/32 [00:00<00:00, 321.19it/s, v_num=2, train_loss=2.880, RMSE=6.850]
Epoch 34: 34%|███▍ | 11/32 [00:00<00:00, 323.82it/s, v_num=2, train_loss=2.880, RMSE=6.850]
Epoch 34: 34%|███▍ | 11/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=3.110, RMSE=6.850]
Epoch 34: 38%|███▊ | 12/32 [00:00<00:00, 324.36it/s, v_num=2, train_loss=3.110, RMSE=6.850]
Epoch 34: 38%|███▊ | 12/32 [00:00<00:00, 322.59it/s, v_num=2, train_loss=3.010, RMSE=6.850]
Epoch 34: 41%|████ | 13/32 [00:00<00:00, 321.46it/s, v_num=2, train_loss=3.010, RMSE=6.850]
Epoch 34: 41%|████ | 13/32 [00:00<00:00, 319.85it/s, v_num=2, train_loss=3.070, RMSE=6.850]
Epoch 34: 44%|████▍ | 14/32 [00:00<00:00, 322.06it/s, v_num=2, train_loss=3.070, RMSE=6.850]
Epoch 34: 44%|████▍ | 14/32 [00:00<00:00, 320.55it/s, v_num=2, train_loss=3.210, RMSE=6.850]
Epoch 34: 47%|████▋ | 15/32 [00:00<00:00, 322.75it/s, v_num=2, train_loss=3.210, RMSE=6.850]
Epoch 34: 47%|████▋ | 15/32 [00:00<00:00, 321.34it/s, v_num=2, train_loss=2.800, RMSE=6.850]
Epoch 34: 50%|█████ | 16/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=2.800, RMSE=6.850]
Epoch 34: 50%|█████ | 16/32 [00:00<00:00, 321.07it/s, v_num=2, train_loss=3.320, RMSE=6.850]
Epoch 34: 53%|█████▎ | 17/32 [00:00<00:00, 322.85it/s, v_num=2, train_loss=3.320, RMSE=6.850]
Epoch 34: 53%|█████▎ | 17/32 [00:00<00:00, 321.60it/s, v_num=2, train_loss=2.600, RMSE=6.850]
Epoch 34: 56%|█████▋ | 18/32 [00:00<00:00, 323.29it/s, v_num=2, train_loss=2.600, RMSE=6.850]
Epoch 34: 56%|█████▋ | 18/32 [00:00<00:00, 322.11it/s, v_num=2, train_loss=3.090, RMSE=6.850]
Epoch 34: 59%|█████▉ | 19/32 [00:00<00:00, 323.84it/s, v_num=2, train_loss=3.090, RMSE=6.850]
Epoch 34: 59%|█████▉ | 19/32 [00:00<00:00, 322.73it/s, v_num=2, train_loss=3.010, RMSE=6.850]
Epoch 34: 62%|██████▎ | 20/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=3.010, RMSE=6.850]
Epoch 34: 62%|██████▎ | 20/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=2.820, RMSE=6.850]
Epoch 34: 66%|██████▌ | 21/32 [00:00<00:00, 324.38it/s, v_num=2, train_loss=2.820, RMSE=6.850]
Epoch 34: 66%|██████▌ | 21/32 [00:00<00:00, 323.37it/s, v_num=2, train_loss=3.030, RMSE=6.850]
Epoch 34: 69%|██████▉ | 22/32 [00:00<00:00, 324.60it/s, v_num=2, train_loss=3.030, RMSE=6.850]
Epoch 34: 69%|██████▉ | 22/32 [00:00<00:00, 323.63it/s, v_num=2, train_loss=2.620, RMSE=6.850]
Epoch 34: 72%|███████▏ | 23/32 [00:00<00:00, 325.01it/s, v_num=2, train_loss=2.620, RMSE=6.850]
Epoch 34: 72%|███████▏ | 23/32 [00:00<00:00, 324.08it/s, v_num=2, train_loss=3.030, RMSE=6.850]
Epoch 34: 75%|███████▌ | 24/32 [00:00<00:00, 325.31it/s, v_num=2, train_loss=3.030, RMSE=6.850]
Epoch 34: 75%|███████▌ | 24/32 [00:00<00:00, 324.41it/s, v_num=2, train_loss=3.270, RMSE=6.850]
Epoch 34: 78%|███████▊ | 25/32 [00:00<00:00, 325.45it/s, v_num=2, train_loss=3.270, RMSE=6.850]
Epoch 34: 78%|███████▊ | 25/32 [00:00<00:00, 324.58it/s, v_num=2, train_loss=2.930, RMSE=6.850]
Epoch 34: 81%|████████▏ | 26/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=2.930, RMSE=6.850]
Epoch 34: 81%|████████▏ | 26/32 [00:00<00:00, 324.66it/s, v_num=2, train_loss=3.360, RMSE=6.850]
Epoch 34: 84%|████████▍ | 27/32 [00:00<00:00, 325.80it/s, v_num=2, train_loss=3.360, RMSE=6.850]
Epoch 34: 84%|████████▍ | 27/32 [00:00<00:00, 325.00it/s, v_num=2, train_loss=2.980, RMSE=6.850]
Epoch 34: 88%|████████▊ | 28/32 [00:00<00:00, 325.94it/s, v_num=2, train_loss=2.980, RMSE=6.850]
Epoch 34: 88%|████████▊ | 28/32 [00:00<00:00, 325.16it/s, v_num=2, train_loss=2.930, RMSE=6.850]
Epoch 34: 91%|█████████ | 29/32 [00:00<00:00, 326.08it/s, v_num=2, train_loss=2.930, RMSE=6.850]
Epoch 34: 91%|█████████ | 29/32 [00:00<00:00, 325.33it/s, v_num=2, train_loss=2.850, RMSE=6.850]
Epoch 34: 94%|█████████▍| 30/32 [00:00<00:00, 326.16it/s, v_num=2, train_loss=2.850, RMSE=6.850]
Epoch 34: 94%|█████████▍| 30/32 [00:00<00:00, 325.44it/s, v_num=2, train_loss=3.150, RMSE=6.850]
Epoch 34: 97%|█████████▋| 31/32 [00:00<00:00, 326.37it/s, v_num=2, train_loss=3.150, RMSE=6.850]
Epoch 34: 97%|█████████▋| 31/32 [00:00<00:00, 325.67it/s, v_num=2, train_loss=2.790, RMSE=6.850]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 326.50it/s, v_num=2, train_loss=2.790, RMSE=6.850]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 325.82it/s, v_num=2, train_loss=3.040, RMSE=6.850]
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Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 266.92it/s, v_num=2, train_loss=3.040, RMSE=6.750]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 265.83it/s, v_num=2, train_loss=3.040, RMSE=6.750]
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Epoch 35: 3%|▎ | 1/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=3.040, RMSE=6.750]
Epoch 35: 3%|▎ | 1/32 [00:00<00:00, 299.66it/s, v_num=2, train_loss=2.850, RMSE=6.750]
Epoch 35: 6%|▋ | 2/32 [00:00<00:00, 322.32it/s, v_num=2, train_loss=2.850, RMSE=6.750]
Epoch 35: 6%|▋ | 2/32 [00:00<00:00, 311.99it/s, v_num=2, train_loss=3.470, RMSE=6.750]
Epoch 35: 9%|▉ | 3/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=3.470, RMSE=6.750]
Epoch 35: 9%|▉ | 3/32 [00:00<00:00, 316.10it/s, v_num=2, train_loss=2.870, RMSE=6.750]
Epoch 35: 12%|█▎ | 4/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=2.870, RMSE=6.750]
Epoch 35: 12%|█▎ | 4/32 [00:00<00:00, 318.85it/s, v_num=2, train_loss=3.080, RMSE=6.750]
Epoch 35: 16%|█▌ | 5/32 [00:00<00:00, 325.19it/s, v_num=2, train_loss=3.080, RMSE=6.750]
Epoch 35: 16%|█▌ | 5/32 [00:00<00:00, 320.87it/s, v_num=2, train_loss=3.050, RMSE=6.750]
Epoch 35: 19%|█▉ | 6/32 [00:00<00:00, 325.82it/s, v_num=2, train_loss=3.050, RMSE=6.750]
Epoch 35: 19%|█▉ | 6/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=3.130, RMSE=6.750]
Epoch 35: 22%|██▏ | 7/32 [00:00<00:00, 326.38it/s, v_num=2, train_loss=3.130, RMSE=6.750]
Epoch 35: 22%|██▏ | 7/32 [00:00<00:00, 323.32it/s, v_num=2, train_loss=3.200, RMSE=6.750]
Epoch 35: 25%|██▌ | 8/32 [00:00<00:00, 326.75it/s, v_num=2, train_loss=3.200, RMSE=6.750]
Epoch 35: 25%|██▌ | 8/32 [00:00<00:00, 324.07it/s, v_num=2, train_loss=3.240, RMSE=6.750]
Epoch 35: 28%|██▊ | 9/32 [00:00<00:00, 327.65it/s, v_num=2, train_loss=3.240, RMSE=6.750]
Epoch 35: 28%|██▊ | 9/32 [00:00<00:00, 325.25it/s, v_num=2, train_loss=2.920, RMSE=6.750]
Epoch 35: 31%|███▏ | 10/32 [00:00<00:00, 327.98it/s, v_num=2, train_loss=2.920, RMSE=6.750]
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Epoch 35: 41%|████ | 13/32 [00:00<00:00, 328.69it/s, v_num=2, train_loss=2.920, RMSE=6.750]
Epoch 35: 41%|████ | 13/32 [00:00<00:00, 327.01it/s, v_num=2, train_loss=3.120, RMSE=6.750]
Epoch 35: 44%|████▍ | 14/32 [00:00<00:00, 328.63it/s, v_num=2, train_loss=3.120, RMSE=6.750]
Epoch 35: 44%|████▍ | 14/32 [00:00<00:00, 327.07it/s, v_num=2, train_loss=2.830, RMSE=6.750]
Epoch 35: 47%|████▋ | 15/32 [00:00<00:00, 328.92it/s, v_num=2, train_loss=2.830, RMSE=6.750]
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Epoch 35: 50%|█████ | 16/32 [00:00<00:00, 328.77it/s, v_num=2, train_loss=3.080, RMSE=6.750]
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Epoch 35: 53%|█████▎ | 17/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=3.280, RMSE=6.750]
Epoch 35: 56%|█████▋ | 18/32 [00:00<00:00, 329.17it/s, v_num=2, train_loss=3.280, RMSE=6.750]
Epoch 35: 56%|█████▋ | 18/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=2.960, RMSE=6.750]
Epoch 35: 59%|█████▉ | 19/32 [00:00<00:00, 329.37it/s, v_num=2, train_loss=2.960, RMSE=6.750]
Epoch 35: 59%|█████▉ | 19/32 [00:00<00:00, 328.20it/s, v_num=2, train_loss=3.220, RMSE=6.750]
Epoch 35: 62%|██████▎ | 20/32 [00:00<00:00, 329.49it/s, v_num=2, train_loss=3.220, RMSE=6.750]
Epoch 35: 62%|██████▎ | 20/32 [00:00<00:00, 328.39it/s, v_num=2, train_loss=2.970, RMSE=6.750]
Epoch 35: 66%|██████▌ | 21/32 [00:00<00:00, 329.71it/s, v_num=2, train_loss=2.970, RMSE=6.750]
Epoch 35: 66%|██████▌ | 21/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=2.790, RMSE=6.750]
Epoch 35: 69%|██████▉ | 22/32 [00:00<00:00, 329.73it/s, v_num=2, train_loss=2.790, RMSE=6.750]
Epoch 35: 69%|██████▉ | 22/32 [00:00<00:00, 328.72it/s, v_num=2, train_loss=2.900, RMSE=6.750]
Epoch 35: 72%|███████▏ | 23/32 [00:00<00:00, 329.79it/s, v_num=2, train_loss=2.900, RMSE=6.750]
Epoch 35: 72%|███████▏ | 23/32 [00:00<00:00, 328.83it/s, v_num=2, train_loss=2.840, RMSE=6.750]
Epoch 35: 75%|███████▌ | 24/32 [00:00<00:00, 329.73it/s, v_num=2, train_loss=2.840, RMSE=6.750]
Epoch 35: 75%|███████▌ | 24/32 [00:00<00:00, 328.81it/s, v_num=2, train_loss=2.470, RMSE=6.750]
Epoch 35: 78%|███████▊ | 25/32 [00:00<00:00, 329.89it/s, v_num=2, train_loss=2.470, RMSE=6.750]
Epoch 35: 78%|███████▊ | 25/32 [00:00<00:00, 328.81it/s, v_num=2, train_loss=3.030, RMSE=6.750]
Epoch 35: 81%|████████▏ | 26/32 [00:00<00:00, 329.51it/s, v_num=2, train_loss=3.030, RMSE=6.750]
Epoch 35: 81%|████████▏ | 26/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=2.730, RMSE=6.750]
Epoch 35: 84%|████████▍ | 27/32 [00:00<00:00, 329.50it/s, v_num=2, train_loss=2.730, RMSE=6.750]
Epoch 35: 84%|████████▍ | 27/32 [00:00<00:00, 328.66it/s, v_num=2, train_loss=3.320, RMSE=6.750]
Epoch 35: 88%|████████▊ | 28/32 [00:00<00:00, 329.40it/s, v_num=2, train_loss=3.320, RMSE=6.750]
Epoch 35: 88%|████████▊ | 28/32 [00:00<00:00, 328.60it/s, v_num=2, train_loss=2.750, RMSE=6.750]
Epoch 35: 91%|█████████ | 29/32 [00:00<00:00, 329.44it/s, v_num=2, train_loss=2.750, RMSE=6.750]
Epoch 35: 91%|█████████ | 29/32 [00:00<00:00, 328.68it/s, v_num=2, train_loss=2.880, RMSE=6.750]
Epoch 35: 94%|█████████▍| 30/32 [00:00<00:00, 329.58it/s, v_num=2, train_loss=2.880, RMSE=6.750]
Epoch 35: 94%|█████████▍| 30/32 [00:00<00:00, 328.82it/s, v_num=2, train_loss=2.980, RMSE=6.750]
Epoch 35: 97%|█████████▋| 31/32 [00:00<00:00, 327.69it/s, v_num=2, train_loss=2.980, RMSE=6.750]
Epoch 35: 97%|█████████▋| 31/32 [00:00<00:00, 326.98it/s, v_num=2, train_loss=2.960, RMSE=6.750]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 327.86it/s, v_num=2, train_loss=2.960, RMSE=6.750]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 327.18it/s, v_num=2, train_loss=2.850, RMSE=6.750]
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Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 268.52it/s, v_num=2, train_loss=2.850, RMSE=6.530]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 267.42it/s, v_num=2, train_loss=2.850, RMSE=6.530]
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Epoch 36: 6%|▋ | 2/32 [00:00<00:00, 309.06it/s, v_num=2, train_loss=3.170, RMSE=6.530]
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Epoch 36: 12%|█▎ | 4/32 [00:00<00:00, 326.28it/s, v_num=2, train_loss=3.030, RMSE=6.530]
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Epoch 36: 16%|█▌ | 5/32 [00:00<00:00, 323.01it/s, v_num=2, train_loss=3.190, RMSE=6.530]
Epoch 36: 19%|█▉ | 6/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.190, RMSE=6.530]
Epoch 36: 19%|█▉ | 6/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=2.910, RMSE=6.530]
Epoch 36: 22%|██▏ | 7/32 [00:00<00:00, 327.34it/s, v_num=2, train_loss=2.910, RMSE=6.530]
Epoch 36: 22%|██▏ | 7/32 [00:00<00:00, 324.07it/s, v_num=2, train_loss=2.980, RMSE=6.530]
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Epoch 36: 28%|██▊ | 9/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=3.090, RMSE=6.530]
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Epoch 36: 31%|███▏ | 10/32 [00:00<00:00, 324.46it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 34%|███▍ | 11/32 [00:00<00:00, 327.04it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 34%|███▍ | 11/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=2.930, RMSE=6.530]
Epoch 36: 38%|███▊ | 12/32 [00:00<00:00, 327.75it/s, v_num=2, train_loss=2.930, RMSE=6.530]
Epoch 36: 38%|███▊ | 12/32 [00:00<00:00, 325.93it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 41%|████ | 13/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 41%|████ | 13/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=2.980, RMSE=6.530]
Epoch 36: 44%|████▍ | 14/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=2.980, RMSE=6.530]
Epoch 36: 44%|████▍ | 14/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=3.220, RMSE=6.530]
Epoch 36: 47%|████▋ | 15/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.220, RMSE=6.530]
Epoch 36: 47%|████▋ | 15/32 [00:00<00:00, 326.68it/s, v_num=2, train_loss=2.840, RMSE=6.530]
Epoch 36: 50%|█████ | 16/32 [00:00<00:00, 328.63it/s, v_num=2, train_loss=2.840, RMSE=6.530]
Epoch 36: 50%|█████ | 16/32 [00:00<00:00, 327.26it/s, v_num=2, train_loss=3.130, RMSE=6.530]
Epoch 36: 53%|█████▎ | 17/32 [00:00<00:00, 328.76it/s, v_num=2, train_loss=3.130, RMSE=6.530]
Epoch 36: 53%|█████▎ | 17/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=2.550, RMSE=6.530]
Epoch 36: 56%|█████▋ | 18/32 [00:00<00:00, 328.80it/s, v_num=2, train_loss=2.550, RMSE=6.530]
Epoch 36: 56%|█████▋ | 18/32 [00:00<00:00, 327.59it/s, v_num=2, train_loss=3.050, RMSE=6.530]
Epoch 36: 59%|█████▉ | 19/32 [00:00<00:00, 328.73it/s, v_num=2, train_loss=3.050, RMSE=6.530]
Epoch 36: 59%|█████▉ | 19/32 [00:00<00:00, 327.58it/s, v_num=2, train_loss=2.820, RMSE=6.530]
Epoch 36: 62%|██████▎ | 20/32 [00:00<00:00, 329.04it/s, v_num=2, train_loss=2.820, RMSE=6.530]
Epoch 36: 62%|██████▎ | 20/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=2.660, RMSE=6.530]
Epoch 36: 66%|██████▌ | 21/32 [00:00<00:00, 329.18it/s, v_num=2, train_loss=2.660, RMSE=6.530]
Epoch 36: 66%|██████▌ | 21/32 [00:00<00:00, 328.07it/s, v_num=2, train_loss=2.870, RMSE=6.530]
Epoch 36: 69%|██████▉ | 22/32 [00:00<00:00, 329.23it/s, v_num=2, train_loss=2.870, RMSE=6.530]
Epoch 36: 69%|██████▉ | 22/32 [00:00<00:00, 328.23it/s, v_num=2, train_loss=2.940, RMSE=6.530]
Epoch 36: 72%|███████▏ | 23/32 [00:00<00:00, 329.12it/s, v_num=2, train_loss=2.940, RMSE=6.530]
Epoch 36: 72%|███████▏ | 23/32 [00:00<00:00, 328.16it/s, v_num=2, train_loss=2.970, RMSE=6.530]
Epoch 36: 75%|███████▌ | 24/32 [00:00<00:00, 329.39it/s, v_num=2, train_loss=2.970, RMSE=6.530]
Epoch 36: 75%|███████▌ | 24/32 [00:00<00:00, 328.47it/s, v_num=2, train_loss=3.120, RMSE=6.530]
Epoch 36: 78%|███████▊ | 25/32 [00:00<00:00, 329.54it/s, v_num=2, train_loss=3.120, RMSE=6.530]
Epoch 36: 78%|███████▊ | 25/32 [00:00<00:00, 328.66it/s, v_num=2, train_loss=3.050, RMSE=6.530]
Epoch 36: 81%|████████▏ | 26/32 [00:00<00:00, 329.72it/s, v_num=2, train_loss=3.050, RMSE=6.530]
Epoch 36: 81%|████████▏ | 26/32 [00:00<00:00, 328.88it/s, v_num=2, train_loss=3.070, RMSE=6.530]
Epoch 36: 84%|████████▍ | 27/32 [00:00<00:00, 329.61it/s, v_num=2, train_loss=3.070, RMSE=6.530]
Epoch 36: 84%|████████▍ | 27/32 [00:00<00:00, 328.78it/s, v_num=2, train_loss=2.870, RMSE=6.530]
Epoch 36: 88%|████████▊ | 28/32 [00:00<00:00, 329.74it/s, v_num=2, train_loss=2.870, RMSE=6.530]
Epoch 36: 88%|████████▊ | 28/32 [00:00<00:00, 328.92it/s, v_num=2, train_loss=3.020, RMSE=6.530]
Epoch 36: 91%|█████████ | 29/32 [00:00<00:00, 329.78it/s, v_num=2, train_loss=3.020, RMSE=6.530]
Epoch 36: 91%|█████████ | 29/32 [00:00<00:00, 329.01it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 94%|█████████▍| 30/32 [00:00<00:00, 329.82it/s, v_num=2, train_loss=2.860, RMSE=6.530]
Epoch 36: 94%|█████████▍| 30/32 [00:00<00:00, 329.08it/s, v_num=2, train_loss=3.000, RMSE=6.530]
Epoch 36: 97%|█████████▋| 31/32 [00:00<00:00, 329.89it/s, v_num=2, train_loss=3.000, RMSE=6.530]
Epoch 36: 97%|█████████▋| 31/32 [00:00<00:00, 329.18it/s, v_num=2, train_loss=3.150, RMSE=6.530]
Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 330.05it/s, v_num=2, train_loss=3.150, RMSE=6.530]
Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 329.36it/s, v_num=2, train_loss=2.500, RMSE=6.530]
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Epoch 37: 12%|█▎ | 4/32 [00:00<00:00, 313.76it/s, v_num=2, train_loss=2.950, RMSE=6.250]
Epoch 37: 16%|█▌ | 5/32 [00:00<00:00, 320.30it/s, v_num=2, train_loss=2.950, RMSE=6.250]
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Epoch 37: 28%|██▊ | 9/32 [00:00<00:00, 325.29it/s, v_num=2, train_loss=2.850, RMSE=6.250]
Epoch 37: 28%|██▊ | 9/32 [00:00<00:00, 322.91it/s, v_num=2, train_loss=2.950, RMSE=6.250]
Epoch 37: 31%|███▏ | 10/32 [00:00<00:00, 325.68it/s, v_num=2, train_loss=2.950, RMSE=6.250]
Epoch 37: 31%|███▏ | 10/32 [00:00<00:00, 323.54it/s, v_num=2, train_loss=2.850, RMSE=6.250]
Epoch 37: 34%|███▍ | 11/32 [00:00<00:00, 326.52it/s, v_num=2, train_loss=2.850, RMSE=6.250]
Epoch 37: 34%|███▍ | 11/32 [00:00<00:00, 324.31it/s, v_num=2, train_loss=3.140, RMSE=6.250]
Epoch 37: 38%|███▊ | 12/32 [00:00<00:00, 326.80it/s, v_num=2, train_loss=3.140, RMSE=6.250]
Epoch 37: 38%|███▊ | 12/32 [00:00<00:00, 324.99it/s, v_num=2, train_loss=3.220, RMSE=6.250]
Epoch 37: 41%|████ | 13/32 [00:00<00:00, 327.21it/s, v_num=2, train_loss=3.220, RMSE=6.250]
Epoch 37: 41%|████ | 13/32 [00:00<00:00, 325.54it/s, v_num=2, train_loss=2.760, RMSE=6.250]
Epoch 37: 44%|████▍ | 14/32 [00:00<00:00, 327.46it/s, v_num=2, train_loss=2.760, RMSE=6.250]
Epoch 37: 44%|████▍ | 14/32 [00:00<00:00, 325.90it/s, v_num=2, train_loss=3.140, RMSE=6.250]
Epoch 37: 47%|████▋ | 15/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=3.140, RMSE=6.250]
Epoch 37: 47%|████▋ | 15/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=3.040, RMSE=6.250]
Epoch 37: 50%|█████ | 16/32 [00:00<00:00, 327.61it/s, v_num=2, train_loss=3.040, RMSE=6.250]
Epoch 37: 50%|█████ | 16/32 [00:00<00:00, 326.25it/s, v_num=2, train_loss=2.740, RMSE=6.250]
Epoch 37: 53%|█████▎ | 17/32 [00:00<00:00, 325.35it/s, v_num=2, train_loss=2.740, RMSE=6.250]
Epoch 37: 53%|█████▎ | 17/32 [00:00<00:00, 324.07it/s, v_num=2, train_loss=3.070, RMSE=6.250]
Epoch 37: 56%|█████▋ | 18/32 [00:00<00:00, 325.65it/s, v_num=2, train_loss=3.070, RMSE=6.250]
Epoch 37: 56%|█████▋ | 18/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=3.320, RMSE=6.250]
Epoch 37: 59%|█████▉ | 19/32 [00:00<00:00, 325.83it/s, v_num=2, train_loss=3.320, RMSE=6.250]
Epoch 37: 59%|█████▉ | 19/32 [00:00<00:00, 324.70it/s, v_num=2, train_loss=2.970, RMSE=6.250]
Epoch 37: 62%|██████▎ | 20/32 [00:00<00:00, 326.27it/s, v_num=2, train_loss=2.970, RMSE=6.250]
Epoch 37: 62%|██████▎ | 20/32 [00:00<00:00, 325.10it/s, v_num=2, train_loss=2.780, RMSE=6.250]
Epoch 37: 66%|██████▌ | 21/32 [00:00<00:00, 326.36it/s, v_num=2, train_loss=2.780, RMSE=6.250]
Epoch 37: 66%|██████▌ | 21/32 [00:00<00:00, 325.34it/s, v_num=2, train_loss=2.750, RMSE=6.250]
Epoch 37: 69%|██████▉ | 22/32 [00:00<00:00, 326.53it/s, v_num=2, train_loss=2.750, RMSE=6.250]
Epoch 37: 69%|██████▉ | 22/32 [00:00<00:00, 325.55it/s, v_num=2, train_loss=3.130, RMSE=6.250]
Epoch 37: 72%|███████▏ | 23/32 [00:00<00:00, 326.69it/s, v_num=2, train_loss=3.130, RMSE=6.250]
Epoch 37: 72%|███████▏ | 23/32 [00:00<00:00, 325.74it/s, v_num=2, train_loss=2.840, RMSE=6.250]
Epoch 37: 75%|███████▌ | 24/32 [00:00<00:00, 327.02it/s, v_num=2, train_loss=2.840, RMSE=6.250]
Epoch 37: 75%|███████▌ | 24/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=2.770, RMSE=6.250]
Epoch 37: 78%|███████▊ | 25/32 [00:00<00:00, 327.10it/s, v_num=2, train_loss=2.770, RMSE=6.250]
Epoch 37: 78%|███████▊ | 25/32 [00:00<00:00, 326.22it/s, v_num=2, train_loss=2.820, RMSE=6.250]
Epoch 37: 81%|████████▏ | 26/32 [00:00<00:00, 327.19it/s, v_num=2, train_loss=2.820, RMSE=6.250]
Epoch 37: 81%|████████▏ | 26/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=2.710, RMSE=6.250]
Epoch 37: 84%|████████▍ | 27/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=2.710, RMSE=6.250]
Epoch 37: 84%|████████▍ | 27/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=3.010, RMSE=6.250]
Epoch 37: 88%|████████▊ | 28/32 [00:00<00:00, 327.56it/s, v_num=2, train_loss=3.010, RMSE=6.250]
Epoch 37: 88%|████████▊ | 28/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=3.190, RMSE=6.250]
Epoch 37: 91%|█████████ | 29/32 [00:00<00:00, 327.70it/s, v_num=2, train_loss=3.190, RMSE=6.250]
Epoch 37: 91%|█████████ | 29/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=3.070, RMSE=6.250]
Epoch 37: 94%|█████████▍| 30/32 [00:00<00:00, 327.78it/s, v_num=2, train_loss=3.070, RMSE=6.250]
Epoch 37: 94%|█████████▍| 30/32 [00:00<00:00, 327.05it/s, v_num=2, train_loss=2.900, RMSE=6.250]
Epoch 37: 97%|█████████▋| 31/32 [00:00<00:00, 327.85it/s, v_num=2, train_loss=2.900, RMSE=6.250]
Epoch 37: 97%|█████████▋| 31/32 [00:00<00:00, 327.15it/s, v_num=2, train_loss=2.840, RMSE=6.250]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 328.14it/s, v_num=2, train_loss=2.840, RMSE=6.250]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 327.45it/s, v_num=2, train_loss=3.120, RMSE=6.250]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 618.59it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 622.80it/s]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 267.62it/s, v_num=2, train_loss=3.120, RMSE=6.150]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 266.44it/s, v_num=2, train_loss=3.120, RMSE=6.150]
Epoch 37: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.120, RMSE=6.150]
Epoch 38: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.120, RMSE=6.150]
Epoch 38: 3%|▎ | 1/32 [00:00<00:00, 281.36it/s, v_num=2, train_loss=3.120, RMSE=6.150]
Epoch 38: 3%|▎ | 1/32 [00:00<00:00, 265.66it/s, v_num=2, train_loss=2.750, RMSE=6.150]
Epoch 38: 6%|▋ | 2/32 [00:00<00:00, 297.11it/s, v_num=2, train_loss=2.750, RMSE=6.150]
Epoch 38: 6%|▋ | 2/32 [00:00<00:00, 288.33it/s, v_num=2, train_loss=2.930, RMSE=6.150]
Epoch 38: 9%|▉ | 3/32 [00:00<00:00, 307.01it/s, v_num=2, train_loss=2.930, RMSE=6.150]
Epoch 38: 9%|▉ | 3/32 [00:00<00:00, 300.71it/s, v_num=2, train_loss=3.010, RMSE=6.150]
Epoch 38: 12%|█▎ | 4/32 [00:00<00:00, 311.94it/s, v_num=2, train_loss=3.010, RMSE=6.150]
Epoch 38: 12%|█▎ | 4/32 [00:00<00:00, 307.06it/s, v_num=2, train_loss=3.060, RMSE=6.150]
Epoch 38: 16%|█▌ | 5/32 [00:00<00:00, 315.30it/s, v_num=2, train_loss=3.060, RMSE=6.150]
Epoch 38: 16%|█▌ | 5/32 [00:00<00:00, 311.29it/s, v_num=2, train_loss=2.730, RMSE=6.150]
Epoch 38: 19%|█▉ | 6/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=2.730, RMSE=6.150]
Epoch 38: 19%|█▉ | 6/32 [00:00<00:00, 314.14it/s, v_num=2, train_loss=2.960, RMSE=6.150]
Epoch 38: 22%|██▏ | 7/32 [00:00<00:00, 319.18it/s, v_num=2, train_loss=2.960, RMSE=6.150]
Epoch 38: 22%|██▏ | 7/32 [00:00<00:00, 316.21it/s, v_num=2, train_loss=3.090, RMSE=6.150]
Epoch 38: 25%|██▌ | 8/32 [00:00<00:00, 320.50it/s, v_num=2, train_loss=3.090, RMSE=6.150]
Epoch 38: 25%|██▌ | 8/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=3.070, RMSE=6.150]
Epoch 38: 28%|██▊ | 9/32 [00:00<00:00, 321.79it/s, v_num=2, train_loss=3.070, RMSE=6.150]
Epoch 38: 28%|██▊ | 9/32 [00:00<00:00, 319.47it/s, v_num=2, train_loss=2.840, RMSE=6.150]
Epoch 38: 31%|███▏ | 10/32 [00:00<00:00, 322.86it/s, v_num=2, train_loss=2.840, RMSE=6.150]
Epoch 38: 31%|███▏ | 10/32 [00:00<00:00, 320.75it/s, v_num=2, train_loss=2.910, RMSE=6.150]
Epoch 38: 34%|███▍ | 11/32 [00:00<00:00, 323.79it/s, v_num=2, train_loss=2.910, RMSE=6.150]
Epoch 38: 34%|███▍ | 11/32 [00:00<00:00, 321.71it/s, v_num=2, train_loss=2.940, RMSE=6.150]
Epoch 38: 38%|███▊ | 12/32 [00:00<00:00, 324.08it/s, v_num=2, train_loss=2.940, RMSE=6.150]
Epoch 38: 38%|███▊ | 12/32 [00:00<00:00, 322.31it/s, v_num=2, train_loss=3.110, RMSE=6.150]
Epoch 38: 41%|████ | 13/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=3.110, RMSE=6.150]
Epoch 38: 41%|████ | 13/32 [00:00<00:00, 322.35it/s, v_num=2, train_loss=2.770, RMSE=6.150]
Epoch 38: 44%|████▍ | 14/32 [00:00<00:00, 324.34it/s, v_num=2, train_loss=2.770, RMSE=6.150]
Epoch 38: 44%|████▍ | 14/32 [00:00<00:00, 322.82it/s, v_num=2, train_loss=2.850, RMSE=6.150]
Epoch 38: 47%|████▋ | 15/32 [00:00<00:00, 325.08it/s, v_num=2, train_loss=2.850, RMSE=6.150]
Epoch 38: 47%|████▋ | 15/32 [00:00<00:00, 323.65it/s, v_num=2, train_loss=2.660, RMSE=6.150]
Epoch 38: 50%|█████ | 16/32 [00:00<00:00, 325.26it/s, v_num=2, train_loss=2.660, RMSE=6.150]
Epoch 38: 50%|█████ | 16/32 [00:00<00:00, 323.91it/s, v_num=2, train_loss=2.860, RMSE=6.150]
Epoch 38: 53%|█████▎ | 17/32 [00:00<00:00, 325.47it/s, v_num=2, train_loss=2.860, RMSE=6.150]
Epoch 38: 53%|█████▎ | 17/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.360, RMSE=6.150]
Epoch 38: 56%|█████▋ | 18/32 [00:00<00:00, 325.87it/s, v_num=2, train_loss=3.360, RMSE=6.150]
Epoch 38: 56%|█████▋ | 18/32 [00:00<00:00, 324.68it/s, v_num=2, train_loss=2.760, RMSE=6.150]
Epoch 38: 59%|█████▉ | 19/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=2.760, RMSE=6.150]
Epoch 38: 59%|█████▉ | 19/32 [00:00<00:00, 325.21it/s, v_num=2, train_loss=2.780, RMSE=6.150]
Epoch 38: 62%|██████▎ | 20/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=2.780, RMSE=6.150]
Epoch 38: 62%|██████▎ | 20/32 [00:00<00:00, 325.46it/s, v_num=2, train_loss=2.620, RMSE=6.150]
Epoch 38: 66%|██████▌ | 21/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=2.620, RMSE=6.150]
Epoch 38: 66%|██████▌ | 21/32 [00:00<00:00, 325.61it/s, v_num=2, train_loss=3.060, RMSE=6.150]
Epoch 38: 69%|██████▉ | 22/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=3.060, RMSE=6.150]
Epoch 38: 69%|██████▉ | 22/32 [00:00<00:00, 325.82it/s, v_num=2, train_loss=2.910, RMSE=6.150]
Epoch 38: 72%|███████▏ | 23/32 [00:00<00:00, 327.15it/s, v_num=2, train_loss=2.910, RMSE=6.150]
Epoch 38: 72%|███████▏ | 23/32 [00:00<00:00, 326.21it/s, v_num=2, train_loss=2.980, RMSE=6.150]
Epoch 38: 75%|███████▌ | 24/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=2.980, RMSE=6.150]
Epoch 38: 75%|███████▌ | 24/32 [00:00<00:00, 326.40it/s, v_num=2, train_loss=3.030, RMSE=6.150]
Epoch 38: 78%|███████▊ | 25/32 [00:00<00:00, 327.42it/s, v_num=2, train_loss=3.030, RMSE=6.150]
Epoch 38: 78%|███████▊ | 25/32 [00:00<00:00, 326.55it/s, v_num=2, train_loss=2.930, RMSE=6.150]
Epoch 38: 81%|████████▏ | 26/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=2.930, RMSE=6.150]
Epoch 38: 81%|████████▏ | 26/32 [00:00<00:00, 326.64it/s, v_num=2, train_loss=3.020, RMSE=6.150]
Epoch 38: 84%|████████▍ | 27/32 [00:00<00:00, 327.76it/s, v_num=2, train_loss=3.020, RMSE=6.150]
Epoch 38: 84%|████████▍ | 27/32 [00:00<00:00, 326.94it/s, v_num=2, train_loss=2.900, RMSE=6.150]
Epoch 38: 88%|████████▊ | 28/32 [00:00<00:00, 327.82it/s, v_num=2, train_loss=2.900, RMSE=6.150]
Epoch 38: 88%|████████▊ | 28/32 [00:00<00:00, 327.05it/s, v_num=2, train_loss=2.870, RMSE=6.150]
Epoch 38: 91%|█████████ | 29/32 [00:00<00:00, 327.96it/s, v_num=2, train_loss=2.870, RMSE=6.150]
Epoch 38: 91%|█████████ | 29/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=3.190, RMSE=6.150]
Epoch 38: 94%|█████████▍| 30/32 [00:00<00:00, 328.09it/s, v_num=2, train_loss=3.190, RMSE=6.150]
Epoch 38: 94%|█████████▍| 30/32 [00:00<00:00, 327.36it/s, v_num=2, train_loss=3.030, RMSE=6.150]
Epoch 38: 97%|█████████▋| 31/32 [00:00<00:00, 328.28it/s, v_num=2, train_loss=3.030, RMSE=6.150]
Epoch 38: 97%|█████████▋| 31/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=3.150, RMSE=6.150]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 328.44it/s, v_num=2, train_loss=3.150, RMSE=6.150]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 327.76it/s, v_num=2, train_loss=3.130, RMSE=6.150]
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Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 268.60it/s, v_num=2, train_loss=3.130, RMSE=5.810]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 267.50it/s, v_num=2, train_loss=3.130, RMSE=5.810]
Epoch 38: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.130, RMSE=5.810]
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Epoch 39: 3%|▎ | 1/32 [00:00<00:00, 317.03it/s, v_num=2, train_loss=3.130, RMSE=5.810]
Epoch 39: 3%|▎ | 1/32 [00:00<00:00, 296.71it/s, v_num=2, train_loss=2.830, RMSE=5.810]
Epoch 39: 6%|▋ | 2/32 [00:00<00:00, 321.77it/s, v_num=2, train_loss=2.830, RMSE=5.810]
Epoch 39: 6%|▋ | 2/32 [00:00<00:00, 311.54it/s, v_num=2, train_loss=2.750, RMSE=5.810]
Epoch 39: 9%|▉ | 3/32 [00:00<00:00, 313.32it/s, v_num=2, train_loss=2.750, RMSE=5.810]
Epoch 39: 9%|▉ | 3/32 [00:00<00:00, 306.71it/s, v_num=2, train_loss=2.860, RMSE=5.810]
Epoch 39: 12%|█▎ | 4/32 [00:00<00:00, 317.13it/s, v_num=2, train_loss=2.860, RMSE=5.810]
Epoch 39: 12%|█▎ | 4/32 [00:00<00:00, 312.04it/s, v_num=2, train_loss=2.980, RMSE=5.810]
Epoch 39: 16%|█▌ | 5/32 [00:00<00:00, 320.43it/s, v_num=2, train_loss=2.980, RMSE=5.810]
Epoch 39: 16%|█▌ | 5/32 [00:00<00:00, 316.33it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 19%|█▉ | 6/32 [00:00<00:00, 322.05it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 19%|█▉ | 6/32 [00:00<00:00, 318.41it/s, v_num=2, train_loss=3.060, RMSE=5.810]
Epoch 39: 22%|██▏ | 7/32 [00:00<00:00, 322.96it/s, v_num=2, train_loss=3.060, RMSE=5.810]
Epoch 39: 22%|██▏ | 7/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=2.850, RMSE=5.810]
Epoch 39: 25%|██▌ | 8/32 [00:00<00:00, 323.65it/s, v_num=2, train_loss=2.850, RMSE=5.810]
Epoch 39: 25%|██▌ | 8/32 [00:00<00:00, 320.99it/s, v_num=2, train_loss=2.870, RMSE=5.810]
Epoch 39: 28%|██▊ | 9/32 [00:00<00:00, 324.87it/s, v_num=2, train_loss=2.870, RMSE=5.810]
Epoch 39: 28%|██▊ | 9/32 [00:00<00:00, 322.50it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 31%|███▏ | 10/32 [00:00<00:00, 325.32it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 31%|███▏ | 10/32 [00:00<00:00, 323.16it/s, v_num=2, train_loss=2.940, RMSE=5.810]
Epoch 39: 34%|███▍ | 11/32 [00:00<00:00, 325.80it/s, v_num=2, train_loss=2.940, RMSE=5.810]
Epoch 39: 34%|███▍ | 11/32 [00:00<00:00, 323.85it/s, v_num=2, train_loss=3.120, RMSE=5.810]
Epoch 39: 38%|███▊ | 12/32 [00:00<00:00, 326.05it/s, v_num=2, train_loss=3.120, RMSE=5.810]
Epoch 39: 38%|███▊ | 12/32 [00:00<00:00, 324.25it/s, v_num=2, train_loss=2.840, RMSE=5.810]
Epoch 39: 41%|████ | 13/32 [00:00<00:00, 326.55it/s, v_num=2, train_loss=2.840, RMSE=5.810]
Epoch 39: 41%|████ | 13/32 [00:00<00:00, 324.69it/s, v_num=2, train_loss=2.740, RMSE=5.810]
Epoch 39: 44%|████▍ | 14/32 [00:00<00:00, 326.72it/s, v_num=2, train_loss=2.740, RMSE=5.810]
Epoch 39: 44%|████▍ | 14/32 [00:00<00:00, 325.17it/s, v_num=2, train_loss=2.970, RMSE=5.810]
Epoch 39: 47%|████▋ | 15/32 [00:00<00:00, 327.13it/s, v_num=2, train_loss=2.970, RMSE=5.810]
Epoch 39: 47%|████▋ | 15/32 [00:00<00:00, 325.68it/s, v_num=2, train_loss=3.260, RMSE=5.810]
Epoch 39: 50%|█████ | 16/32 [00:00<00:00, 327.29it/s, v_num=2, train_loss=3.260, RMSE=5.810]
Epoch 39: 50%|█████ | 16/32 [00:00<00:00, 325.93it/s, v_num=2, train_loss=2.760, RMSE=5.810]
Epoch 39: 53%|█████▎ | 17/32 [00:00<00:00, 327.60it/s, v_num=2, train_loss=2.760, RMSE=5.810]
Epoch 39: 53%|█████▎ | 17/32 [00:00<00:00, 326.13it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 56%|█████▋ | 18/32 [00:00<00:00, 327.69it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 56%|█████▋ | 18/32 [00:00<00:00, 326.46it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 59%|█████▉ | 19/32 [00:00<00:00, 327.82it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 59%|█████▉ | 19/32 [00:00<00:00, 326.66it/s, v_num=2, train_loss=3.000, RMSE=5.810]
Epoch 39: 62%|██████▎ | 20/32 [00:00<00:00, 327.95it/s, v_num=2, train_loss=3.000, RMSE=5.810]
Epoch 39: 62%|██████▎ | 20/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=2.740, RMSE=5.810]
Epoch 39: 66%|██████▌ | 21/32 [00:00<00:00, 328.32it/s, v_num=2, train_loss=2.740, RMSE=5.810]
Epoch 39: 66%|██████▌ | 21/32 [00:00<00:00, 327.13it/s, v_num=2, train_loss=3.020, RMSE=5.810]
Epoch 39: 69%|██████▉ | 22/32 [00:00<00:00, 328.41it/s, v_num=2, train_loss=3.020, RMSE=5.810]
Epoch 39: 69%|██████▉ | 22/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 72%|███████▏ | 23/32 [00:00<00:00, 328.41it/s, v_num=2, train_loss=2.910, RMSE=5.810]
Epoch 39: 72%|███████▏ | 23/32 [00:00<00:00, 327.46it/s, v_num=2, train_loss=3.100, RMSE=5.810]
Epoch 39: 75%|███████▌ | 24/32 [00:00<00:00, 328.54it/s, v_num=2, train_loss=3.100, RMSE=5.810]
Epoch 39: 75%|███████▌ | 24/32 [00:00<00:00, 327.63it/s, v_num=2, train_loss=2.920, RMSE=5.810]
Epoch 39: 78%|███████▊ | 25/32 [00:00<00:00, 328.70it/s, v_num=2, train_loss=2.920, RMSE=5.810]
Epoch 39: 78%|███████▊ | 25/32 [00:00<00:00, 327.81it/s, v_num=2, train_loss=2.700, RMSE=5.810]
Epoch 39: 81%|████████▏ | 26/32 [00:00<00:00, 328.86it/s, v_num=2, train_loss=2.700, RMSE=5.810]
Epoch 39: 81%|████████▏ | 26/32 [00:00<00:00, 328.02it/s, v_num=2, train_loss=2.860, RMSE=5.810]
Epoch 39: 84%|████████▍ | 27/32 [00:00<00:00, 328.95it/s, v_num=2, train_loss=2.860, RMSE=5.810]
Epoch 39: 84%|████████▍ | 27/32 [00:00<00:00, 328.14it/s, v_num=2, train_loss=2.930, RMSE=5.810]
Epoch 39: 88%|████████▊ | 28/32 [00:00<00:00, 329.05it/s, v_num=2, train_loss=2.930, RMSE=5.810]
Epoch 39: 88%|████████▊ | 28/32 [00:00<00:00, 328.27it/s, v_num=2, train_loss=2.960, RMSE=5.810]
Epoch 39: 91%|█████████ | 29/32 [00:00<00:00, 329.11it/s, v_num=2, train_loss=2.960, RMSE=5.810]
Epoch 39: 91%|█████████ | 29/32 [00:00<00:00, 328.35it/s, v_num=2, train_loss=2.840, RMSE=5.810]
Epoch 39: 94%|█████████▍| 30/32 [00:00<00:00, 329.29it/s, v_num=2, train_loss=2.840, RMSE=5.810]
Epoch 39: 94%|█████████▍| 30/32 [00:00<00:00, 328.56it/s, v_num=2, train_loss=2.900, RMSE=5.810]
Epoch 39: 97%|█████████▋| 31/32 [00:00<00:00, 329.41it/s, v_num=2, train_loss=2.900, RMSE=5.810]
Epoch 39: 97%|█████████▋| 31/32 [00:00<00:00, 328.70it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 329.59it/s, v_num=2, train_loss=3.070, RMSE=5.810]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 328.90it/s, v_num=2, train_loss=2.450, RMSE=5.810]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 635.65it/s]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 270.00it/s, v_num=2, train_loss=2.450, RMSE=5.590]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 268.88it/s, v_num=2, train_loss=2.450, RMSE=5.590]
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Epoch 40: 3%|▎ | 1/32 [00:00<00:00, 310.74it/s, v_num=2, train_loss=2.450, RMSE=5.590]
Epoch 40: 3%|▎ | 1/32 [00:00<00:00, 292.00it/s, v_num=2, train_loss=2.970, RMSE=5.590]
Epoch 40: 6%|▋ | 2/32 [00:00<00:00, 316.03it/s, v_num=2, train_loss=2.970, RMSE=5.590]
Epoch 40: 6%|▋ | 2/32 [00:00<00:00, 306.12it/s, v_num=2, train_loss=2.770, RMSE=5.590]
Epoch 40: 9%|▉ | 3/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=2.770, RMSE=5.590]
Epoch 40: 9%|▉ | 3/32 [00:00<00:00, 314.34it/s, v_num=2, train_loss=2.960, RMSE=5.590]
Epoch 40: 12%|█▎ | 4/32 [00:00<00:00, 322.74it/s, v_num=2, train_loss=2.960, RMSE=5.590]
Epoch 40: 12%|█▎ | 4/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=2.840, RMSE=5.590]
Epoch 40: 16%|█▌ | 5/32 [00:00<00:00, 324.11it/s, v_num=2, train_loss=2.840, RMSE=5.590]
Epoch 40: 16%|█▌ | 5/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=2.970, RMSE=5.590]
Epoch 40: 19%|█▉ | 6/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=2.970, RMSE=5.590]
Epoch 40: 19%|█▉ | 6/32 [00:00<00:00, 321.49it/s, v_num=2, train_loss=2.920, RMSE=5.590]
Epoch 40: 22%|██▏ | 7/32 [00:00<00:00, 326.44it/s, v_num=2, train_loss=2.920, RMSE=5.590]
Epoch 40: 22%|██▏ | 7/32 [00:00<00:00, 323.39it/s, v_num=2, train_loss=3.210, RMSE=5.590]
Epoch 40: 25%|██▌ | 8/32 [00:00<00:00, 326.89it/s, v_num=2, train_loss=3.210, RMSE=5.590]
Epoch 40: 25%|██▌ | 8/32 [00:00<00:00, 324.21it/s, v_num=2, train_loss=3.010, RMSE=5.590]
Epoch 40: 28%|██▊ | 9/32 [00:00<00:00, 326.30it/s, v_num=2, train_loss=3.010, RMSE=5.590]
Epoch 40: 28%|██▊ | 9/32 [00:00<00:00, 323.89it/s, v_num=2, train_loss=3.170, RMSE=5.590]
Epoch 40: 31%|███▏ | 10/32 [00:00<00:00, 326.24it/s, v_num=2, train_loss=3.170, RMSE=5.590]
Epoch 40: 31%|███▏ | 10/32 [00:00<00:00, 324.07it/s, v_num=2, train_loss=2.660, RMSE=5.590]
Epoch 40: 34%|███▍ | 11/32 [00:00<00:00, 327.03it/s, v_num=2, train_loss=2.660, RMSE=5.590]
Epoch 40: 34%|███▍ | 11/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=2.780, RMSE=5.590]
Epoch 40: 38%|███▊ | 12/32 [00:00<00:00, 327.35it/s, v_num=2, train_loss=2.780, RMSE=5.590]
Epoch 40: 38%|███▊ | 12/32 [00:00<00:00, 325.54it/s, v_num=2, train_loss=2.650, RMSE=5.590]
Epoch 40: 41%|████ | 13/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=2.650, RMSE=5.590]
Epoch 40: 41%|████ | 13/32 [00:00<00:00, 325.95it/s, v_num=2, train_loss=2.900, RMSE=5.590]
Epoch 40: 44%|████▍ | 14/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=2.900, RMSE=5.590]
Epoch 40: 44%|████▍ | 14/32 [00:00<00:00, 326.25it/s, v_num=2, train_loss=2.750, RMSE=5.590]
Epoch 40: 47%|████▋ | 15/32 [00:00<00:00, 328.36it/s, v_num=2, train_loss=2.750, RMSE=5.590]
Epoch 40: 47%|████▋ | 15/32 [00:00<00:00, 326.88it/s, v_num=2, train_loss=2.820, RMSE=5.590]
Epoch 40: 50%|█████ | 16/32 [00:00<00:00, 328.48it/s, v_num=2, train_loss=2.820, RMSE=5.590]
Epoch 40: 50%|█████ | 16/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=3.030, RMSE=5.590]
Epoch 40: 53%|█████▎ | 17/32 [00:00<00:00, 328.69it/s, v_num=2, train_loss=3.030, RMSE=5.590]
Epoch 40: 53%|█████▎ | 17/32 [00:00<00:00, 327.41it/s, v_num=2, train_loss=2.770, RMSE=5.590]
Epoch 40: 56%|█████▋ | 18/32 [00:00<00:00, 328.78it/s, v_num=2, train_loss=2.770, RMSE=5.590]
Epoch 40: 56%|█████▋ | 18/32 [00:00<00:00, 327.56it/s, v_num=2, train_loss=2.880, RMSE=5.590]
Epoch 40: 59%|█████▉ | 19/32 [00:00<00:00, 328.93it/s, v_num=2, train_loss=2.880, RMSE=5.590]
Epoch 40: 59%|█████▉ | 19/32 [00:00<00:00, 327.78it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 62%|██████▎ | 20/32 [00:00<00:00, 329.00it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 62%|██████▎ | 20/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=2.810, RMSE=5.590]
Epoch 40: 66%|██████▌ | 21/32 [00:00<00:00, 324.02it/s, v_num=2, train_loss=2.810, RMSE=5.590]
Epoch 40: 66%|██████▌ | 21/32 [00:00<00:00, 322.99it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 69%|██████▉ | 22/32 [00:00<00:00, 324.30it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 69%|██████▉ | 22/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 72%|███████▏ | 23/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=2.850, RMSE=5.590]
Epoch 40: 72%|███████▏ | 23/32 [00:00<00:00, 323.52it/s, v_num=2, train_loss=2.880, RMSE=5.590]
Epoch 40: 75%|███████▌ | 24/32 [00:00<00:00, 324.84it/s, v_num=2, train_loss=2.880, RMSE=5.590]
Epoch 40: 75%|███████▌ | 24/32 [00:00<00:00, 323.78it/s, v_num=2, train_loss=2.790, RMSE=5.590]
Epoch 40: 78%|███████▊ | 25/32 [00:00<00:00, 324.96it/s, v_num=2, train_loss=2.790, RMSE=5.590]
Epoch 40: 78%|███████▊ | 25/32 [00:00<00:00, 324.10it/s, v_num=2, train_loss=2.800, RMSE=5.590]
Epoch 40: 81%|████████▏ | 26/32 [00:00<00:00, 325.10it/s, v_num=2, train_loss=2.800, RMSE=5.590]
Epoch 40: 81%|████████▏ | 26/32 [00:00<00:00, 324.27it/s, v_num=2, train_loss=3.280, RMSE=5.590]
Epoch 40: 84%|████████▍ | 27/32 [00:00<00:00, 325.37it/s, v_num=2, train_loss=3.280, RMSE=5.590]
Epoch 40: 84%|████████▍ | 27/32 [00:00<00:00, 324.57it/s, v_num=2, train_loss=3.140, RMSE=5.590]
Epoch 40: 88%|████████▊ | 28/32 [00:00<00:00, 325.67it/s, v_num=2, train_loss=3.140, RMSE=5.590]
Epoch 40: 88%|████████▊ | 28/32 [00:00<00:00, 324.79it/s, v_num=2, train_loss=2.800, RMSE=5.590]
Epoch 40: 91%|█████████ | 29/32 [00:00<00:00, 325.83it/s, v_num=2, train_loss=2.800, RMSE=5.590]
Epoch 40: 91%|█████████ | 29/32 [00:00<00:00, 325.09it/s, v_num=2, train_loss=3.210, RMSE=5.590]
Epoch 40: 94%|█████████▍| 30/32 [00:00<00:00, 325.97it/s, v_num=2, train_loss=3.210, RMSE=5.590]
Epoch 40: 94%|█████████▍| 30/32 [00:00<00:00, 325.25it/s, v_num=2, train_loss=2.870, RMSE=5.590]
Epoch 40: 97%|█████████▋| 31/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=2.870, RMSE=5.590]
Epoch 40: 97%|█████████▋| 31/32 [00:00<00:00, 325.36it/s, v_num=2, train_loss=2.840, RMSE=5.590]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 326.51it/s, v_num=2, train_loss=2.840, RMSE=5.590]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 325.73it/s, v_num=2, train_loss=3.120, RMSE=5.590]
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Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 267.60it/s, v_num=2, train_loss=3.120, RMSE=5.620]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 266.47it/s, v_num=2, train_loss=3.120, RMSE=5.620]
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Epoch 41: 3%|▎ | 1/32 [00:00<00:00, 309.02it/s, v_num=2, train_loss=3.120, RMSE=5.620]
Epoch 41: 3%|▎ | 1/32 [00:00<00:00, 290.46it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 6%|▋ | 2/32 [00:00<00:00, 319.49it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 6%|▋ | 2/32 [00:00<00:00, 309.50it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 9%|▉ | 3/32 [00:00<00:00, 319.31it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 9%|▉ | 3/32 [00:00<00:00, 312.57it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 12%|█▎ | 4/32 [00:00<00:00, 321.91it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 12%|█▎ | 4/32 [00:00<00:00, 316.77it/s, v_num=2, train_loss=2.360, RMSE=5.620]
Epoch 41: 16%|█▌ | 5/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=2.360, RMSE=5.620]
Epoch 41: 16%|█▌ | 5/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 41: 19%|█▉ | 6/32 [00:00<00:00, 325.25it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 41: 19%|█▉ | 6/32 [00:00<00:00, 321.71it/s, v_num=2, train_loss=3.110, RMSE=5.620]
Epoch 41: 22%|██▏ | 7/32 [00:00<00:00, 325.87it/s, v_num=2, train_loss=3.110, RMSE=5.620]
Epoch 41: 22%|██▏ | 7/32 [00:00<00:00, 322.84it/s, v_num=2, train_loss=2.900, RMSE=5.620]
Epoch 41: 25%|██▌ | 8/32 [00:00<00:00, 326.45it/s, v_num=2, train_loss=2.900, RMSE=5.620]
Epoch 41: 25%|██▌ | 8/32 [00:00<00:00, 323.74it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 28%|██▊ | 9/32 [00:00<00:00, 326.86it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 28%|██▊ | 9/32 [00:00<00:00, 324.48it/s, v_num=2, train_loss=3.030, RMSE=5.620]
Epoch 41: 31%|███▏ | 10/32 [00:00<00:00, 327.45it/s, v_num=2, train_loss=3.030, RMSE=5.620]
Epoch 41: 31%|███▏ | 10/32 [00:00<00:00, 325.29it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 34%|███▍ | 11/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 34%|███▍ | 11/32 [00:00<00:00, 325.91it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 41: 38%|███▊ | 12/32 [00:00<00:00, 327.94it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 41: 38%|███▊ | 12/32 [00:00<00:00, 326.08it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 41%|████ | 13/32 [00:00<00:00, 328.00it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 41: 41%|████ | 13/32 [00:00<00:00, 326.33it/s, v_num=2, train_loss=2.840, RMSE=5.620]
Epoch 41: 44%|████▍ | 14/32 [00:00<00:00, 328.52it/s, v_num=2, train_loss=2.840, RMSE=5.620]
Epoch 41: 44%|████▍ | 14/32 [00:00<00:00, 326.96it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 47%|████▋ | 15/32 [00:00<00:00, 328.62it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 47%|████▋ | 15/32 [00:00<00:00, 327.16it/s, v_num=2, train_loss=2.880, RMSE=5.620]
Epoch 41: 50%|█████ | 16/32 [00:00<00:00, 328.80it/s, v_num=2, train_loss=2.880, RMSE=5.620]
Epoch 41: 50%|█████ | 16/32 [00:00<00:00, 327.44it/s, v_num=2, train_loss=2.600, RMSE=5.620]
Epoch 41: 53%|█████▎ | 17/32 [00:00<00:00, 328.91it/s, v_num=2, train_loss=2.600, RMSE=5.620]
Epoch 41: 53%|█████▎ | 17/32 [00:00<00:00, 327.57it/s, v_num=2, train_loss=2.960, RMSE=5.620]
Epoch 41: 56%|█████▋ | 18/32 [00:00<00:00, 329.15it/s, v_num=2, train_loss=2.960, RMSE=5.620]
Epoch 41: 56%|█████▋ | 18/32 [00:00<00:00, 327.89it/s, v_num=2, train_loss=2.920, RMSE=5.620]
Epoch 41: 59%|█████▉ | 19/32 [00:00<00:00, 329.14it/s, v_num=2, train_loss=2.920, RMSE=5.620]
Epoch 41: 59%|█████▉ | 19/32 [00:00<00:00, 327.99it/s, v_num=2, train_loss=3.020, RMSE=5.620]
Epoch 41: 62%|██████▎ | 20/32 [00:00<00:00, 329.14it/s, v_num=2, train_loss=3.020, RMSE=5.620]
Epoch 41: 62%|██████▎ | 20/32 [00:00<00:00, 327.98it/s, v_num=2, train_loss=2.890, RMSE=5.620]
Epoch 41: 66%|██████▌ | 21/32 [00:00<00:00, 328.90it/s, v_num=2, train_loss=2.890, RMSE=5.620]
Epoch 41: 66%|██████▌ | 21/32 [00:00<00:00, 327.81it/s, v_num=2, train_loss=3.160, RMSE=5.620]
Epoch 41: 69%|██████▉ | 22/32 [00:00<00:00, 328.87it/s, v_num=2, train_loss=3.160, RMSE=5.620]
Epoch 41: 69%|██████▉ | 22/32 [00:00<00:00, 327.73it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 72%|███████▏ | 23/32 [00:00<00:00, 328.92it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 41: 72%|███████▏ | 23/32 [00:00<00:00, 327.97it/s, v_num=2, train_loss=2.660, RMSE=5.620]
Epoch 41: 75%|███████▌ | 24/32 [00:00<00:00, 329.02it/s, v_num=2, train_loss=2.660, RMSE=5.620]
Epoch 41: 75%|███████▌ | 24/32 [00:00<00:00, 328.09it/s, v_num=2, train_loss=2.930, RMSE=5.620]
Epoch 41: 78%|███████▊ | 25/32 [00:00<00:00, 328.99it/s, v_num=2, train_loss=2.930, RMSE=5.620]
Epoch 41: 78%|███████▊ | 25/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 81%|████████▏ | 26/32 [00:00<00:00, 329.26it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 41: 81%|████████▏ | 26/32 [00:00<00:00, 328.29it/s, v_num=2, train_loss=2.760, RMSE=5.620]
Epoch 41: 84%|████████▍ | 27/32 [00:00<00:00, 329.33it/s, v_num=2, train_loss=2.760, RMSE=5.620]
Epoch 41: 84%|████████▍ | 27/32 [00:00<00:00, 328.51it/s, v_num=2, train_loss=2.760, RMSE=5.620]
Epoch 41: 88%|████████▊ | 28/32 [00:00<00:00, 329.27it/s, v_num=2, train_loss=2.760, RMSE=5.620]
Epoch 41: 88%|████████▊ | 28/32 [00:00<00:00, 328.48it/s, v_num=2, train_loss=3.040, RMSE=5.620]
Epoch 41: 91%|█████████ | 29/32 [00:00<00:00, 329.27it/s, v_num=2, train_loss=3.040, RMSE=5.620]
Epoch 41: 91%|█████████ | 29/32 [00:00<00:00, 328.51it/s, v_num=2, train_loss=3.120, RMSE=5.620]
Epoch 41: 94%|█████████▍| 30/32 [00:00<00:00, 329.31it/s, v_num=2, train_loss=3.120, RMSE=5.620]
Epoch 41: 94%|█████████▍| 30/32 [00:00<00:00, 328.57it/s, v_num=2, train_loss=2.930, RMSE=5.620]
Epoch 41: 97%|█████████▋| 31/32 [00:00<00:00, 329.50it/s, v_num=2, train_loss=2.930, RMSE=5.620]
Epoch 41: 97%|█████████▋| 31/32 [00:00<00:00, 328.78it/s, v_num=2, train_loss=2.740, RMSE=5.620]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 329.64it/s, v_num=2, train_loss=2.740, RMSE=5.620]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 328.95it/s, v_num=2, train_loss=2.770, RMSE=5.620]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 634.57it/s]
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Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 630.89it/s]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 629.85it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 634.10it/s]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 269.91it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 268.75it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 41: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 42: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 42: 3%|▎ | 1/32 [00:00<00:00, 318.72it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 42: 3%|▎ | 1/32 [00:00<00:00, 298.40it/s, v_num=2, train_loss=2.620, RMSE=5.400]
Epoch 42: 6%|▋ | 2/32 [00:00<00:00, 322.32it/s, v_num=2, train_loss=2.620, RMSE=5.400]
Epoch 42: 6%|▋ | 2/32 [00:00<00:00, 311.97it/s, v_num=2, train_loss=2.650, RMSE=5.400]
Epoch 42: 9%|▉ | 3/32 [00:00<00:00, 324.53it/s, v_num=2, train_loss=2.650, RMSE=5.400]
Epoch 42: 9%|▉ | 3/32 [00:00<00:00, 317.53it/s, v_num=2, train_loss=2.980, RMSE=5.400]
Epoch 42: 12%|█▎ | 4/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=2.980, RMSE=5.400]
Epoch 42: 12%|█▎ | 4/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=3.040, RMSE=5.400]
Epoch 42: 16%|█▌ | 5/32 [00:00<00:00, 326.78it/s, v_num=2, train_loss=3.040, RMSE=5.400]
Epoch 42: 16%|█▌ | 5/32 [00:00<00:00, 322.18it/s, v_num=2, train_loss=3.310, RMSE=5.400]
Epoch 42: 19%|█▉ | 6/32 [00:00<00:00, 327.08it/s, v_num=2, train_loss=3.310, RMSE=5.400]
Epoch 42: 19%|█▉ | 6/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=2.660, RMSE=5.400]
Epoch 42: 22%|██▏ | 7/32 [00:00<00:00, 322.24it/s, v_num=2, train_loss=2.660, RMSE=5.400]
Epoch 42: 22%|██▏ | 7/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=2.630, RMSE=5.400]
Epoch 42: 25%|██▌ | 8/32 [00:00<00:00, 323.47it/s, v_num=2, train_loss=2.630, RMSE=5.400]
Epoch 42: 25%|██▌ | 8/32 [00:00<00:00, 320.78it/s, v_num=2, train_loss=2.930, RMSE=5.400]
Epoch 42: 28%|██▊ | 9/32 [00:00<00:00, 324.19it/s, v_num=2, train_loss=2.930, RMSE=5.400]
Epoch 42: 28%|██▊ | 9/32 [00:00<00:00, 321.79it/s, v_num=2, train_loss=2.830, RMSE=5.400]
Epoch 42: 31%|███▏ | 10/32 [00:00<00:00, 324.70it/s, v_num=2, train_loss=2.830, RMSE=5.400]
Epoch 42: 31%|███▏ | 10/32 [00:00<00:00, 322.27it/s, v_num=2, train_loss=2.980, RMSE=5.400]
Epoch 42: 34%|███▍ | 11/32 [00:00<00:00, 324.58it/s, v_num=2, train_loss=2.980, RMSE=5.400]
Epoch 42: 34%|███▍ | 11/32 [00:00<00:00, 322.63it/s, v_num=2, train_loss=2.810, RMSE=5.400]
Epoch 42: 38%|███▊ | 12/32 [00:00<00:00, 325.53it/s, v_num=2, train_loss=2.810, RMSE=5.400]
Epoch 42: 38%|███▊ | 12/32 [00:00<00:00, 323.50it/s, v_num=2, train_loss=3.030, RMSE=5.400]
Epoch 42: 41%|████ | 13/32 [00:00<00:00, 325.97it/s, v_num=2, train_loss=3.030, RMSE=5.400]
Epoch 42: 41%|████ | 13/32 [00:00<00:00, 324.31it/s, v_num=2, train_loss=2.890, RMSE=5.400]
Epoch 42: 44%|████▍ | 14/32 [00:00<00:00, 326.19it/s, v_num=2, train_loss=2.890, RMSE=5.400]
Epoch 42: 44%|████▍ | 14/32 [00:00<00:00, 324.65it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 42: 47%|████▋ | 15/32 [00:00<00:00, 326.39it/s, v_num=2, train_loss=2.770, RMSE=5.400]
Epoch 42: 47%|████▋ | 15/32 [00:00<00:00, 324.95it/s, v_num=2, train_loss=3.270, RMSE=5.400]
Epoch 42: 50%|█████ | 16/32 [00:00<00:00, 326.79it/s, v_num=2, train_loss=3.270, RMSE=5.400]
Epoch 42: 50%|█████ | 16/32 [00:00<00:00, 325.22it/s, v_num=2, train_loss=2.960, RMSE=5.400]
Epoch 42: 53%|█████▎ | 17/32 [00:00<00:00, 327.07it/s, v_num=2, train_loss=2.960, RMSE=5.400]
Epoch 42: 53%|█████▎ | 17/32 [00:00<00:00, 325.78it/s, v_num=2, train_loss=2.860, RMSE=5.400]
Epoch 42: 56%|█████▋ | 18/32 [00:00<00:00, 327.25it/s, v_num=2, train_loss=2.860, RMSE=5.400]
Epoch 42: 56%|█████▋ | 18/32 [00:00<00:00, 326.06it/s, v_num=2, train_loss=2.790, RMSE=5.400]
Epoch 42: 59%|█████▉ | 19/32 [00:00<00:00, 327.48it/s, v_num=2, train_loss=2.790, RMSE=5.400]
Epoch 42: 59%|█████▉ | 19/32 [00:00<00:00, 326.24it/s, v_num=2, train_loss=2.800, RMSE=5.400]
Epoch 42: 62%|██████▎ | 20/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=2.800, RMSE=5.400]
Epoch 42: 62%|██████▎ | 20/32 [00:00<00:00, 326.52it/s, v_num=2, train_loss=2.940, RMSE=5.400]
Epoch 42: 66%|██████▌ | 21/32 [00:00<00:00, 327.38it/s, v_num=2, train_loss=2.940, RMSE=5.400]
Epoch 42: 66%|██████▌ | 21/32 [00:00<00:00, 326.35it/s, v_num=2, train_loss=2.690, RMSE=5.400]
Epoch 42: 69%|██████▉ | 22/32 [00:00<00:00, 327.61it/s, v_num=2, train_loss=2.690, RMSE=5.400]
Epoch 42: 69%|██████▉ | 22/32 [00:00<00:00, 326.62it/s, v_num=2, train_loss=2.870, RMSE=5.400]
Epoch 42: 72%|███████▏ | 23/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=2.870, RMSE=5.400]
Epoch 42: 72%|███████▏ | 23/32 [00:00<00:00, 326.85it/s, v_num=2, train_loss=2.780, RMSE=5.400]
Epoch 42: 75%|███████▌ | 24/32 [00:00<00:00, 327.93it/s, v_num=2, train_loss=2.780, RMSE=5.400]
Epoch 42: 75%|███████▌ | 24/32 [00:00<00:00, 327.00it/s, v_num=2, train_loss=2.760, RMSE=5.400]
Epoch 42: 78%|███████▊ | 25/32 [00:00<00:00, 328.16it/s, v_num=2, train_loss=2.760, RMSE=5.400]
Epoch 42: 78%|███████▊ | 25/32 [00:00<00:00, 327.28it/s, v_num=2, train_loss=2.870, RMSE=5.400]
Epoch 42: 81%|████████▏ | 26/32 [00:00<00:00, 328.28it/s, v_num=2, train_loss=2.870, RMSE=5.400]
Epoch 42: 81%|████████▏ | 26/32 [00:00<00:00, 327.44it/s, v_num=2, train_loss=2.860, RMSE=5.400]
Epoch 42: 84%|████████▍ | 27/32 [00:00<00:00, 328.44it/s, v_num=2, train_loss=2.860, RMSE=5.400]
Epoch 42: 84%|████████▍ | 27/32 [00:00<00:00, 327.63it/s, v_num=2, train_loss=2.730, RMSE=5.400]
Epoch 42: 88%|████████▊ | 28/32 [00:00<00:00, 328.49it/s, v_num=2, train_loss=2.730, RMSE=5.400]
Epoch 42: 88%|████████▊ | 28/32 [00:00<00:00, 327.67it/s, v_num=2, train_loss=3.040, RMSE=5.400]
Epoch 42: 91%|█████████ | 29/32 [00:00<00:00, 328.65it/s, v_num=2, train_loss=3.040, RMSE=5.400]
Epoch 42: 91%|█████████ | 29/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=2.950, RMSE=5.400]
Epoch 42: 94%|█████████▍| 30/32 [00:00<00:00, 328.62it/s, v_num=2, train_loss=2.950, RMSE=5.400]
Epoch 42: 94%|█████████▍| 30/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=3.080, RMSE=5.400]
Epoch 42: 97%|█████████▋| 31/32 [00:00<00:00, 328.61it/s, v_num=2, train_loss=3.080, RMSE=5.400]
Epoch 42: 97%|█████████▋| 31/32 [00:00<00:00, 327.90it/s, v_num=2, train_loss=2.850, RMSE=5.400]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 328.85it/s, v_num=2, train_loss=2.850, RMSE=5.400]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 328.16it/s, v_num=2, train_loss=2.880, RMSE=5.400]
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Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 269.39it/s, v_num=2, train_loss=2.880, RMSE=5.120]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 268.29it/s, v_num=2, train_loss=2.880, RMSE=5.120]
Epoch 42: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.880, RMSE=5.120]
Epoch 43: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.880, RMSE=5.120]
Epoch 43: 3%|▎ | 1/32 [00:00<00:00, 312.73it/s, v_num=2, train_loss=2.880, RMSE=5.120]
Epoch 43: 3%|▎ | 1/32 [00:00<00:00, 293.06it/s, v_num=2, train_loss=3.010, RMSE=5.120]
Epoch 43: 6%|▋ | 2/32 [00:00<00:00, 320.37it/s, v_num=2, train_loss=3.010, RMSE=5.120]
Epoch 43: 6%|▋ | 2/32 [00:00<00:00, 310.24it/s, v_num=2, train_loss=3.020, RMSE=5.120]
Epoch 43: 9%|▉ | 3/32 [00:00<00:00, 322.67it/s, v_num=2, train_loss=3.020, RMSE=5.120]
Epoch 43: 9%|▉ | 3/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=2.870, RMSE=5.120]
Epoch 43: 12%|█▎ | 4/32 [00:00<00:00, 324.06it/s, v_num=2, train_loss=2.870, RMSE=5.120]
Epoch 43: 12%|█▎ | 4/32 [00:00<00:00, 318.81it/s, v_num=2, train_loss=2.870, RMSE=5.120]
Epoch 43: 16%|█▌ | 5/32 [00:00<00:00, 325.45it/s, v_num=2, train_loss=2.870, RMSE=5.120]
Epoch 43: 16%|█▌ | 5/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=2.980, RMSE=5.120]
Epoch 43: 19%|█▉ | 6/32 [00:00<00:00, 326.65it/s, v_num=2, train_loss=2.980, RMSE=5.120]
Epoch 43: 19%|█▉ | 6/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=2.830, RMSE=5.120]
Epoch 43: 22%|██▏ | 7/32 [00:00<00:00, 326.90it/s, v_num=2, train_loss=2.830, RMSE=5.120]
Epoch 43: 22%|██▏ | 7/32 [00:00<00:00, 323.71it/s, v_num=2, train_loss=3.030, RMSE=5.120]
Epoch 43: 25%|██▌ | 8/32 [00:00<00:00, 327.35it/s, v_num=2, train_loss=3.030, RMSE=5.120]
Epoch 43: 25%|██▌ | 8/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=2.960, RMSE=5.120]
Epoch 43: 28%|██▊ | 9/32 [00:00<00:00, 327.49it/s, v_num=2, train_loss=2.960, RMSE=5.120]
Epoch 43: 28%|██▊ | 9/32 [00:00<00:00, 325.08it/s, v_num=2, train_loss=2.790, RMSE=5.120]
Epoch 43: 31%|███▏ | 10/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=2.790, RMSE=5.120]
Epoch 43: 31%|███▏ | 10/32 [00:00<00:00, 325.99it/s, v_num=2, train_loss=2.990, RMSE=5.120]
Epoch 43: 34%|███▍ | 11/32 [00:00<00:00, 328.55it/s, v_num=2, train_loss=2.990, RMSE=5.120]
Epoch 43: 34%|███▍ | 11/32 [00:00<00:00, 326.56it/s, v_num=2, train_loss=2.630, RMSE=5.120]
Epoch 43: 38%|███▊ | 12/32 [00:00<00:00, 328.72it/s, v_num=2, train_loss=2.630, RMSE=5.120]
Epoch 43: 38%|███▊ | 12/32 [00:00<00:00, 326.91it/s, v_num=2, train_loss=3.180, RMSE=5.120]
Epoch 43: 41%|████ | 13/32 [00:00<00:00, 328.93it/s, v_num=2, train_loss=3.180, RMSE=5.120]
Epoch 43: 41%|████ | 13/32 [00:00<00:00, 327.24it/s, v_num=2, train_loss=2.920, RMSE=5.120]
Epoch 43: 44%|████▍ | 14/32 [00:00<00:00, 329.31it/s, v_num=2, train_loss=2.920, RMSE=5.120]
Epoch 43: 44%|████▍ | 14/32 [00:00<00:00, 327.69it/s, v_num=2, train_loss=2.650, RMSE=5.120]
Epoch 43: 47%|████▋ | 15/32 [00:00<00:00, 329.33it/s, v_num=2, train_loss=2.650, RMSE=5.120]
Epoch 43: 47%|████▋ | 15/32 [00:00<00:00, 327.87it/s, v_num=2, train_loss=2.740, RMSE=5.120]
Epoch 43: 50%|█████ | 16/32 [00:00<00:00, 329.47it/s, v_num=2, train_loss=2.740, RMSE=5.120]
Epoch 43: 50%|█████ | 16/32 [00:00<00:00, 328.09it/s, v_num=2, train_loss=2.850, RMSE=5.120]
Epoch 43: 53%|█████▎ | 17/32 [00:00<00:00, 329.42it/s, v_num=2, train_loss=2.850, RMSE=5.120]
Epoch 43: 53%|█████▎ | 17/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=2.840, RMSE=5.120]
Epoch 43: 56%|█████▋ | 18/32 [00:00<00:00, 329.75it/s, v_num=2, train_loss=2.840, RMSE=5.120]
Epoch 43: 56%|█████▋ | 18/32 [00:00<00:00, 328.52it/s, v_num=2, train_loss=2.710, RMSE=5.120]
Epoch 43: 59%|█████▉ | 19/32 [00:00<00:00, 329.87it/s, v_num=2, train_loss=2.710, RMSE=5.120]
Epoch 43: 59%|█████▉ | 19/32 [00:00<00:00, 328.71it/s, v_num=2, train_loss=2.780, RMSE=5.120]
Epoch 43: 62%|██████▎ | 20/32 [00:00<00:00, 330.00it/s, v_num=2, train_loss=2.780, RMSE=5.120]
Epoch 43: 62%|██████▎ | 20/32 [00:00<00:00, 328.88it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 66%|██████▌ | 21/32 [00:00<00:00, 329.82it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 66%|██████▌ | 21/32 [00:00<00:00, 328.76it/s, v_num=2, train_loss=2.810, RMSE=5.120]
Epoch 43: 69%|██████▉ | 22/32 [00:00<00:00, 330.01it/s, v_num=2, train_loss=2.810, RMSE=5.120]
Epoch 43: 69%|██████▉ | 22/32 [00:00<00:00, 328.90it/s, v_num=2, train_loss=3.020, RMSE=5.120]
Epoch 43: 72%|███████▏ | 23/32 [00:00<00:00, 330.04it/s, v_num=2, train_loss=3.020, RMSE=5.120]
Epoch 43: 72%|███████▏ | 23/32 [00:00<00:00, 329.08it/s, v_num=2, train_loss=2.540, RMSE=5.120]
Epoch 43: 75%|███████▌ | 24/32 [00:00<00:00, 330.07it/s, v_num=2, train_loss=2.540, RMSE=5.120]
Epoch 43: 75%|███████▌ | 24/32 [00:00<00:00, 329.15it/s, v_num=2, train_loss=2.960, RMSE=5.120]
Epoch 43: 78%|███████▊ | 25/32 [00:00<00:00, 328.42it/s, v_num=2, train_loss=2.960, RMSE=5.120]
Epoch 43: 78%|███████▊ | 25/32 [00:00<00:00, 327.54it/s, v_num=2, train_loss=2.940, RMSE=5.120]
Epoch 43: 81%|████████▏ | 26/32 [00:00<00:00, 328.35it/s, v_num=2, train_loss=2.940, RMSE=5.120]
Epoch 43: 81%|████████▏ | 26/32 [00:00<00:00, 327.50it/s, v_num=2, train_loss=3.010, RMSE=5.120]
Epoch 43: 84%|████████▍ | 27/32 [00:00<00:00, 328.22it/s, v_num=2, train_loss=3.010, RMSE=5.120]
Epoch 43: 84%|████████▍ | 27/32 [00:00<00:00, 327.36it/s, v_num=2, train_loss=2.670, RMSE=5.120]
Epoch 43: 88%|████████▊ | 28/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=2.670, RMSE=5.120]
Epoch 43: 88%|████████▊ | 28/32 [00:00<00:00, 327.09it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 91%|█████████ | 29/32 [00:00<00:00, 327.85it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 91%|█████████ | 29/32 [00:00<00:00, 327.10it/s, v_num=2, train_loss=2.720, RMSE=5.120]
Epoch 43: 94%|█████████▍| 30/32 [00:00<00:00, 327.82it/s, v_num=2, train_loss=2.720, RMSE=5.120]
Epoch 43: 94%|█████████▍| 30/32 [00:00<00:00, 327.08it/s, v_num=2, train_loss=2.790, RMSE=5.120]
Epoch 43: 97%|█████████▋| 31/32 [00:00<00:00, 328.02it/s, v_num=2, train_loss=2.790, RMSE=5.120]
Epoch 43: 97%|█████████▋| 31/32 [00:00<00:00, 327.31it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 328.25it/s, v_num=2, train_loss=2.930, RMSE=5.120]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 327.57it/s, v_num=2, train_loss=2.420, RMSE=5.120]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 629.44it/s]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 268.73it/s, v_num=2, train_loss=2.420, RMSE=5.000]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 267.65it/s, v_num=2, train_loss=2.420, RMSE=5.000]
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Epoch 44: 3%|▎ | 1/32 [00:00<00:00, 315.91it/s, v_num=2, train_loss=2.420, RMSE=5.000]
Epoch 44: 3%|▎ | 1/32 [00:00<00:00, 296.48it/s, v_num=2, train_loss=2.960, RMSE=5.000]
Epoch 44: 6%|▋ | 2/32 [00:00<00:00, 320.47it/s, v_num=2, train_loss=2.960, RMSE=5.000]
Epoch 44: 6%|▋ | 2/32 [00:00<00:00, 310.33it/s, v_num=2, train_loss=2.850, RMSE=5.000]
Epoch 44: 9%|▉ | 3/32 [00:00<00:00, 323.79it/s, v_num=2, train_loss=2.850, RMSE=5.000]
Epoch 44: 9%|▉ | 3/32 [00:00<00:00, 316.73it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 12%|█▎ | 4/32 [00:00<00:00, 326.58it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 12%|█▎ | 4/32 [00:00<00:00, 320.48it/s, v_num=2, train_loss=2.980, RMSE=5.000]
Epoch 44: 16%|█▌ | 5/32 [00:00<00:00, 327.67it/s, v_num=2, train_loss=2.980, RMSE=5.000]
Epoch 44: 16%|█▌ | 5/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=2.650, RMSE=5.000]
Epoch 44: 19%|█▉ | 6/32 [00:00<00:00, 328.06it/s, v_num=2, train_loss=2.650, RMSE=5.000]
Epoch 44: 19%|█▉ | 6/32 [00:00<00:00, 324.44it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 22%|██▏ | 7/32 [00:00<00:00, 328.20it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 22%|██▏ | 7/32 [00:00<00:00, 325.07it/s, v_num=2, train_loss=2.650, RMSE=5.000]
Epoch 44: 25%|██▌ | 8/32 [00:00<00:00, 329.18it/s, v_num=2, train_loss=2.650, RMSE=5.000]
Epoch 44: 25%|██▌ | 8/32 [00:00<00:00, 326.02it/s, v_num=2, train_loss=3.170, RMSE=5.000]
Epoch 44: 28%|██▊ | 9/32 [00:00<00:00, 329.66it/s, v_num=2, train_loss=3.170, RMSE=5.000]
Epoch 44: 28%|██▊ | 9/32 [00:00<00:00, 327.21it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 31%|███▏ | 10/32 [00:00<00:00, 329.84it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 31%|███▏ | 10/32 [00:00<00:00, 327.63it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 34%|███▍ | 11/32 [00:00<00:00, 329.92it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 34%|███▍ | 11/32 [00:00<00:00, 327.91it/s, v_num=2, train_loss=2.670, RMSE=5.000]
Epoch 44: 38%|███▊ | 12/32 [00:00<00:00, 330.36it/s, v_num=2, train_loss=2.670, RMSE=5.000]
Epoch 44: 38%|███▊ | 12/32 [00:00<00:00, 328.20it/s, v_num=2, train_loss=3.020, RMSE=5.000]
Epoch 44: 41%|████ | 13/32 [00:00<00:00, 329.53it/s, v_num=2, train_loss=3.020, RMSE=5.000]
Epoch 44: 41%|████ | 13/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 44%|████▍ | 14/32 [00:00<00:00, 329.46it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 44%|████▍ | 14/32 [00:00<00:00, 327.89it/s, v_num=2, train_loss=2.760, RMSE=5.000]
Epoch 44: 47%|████▋ | 15/32 [00:00<00:00, 329.61it/s, v_num=2, train_loss=2.760, RMSE=5.000]
Epoch 44: 47%|████▋ | 15/32 [00:00<00:00, 328.15it/s, v_num=2, train_loss=3.010, RMSE=5.000]
Epoch 44: 50%|█████ | 16/32 [00:00<00:00, 329.71it/s, v_num=2, train_loss=3.010, RMSE=5.000]
Epoch 44: 50%|█████ | 16/32 [00:00<00:00, 328.32it/s, v_num=2, train_loss=3.010, RMSE=5.000]
Epoch 44: 53%|█████▎ | 17/32 [00:00<00:00, 329.99it/s, v_num=2, train_loss=3.010, RMSE=5.000]
Epoch 44: 53%|█████▎ | 17/32 [00:00<00:00, 328.70it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 56%|█████▋ | 18/32 [00:00<00:00, 329.97it/s, v_num=2, train_loss=2.710, RMSE=5.000]
Epoch 44: 56%|█████▋ | 18/32 [00:00<00:00, 328.73it/s, v_num=2, train_loss=3.140, RMSE=5.000]
Epoch 44: 59%|█████▉ | 19/32 [00:00<00:00, 329.99it/s, v_num=2, train_loss=3.140, RMSE=5.000]
Epoch 44: 59%|█████▉ | 19/32 [00:00<00:00, 328.83it/s, v_num=2, train_loss=2.690, RMSE=5.000]
Epoch 44: 62%|██████▎ | 20/32 [00:00<00:00, 329.96it/s, v_num=2, train_loss=2.690, RMSE=5.000]
Epoch 44: 62%|██████▎ | 20/32 [00:00<00:00, 328.84it/s, v_num=2, train_loss=2.850, RMSE=5.000]
Epoch 44: 66%|██████▌ | 21/32 [00:00<00:00, 330.17it/s, v_num=2, train_loss=2.850, RMSE=5.000]
Epoch 44: 66%|██████▌ | 21/32 [00:00<00:00, 329.10it/s, v_num=2, train_loss=2.700, RMSE=5.000]
Epoch 44: 69%|██████▉ | 22/32 [00:00<00:00, 330.27it/s, v_num=2, train_loss=2.700, RMSE=5.000]
Epoch 44: 69%|██████▉ | 22/32 [00:00<00:00, 329.27it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 72%|███████▏ | 23/32 [00:00<00:00, 330.09it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 72%|███████▏ | 23/32 [00:00<00:00, 329.12it/s, v_num=2, train_loss=2.890, RMSE=5.000]
Epoch 44: 75%|███████▌ | 24/32 [00:00<00:00, 329.94it/s, v_num=2, train_loss=2.890, RMSE=5.000]
Epoch 44: 75%|███████▌ | 24/32 [00:00<00:00, 329.01it/s, v_num=2, train_loss=2.890, RMSE=5.000]
Epoch 44: 78%|███████▊ | 25/32 [00:00<00:00, 330.10it/s, v_num=2, train_loss=2.890, RMSE=5.000]
Epoch 44: 78%|███████▊ | 25/32 [00:00<00:00, 329.21it/s, v_num=2, train_loss=3.050, RMSE=5.000]
Epoch 44: 81%|████████▏ | 26/32 [00:00<00:00, 330.12it/s, v_num=2, train_loss=3.050, RMSE=5.000]
Epoch 44: 81%|████████▏ | 26/32 [00:00<00:00, 329.25it/s, v_num=2, train_loss=2.990, RMSE=5.000]
Epoch 44: 84%|████████▍ | 27/32 [00:00<00:00, 330.08it/s, v_num=2, train_loss=2.990, RMSE=5.000]
Epoch 44: 84%|████████▍ | 27/32 [00:00<00:00, 329.25it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 88%|████████▊ | 28/32 [00:00<00:00, 330.12it/s, v_num=2, train_loss=2.800, RMSE=5.000]
Epoch 44: 88%|████████▊ | 28/32 [00:00<00:00, 329.32it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 91%|█████████ | 29/32 [00:00<00:00, 330.31it/s, v_num=2, train_loss=2.870, RMSE=5.000]
Epoch 44: 91%|█████████ | 29/32 [00:00<00:00, 329.54it/s, v_num=2, train_loss=2.730, RMSE=5.000]
Epoch 44: 94%|█████████▍| 30/32 [00:00<00:00, 330.26it/s, v_num=2, train_loss=2.730, RMSE=5.000]
Epoch 44: 94%|█████████▍| 30/32 [00:00<00:00, 329.51it/s, v_num=2, train_loss=2.660, RMSE=5.000]
Epoch 44: 97%|█████████▋| 31/32 [00:00<00:00, 330.30it/s, v_num=2, train_loss=2.660, RMSE=5.000]
Epoch 44: 97%|█████████▋| 31/32 [00:00<00:00, 329.57it/s, v_num=2, train_loss=2.790, RMSE=5.000]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 330.50it/s, v_num=2, train_loss=2.790, RMSE=5.000]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 329.80it/s, v_num=2, train_loss=3.080, RMSE=5.000]
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Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 270.38it/s, v_num=2, train_loss=3.080, RMSE=5.030]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 269.27it/s, v_num=2, train_loss=3.080, RMSE=5.030]
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Epoch 45: 3%|▎ | 1/32 [00:00<00:00, 316.22it/s, v_num=2, train_loss=3.080, RMSE=5.030]
Epoch 45: 3%|▎ | 1/32 [00:00<00:00, 296.86it/s, v_num=2, train_loss=2.980, RMSE=5.030]
Epoch 45: 6%|▋ | 2/32 [00:00<00:00, 322.65it/s, v_num=2, train_loss=2.980, RMSE=5.030]
Epoch 45: 6%|▋ | 2/32 [00:00<00:00, 312.22it/s, v_num=2, train_loss=3.040, RMSE=5.030]
Epoch 45: 9%|▉ | 3/32 [00:00<00:00, 324.00it/s, v_num=2, train_loss=3.040, RMSE=5.030]
Epoch 45: 9%|▉ | 3/32 [00:00<00:00, 317.02it/s, v_num=2, train_loss=2.820, RMSE=5.030]
Epoch 45: 12%|█▎ | 4/32 [00:00<00:00, 325.99it/s, v_num=2, train_loss=2.820, RMSE=5.030]
Epoch 45: 12%|█▎ | 4/32 [00:00<00:00, 319.32it/s, v_num=2, train_loss=2.710, RMSE=5.030]
Epoch 45: 16%|█▌ | 5/32 [00:00<00:00, 310.39it/s, v_num=2, train_loss=2.710, RMSE=5.030]
Epoch 45: 16%|█▌ | 5/32 [00:00<00:00, 305.70it/s, v_num=2, train_loss=2.850, RMSE=5.030]
Epoch 45: 19%|█▉ | 6/32 [00:00<00:00, 298.94it/s, v_num=2, train_loss=2.850, RMSE=5.030]
Epoch 45: 19%|█▉ | 6/32 [00:00<00:00, 295.17it/s, v_num=2, train_loss=2.540, RMSE=5.030]
Epoch 45: 22%|██▏ | 7/32 [00:00<00:00, 291.17it/s, v_num=2, train_loss=2.540, RMSE=5.030]
Epoch 45: 22%|██▏ | 7/32 [00:00<00:00, 288.15it/s, v_num=2, train_loss=2.680, RMSE=5.030]
Epoch 45: 25%|██▌ | 8/32 [00:00<00:00, 286.05it/s, v_num=2, train_loss=2.680, RMSE=5.030]
Epoch 45: 25%|██▌ | 8/32 [00:00<00:00, 283.42it/s, v_num=2, train_loss=2.790, RMSE=5.030]
Epoch 45: 28%|██▊ | 9/32 [00:00<00:00, 281.99it/s, v_num=2, train_loss=2.790, RMSE=5.030]
Epoch 45: 28%|██▊ | 9/32 [00:00<00:00, 279.80it/s, v_num=2, train_loss=2.770, RMSE=5.030]
Epoch 45: 31%|███▏ | 10/32 [00:00<00:00, 279.16it/s, v_num=2, train_loss=2.770, RMSE=5.030]
Epoch 45: 31%|███▏ | 10/32 [00:00<00:00, 277.57it/s, v_num=2, train_loss=2.890, RMSE=5.030]
Epoch 45: 34%|███▍ | 11/32 [00:00<00:00, 280.04it/s, v_num=2, train_loss=2.890, RMSE=5.030]
Epoch 45: 34%|███▍ | 11/32 [00:00<00:00, 278.58it/s, v_num=2, train_loss=2.880, RMSE=5.030]
Epoch 45: 38%|███▊ | 12/32 [00:00<00:00, 283.81it/s, v_num=2, train_loss=2.880, RMSE=5.030]
Epoch 45: 38%|███▊ | 12/32 [00:00<00:00, 282.44it/s, v_num=2, train_loss=3.040, RMSE=5.030]
Epoch 45: 41%|████ | 13/32 [00:00<00:00, 286.96it/s, v_num=2, train_loss=3.040, RMSE=5.030]
Epoch 45: 41%|████ | 13/32 [00:00<00:00, 285.67it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 44%|████▍ | 14/32 [00:00<00:00, 289.73it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 44%|████▍ | 14/32 [00:00<00:00, 288.51it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 47%|████▋ | 15/32 [00:00<00:00, 292.00it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 47%|████▋ | 15/32 [00:00<00:00, 290.85it/s, v_num=2, train_loss=2.780, RMSE=5.030]
Epoch 45: 50%|█████ | 16/32 [00:00<00:00, 294.30it/s, v_num=2, train_loss=2.780, RMSE=5.030]
Epoch 45: 50%|█████ | 16/32 [00:00<00:00, 293.20it/s, v_num=2, train_loss=2.840, RMSE=5.030]
Epoch 45: 53%|█████▎ | 17/32 [00:00<00:00, 296.14it/s, v_num=2, train_loss=2.840, RMSE=5.030]
Epoch 45: 53%|█████▎ | 17/32 [00:00<00:00, 295.10it/s, v_num=2, train_loss=2.690, RMSE=5.030]
Epoch 45: 56%|█████▋ | 18/32 [00:00<00:00, 297.83it/s, v_num=2, train_loss=2.690, RMSE=5.030]
Epoch 45: 56%|█████▋ | 18/32 [00:00<00:00, 296.82it/s, v_num=2, train_loss=2.950, RMSE=5.030]
Epoch 45: 59%|█████▉ | 19/32 [00:00<00:00, 299.29it/s, v_num=2, train_loss=2.950, RMSE=5.030]
Epoch 45: 59%|█████▉ | 19/32 [00:00<00:00, 298.33it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 62%|██████▎ | 20/32 [00:00<00:00, 300.69it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 62%|██████▎ | 20/32 [00:00<00:00, 299.63it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 66%|██████▌ | 21/32 [00:00<00:00, 301.55it/s, v_num=2, train_loss=2.930, RMSE=5.030]
Epoch 45: 66%|██████▌ | 21/32 [00:00<00:00, 300.68it/s, v_num=2, train_loss=2.880, RMSE=5.030]
Epoch 45: 69%|██████▉ | 22/32 [00:00<00:00, 302.72it/s, v_num=2, train_loss=2.880, RMSE=5.030]
Epoch 45: 69%|██████▉ | 22/32 [00:00<00:00, 301.87it/s, v_num=2, train_loss=2.730, RMSE=5.030]
Epoch 45: 72%|███████▏ | 23/32 [00:00<00:00, 303.83it/s, v_num=2, train_loss=2.730, RMSE=5.030]
Epoch 45: 72%|███████▏ | 23/32 [00:00<00:00, 303.01it/s, v_num=2, train_loss=2.710, RMSE=5.030]
Epoch 45: 75%|███████▌ | 24/32 [00:00<00:00, 304.88it/s, v_num=2, train_loss=2.710, RMSE=5.030]
Epoch 45: 75%|███████▌ | 24/32 [00:00<00:00, 304.08it/s, v_num=2, train_loss=3.010, RMSE=5.030]
Epoch 45: 78%|███████▊ | 25/32 [00:00<00:00, 306.00it/s, v_num=2, train_loss=3.010, RMSE=5.030]
Epoch 45: 78%|███████▊ | 25/32 [00:00<00:00, 305.23it/s, v_num=2, train_loss=2.620, RMSE=5.030]
Epoch 45: 81%|████████▏ | 26/32 [00:00<00:00, 306.86it/s, v_num=2, train_loss=2.620, RMSE=5.030]
Epoch 45: 81%|████████▏ | 26/32 [00:00<00:00, 306.12it/s, v_num=2, train_loss=2.740, RMSE=5.030]
Epoch 45: 84%|████████▍ | 27/32 [00:00<00:00, 307.65it/s, v_num=2, train_loss=2.740, RMSE=5.030]
Epoch 45: 84%|████████▍ | 27/32 [00:00<00:00, 306.94it/s, v_num=2, train_loss=2.580, RMSE=5.030]
Epoch 45: 88%|████████▊ | 28/32 [00:00<00:00, 308.46it/s, v_num=2, train_loss=2.580, RMSE=5.030]
Epoch 45: 88%|████████▊ | 28/32 [00:00<00:00, 307.76it/s, v_num=2, train_loss=2.990, RMSE=5.030]
Epoch 45: 91%|█████████ | 29/32 [00:00<00:00, 309.28it/s, v_num=2, train_loss=2.990, RMSE=5.030]
Epoch 45: 91%|█████████ | 29/32 [00:00<00:00, 308.61it/s, v_num=2, train_loss=2.780, RMSE=5.030]
Epoch 45: 94%|█████████▍| 30/32 [00:00<00:00, 309.96it/s, v_num=2, train_loss=2.780, RMSE=5.030]
Epoch 45: 94%|█████████▍| 30/32 [00:00<00:00, 309.30it/s, v_num=2, train_loss=2.950, RMSE=5.030]
Epoch 45: 97%|█████████▋| 31/32 [00:00<00:00, 310.48it/s, v_num=2, train_loss=2.950, RMSE=5.030]
Epoch 45: 97%|█████████▋| 31/32 [00:00<00:00, 309.85it/s, v_num=2, train_loss=3.060, RMSE=5.030]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 311.19it/s, v_num=2, train_loss=3.060, RMSE=5.030]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 310.57it/s, v_num=2, train_loss=2.530, RMSE=5.030]
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Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 256.38it/s, v_num=2, train_loss=2.530, RMSE=4.690]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 255.30it/s, v_num=2, train_loss=2.530, RMSE=4.690]
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Epoch 46: 3%|▎ | 1/32 [00:00<00:00, 282.64it/s, v_num=2, train_loss=2.530, RMSE=4.690]
Epoch 46: 3%|▎ | 1/32 [00:00<00:00, 266.71it/s, v_num=2, train_loss=2.860, RMSE=4.690]
Epoch 46: 6%|▋ | 2/32 [00:00<00:00, 295.85it/s, v_num=2, train_loss=2.860, RMSE=4.690]
Epoch 46: 6%|▋ | 2/32 [00:00<00:00, 287.07it/s, v_num=2, train_loss=2.980, RMSE=4.690]
Epoch 46: 9%|▉ | 3/32 [00:00<00:00, 304.80it/s, v_num=2, train_loss=2.980, RMSE=4.690]
Epoch 46: 9%|▉ | 3/32 [00:00<00:00, 298.62it/s, v_num=2, train_loss=2.730, RMSE=4.690]
Epoch 46: 12%|█▎ | 4/32 [00:00<00:00, 309.79it/s, v_num=2, train_loss=2.730, RMSE=4.690]
Epoch 46: 12%|█▎ | 4/32 [00:00<00:00, 305.01it/s, v_num=2, train_loss=2.750, RMSE=4.690]
Epoch 46: 16%|█▌ | 5/32 [00:00<00:00, 313.11it/s, v_num=2, train_loss=2.750, RMSE=4.690]
Epoch 46: 16%|█▌ | 5/32 [00:00<00:00, 309.17it/s, v_num=2, train_loss=2.850, RMSE=4.690]
Epoch 46: 19%|█▉ | 6/32 [00:00<00:00, 315.40it/s, v_num=2, train_loss=2.850, RMSE=4.690]
Epoch 46: 19%|█▉ | 6/32 [00:00<00:00, 312.05it/s, v_num=2, train_loss=2.780, RMSE=4.690]
Epoch 46: 22%|██▏ | 7/32 [00:00<00:00, 317.37it/s, v_num=2, train_loss=2.780, RMSE=4.690]
Epoch 46: 22%|██▏ | 7/32 [00:00<00:00, 313.87it/s, v_num=2, train_loss=3.030, RMSE=4.690]
Epoch 46: 25%|██▌ | 8/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.030, RMSE=4.690]
Epoch 46: 25%|██▌ | 8/32 [00:00<00:00, 315.47it/s, v_num=2, train_loss=2.900, RMSE=4.690]
Epoch 46: 28%|██▊ | 9/32 [00:00<00:00, 318.60it/s, v_num=2, train_loss=2.900, RMSE=4.690]
Epoch 46: 28%|██▊ | 9/32 [00:00<00:00, 316.24it/s, v_num=2, train_loss=2.820, RMSE=4.690]
Epoch 46: 31%|███▏ | 10/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=2.820, RMSE=4.690]
Epoch 46: 31%|███▏ | 10/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=2.990, RMSE=4.690]
Epoch 46: 34%|███▍ | 11/32 [00:00<00:00, 319.25it/s, v_num=2, train_loss=2.990, RMSE=4.690]
Epoch 46: 34%|███▍ | 11/32 [00:00<00:00, 317.32it/s, v_num=2, train_loss=2.820, RMSE=4.690]
Epoch 46: 38%|███▊ | 12/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=2.820, RMSE=4.690]
Epoch 46: 38%|███▊ | 12/32 [00:00<00:00, 318.12it/s, v_num=2, train_loss=2.670, RMSE=4.690]
Epoch 46: 41%|████ | 13/32 [00:00<00:00, 320.06it/s, v_num=2, train_loss=2.670, RMSE=4.690]
Epoch 46: 41%|████ | 13/32 [00:00<00:00, 318.45it/s, v_num=2, train_loss=2.760, RMSE=4.690]
Epoch 46: 44%|████▍ | 14/32 [00:00<00:00, 320.60it/s, v_num=2, train_loss=2.760, RMSE=4.690]
Epoch 46: 44%|████▍ | 14/32 [00:00<00:00, 319.09it/s, v_num=2, train_loss=2.830, RMSE=4.690]
Epoch 46: 47%|████▋ | 15/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=2.830, RMSE=4.690]
Epoch 46: 47%|████▋ | 15/32 [00:00<00:00, 319.70it/s, v_num=2, train_loss=2.740, RMSE=4.690]
Epoch 46: 50%|█████ | 16/32 [00:00<00:00, 321.76it/s, v_num=2, train_loss=2.740, RMSE=4.690]
Epoch 46: 50%|█████ | 16/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=2.550, RMSE=4.690]
Epoch 46: 53%|█████▎ | 17/32 [00:00<00:00, 322.10it/s, v_num=2, train_loss=2.550, RMSE=4.690]
Epoch 46: 53%|█████▎ | 17/32 [00:00<00:00, 320.84it/s, v_num=2, train_loss=3.070, RMSE=4.690]
Epoch 46: 56%|█████▋ | 18/32 [00:00<00:00, 322.45it/s, v_num=2, train_loss=3.070, RMSE=4.690]
Epoch 46: 56%|█████▋ | 18/32 [00:00<00:00, 321.27it/s, v_num=2, train_loss=3.060, RMSE=4.690]
Epoch 46: 59%|█████▉ | 19/32 [00:00<00:00, 322.79it/s, v_num=2, train_loss=3.060, RMSE=4.690]
Epoch 46: 59%|█████▉ | 19/32 [00:00<00:00, 321.67it/s, v_num=2, train_loss=2.730, RMSE=4.690]
Epoch 46: 62%|██████▎ | 20/32 [00:00<00:00, 323.23it/s, v_num=2, train_loss=2.730, RMSE=4.690]
Epoch 46: 62%|██████▎ | 20/32 [00:00<00:00, 322.16it/s, v_num=2, train_loss=2.660, RMSE=4.690]
Epoch 46: 66%|██████▌ | 21/32 [00:00<00:00, 322.91it/s, v_num=2, train_loss=2.660, RMSE=4.690]
Epoch 46: 66%|██████▌ | 21/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=2.690, RMSE=4.690]
Epoch 46: 69%|██████▉ | 22/32 [00:00<00:00, 323.15it/s, v_num=2, train_loss=2.690, RMSE=4.690]
Epoch 46: 69%|██████▉ | 22/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=2.880, RMSE=4.690]
Epoch 46: 72%|███████▏ | 23/32 [00:00<00:00, 323.46it/s, v_num=2, train_loss=2.880, RMSE=4.690]
Epoch 46: 72%|███████▏ | 23/32 [00:00<00:00, 322.54it/s, v_num=2, train_loss=2.970, RMSE=4.690]
Epoch 46: 75%|███████▌ | 24/32 [00:00<00:00, 323.83it/s, v_num=2, train_loss=2.970, RMSE=4.690]
Epoch 46: 75%|███████▌ | 24/32 [00:00<00:00, 322.79it/s, v_num=2, train_loss=2.810, RMSE=4.690]
Epoch 46: 78%|███████▊ | 25/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=2.810, RMSE=4.690]
Epoch 46: 78%|███████▊ | 25/32 [00:00<00:00, 323.13it/s, v_num=2, train_loss=2.850, RMSE=4.690]
Epoch 46: 81%|████████▏ | 26/32 [00:00<00:00, 324.22it/s, v_num=2, train_loss=2.850, RMSE=4.690]
Epoch 46: 81%|████████▏ | 26/32 [00:00<00:00, 323.39it/s, v_num=2, train_loss=2.930, RMSE=4.690]
Epoch 46: 84%|████████▍ | 27/32 [00:00<00:00, 324.40it/s, v_num=2, train_loss=2.930, RMSE=4.690]
Epoch 46: 84%|████████▍ | 27/32 [00:00<00:00, 323.58it/s, v_num=2, train_loss=2.940, RMSE=4.690]
Epoch 46: 88%|████████▊ | 28/32 [00:00<00:00, 324.55it/s, v_num=2, train_loss=2.940, RMSE=4.690]
Epoch 46: 88%|████████▊ | 28/32 [00:00<00:00, 323.78it/s, v_num=2, train_loss=2.720, RMSE=4.690]
Epoch 46: 91%|█████████ | 29/32 [00:00<00:00, 322.97it/s, v_num=2, train_loss=2.720, RMSE=4.690]
Epoch 46: 91%|█████████ | 29/32 [00:00<00:00, 322.23it/s, v_num=2, train_loss=2.830, RMSE=4.690]
Epoch 46: 94%|█████████▍| 30/32 [00:00<00:00, 323.14it/s, v_num=2, train_loss=2.830, RMSE=4.690]
Epoch 46: 94%|█████████▍| 30/32 [00:00<00:00, 322.43it/s, v_num=2, train_loss=2.650, RMSE=4.690]
Epoch 46: 97%|█████████▋| 31/32 [00:00<00:00, 323.20it/s, v_num=2, train_loss=2.650, RMSE=4.690]
Epoch 46: 97%|█████████▋| 31/32 [00:00<00:00, 322.52it/s, v_num=2, train_loss=2.660, RMSE=4.690]
Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 323.48it/s, v_num=2, train_loss=2.660, RMSE=4.690]
Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 322.81it/s, v_num=2, train_loss=3.200, RMSE=4.690]
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Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 264.78it/s, v_num=2, train_loss=3.200, RMSE=4.650]
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Epoch 47: 3%|▎ | 1/32 [00:00<00:00, 270.36it/s, v_num=2, train_loss=3.030, RMSE=4.650]
Epoch 47: 6%|▋ | 2/32 [00:00<00:00, 300.58it/s, v_num=2, train_loss=3.030, RMSE=4.650]
Epoch 47: 6%|▋ | 2/32 [00:00<00:00, 291.56it/s, v_num=2, train_loss=2.820, RMSE=4.650]
Epoch 47: 9%|▉ | 3/32 [00:00<00:00, 308.56it/s, v_num=2, train_loss=2.820, RMSE=4.650]
Epoch 47: 9%|▉ | 3/32 [00:00<00:00, 302.21it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 12%|█▎ | 4/32 [00:00<00:00, 313.36it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 12%|█▎ | 4/32 [00:00<00:00, 308.43it/s, v_num=2, train_loss=2.870, RMSE=4.650]
Epoch 47: 16%|█▌ | 5/32 [00:00<00:00, 315.37it/s, v_num=2, train_loss=2.870, RMSE=4.650]
Epoch 47: 16%|█▌ | 5/32 [00:00<00:00, 311.38it/s, v_num=2, train_loss=2.870, RMSE=4.650]
Epoch 47: 19%|█▉ | 6/32 [00:00<00:00, 316.94it/s, v_num=2, train_loss=2.870, RMSE=4.650]
Epoch 47: 19%|█▉ | 6/32 [00:00<00:00, 313.57it/s, v_num=2, train_loss=2.800, RMSE=4.650]
Epoch 47: 22%|██▏ | 7/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=2.800, RMSE=4.650]
Epoch 47: 22%|██▏ | 7/32 [00:00<00:00, 315.92it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 25%|██▌ | 8/32 [00:00<00:00, 320.53it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 25%|██▌ | 8/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=2.800, RMSE=4.650]
Epoch 47: 28%|██▊ | 9/32 [00:00<00:00, 321.57it/s, v_num=2, train_loss=2.800, RMSE=4.650]
Epoch 47: 28%|██▊ | 9/32 [00:00<00:00, 319.23it/s, v_num=2, train_loss=2.700, RMSE=4.650]
Epoch 47: 31%|███▏ | 10/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=2.700, RMSE=4.650]
Epoch 47: 31%|███▏ | 10/32 [00:00<00:00, 320.25it/s, v_num=2, train_loss=2.960, RMSE=4.650]
Epoch 47: 34%|███▍ | 11/32 [00:00<00:00, 323.07it/s, v_num=2, train_loss=2.960, RMSE=4.650]
Epoch 47: 34%|███▍ | 11/32 [00:00<00:00, 321.14it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 38%|███▊ | 12/32 [00:00<00:00, 323.94it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 38%|███▊ | 12/32 [00:00<00:00, 322.17it/s, v_num=2, train_loss=2.710, RMSE=4.650]
Epoch 47: 41%|████ | 13/32 [00:00<00:00, 324.49it/s, v_num=2, train_loss=2.710, RMSE=4.650]
Epoch 47: 41%|████ | 13/32 [00:00<00:00, 322.84it/s, v_num=2, train_loss=2.570, RMSE=4.650]
Epoch 47: 44%|████▍ | 14/32 [00:00<00:00, 324.88it/s, v_num=2, train_loss=2.570, RMSE=4.650]
Epoch 47: 44%|████▍ | 14/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=2.930, RMSE=4.650]
Epoch 47: 47%|████▋ | 15/32 [00:00<00:00, 325.31it/s, v_num=2, train_loss=2.930, RMSE=4.650]
Epoch 47: 47%|████▋ | 15/32 [00:00<00:00, 323.84it/s, v_num=2, train_loss=2.860, RMSE=4.650]
Epoch 47: 50%|█████ | 16/32 [00:00<00:00, 325.80it/s, v_num=2, train_loss=2.860, RMSE=4.650]
Epoch 47: 50%|█████ | 16/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 53%|█████▎ | 17/32 [00:00<00:00, 326.12it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 53%|█████▎ | 17/32 [00:00<00:00, 324.85it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 56%|█████▋ | 18/32 [00:00<00:00, 326.36it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 56%|█████▋ | 18/32 [00:00<00:00, 325.15it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 59%|█████▉ | 19/32 [00:00<00:00, 326.55it/s, v_num=2, train_loss=2.720, RMSE=4.650]
Epoch 47: 59%|█████▉ | 19/32 [00:00<00:00, 325.41it/s, v_num=2, train_loss=2.770, RMSE=4.650]
Epoch 47: 62%|██████▎ | 20/32 [00:00<00:00, 326.95it/s, v_num=2, train_loss=2.770, RMSE=4.650]
Epoch 47: 62%|██████▎ | 20/32 [00:00<00:00, 325.87it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 66%|██████▌ | 21/32 [00:00<00:00, 327.13it/s, v_num=2, train_loss=2.900, RMSE=4.650]
Epoch 47: 66%|██████▌ | 21/32 [00:00<00:00, 326.10it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 69%|██████▉ | 22/32 [00:00<00:00, 327.22it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 69%|██████▉ | 22/32 [00:00<00:00, 326.24it/s, v_num=2, train_loss=2.760, RMSE=4.650]
Epoch 47: 72%|███████▏ | 23/32 [00:00<00:00, 327.39it/s, v_num=2, train_loss=2.760, RMSE=4.650]
Epoch 47: 72%|███████▏ | 23/32 [00:00<00:00, 326.45it/s, v_num=2, train_loss=3.240, RMSE=4.650]
Epoch 47: 75%|███████▌ | 24/32 [00:00<00:00, 327.69it/s, v_num=2, train_loss=3.240, RMSE=4.650]
Epoch 47: 75%|███████▌ | 24/32 [00:00<00:00, 326.77it/s, v_num=2, train_loss=2.700, RMSE=4.650]
Epoch 47: 78%|███████▊ | 25/32 [00:00<00:00, 327.83it/s, v_num=2, train_loss=2.700, RMSE=4.650]
Epoch 47: 78%|███████▊ | 25/32 [00:00<00:00, 326.96it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 81%|████████▏ | 26/32 [00:00<00:00, 327.96it/s, v_num=2, train_loss=2.750, RMSE=4.650]
Epoch 47: 81%|████████▏ | 26/32 [00:00<00:00, 327.12it/s, v_num=2, train_loss=2.730, RMSE=4.650]
Epoch 47: 84%|████████▍ | 27/32 [00:00<00:00, 327.96it/s, v_num=2, train_loss=2.730, RMSE=4.650]
Epoch 47: 84%|████████▍ | 27/32 [00:00<00:00, 327.15it/s, v_num=2, train_loss=2.650, RMSE=4.650]
Epoch 47: 88%|████████▊ | 28/32 [00:00<00:00, 328.23it/s, v_num=2, train_loss=2.650, RMSE=4.650]
Epoch 47: 88%|████████▊ | 28/32 [00:00<00:00, 327.45it/s, v_num=2, train_loss=2.680, RMSE=4.650]
Epoch 47: 91%|█████████ | 29/32 [00:00<00:00, 328.30it/s, v_num=2, train_loss=2.680, RMSE=4.650]
Epoch 47: 91%|█████████ | 29/32 [00:00<00:00, 327.55it/s, v_num=2, train_loss=2.710, RMSE=4.650]
Epoch 47: 94%|█████████▍| 30/32 [00:00<00:00, 328.38it/s, v_num=2, train_loss=2.710, RMSE=4.650]
Epoch 47: 94%|█████████▍| 30/32 [00:00<00:00, 327.65it/s, v_num=2, train_loss=2.960, RMSE=4.650]
Epoch 47: 97%|█████████▋| 31/32 [00:00<00:00, 328.51it/s, v_num=2, train_loss=2.960, RMSE=4.650]
Epoch 47: 97%|█████████▋| 31/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=2.730, RMSE=4.650]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 328.77it/s, v_num=2, train_loss=2.730, RMSE=4.650]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 328.08it/s, v_num=2, train_loss=2.660, RMSE=4.650]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 634.85it/s]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 269.34it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 268.26it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 47: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 48: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 48: 3%|▎ | 1/32 [00:00<00:00, 321.30it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 48: 3%|▎ | 1/32 [00:00<00:00, 301.31it/s, v_num=2, train_loss=3.160, RMSE=4.510]
Epoch 48: 6%|▋ | 2/32 [00:00<00:00, 323.90it/s, v_num=2, train_loss=3.160, RMSE=4.510]
Epoch 48: 6%|▋ | 2/32 [00:00<00:00, 313.52it/s, v_num=2, train_loss=2.720, RMSE=4.510]
Epoch 48: 9%|▉ | 3/32 [00:00<00:00, 325.98it/s, v_num=2, train_loss=2.720, RMSE=4.510]
Epoch 48: 9%|▉ | 3/32 [00:00<00:00, 318.93it/s, v_num=2, train_loss=2.980, RMSE=4.510]
Epoch 48: 12%|█▎ | 4/32 [00:00<00:00, 327.17it/s, v_num=2, train_loss=2.980, RMSE=4.510]
Epoch 48: 12%|█▎ | 4/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.020, RMSE=4.510]
Epoch 48: 16%|█▌ | 5/32 [00:00<00:00, 328.50it/s, v_num=2, train_loss=3.020, RMSE=4.510]
Epoch 48: 16%|█▌ | 5/32 [00:00<00:00, 324.13it/s, v_num=2, train_loss=2.710, RMSE=4.510]
Epoch 48: 19%|█▉ | 6/32 [00:00<00:00, 328.70it/s, v_num=2, train_loss=2.710, RMSE=4.510]
Epoch 48: 19%|█▉ | 6/32 [00:00<00:00, 325.10it/s, v_num=2, train_loss=2.820, RMSE=4.510]
Epoch 48: 22%|██▏ | 7/32 [00:00<00:00, 328.78it/s, v_num=2, train_loss=2.820, RMSE=4.510]
Epoch 48: 22%|██▏ | 7/32 [00:00<00:00, 325.45it/s, v_num=2, train_loss=2.620, RMSE=4.510]
Epoch 48: 25%|██▌ | 8/32 [00:00<00:00, 328.80it/s, v_num=2, train_loss=2.620, RMSE=4.510]
Epoch 48: 25%|██▌ | 8/32 [00:00<00:00, 325.95it/s, v_num=2, train_loss=2.920, RMSE=4.510]
Epoch 48: 28%|██▊ | 9/32 [00:00<00:00, 329.44it/s, v_num=2, train_loss=2.920, RMSE=4.510]
Epoch 48: 28%|██▊ | 9/32 [00:00<00:00, 326.63it/s, v_num=2, train_loss=2.540, RMSE=4.510]
Epoch 48: 31%|███▏ | 10/32 [00:00<00:00, 329.58it/s, v_num=2, train_loss=2.540, RMSE=4.510]
Epoch 48: 31%|███▏ | 10/32 [00:00<00:00, 327.39it/s, v_num=2, train_loss=2.840, RMSE=4.510]
Epoch 48: 34%|███▍ | 11/32 [00:00<00:00, 329.77it/s, v_num=2, train_loss=2.840, RMSE=4.510]
Epoch 48: 34%|███▍ | 11/32 [00:00<00:00, 327.77it/s, v_num=2, train_loss=2.730, RMSE=4.510]
Epoch 48: 38%|███▊ | 12/32 [00:00<00:00, 329.72it/s, v_num=2, train_loss=2.730, RMSE=4.510]
Epoch 48: 38%|███▊ | 12/32 [00:00<00:00, 327.88it/s, v_num=2, train_loss=2.990, RMSE=4.510]
Epoch 48: 41%|████ | 13/32 [00:00<00:00, 330.23it/s, v_num=2, train_loss=2.990, RMSE=4.510]
Epoch 48: 41%|████ | 13/32 [00:00<00:00, 328.31it/s, v_num=2, train_loss=2.830, RMSE=4.510]
Epoch 48: 44%|████▍ | 14/32 [00:00<00:00, 329.69it/s, v_num=2, train_loss=2.830, RMSE=4.510]
Epoch 48: 44%|████▍ | 14/32 [00:00<00:00, 328.11it/s, v_num=2, train_loss=2.690, RMSE=4.510]
Epoch 48: 47%|████▋ | 15/32 [00:00<00:00, 327.18it/s, v_num=2, train_loss=2.690, RMSE=4.510]
Epoch 48: 47%|████▋ | 15/32 [00:00<00:00, 325.72it/s, v_num=2, train_loss=2.730, RMSE=4.510]
Epoch 48: 50%|█████ | 16/32 [00:00<00:00, 327.47it/s, v_num=2, train_loss=2.730, RMSE=4.510]
Epoch 48: 50%|█████ | 16/32 [00:00<00:00, 326.11it/s, v_num=2, train_loss=2.750, RMSE=4.510]
Epoch 48: 53%|█████▎ | 17/32 [00:00<00:00, 327.62it/s, v_num=2, train_loss=2.750, RMSE=4.510]
Epoch 48: 53%|█████▎ | 17/32 [00:00<00:00, 326.34it/s, v_num=2, train_loss=2.770, RMSE=4.510]
Epoch 48: 56%|█████▋ | 18/32 [00:00<00:00, 328.01it/s, v_num=2, train_loss=2.770, RMSE=4.510]
Epoch 48: 56%|█████▋ | 18/32 [00:00<00:00, 326.80it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 48: 59%|█████▉ | 19/32 [00:00<00:00, 328.21it/s, v_num=2, train_loss=2.660, RMSE=4.510]
Epoch 48: 59%|█████▉ | 19/32 [00:00<00:00, 327.02it/s, v_num=2, train_loss=2.920, RMSE=4.510]
Epoch 48: 62%|██████▎ | 20/32 [00:00<00:00, 328.34it/s, v_num=2, train_loss=2.920, RMSE=4.510]
Epoch 48: 62%|██████▎ | 20/32 [00:00<00:00, 327.24it/s, v_num=2, train_loss=2.690, RMSE=4.510]
Epoch 48: 66%|██████▌ | 21/32 [00:00<00:00, 328.49it/s, v_num=2, train_loss=2.690, RMSE=4.510]
Epoch 48: 66%|██████▌ | 21/32 [00:00<00:00, 327.43it/s, v_num=2, train_loss=2.610, RMSE=4.510]
Epoch 48: 69%|██████▉ | 22/32 [00:00<00:00, 328.68it/s, v_num=2, train_loss=2.610, RMSE=4.510]
Epoch 48: 69%|██████▉ | 22/32 [00:00<00:00, 327.68it/s, v_num=2, train_loss=2.480, RMSE=4.510]
Epoch 48: 72%|███████▏ | 23/32 [00:00<00:00, 328.60it/s, v_num=2, train_loss=2.480, RMSE=4.510]
Epoch 48: 72%|███████▏ | 23/32 [00:00<00:00, 327.65it/s, v_num=2, train_loss=2.900, RMSE=4.510]
Epoch 48: 75%|███████▌ | 24/32 [00:00<00:00, 328.72it/s, v_num=2, train_loss=2.900, RMSE=4.510]
Epoch 48: 75%|███████▌ | 24/32 [00:00<00:00, 327.80it/s, v_num=2, train_loss=2.770, RMSE=4.510]
Epoch 48: 78%|███████▊ | 25/32 [00:00<00:00, 328.55it/s, v_num=2, train_loss=2.770, RMSE=4.510]
Epoch 48: 78%|███████▊ | 25/32 [00:00<00:00, 327.68it/s, v_num=2, train_loss=2.880, RMSE=4.510]
Epoch 48: 81%|████████▏ | 26/32 [00:00<00:00, 328.86it/s, v_num=2, train_loss=2.880, RMSE=4.510]
Epoch 48: 81%|████████▏ | 26/32 [00:00<00:00, 328.02it/s, v_num=2, train_loss=2.780, RMSE=4.510]
Epoch 48: 84%|████████▍ | 27/32 [00:00<00:00, 328.97it/s, v_num=2, train_loss=2.780, RMSE=4.510]
Epoch 48: 84%|████████▍ | 27/32 [00:00<00:00, 328.12it/s, v_num=2, train_loss=2.760, RMSE=4.510]
Epoch 48: 88%|████████▊ | 28/32 [00:00<00:00, 329.01it/s, v_num=2, train_loss=2.760, RMSE=4.510]
Epoch 48: 88%|████████▊ | 28/32 [00:00<00:00, 328.23it/s, v_num=2, train_loss=2.600, RMSE=4.510]
Epoch 48: 91%|█████████ | 29/32 [00:00<00:00, 329.01it/s, v_num=2, train_loss=2.600, RMSE=4.510]
Epoch 48: 91%|█████████ | 29/32 [00:00<00:00, 328.26it/s, v_num=2, train_loss=2.710, RMSE=4.510]
Epoch 48: 94%|█████████▍| 30/32 [00:00<00:00, 329.14it/s, v_num=2, train_loss=2.710, RMSE=4.510]
Epoch 48: 94%|█████████▍| 30/32 [00:00<00:00, 328.41it/s, v_num=2, train_loss=2.890, RMSE=4.510]
Epoch 48: 97%|█████████▋| 31/32 [00:00<00:00, 329.18it/s, v_num=2, train_loss=2.890, RMSE=4.510]
Epoch 48: 97%|█████████▋| 31/32 [00:00<00:00, 328.47it/s, v_num=2, train_loss=3.140, RMSE=4.510]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 329.37it/s, v_num=2, train_loss=3.140, RMSE=4.510]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 328.69it/s, v_num=2, train_loss=3.010, RMSE=4.510]
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Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 269.93it/s, v_num=2, train_loss=3.010, RMSE=4.530]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 268.85it/s, v_num=2, train_loss=3.010, RMSE=4.530]
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Epoch 49: 3%|▎ | 1/32 [00:00<00:00, 314.44it/s, v_num=2, train_loss=3.010, RMSE=4.530]
Epoch 49: 3%|▎ | 1/32 [00:00<00:00, 295.17it/s, v_num=2, train_loss=2.780, RMSE=4.530]
Epoch 49: 6%|▋ | 2/32 [00:00<00:00, 320.59it/s, v_num=2, train_loss=2.780, RMSE=4.530]
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/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
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┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Regression ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ MAE │ 3.14331 │
│ MSE │ 19.08153 │
│ NLL │ 2.75876 │
│ RMSE │ 4.36824 │
└──────────────┴───────────────────────────┘
[{'test/reg/MAE': 3.143312931060791, 'test/reg/MSE': 19.081531524658203, 'test/reg/RMSE': 4.368241310119629, 'test/reg/NLL': 2.758760929107666}]
6. Testing the Model¶
We can now test the model by plotting the predictions and the uncertainty estimates. In this specific case, we can reproduce the results of the paper.
import matplotlib.pyplot as plt
with torch.no_grad():
x = torch.linspace(-7, 7, 1000)
dists = model(x.unsqueeze(-1))
means = dists.loc.squeeze(1)
variances = torch.sqrt(dists.variance_loc).squeeze(1)
fig, ax = plt.subplots(1, 1)
ax.plot(x, x**3, "--r", label="ground truth", zorder=3)
ax.plot(x, means, "-k", label="predictions")
for k in torch.linspace(0, 4, 4):
ax.fill_between(
x,
means - k * variances,
means + k * variances,
linewidth=0,
alpha=0.3,
edgecolor=None,
facecolor="blue",
label="epistemic uncertainty" if not k else None,
)
plt.gca().set_ylim(-150, 150)
plt.gca().set_xlim(-7, 7)
plt.legend(loc="upper left")
plt.grid()
Reference¶
Deep Evidential Regression: Alexander Amini, Wilko Schwarting, Ava Soleimany, & Daniela Rus. NeurIPS 2020.
Total running time of the script: (0 minutes 6.355 seconds)