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, 314.29it/s, v_num=2, train_loss=4.470, RMSE=24.50]
Epoch 1: 88%|████████▊ | 28/32 [00:00<00:00, 315.33it/s, v_num=2, train_loss=4.470, RMSE=24.50]
Epoch 1: 88%|████████▊ | 28/32 [00:00<00:00, 314.57it/s, v_num=2, train_loss=4.820, RMSE=24.50]
Epoch 1: 91%|█████████ | 29/32 [00:00<00:00, 315.61it/s, v_num=2, train_loss=4.820, RMSE=24.50]
Epoch 1: 91%|█████████ | 29/32 [00:00<00:00, 314.86it/s, v_num=2, train_loss=5.170, RMSE=24.50]
Epoch 1: 94%|█████████▍| 30/32 [00:00<00:00, 315.89it/s, v_num=2, train_loss=5.170, RMSE=24.50]
Epoch 1: 94%|█████████▍| 30/32 [00:00<00:00, 315.18it/s, v_num=2, train_loss=5.660, RMSE=24.50]
Epoch 1: 97%|█████████▋| 31/32 [00:00<00:00, 316.10it/s, v_num=2, train_loss=5.660, RMSE=24.50]
Epoch 1: 97%|█████████▋| 31/32 [00:00<00:00, 315.42it/s, v_num=2, train_loss=5.950, RMSE=24.50]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 316.38it/s, v_num=2, train_loss=5.950, RMSE=24.50]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 315.73it/s, v_num=2, train_loss=5.720, RMSE=24.50]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 608.44it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 605.09it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 609.59it/s]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 258.98it/s, v_num=2, train_loss=5.720, RMSE=24.30]
Epoch 1: 100%|██████████| 32/32 [00:00<00:00, 257.77it/s, v_num=2, train_loss=5.720, RMSE=24.30]
Epoch 1: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=5.720, RMSE=24.30]
Epoch 2: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=5.720, RMSE=24.30]
Epoch 2: 3%|▎ | 1/32 [00:00<00:00, 279.30it/s, v_num=2, train_loss=5.720, RMSE=24.30]
Epoch 2: 3%|▎ | 1/32 [00:00<00:00, 263.11it/s, v_num=2, train_loss=4.900, RMSE=24.30]
Epoch 2: 6%|▋ | 2/32 [00:00<00:00, 294.18it/s, v_num=2, train_loss=4.900, RMSE=24.30]
Epoch 2: 6%|▋ | 2/32 [00:00<00:00, 284.96it/s, v_num=2, train_loss=5.050, RMSE=24.30]
Epoch 2: 9%|▉ | 3/32 [00:00<00:00, 302.39it/s, v_num=2, train_loss=5.050, RMSE=24.30]
Epoch 2: 9%|▉ | 3/32 [00:00<00:00, 296.10it/s, v_num=2, train_loss=4.690, RMSE=24.30]
Epoch 2: 12%|█▎ | 4/32 [00:00<00:00, 307.35it/s, v_num=2, train_loss=4.690, RMSE=24.30]
Epoch 2: 12%|█▎ | 4/32 [00:00<00:00, 302.42it/s, v_num=2, train_loss=5.160, RMSE=24.30]
Epoch 2: 16%|█▌ | 5/32 [00:00<00:00, 310.43it/s, v_num=2, train_loss=5.160, RMSE=24.30]
Epoch 2: 16%|█▌ | 5/32 [00:00<00:00, 306.40it/s, v_num=2, train_loss=4.310, RMSE=24.30]
Epoch 2: 19%|█▉ | 6/32 [00:00<00:00, 312.56it/s, v_num=2, train_loss=4.310, RMSE=24.30]
Epoch 2: 19%|█▉ | 6/32 [00:00<00:00, 309.19it/s, v_num=2, train_loss=5.160, RMSE=24.30]
Epoch 2: 22%|██▏ | 7/32 [00:00<00:00, 313.67it/s, v_num=2, train_loss=5.160, RMSE=24.30]
Epoch 2: 22%|██▏ | 7/32 [00:00<00:00, 310.71it/s, v_num=2, train_loss=3.800, RMSE=24.30]
Epoch 2: 25%|██▌ | 8/32 [00:00<00:00, 314.85it/s, v_num=2, train_loss=3.800, RMSE=24.30]
Epoch 2: 25%|██▌ | 8/32 [00:00<00:00, 312.24it/s, v_num=2, train_loss=4.650, RMSE=24.30]
Epoch 2: 28%|██▊ | 9/32 [00:00<00:00, 316.29it/s, v_num=2, train_loss=4.650, RMSE=24.30]
Epoch 2: 28%|██▊ | 9/32 [00:00<00:00, 313.51it/s, v_num=2, train_loss=4.760, RMSE=24.30]
Epoch 2: 31%|███▏ | 10/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=4.760, RMSE=24.30]
Epoch 2: 31%|███▏ | 10/32 [00:00<00:00, 314.76it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 34%|███▍ | 11/32 [00:00<00:00, 317.65it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 34%|███▍ | 11/32 [00:00<00:00, 315.74it/s, v_num=2, train_loss=4.490, RMSE=24.30]
Epoch 2: 38%|███▊ | 12/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=4.490, RMSE=24.30]
Epoch 2: 38%|███▊ | 12/32 [00:00<00:00, 316.13it/s, v_num=2, train_loss=4.690, RMSE=24.30]
Epoch 2: 41%|████ | 13/32 [00:00<00:00, 318.49it/s, v_num=2, train_loss=4.690, RMSE=24.30]
Epoch 2: 41%|████ | 13/32 [00:00<00:00, 316.84it/s, v_num=2, train_loss=4.190, RMSE=24.30]
Epoch 2: 44%|████▍ | 14/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=4.190, RMSE=24.30]
Epoch 2: 44%|████▍ | 14/32 [00:00<00:00, 317.40it/s, v_num=2, train_loss=4.060, RMSE=24.30]
Epoch 2: 47%|████▋ | 15/32 [00:00<00:00, 319.12it/s, v_num=2, train_loss=4.060, RMSE=24.30]
Epoch 2: 47%|████▋ | 15/32 [00:00<00:00, 317.70it/s, v_num=2, train_loss=4.050, RMSE=24.30]
Epoch 2: 50%|█████ | 16/32 [00:00<00:00, 319.47it/s, v_num=2, train_loss=4.050, RMSE=24.30]
Epoch 2: 50%|█████ | 16/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=4.820, RMSE=24.30]
Epoch 2: 53%|█████▎ | 17/32 [00:00<00:00, 319.50it/s, v_num=2, train_loss=4.820, RMSE=24.30]
Epoch 2: 53%|█████▎ | 17/32 [00:00<00:00, 318.23it/s, v_num=2, train_loss=4.340, RMSE=24.30]
Epoch 2: 56%|█████▋ | 18/32 [00:00<00:00, 319.97it/s, v_num=2, train_loss=4.340, RMSE=24.30]
Epoch 2: 56%|█████▋ | 18/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=4.090, RMSE=24.30]
Epoch 2: 59%|█████▉ | 19/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=4.090, RMSE=24.30]
Epoch 2: 59%|█████▉ | 19/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 62%|██████▎ | 20/32 [00:00<00:00, 320.00it/s, v_num=2, train_loss=4.380, RMSE=24.30]
Epoch 2: 62%|██████▎ | 20/32 [00:00<00:00, 318.91it/s, v_num=2, train_loss=4.040, RMSE=24.30]
Epoch 2: 66%|██████▌ | 21/32 [00:00<00:00, 320.08it/s, v_num=2, train_loss=4.040, RMSE=24.30]
Epoch 2: 66%|██████▌ | 21/32 [00:00<00:00, 319.05it/s, v_num=2, train_loss=3.960, RMSE=24.30]
Epoch 2: 69%|██████▉ | 22/32 [00:00<00:00, 320.05it/s, v_num=2, train_loss=3.960, RMSE=24.30]
Epoch 2: 69%|██████▉ | 22/32 [00:00<00:00, 319.09it/s, v_num=2, train_loss=4.020, RMSE=24.30]
Epoch 2: 72%|███████▏ | 23/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=4.020, RMSE=24.30]
Epoch 2: 72%|███████▏ | 23/32 [00:00<00:00, 319.52it/s, v_num=2, train_loss=4.410, RMSE=24.30]
Epoch 2: 75%|███████▌ | 24/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=4.410, RMSE=24.30]
Epoch 2: 75%|███████▌ | 24/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=4.210, RMSE=24.30]
Epoch 2: 78%|███████▊ | 25/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=4.210, RMSE=24.30]
Epoch 2: 78%|███████▊ | 25/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=4.020, RMSE=24.30]
Epoch 2: 81%|████████▏ | 26/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=4.020, RMSE=24.30]
Epoch 2: 81%|████████▏ | 26/32 [00:00<00:00, 319.55it/s, v_num=2, train_loss=3.570, RMSE=24.30]
Epoch 2: 84%|████████▍ | 27/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=3.570, RMSE=24.30]
Epoch 2: 84%|████████▍ | 27/32 [00:00<00:00, 319.66it/s, v_num=2, train_loss=4.240, RMSE=24.30]
Epoch 2: 88%|████████▊ | 28/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=4.240, RMSE=24.30]
Epoch 2: 88%|████████▊ | 28/32 [00:00<00:00, 319.80it/s, v_num=2, train_loss=4.300, RMSE=24.30]
Epoch 2: 91%|█████████ | 29/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=4.300, RMSE=24.30]
Epoch 2: 91%|█████████ | 29/32 [00:00<00:00, 320.00it/s, v_num=2, train_loss=3.750, RMSE=24.30]
Epoch 2: 94%|█████████▍| 30/32 [00:00<00:00, 320.82it/s, v_num=2, train_loss=3.750, RMSE=24.30]
Epoch 2: 94%|█████████▍| 30/32 [00:00<00:00, 320.10it/s, v_num=2, train_loss=3.660, RMSE=24.30]
Epoch 2: 97%|█████████▋| 31/32 [00:00<00:00, 321.03it/s, v_num=2, train_loss=3.660, RMSE=24.30]
Epoch 2: 97%|█████████▋| 31/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=4.130, RMSE=24.30]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=4.130, RMSE=24.30]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 320.53it/s, v_num=2, train_loss=3.950, RMSE=24.30]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 616.39it/s]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 262.42it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 2: 100%|██████████| 32/32 [00:00<00:00, 261.25it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 2: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 3: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 3: 3%|▎ | 1/32 [00:00<00:00, 277.86it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 3: 3%|▎ | 1/32 [00:00<00:00, 261.72it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 6%|▋ | 2/32 [00:00<00:00, 295.46it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 6%|▋ | 2/32 [00:00<00:00, 284.77it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 9%|▉ | 3/32 [00:00<00:00, 303.12it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 9%|▉ | 3/32 [00:00<00:00, 296.74it/s, v_num=2, train_loss=4.660, RMSE=24.10]
Epoch 3: 12%|█▎ | 4/32 [00:00<00:00, 307.55it/s, v_num=2, train_loss=4.660, RMSE=24.10]
Epoch 3: 12%|█▎ | 4/32 [00:00<00:00, 302.59it/s, v_num=2, train_loss=3.670, RMSE=24.10]
Epoch 3: 16%|█▌ | 5/32 [00:00<00:00, 300.77it/s, v_num=2, train_loss=3.670, RMSE=24.10]
Epoch 3: 16%|█▌ | 5/32 [00:00<00:00, 296.91it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 19%|█▉ | 6/32 [00:00<00:00, 303.99it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 19%|█▉ | 6/32 [00:00<00:00, 300.71it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 22%|██▏ | 7/32 [00:00<00:00, 306.71it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 22%|██▏ | 7/32 [00:00<00:00, 303.24it/s, v_num=2, train_loss=3.800, RMSE=24.10]
Epoch 3: 25%|██▌ | 8/32 [00:00<00:00, 308.59it/s, v_num=2, train_loss=3.800, RMSE=24.10]
Epoch 3: 25%|██▌ | 8/32 [00:00<00:00, 306.09it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 28%|██▊ | 9/32 [00:00<00:00, 310.14it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 28%|██▊ | 9/32 [00:00<00:00, 307.77it/s, v_num=2, train_loss=4.050, RMSE=24.10]
Epoch 3: 31%|███▏ | 10/32 [00:00<00:00, 311.05it/s, v_num=2, train_loss=4.050, RMSE=24.10]
Epoch 3: 31%|███▏ | 10/32 [00:00<00:00, 309.01it/s, v_num=2, train_loss=4.180, RMSE=24.10]
Epoch 3: 34%|███▍ | 11/32 [00:00<00:00, 312.05it/s, v_num=2, train_loss=4.180, RMSE=24.10]
Epoch 3: 34%|███▍ | 11/32 [00:00<00:00, 310.18it/s, v_num=2, train_loss=4.580, RMSE=24.10]
Epoch 3: 38%|███▊ | 12/32 [00:00<00:00, 313.30it/s, v_num=2, train_loss=4.580, RMSE=24.10]
Epoch 3: 38%|███▊ | 12/32 [00:00<00:00, 311.58it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 41%|████ | 13/32 [00:00<00:00, 314.04it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 41%|████ | 13/32 [00:00<00:00, 312.45it/s, v_num=2, train_loss=4.440, RMSE=24.10]
Epoch 3: 44%|████▍ | 14/32 [00:00<00:00, 314.71it/s, v_num=2, train_loss=4.440, RMSE=24.10]
Epoch 3: 44%|████▍ | 14/32 [00:00<00:00, 313.21it/s, v_num=2, train_loss=3.900, RMSE=24.10]
Epoch 3: 47%|████▋ | 15/32 [00:00<00:00, 315.09it/s, v_num=2, train_loss=3.900, RMSE=24.10]
Epoch 3: 47%|████▋ | 15/32 [00:00<00:00, 313.68it/s, v_num=2, train_loss=3.860, RMSE=24.10]
Epoch 3: 50%|█████ | 16/32 [00:00<00:00, 315.79it/s, v_num=2, train_loss=3.860, RMSE=24.10]
Epoch 3: 50%|█████ | 16/32 [00:00<00:00, 314.27it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 53%|█████▎ | 17/32 [00:00<00:00, 316.19it/s, v_num=2, train_loss=3.850, RMSE=24.10]
Epoch 3: 53%|█████▎ | 17/32 [00:00<00:00, 314.95it/s, v_num=2, train_loss=3.990, RMSE=24.10]
Epoch 3: 56%|█████▋ | 18/32 [00:00<00:00, 316.66it/s, v_num=2, train_loss=3.990, RMSE=24.10]
Epoch 3: 56%|█████▋ | 18/32 [00:00<00:00, 315.48it/s, v_num=2, train_loss=4.420, RMSE=24.10]
Epoch 3: 59%|█████▉ | 19/32 [00:00<00:00, 317.04it/s, v_num=2, train_loss=4.420, RMSE=24.10]
Epoch 3: 59%|█████▉ | 19/32 [00:00<00:00, 315.94it/s, v_num=2, train_loss=3.820, RMSE=24.10]
Epoch 3: 62%|██████▎ | 20/32 [00:00<00:00, 317.14it/s, v_num=2, train_loss=3.820, RMSE=24.10]
Epoch 3: 62%|██████▎ | 20/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=3.800, RMSE=24.10]
Epoch 3: 66%|██████▌ | 21/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=3.800, RMSE=24.10]
Epoch 3: 66%|██████▌ | 21/32 [00:00<00:00, 316.46it/s, v_num=2, train_loss=3.880, RMSE=24.10]
Epoch 3: 69%|██████▉ | 22/32 [00:00<00:00, 317.81it/s, v_num=2, train_loss=3.880, RMSE=24.10]
Epoch 3: 69%|██████▉ | 22/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=4.040, RMSE=24.10]
Epoch 3: 72%|███████▏ | 23/32 [00:00<00:00, 318.04it/s, v_num=2, train_loss=4.040, RMSE=24.10]
Epoch 3: 72%|███████▏ | 23/32 [00:00<00:00, 317.11it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 75%|███████▌ | 24/32 [00:00<00:00, 318.31it/s, v_num=2, train_loss=3.840, RMSE=24.10]
Epoch 3: 75%|███████▌ | 24/32 [00:00<00:00, 317.42it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 78%|███████▊ | 25/32 [00:00<00:00, 318.63it/s, v_num=2, train_loss=4.190, RMSE=24.10]
Epoch 3: 78%|███████▊ | 25/32 [00:00<00:00, 317.77it/s, v_num=2, train_loss=3.920, RMSE=24.10]
Epoch 3: 81%|████████▏ | 26/32 [00:00<00:00, 318.78it/s, v_num=2, train_loss=3.920, RMSE=24.10]
Epoch 3: 81%|████████▏ | 26/32 [00:00<00:00, 317.90it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 84%|████████▍ | 27/32 [00:00<00:00, 318.74it/s, v_num=2, train_loss=4.150, RMSE=24.10]
Epoch 3: 84%|████████▍ | 27/32 [00:00<00:00, 317.95it/s, v_num=2, train_loss=4.110, RMSE=24.10]
Epoch 3: 88%|████████▊ | 28/32 [00:00<00:00, 315.92it/s, v_num=2, train_loss=4.110, RMSE=24.10]
Epoch 3: 88%|████████▊ | 28/32 [00:00<00:00, 314.99it/s, v_num=2, train_loss=4.080, RMSE=24.10]
Epoch 3: 91%|█████████ | 29/32 [00:00<00:00, 312.76it/s, v_num=2, train_loss=4.080, RMSE=24.10]
Epoch 3: 91%|█████████ | 29/32 [00:00<00:00, 311.88it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 3: 94%|█████████▍| 30/32 [00:00<00:00, 310.14it/s, v_num=2, train_loss=3.950, RMSE=24.10]
Epoch 3: 94%|█████████▍| 30/32 [00:00<00:00, 309.29it/s, v_num=2, train_loss=3.920, RMSE=24.10]
Epoch 3: 97%|█████████▋| 31/32 [00:00<00:00, 307.59it/s, v_num=2, train_loss=3.920, RMSE=24.10]
Epoch 3: 97%|█████████▋| 31/32 [00:00<00:00, 306.78it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 305.68it/s, v_num=2, train_loss=3.960, RMSE=24.10]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 304.91it/s, v_num=2, train_loss=4.290, RMSE=24.10]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 591.46it/s]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 249.38it/s, v_num=2, train_loss=4.290, RMSE=23.80]
Epoch 3: 100%|██████████| 32/32 [00:00<00:00, 248.28it/s, v_num=2, train_loss=4.290, RMSE=23.80]
Epoch 3: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.290, RMSE=23.80]
Epoch 4: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.290, RMSE=23.80]
Epoch 4: 3%|▎ | 1/32 [00:00<00:00, 274.82it/s, v_num=2, train_loss=4.290, RMSE=23.80]
Epoch 4: 3%|▎ | 1/32 [00:00<00:00, 259.31it/s, v_num=2, train_loss=3.980, RMSE=23.80]
Epoch 4: 6%|▋ | 2/32 [00:00<00:00, 293.74it/s, v_num=2, train_loss=3.980, RMSE=23.80]
Epoch 4: 6%|▋ | 2/32 [00:00<00:00, 284.68it/s, v_num=2, train_loss=3.860, RMSE=23.80]
Epoch 4: 9%|▉ | 3/32 [00:00<00:00, 302.13it/s, v_num=2, train_loss=3.860, RMSE=23.80]
Epoch 4: 9%|▉ | 3/32 [00:00<00:00, 295.77it/s, v_num=2, train_loss=3.670, RMSE=23.80]
Epoch 4: 12%|█▎ | 4/32 [00:00<00:00, 306.08it/s, v_num=2, train_loss=3.670, RMSE=23.80]
Epoch 4: 12%|█▎ | 4/32 [00:00<00:00, 301.12it/s, v_num=2, train_loss=4.330, RMSE=23.80]
Epoch 4: 16%|█▌ | 5/32 [00:00<00:00, 308.90it/s, v_num=2, train_loss=4.330, RMSE=23.80]
Epoch 4: 16%|█▌ | 5/32 [00:00<00:00, 304.90it/s, v_num=2, train_loss=4.140, RMSE=23.80]
Epoch 4: 19%|█▉ | 6/32 [00:00<00:00, 311.25it/s, v_num=2, train_loss=4.140, RMSE=23.80]
Epoch 4: 19%|█▉ | 6/32 [00:00<00:00, 307.87it/s, v_num=2, train_loss=3.400, RMSE=23.80]
Epoch 4: 22%|██▏ | 7/32 [00:00<00:00, 313.38it/s, v_num=2, train_loss=3.400, RMSE=23.80]
Epoch 4: 22%|██▏ | 7/32 [00:00<00:00, 310.42it/s, v_num=2, train_loss=4.280, RMSE=23.80]
Epoch 4: 25%|██▌ | 8/32 [00:00<00:00, 314.44it/s, v_num=2, train_loss=4.280, RMSE=23.80]
Epoch 4: 25%|██▌ | 8/32 [00:00<00:00, 311.82it/s, v_num=2, train_loss=4.200, RMSE=23.80]
Epoch 4: 28%|██▊ | 9/32 [00:00<00:00, 315.20it/s, v_num=2, train_loss=4.200, RMSE=23.80]
Epoch 4: 28%|██▊ | 9/32 [00:00<00:00, 312.88it/s, v_num=2, train_loss=3.980, RMSE=23.80]
Epoch 4: 31%|███▏ | 10/32 [00:00<00:00, 315.94it/s, v_num=2, train_loss=3.980, RMSE=23.80]
Epoch 4: 31%|███▏ | 10/32 [00:00<00:00, 313.83it/s, v_num=2, train_loss=4.050, RMSE=23.80]
Epoch 4: 34%|███▍ | 11/32 [00:00<00:00, 317.04it/s, v_num=2, train_loss=4.050, RMSE=23.80]
Epoch 4: 34%|███▍ | 11/32 [00:00<00:00, 314.86it/s, v_num=2, train_loss=4.140, RMSE=23.80]
Epoch 4: 38%|███▊ | 12/32 [00:00<00:00, 317.57it/s, v_num=2, train_loss=4.140, RMSE=23.80]
Epoch 4: 38%|███▊ | 12/32 [00:00<00:00, 315.78it/s, v_num=2, train_loss=3.860, RMSE=23.80]
Epoch 4: 41%|████ | 13/32 [00:00<00:00, 317.98it/s, v_num=2, train_loss=3.860, RMSE=23.80]
Epoch 4: 41%|████ | 13/32 [00:00<00:00, 316.33it/s, v_num=2, train_loss=4.190, RMSE=23.80]
Epoch 4: 44%|████▍ | 14/32 [00:00<00:00, 317.83it/s, v_num=2, train_loss=4.190, RMSE=23.80]
Epoch 4: 44%|████▍ | 14/32 [00:00<00:00, 316.25it/s, v_num=2, train_loss=3.900, RMSE=23.80]
Epoch 4: 47%|████▋ | 15/32 [00:00<00:00, 317.99it/s, v_num=2, train_loss=3.900, RMSE=23.80]
Epoch 4: 47%|████▋ | 15/32 [00:00<00:00, 316.54it/s, v_num=2, train_loss=4.110, RMSE=23.80]
Epoch 4: 50%|█████ | 16/32 [00:00<00:00, 318.29it/s, v_num=2, train_loss=4.110, RMSE=23.80]
Epoch 4: 50%|█████ | 16/32 [00:00<00:00, 316.95it/s, v_num=2, train_loss=4.180, RMSE=23.80]
Epoch 4: 53%|█████▎ | 17/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=4.180, RMSE=23.80]
Epoch 4: 53%|█████▎ | 17/32 [00:00<00:00, 317.18it/s, v_num=2, train_loss=4.320, RMSE=23.80]
Epoch 4: 56%|█████▋ | 18/32 [00:00<00:00, 318.26it/s, v_num=2, train_loss=4.320, RMSE=23.80]
Epoch 4: 56%|█████▋ | 18/32 [00:00<00:00, 317.06it/s, v_num=2, train_loss=4.020, RMSE=23.80]
Epoch 4: 59%|█████▉ | 19/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=4.020, RMSE=23.80]
Epoch 4: 59%|█████▉ | 19/32 [00:00<00:00, 317.07it/s, v_num=2, train_loss=4.340, RMSE=23.80]
Epoch 4: 62%|██████▎ | 20/32 [00:00<00:00, 318.37it/s, v_num=2, train_loss=4.340, RMSE=23.80]
Epoch 4: 62%|██████▎ | 20/32 [00:00<00:00, 317.26it/s, v_num=2, train_loss=4.150, RMSE=23.80]
Epoch 4: 66%|██████▌ | 21/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=4.150, RMSE=23.80]
Epoch 4: 66%|██████▌ | 21/32 [00:00<00:00, 317.66it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 69%|██████▉ | 22/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 69%|██████▉ | 22/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=3.790, RMSE=23.80]
Epoch 4: 72%|███████▏ | 23/32 [00:00<00:00, 316.26it/s, v_num=2, train_loss=3.790, RMSE=23.80]
Epoch 4: 72%|███████▏ | 23/32 [00:00<00:00, 315.27it/s, v_num=2, train_loss=4.090, RMSE=23.80]
Epoch 4: 75%|███████▌ | 24/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=4.090, RMSE=23.80]
Epoch 4: 75%|███████▌ | 24/32 [00:00<00:00, 315.15it/s, v_num=2, train_loss=4.080, RMSE=23.80]
Epoch 4: 78%|███████▊ | 25/32 [00:00<00:00, 315.95it/s, v_num=2, train_loss=4.080, RMSE=23.80]
Epoch 4: 78%|███████▊ | 25/32 [00:00<00:00, 315.06it/s, v_num=2, train_loss=3.800, RMSE=23.80]
Epoch 4: 81%|████████▏ | 26/32 [00:00<00:00, 315.98it/s, v_num=2, train_loss=3.800, RMSE=23.80]
Epoch 4: 81%|████████▏ | 26/32 [00:00<00:00, 315.14it/s, v_num=2, train_loss=3.870, RMSE=23.80]
Epoch 4: 84%|████████▍ | 27/32 [00:00<00:00, 315.97it/s, v_num=2, train_loss=3.870, RMSE=23.80]
Epoch 4: 84%|████████▍ | 27/32 [00:00<00:00, 315.17it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 88%|████████▊ | 28/32 [00:00<00:00, 316.00it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 88%|████████▊ | 28/32 [00:00<00:00, 315.23it/s, v_num=2, train_loss=3.920, RMSE=23.80]
Epoch 4: 91%|█████████ | 29/32 [00:00<00:00, 315.92it/s, v_num=2, train_loss=3.920, RMSE=23.80]
Epoch 4: 91%|█████████ | 29/32 [00:00<00:00, 315.18it/s, v_num=2, train_loss=3.950, RMSE=23.80]
Epoch 4: 94%|█████████▍| 30/32 [00:00<00:00, 316.02it/s, v_num=2, train_loss=3.950, RMSE=23.80]
Epoch 4: 94%|█████████▍| 30/32 [00:00<00:00, 315.30it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 97%|█████████▋| 31/32 [00:00<00:00, 316.26it/s, v_num=2, train_loss=3.960, RMSE=23.80]
Epoch 4: 97%|█████████▋| 31/32 [00:00<00:00, 315.58it/s, v_num=2, train_loss=3.610, RMSE=23.80]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=3.610, RMSE=23.80]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 315.86it/s, v_num=2, train_loss=4.800, RMSE=23.80]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 608.18it/s]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 258.74it/s, v_num=2, train_loss=4.800, RMSE=23.60]
Epoch 4: 100%|██████████| 32/32 [00:00<00:00, 257.60it/s, v_num=2, train_loss=4.800, RMSE=23.60]
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Epoch 5: 3%|▎ | 1/32 [00:00<00:00, 272.59it/s, v_num=2, train_loss=4.800, RMSE=23.60]
Epoch 5: 3%|▎ | 1/32 [00:00<00:00, 257.08it/s, v_num=2, train_loss=4.070, RMSE=23.60]
Epoch 5: 6%|▋ | 2/32 [00:00<00:00, 291.10it/s, v_num=2, train_loss=4.070, RMSE=23.60]
Epoch 5: 6%|▋ | 2/32 [00:00<00:00, 282.16it/s, v_num=2, train_loss=3.870, RMSE=23.60]
Epoch 5: 9%|▉ | 3/32 [00:00<00:00, 298.25it/s, v_num=2, train_loss=3.870, RMSE=23.60]
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Epoch 5: 12%|█▎ | 4/32 [00:00<00:00, 302.87it/s, v_num=2, train_loss=4.020, RMSE=23.60]
Epoch 5: 12%|█▎ | 4/32 [00:00<00:00, 298.01it/s, v_num=2, train_loss=3.900, RMSE=23.60]
Epoch 5: 16%|█▌ | 5/32 [00:00<00:00, 306.57it/s, v_num=2, train_loss=3.900, RMSE=23.60]
Epoch 5: 16%|█▌ | 5/32 [00:00<00:00, 302.61it/s, v_num=2, train_loss=4.220, RMSE=23.60]
Epoch 5: 19%|█▉ | 6/32 [00:00<00:00, 309.22it/s, v_num=2, train_loss=4.220, RMSE=23.60]
Epoch 5: 19%|█▉ | 6/32 [00:00<00:00, 305.77it/s, v_num=2, train_loss=3.860, RMSE=23.60]
Epoch 5: 22%|██▏ | 7/32 [00:00<00:00, 311.33it/s, v_num=2, train_loss=3.860, RMSE=23.60]
Epoch 5: 22%|██▏ | 7/32 [00:00<00:00, 308.42it/s, v_num=2, train_loss=4.170, RMSE=23.60]
Epoch 5: 25%|██▌ | 8/32 [00:00<00:00, 312.76it/s, v_num=2, train_loss=4.170, RMSE=23.60]
Epoch 5: 25%|██▌ | 8/32 [00:00<00:00, 310.21it/s, v_num=2, train_loss=4.050, RMSE=23.60]
Epoch 5: 28%|██▊ | 9/32 [00:00<00:00, 313.29it/s, v_num=2, train_loss=4.050, RMSE=23.60]
Epoch 5: 28%|██▊ | 9/32 [00:00<00:00, 310.99it/s, v_num=2, train_loss=4.230, RMSE=23.60]
Epoch 5: 31%|███▏ | 10/32 [00:00<00:00, 313.74it/s, v_num=2, train_loss=4.230, RMSE=23.60]
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Epoch 5: 34%|███▍ | 11/32 [00:00<00:00, 314.91it/s, v_num=2, train_loss=4.150, RMSE=23.60]
Epoch 5: 34%|███▍ | 11/32 [00:00<00:00, 313.01it/s, v_num=2, train_loss=3.860, RMSE=23.60]
Epoch 5: 38%|███▊ | 12/32 [00:00<00:00, 314.94it/s, v_num=2, train_loss=3.860, RMSE=23.60]
Epoch 5: 38%|███▊ | 12/32 [00:00<00:00, 313.17it/s, v_num=2, train_loss=4.100, RMSE=23.60]
Epoch 5: 41%|████ | 13/32 [00:00<00:00, 315.01it/s, v_num=2, train_loss=4.100, RMSE=23.60]
Epoch 5: 41%|████ | 13/32 [00:00<00:00, 313.38it/s, v_num=2, train_loss=3.790, RMSE=23.60]
Epoch 5: 44%|████▍ | 14/32 [00:00<00:00, 315.22it/s, v_num=2, train_loss=3.790, RMSE=23.60]
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Epoch 5: 47%|████▋ | 15/32 [00:00<00:00, 314.92it/s, v_num=2, train_loss=3.580, RMSE=23.60]
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Epoch 5: 50%|█████ | 16/32 [00:00<00:00, 315.65it/s, v_num=2, train_loss=3.990, RMSE=23.60]
Epoch 5: 50%|█████ | 16/32 [00:00<00:00, 314.33it/s, v_num=2, train_loss=3.880, RMSE=23.60]
Epoch 5: 53%|█████▎ | 17/32 [00:00<00:00, 315.75it/s, v_num=2, train_loss=3.880, RMSE=23.60]
Epoch 5: 53%|█████▎ | 17/32 [00:00<00:00, 314.44it/s, v_num=2, train_loss=3.890, RMSE=23.60]
Epoch 5: 56%|█████▋ | 18/32 [00:00<00:00, 315.41it/s, v_num=2, train_loss=3.890, RMSE=23.60]
Epoch 5: 56%|█████▋ | 18/32 [00:00<00:00, 314.21it/s, v_num=2, train_loss=4.210, RMSE=23.60]
Epoch 5: 59%|█████▉ | 19/32 [00:00<00:00, 315.43it/s, v_num=2, train_loss=4.210, RMSE=23.60]
Epoch 5: 59%|█████▉ | 19/32 [00:00<00:00, 314.32it/s, v_num=2, train_loss=3.800, RMSE=23.60]
Epoch 5: 62%|██████▎ | 20/32 [00:00<00:00, 315.87it/s, v_num=2, train_loss=3.800, RMSE=23.60]
Epoch 5: 62%|██████▎ | 20/32 [00:00<00:00, 314.48it/s, v_num=2, train_loss=3.680, RMSE=23.60]
Epoch 5: 66%|██████▌ | 21/32 [00:00<00:00, 315.91it/s, v_num=2, train_loss=3.680, RMSE=23.60]
Epoch 5: 66%|██████▌ | 21/32 [00:00<00:00, 314.87it/s, v_num=2, train_loss=4.230, RMSE=23.60]
Epoch 5: 69%|██████▉ | 22/32 [00:00<00:00, 316.08it/s, v_num=2, train_loss=4.230, RMSE=23.60]
Epoch 5: 69%|██████▉ | 22/32 [00:00<00:00, 315.11it/s, v_num=2, train_loss=3.910, RMSE=23.60]
Epoch 5: 72%|███████▏ | 23/32 [00:00<00:00, 316.22it/s, v_num=2, train_loss=3.910, RMSE=23.60]
Epoch 5: 72%|███████▏ | 23/32 [00:00<00:00, 315.30it/s, v_num=2, train_loss=3.770, RMSE=23.60]
Epoch 5: 75%|███████▌ | 24/32 [00:00<00:00, 316.46it/s, v_num=2, train_loss=3.770, RMSE=23.60]
Epoch 5: 75%|███████▌ | 24/32 [00:00<00:00, 315.57it/s, v_num=2, train_loss=3.560, RMSE=23.60]
Epoch 5: 78%|███████▊ | 25/32 [00:00<00:00, 316.90it/s, v_num=2, train_loss=3.560, RMSE=23.60]
Epoch 5: 78%|███████▊ | 25/32 [00:00<00:00, 316.05it/s, v_num=2, train_loss=4.170, RMSE=23.60]
Epoch 5: 81%|████████▏ | 26/32 [00:00<00:00, 317.06it/s, v_num=2, train_loss=4.170, RMSE=23.60]
Epoch 5: 81%|████████▏ | 26/32 [00:00<00:00, 316.23it/s, v_num=2, train_loss=4.240, RMSE=23.60]
Epoch 5: 84%|████████▍ | 27/32 [00:00<00:00, 317.15it/s, v_num=2, train_loss=4.240, RMSE=23.60]
Epoch 5: 84%|████████▍ | 27/32 [00:00<00:00, 316.35it/s, v_num=2, train_loss=3.740, RMSE=23.60]
Epoch 5: 88%|████████▊ | 28/32 [00:00<00:00, 317.23it/s, v_num=2, train_loss=3.740, RMSE=23.60]
Epoch 5: 88%|████████▊ | 28/32 [00:00<00:00, 316.45it/s, v_num=2, train_loss=4.490, RMSE=23.60]
Epoch 5: 91%|█████████ | 29/32 [00:00<00:00, 317.20it/s, v_num=2, train_loss=4.490, RMSE=23.60]
Epoch 5: 91%|█████████ | 29/32 [00:00<00:00, 316.45it/s, v_num=2, train_loss=3.780, RMSE=23.60]
Epoch 5: 94%|█████████▍| 30/32 [00:00<00:00, 317.50it/s, v_num=2, train_loss=3.780, RMSE=23.60]
Epoch 5: 94%|█████████▍| 30/32 [00:00<00:00, 316.79it/s, v_num=2, train_loss=4.300, RMSE=23.60]
Epoch 5: 97%|█████████▋| 31/32 [00:00<00:00, 317.55it/s, v_num=2, train_loss=4.300, RMSE=23.60]
Epoch 5: 97%|█████████▋| 31/32 [00:00<00:00, 316.87it/s, v_num=2, train_loss=4.320, RMSE=23.60]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 317.79it/s, v_num=2, train_loss=4.320, RMSE=23.60]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 317.12it/s, v_num=2, train_loss=3.210, RMSE=23.60]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 608.53it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 612.35it/s]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 259.91it/s, v_num=2, train_loss=3.210, RMSE=23.30]
Epoch 5: 100%|██████████| 32/32 [00:00<00:00, 258.65it/s, v_num=2, train_loss=3.210, RMSE=23.30]
Epoch 5: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.210, RMSE=23.30]
Epoch 6: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.210, RMSE=23.30]
Epoch 6: 3%|▎ | 1/32 [00:00<00:00, 276.56it/s, v_num=2, train_loss=3.210, RMSE=23.30]
Epoch 6: 3%|▎ | 1/32 [00:00<00:00, 260.76it/s, v_num=2, train_loss=4.020, RMSE=23.30]
Epoch 6: 6%|▋ | 2/32 [00:00<00:00, 292.58it/s, v_num=2, train_loss=4.020, RMSE=23.30]
Epoch 6: 6%|▋ | 2/32 [00:00<00:00, 283.69it/s, v_num=2, train_loss=4.210, RMSE=23.30]
Epoch 6: 9%|▉ | 3/32 [00:00<00:00, 298.24it/s, v_num=2, train_loss=4.210, RMSE=23.30]
Epoch 6: 9%|▉ | 3/32 [00:00<00:00, 291.70it/s, v_num=2, train_loss=3.810, RMSE=23.30]
Epoch 6: 12%|█▎ | 4/32 [00:00<00:00, 302.53it/s, v_num=2, train_loss=3.810, RMSE=23.30]
Epoch 6: 12%|█▎ | 4/32 [00:00<00:00, 297.67it/s, v_num=2, train_loss=4.340, RMSE=23.30]
Epoch 6: 16%|█▌ | 5/32 [00:00<00:00, 306.86it/s, v_num=2, train_loss=4.340, RMSE=23.30]
Epoch 6: 16%|█▌ | 5/32 [00:00<00:00, 302.14it/s, v_num=2, train_loss=3.900, RMSE=23.30]
Epoch 6: 19%|█▉ | 6/32 [00:00<00:00, 308.19it/s, v_num=2, train_loss=3.900, RMSE=23.30]
Epoch 6: 19%|█▉ | 6/32 [00:00<00:00, 304.60it/s, v_num=2, train_loss=3.720, RMSE=23.30]
Epoch 6: 22%|██▏ | 7/32 [00:00<00:00, 308.51it/s, v_num=2, train_loss=3.720, RMSE=23.30]
Epoch 6: 22%|██▏ | 7/32 [00:00<00:00, 305.61it/s, v_num=2, train_loss=4.350, RMSE=23.30]
Epoch 6: 25%|██▌ | 8/32 [00:00<00:00, 309.45it/s, v_num=2, train_loss=4.350, RMSE=23.30]
Epoch 6: 25%|██▌ | 8/32 [00:00<00:00, 306.93it/s, v_num=2, train_loss=3.830, RMSE=23.30]
Epoch 6: 28%|██▊ | 9/32 [00:00<00:00, 304.79it/s, v_num=2, train_loss=3.830, RMSE=23.30]
Epoch 6: 28%|██▊ | 9/32 [00:00<00:00, 302.60it/s, v_num=2, train_loss=3.560, RMSE=23.30]
Epoch 6: 31%|███▏ | 10/32 [00:00<00:00, 306.32it/s, v_num=2, train_loss=3.560, RMSE=23.30]
Epoch 6: 31%|███▏ | 10/32 [00:00<00:00, 304.31it/s, v_num=2, train_loss=3.860, RMSE=23.30]
Epoch 6: 34%|███▍ | 11/32 [00:00<00:00, 307.54it/s, v_num=2, train_loss=3.860, RMSE=23.30]
Epoch 6: 34%|███▍ | 11/32 [00:00<00:00, 305.73it/s, v_num=2, train_loss=4.060, RMSE=23.30]
Epoch 6: 38%|███▊ | 12/32 [00:00<00:00, 308.46it/s, v_num=2, train_loss=4.060, RMSE=23.30]
Epoch 6: 38%|███▊ | 12/32 [00:00<00:00, 306.61it/s, v_num=2, train_loss=4.540, RMSE=23.30]
Epoch 6: 41%|████ | 13/32 [00:00<00:00, 309.16it/s, v_num=2, train_loss=4.540, RMSE=23.30]
Epoch 6: 41%|████ | 13/32 [00:00<00:00, 307.57it/s, v_num=2, train_loss=3.740, RMSE=23.30]
Epoch 6: 44%|████▍ | 14/32 [00:00<00:00, 309.68it/s, v_num=2, train_loss=3.740, RMSE=23.30]
Epoch 6: 44%|████▍ | 14/32 [00:00<00:00, 308.20it/s, v_num=2, train_loss=3.640, RMSE=23.30]
Epoch 6: 47%|████▋ | 15/32 [00:00<00:00, 310.60it/s, v_num=2, train_loss=3.640, RMSE=23.30]
Epoch 6: 47%|████▋ | 15/32 [00:00<00:00, 309.00it/s, v_num=2, train_loss=3.670, RMSE=23.30]
Epoch 6: 50%|█████ | 16/32 [00:00<00:00, 311.13it/s, v_num=2, train_loss=3.670, RMSE=23.30]
Epoch 6: 50%|█████ | 16/32 [00:00<00:00, 309.84it/s, v_num=2, train_loss=4.020, RMSE=23.30]
Epoch 6: 53%|█████▎ | 17/32 [00:00<00:00, 311.47it/s, v_num=2, train_loss=4.020, RMSE=23.30]
Epoch 6: 53%|█████▎ | 17/32 [00:00<00:00, 310.24it/s, v_num=2, train_loss=3.920, RMSE=23.30]
Epoch 6: 56%|█████▋ | 18/32 [00:00<00:00, 311.76it/s, v_num=2, train_loss=3.920, RMSE=23.30]
Epoch 6: 56%|█████▋ | 18/32 [00:00<00:00, 310.62it/s, v_num=2, train_loss=4.380, RMSE=23.30]
Epoch 6: 59%|█████▉ | 19/32 [00:00<00:00, 312.31it/s, v_num=2, train_loss=4.380, RMSE=23.30]
Epoch 6: 59%|█████▉ | 19/32 [00:00<00:00, 311.24it/s, v_num=2, train_loss=4.080, RMSE=23.30]
Epoch 6: 62%|██████▎ | 20/32 [00:00<00:00, 312.99it/s, v_num=2, train_loss=4.080, RMSE=23.30]
Epoch 6: 62%|██████▎ | 20/32 [00:00<00:00, 311.97it/s, v_num=2, train_loss=3.600, RMSE=23.30]
Epoch 6: 66%|██████▌ | 21/32 [00:00<00:00, 313.45it/s, v_num=2, train_loss=3.600, RMSE=23.30]
Epoch 6: 66%|██████▌ | 21/32 [00:00<00:00, 312.46it/s, v_num=2, train_loss=3.540, RMSE=23.30]
Epoch 6: 69%|██████▉ | 22/32 [00:00<00:00, 313.72it/s, v_num=2, train_loss=3.540, RMSE=23.30]
Epoch 6: 69%|██████▉ | 22/32 [00:00<00:00, 312.72it/s, v_num=2, train_loss=3.930, RMSE=23.30]
Epoch 6: 72%|███████▏ | 23/32 [00:00<00:00, 313.92it/s, v_num=2, train_loss=3.930, RMSE=23.30]
Epoch 6: 72%|███████▏ | 23/32 [00:00<00:00, 313.01it/s, v_num=2, train_loss=3.970, RMSE=23.30]
Epoch 6: 75%|███████▌ | 24/32 [00:00<00:00, 314.29it/s, v_num=2, train_loss=3.970, RMSE=23.30]
Epoch 6: 75%|███████▌ | 24/32 [00:00<00:00, 313.25it/s, v_num=2, train_loss=3.970, RMSE=23.30]
Epoch 6: 78%|███████▊ | 25/32 [00:00<00:00, 314.21it/s, v_num=2, train_loss=3.970, RMSE=23.30]
Epoch 6: 78%|███████▊ | 25/32 [00:00<00:00, 313.37it/s, v_num=2, train_loss=3.950, RMSE=23.30]
Epoch 6: 81%|████████▏ | 26/32 [00:00<00:00, 314.45it/s, v_num=2, train_loss=3.950, RMSE=23.30]
Epoch 6: 81%|████████▏ | 26/32 [00:00<00:00, 313.63it/s, v_num=2, train_loss=4.120, RMSE=23.30]
Epoch 6: 84%|████████▍ | 27/32 [00:00<00:00, 314.54it/s, v_num=2, train_loss=4.120, RMSE=23.30]
Epoch 6: 84%|████████▍ | 27/32 [00:00<00:00, 313.72it/s, v_num=2, train_loss=4.160, RMSE=23.30]
Epoch 6: 88%|████████▊ | 28/32 [00:00<00:00, 314.75it/s, v_num=2, train_loss=4.160, RMSE=23.30]
Epoch 6: 88%|████████▊ | 28/32 [00:00<00:00, 313.99it/s, v_num=2, train_loss=4.160, RMSE=23.30]
Epoch 6: 91%|█████████ | 29/32 [00:00<00:00, 315.14it/s, v_num=2, train_loss=4.160, RMSE=23.30]
Epoch 6: 91%|█████████ | 29/32 [00:00<00:00, 314.42it/s, v_num=2, train_loss=3.940, RMSE=23.30]
Epoch 6: 94%|█████████▍| 30/32 [00:00<00:00, 315.22it/s, v_num=2, train_loss=3.940, RMSE=23.30]
Epoch 6: 94%|█████████▍| 30/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=3.770, RMSE=23.30]
Epoch 6: 97%|█████████▋| 31/32 [00:00<00:00, 315.43it/s, v_num=2, train_loss=3.770, RMSE=23.30]
Epoch 6: 97%|█████████▋| 31/32 [00:00<00:00, 314.73it/s, v_num=2, train_loss=4.080, RMSE=23.30]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 315.59it/s, v_num=2, train_loss=4.080, RMSE=23.30]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 314.93it/s, v_num=2, train_loss=4.250, RMSE=23.30]
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Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 258.32it/s, v_num=2, train_loss=4.250, RMSE=22.90]
Epoch 6: 100%|██████████| 32/32 [00:00<00:00, 257.19it/s, v_num=2, train_loss=4.250, RMSE=22.90]
Epoch 6: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.250, RMSE=22.90]
Epoch 7: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.250, RMSE=22.90]
Epoch 7: 3%|▎ | 1/32 [00:00<00:00, 266.93it/s, v_num=2, train_loss=4.250, RMSE=22.90]
Epoch 7: 3%|▎ | 1/32 [00:00<00:00, 251.77it/s, v_num=2, train_loss=4.080, RMSE=22.90]
Epoch 7: 6%|▋ | 2/32 [00:00<00:00, 282.95it/s, v_num=2, train_loss=4.080, RMSE=22.90]
Epoch 7: 6%|▋ | 2/32 [00:00<00:00, 274.47it/s, v_num=2, train_loss=4.010, RMSE=22.90]
Epoch 7: 9%|▉ | 3/32 [00:00<00:00, 291.61it/s, v_num=2, train_loss=4.010, RMSE=22.90]
Epoch 7: 9%|▉ | 3/32 [00:00<00:00, 285.64it/s, v_num=2, train_loss=3.870, RMSE=22.90]
Epoch 7: 12%|█▎ | 4/32 [00:00<00:00, 297.67it/s, v_num=2, train_loss=3.870, RMSE=22.90]
Epoch 7: 12%|█▎ | 4/32 [00:00<00:00, 293.04it/s, v_num=2, train_loss=4.190, RMSE=22.90]
Epoch 7: 16%|█▌ | 5/32 [00:00<00:00, 302.75it/s, v_num=2, train_loss=4.190, RMSE=22.90]
Epoch 7: 16%|█▌ | 5/32 [00:00<00:00, 298.88it/s, v_num=2, train_loss=3.730, RMSE=22.90]
Epoch 7: 19%|█▉ | 6/32 [00:00<00:00, 304.90it/s, v_num=2, train_loss=3.730, RMSE=22.90]
Epoch 7: 19%|█▉ | 6/32 [00:00<00:00, 301.57it/s, v_num=2, train_loss=4.030, RMSE=22.90]
Epoch 7: 22%|██▏ | 7/32 [00:00<00:00, 306.32it/s, v_num=2, train_loss=4.030, RMSE=22.90]
Epoch 7: 22%|██▏ | 7/32 [00:00<00:00, 303.42it/s, v_num=2, train_loss=4.230, RMSE=22.90]
Epoch 7: 25%|██▌ | 8/32 [00:00<00:00, 307.40it/s, v_num=2, train_loss=4.230, RMSE=22.90]
Epoch 7: 25%|██▌ | 8/32 [00:00<00:00, 304.78it/s, v_num=2, train_loss=4.020, RMSE=22.90]
Epoch 7: 28%|██▊ | 9/32 [00:00<00:00, 308.17it/s, v_num=2, train_loss=4.020, RMSE=22.90]
Epoch 7: 28%|██▊ | 9/32 [00:00<00:00, 305.88it/s, v_num=2, train_loss=4.070, RMSE=22.90]
Epoch 7: 31%|███▏ | 10/32 [00:00<00:00, 309.25it/s, v_num=2, train_loss=4.070, RMSE=22.90]
Epoch 7: 31%|███▏ | 10/32 [00:00<00:00, 307.22it/s, v_num=2, train_loss=3.910, RMSE=22.90]
Epoch 7: 34%|███▍ | 11/32 [00:00<00:00, 310.17it/s, v_num=2, train_loss=3.910, RMSE=22.90]
Epoch 7: 34%|███▍ | 11/32 [00:00<00:00, 308.31it/s, v_num=2, train_loss=3.990, RMSE=22.90]
Epoch 7: 38%|███▊ | 12/32 [00:00<00:00, 310.63it/s, v_num=2, train_loss=3.990, RMSE=22.90]
Epoch 7: 38%|███▊ | 12/32 [00:00<00:00, 308.88it/s, v_num=2, train_loss=4.420, RMSE=22.90]
Epoch 7: 41%|████ | 13/32 [00:00<00:00, 311.09it/s, v_num=2, train_loss=4.420, RMSE=22.90]
Epoch 7: 41%|████ | 13/32 [00:00<00:00, 309.49it/s, v_num=2, train_loss=3.690, RMSE=22.90]
Epoch 7: 44%|████▍ | 14/32 [00:00<00:00, 311.84it/s, v_num=2, train_loss=3.690, RMSE=22.90]
Epoch 7: 44%|████▍ | 14/32 [00:00<00:00, 310.33it/s, v_num=2, train_loss=3.800, RMSE=22.90]
Epoch 7: 47%|████▋ | 15/32 [00:00<00:00, 312.48it/s, v_num=2, train_loss=3.800, RMSE=22.90]
Epoch 7: 47%|████▋ | 15/32 [00:00<00:00, 311.09it/s, v_num=2, train_loss=4.080, RMSE=22.90]
Epoch 7: 50%|█████ | 16/32 [00:00<00:00, 312.74it/s, v_num=2, train_loss=4.080, RMSE=22.90]
Epoch 7: 50%|█████ | 16/32 [00:00<00:00, 311.43it/s, v_num=2, train_loss=3.730, RMSE=22.90]
Epoch 7: 53%|█████▎ | 17/32 [00:00<00:00, 313.04it/s, v_num=2, train_loss=3.730, RMSE=22.90]
Epoch 7: 53%|█████▎ | 17/32 [00:00<00:00, 311.79it/s, v_num=2, train_loss=3.780, RMSE=22.90]
Epoch 7: 56%|█████▋ | 18/32 [00:00<00:00, 313.14it/s, v_num=2, train_loss=3.780, RMSE=22.90]
Epoch 7: 56%|█████▋ | 18/32 [00:00<00:00, 311.98it/s, v_num=2, train_loss=3.500, RMSE=22.90]
Epoch 7: 59%|█████▉ | 19/32 [00:00<00:00, 313.76it/s, v_num=2, train_loss=3.500, RMSE=22.90]
Epoch 7: 59%|█████▉ | 19/32 [00:00<00:00, 312.65it/s, v_num=2, train_loss=3.920, RMSE=22.90]
Epoch 7: 62%|██████▎ | 20/32 [00:00<00:00, 314.12it/s, v_num=2, train_loss=3.920, RMSE=22.90]
Epoch 7: 62%|██████▎ | 20/32 [00:00<00:00, 313.07it/s, v_num=2, train_loss=3.800, RMSE=22.90]
Epoch 7: 66%|██████▌ | 21/32 [00:00<00:00, 314.37it/s, v_num=2, train_loss=3.800, RMSE=22.90]
Epoch 7: 66%|██████▌ | 21/32 [00:00<00:00, 313.36it/s, v_num=2, train_loss=3.890, RMSE=22.90]
Epoch 7: 69%|██████▉ | 22/32 [00:00<00:00, 314.36it/s, v_num=2, train_loss=3.890, RMSE=22.90]
Epoch 7: 69%|██████▉ | 22/32 [00:00<00:00, 313.29it/s, v_num=2, train_loss=3.870, RMSE=22.90]
Epoch 7: 72%|███████▏ | 23/32 [00:00<00:00, 314.42it/s, v_num=2, train_loss=3.870, RMSE=22.90]
Epoch 7: 72%|███████▏ | 23/32 [00:00<00:00, 313.50it/s, v_num=2, train_loss=3.680, RMSE=22.90]
Epoch 7: 75%|███████▌ | 24/32 [00:00<00:00, 314.85it/s, v_num=2, train_loss=3.680, RMSE=22.90]
Epoch 7: 75%|███████▌ | 24/32 [00:00<00:00, 313.94it/s, v_num=2, train_loss=4.140, RMSE=22.90]
Epoch 7: 78%|███████▊ | 25/32 [00:00<00:00, 315.04it/s, v_num=2, train_loss=4.140, RMSE=22.90]
Epoch 7: 78%|███████▊ | 25/32 [00:00<00:00, 314.20it/s, v_num=2, train_loss=3.990, RMSE=22.90]
Epoch 7: 81%|████████▏ | 26/32 [00:00<00:00, 315.36it/s, v_num=2, train_loss=3.990, RMSE=22.90]
Epoch 7: 81%|████████▏ | 26/32 [00:00<00:00, 314.54it/s, v_num=2, train_loss=3.970, RMSE=22.90]
Epoch 7: 84%|████████▍ | 27/32 [00:00<00:00, 313.70it/s, v_num=2, train_loss=3.970, RMSE=22.90]
Epoch 7: 84%|████████▍ | 27/32 [00:00<00:00, 312.92it/s, v_num=2, train_loss=4.040, RMSE=22.90]
Epoch 7: 88%|████████▊ | 28/32 [00:00<00:00, 313.90it/s, v_num=2, train_loss=4.040, RMSE=22.90]
Epoch 7: 88%|████████▊ | 28/32 [00:00<00:00, 313.16it/s, v_num=2, train_loss=4.210, RMSE=22.90]
Epoch 7: 91%|█████████ | 29/32 [00:00<00:00, 314.34it/s, v_num=2, train_loss=4.210, RMSE=22.90]
Epoch 7: 91%|█████████ | 29/32 [00:00<00:00, 313.62it/s, v_num=2, train_loss=4.110, RMSE=22.90]
Epoch 7: 94%|█████████▍| 30/32 [00:00<00:00, 314.50it/s, v_num=2, train_loss=4.110, RMSE=22.90]
Epoch 7: 94%|█████████▍| 30/32 [00:00<00:00, 313.78it/s, v_num=2, train_loss=3.560, RMSE=22.90]
Epoch 7: 97%|█████████▋| 31/32 [00:00<00:00, 314.66it/s, v_num=2, train_loss=3.560, RMSE=22.90]
Epoch 7: 97%|█████████▋| 31/32 [00:00<00:00, 313.99it/s, v_num=2, train_loss=3.750, RMSE=22.90]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 314.70it/s, v_num=2, train_loss=3.750, RMSE=22.90]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 314.02it/s, v_num=2, train_loss=4.440, RMSE=22.90]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 613.53it/s]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 609.22it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 607.93it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 611.99it/s]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 257.76it/s, v_num=2, train_loss=4.440, RMSE=22.50]
Epoch 7: 100%|██████████| 32/32 [00:00<00:00, 256.61it/s, v_num=2, train_loss=4.440, RMSE=22.50]
Epoch 7: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.440, RMSE=22.50]
Epoch 8: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.440, RMSE=22.50]
Epoch 8: 3%|▎ | 1/32 [00:00<00:00, 257.45it/s, v_num=2, train_loss=4.440, RMSE=22.50]
Epoch 8: 3%|▎ | 1/32 [00:00<00:00, 242.18it/s, v_num=2, train_loss=4.170, RMSE=22.50]
Epoch 8: 6%|▋ | 2/32 [00:00<00:00, 276.33it/s, v_num=2, train_loss=4.170, RMSE=22.50]
Epoch 8: 6%|▋ | 2/32 [00:00<00:00, 268.22it/s, v_num=2, train_loss=4.000, RMSE=22.50]
Epoch 8: 9%|▉ | 3/32 [00:00<00:00, 287.54it/s, v_num=2, train_loss=4.000, RMSE=22.50]
Epoch 8: 9%|▉ | 3/32 [00:00<00:00, 281.78it/s, v_num=2, train_loss=3.720, RMSE=22.50]
Epoch 8: 12%|█▎ | 4/32 [00:00<00:00, 293.86it/s, v_num=2, train_loss=3.720, RMSE=22.50]
Epoch 8: 12%|█▎ | 4/32 [00:00<00:00, 289.28it/s, v_num=2, train_loss=3.540, RMSE=22.50]
Epoch 8: 16%|█▌ | 5/32 [00:00<00:00, 299.22it/s, v_num=2, train_loss=3.540, RMSE=22.50]
Epoch 8: 16%|█▌ | 5/32 [00:00<00:00, 295.33it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 8: 19%|█▉ | 6/32 [00:00<00:00, 302.33it/s, v_num=2, train_loss=4.130, RMSE=22.50]
Epoch 8: 19%|█▉ | 6/32 [00:00<00:00, 299.11it/s, v_num=2, train_loss=4.180, RMSE=22.50]
Epoch 8: 22%|██▏ | 7/32 [00:00<00:00, 304.86it/s, v_num=2, train_loss=4.180, RMSE=22.50]
Epoch 8: 22%|██▏ | 7/32 [00:00<00:00, 302.07it/s, v_num=2, train_loss=3.920, RMSE=22.50]
Epoch 8: 25%|██▌ | 8/32 [00:00<00:00, 307.06it/s, v_num=2, train_loss=3.920, RMSE=22.50]
Epoch 8: 25%|██▌ | 8/32 [00:00<00:00, 304.56it/s, v_num=2, train_loss=4.050, RMSE=22.50]
Epoch 8: 28%|██▊ | 9/32 [00:00<00:00, 308.71it/s, v_num=2, train_loss=4.050, RMSE=22.50]
Epoch 8: 28%|██▊ | 9/32 [00:00<00:00, 306.48it/s, v_num=2, train_loss=3.980, RMSE=22.50]
Epoch 8: 31%|███▏ | 10/32 [00:00<00:00, 310.24it/s, v_num=2, train_loss=3.980, RMSE=22.50]
Epoch 8: 31%|███▏ | 10/32 [00:00<00:00, 308.15it/s, v_num=2, train_loss=4.010, RMSE=22.50]
Epoch 8: 34%|███▍ | 11/32 [00:00<00:00, 310.97it/s, v_num=2, train_loss=4.010, RMSE=22.50]
Epoch 8: 34%|███▍ | 11/32 [00:00<00:00, 309.09it/s, v_num=2, train_loss=3.610, RMSE=22.50]
Epoch 8: 38%|███▊ | 12/32 [00:00<00:00, 311.85it/s, v_num=2, train_loss=3.610, RMSE=22.50]
Epoch 8: 38%|███▊ | 12/32 [00:00<00:00, 310.14it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 8: 41%|████ | 13/32 [00:00<00:00, 312.42it/s, v_num=2, train_loss=3.960, RMSE=22.50]
Epoch 8: 41%|████ | 13/32 [00:00<00:00, 310.85it/s, v_num=2, train_loss=3.940, RMSE=22.50]
Epoch 8: 44%|████▍ | 14/32 [00:00<00:00, 313.49it/s, v_num=2, train_loss=3.940, RMSE=22.50]
Epoch 8: 44%|████▍ | 14/32 [00:00<00:00, 312.02it/s, v_num=2, train_loss=4.140, RMSE=22.50]
Epoch 8: 47%|████▋ | 15/32 [00:00<00:00, 314.23it/s, v_num=2, train_loss=4.140, RMSE=22.50]
Epoch 8: 47%|████▋ | 15/32 [00:00<00:00, 312.85it/s, v_num=2, train_loss=3.650, RMSE=22.50]
Epoch 8: 50%|█████ | 16/32 [00:00<00:00, 314.50it/s, v_num=2, train_loss=3.650, RMSE=22.50]
Epoch 8: 50%|█████ | 16/32 [00:00<00:00, 313.21it/s, v_num=2, train_loss=3.900, RMSE=22.50]
Epoch 8: 53%|█████▎ | 17/32 [00:00<00:00, 315.00it/s, v_num=2, train_loss=3.900, RMSE=22.50]
Epoch 8: 53%|█████▎ | 17/32 [00:00<00:00, 313.76it/s, v_num=2, train_loss=4.010, RMSE=22.50]
Epoch 8: 56%|█████▋ | 18/32 [00:00<00:00, 315.45it/s, v_num=2, train_loss=4.010, RMSE=22.50]
Epoch 8: 56%|█████▋ | 18/32 [00:00<00:00, 314.30it/s, v_num=2, train_loss=3.920, RMSE=22.50]
Epoch 8: 59%|█████▉ | 19/32 [00:00<00:00, 315.70it/s, v_num=2, train_loss=3.920, RMSE=22.50]
Epoch 8: 59%|█████▉ | 19/32 [00:00<00:00, 314.59it/s, v_num=2, train_loss=3.740, RMSE=22.50]
Epoch 8: 62%|██████▎ | 20/32 [00:00<00:00, 316.01it/s, v_num=2, train_loss=3.740, RMSE=22.50]
Epoch 8: 62%|██████▎ | 20/32 [00:00<00:00, 314.96it/s, v_num=2, train_loss=3.810, RMSE=22.50]
Epoch 8: 66%|██████▌ | 21/32 [00:00<00:00, 316.23it/s, v_num=2, train_loss=3.810, RMSE=22.50]
Epoch 8: 66%|██████▌ | 21/32 [00:00<00:00, 315.21it/s, v_num=2, train_loss=4.200, RMSE=22.50]
Epoch 8: 69%|██████▉ | 22/32 [00:00<00:00, 316.43it/s, v_num=2, train_loss=4.200, RMSE=22.50]
Epoch 8: 69%|██████▉ | 22/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=3.840, RMSE=22.50]
Epoch 8: 72%|███████▏ | 23/32 [00:00<00:00, 316.88it/s, v_num=2, train_loss=3.840, RMSE=22.50]
Epoch 8: 72%|███████▏ | 23/32 [00:00<00:00, 315.97it/s, v_num=2, train_loss=3.420, RMSE=22.50]
Epoch 8: 75%|███████▌ | 24/32 [00:00<00:00, 317.18it/s, v_num=2, train_loss=3.420, RMSE=22.50]
Epoch 8: 75%|███████▌ | 24/32 [00:00<00:00, 316.30it/s, v_num=2, train_loss=3.890, RMSE=22.50]
Epoch 8: 78%|███████▊ | 25/32 [00:00<00:00, 317.42it/s, v_num=2, train_loss=3.890, RMSE=22.50]
Epoch 8: 78%|███████▊ | 25/32 [00:00<00:00, 316.53it/s, v_num=2, train_loss=4.100, RMSE=22.50]
Epoch 8: 81%|████████▏ | 26/32 [00:00<00:00, 317.48it/s, v_num=2, train_loss=4.100, RMSE=22.50]
Epoch 8: 81%|████████▏ | 26/32 [00:00<00:00, 316.65it/s, v_num=2, train_loss=3.900, RMSE=22.50]
Epoch 8: 84%|████████▍ | 27/32 [00:00<00:00, 317.63it/s, v_num=2, train_loss=3.900, RMSE=22.50]
Epoch 8: 84%|████████▍ | 27/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=4.140, RMSE=22.50]
Epoch 8: 88%|████████▊ | 28/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=4.140, RMSE=22.50]
Epoch 8: 88%|████████▊ | 28/32 [00:00<00:00, 317.23it/s, v_num=2, train_loss=3.980, RMSE=22.50]
Epoch 8: 91%|█████████ | 29/32 [00:00<00:00, 318.11it/s, v_num=2, train_loss=3.980, RMSE=22.50]
Epoch 8: 91%|█████████ | 29/32 [00:00<00:00, 317.38it/s, v_num=2, train_loss=3.950, RMSE=22.50]
Epoch 8: 94%|█████████▍| 30/32 [00:00<00:00, 318.24it/s, v_num=2, train_loss=3.950, RMSE=22.50]
Epoch 8: 94%|█████████▍| 30/32 [00:00<00:00, 317.49it/s, v_num=2, train_loss=3.800, RMSE=22.50]
Epoch 8: 97%|█████████▋| 31/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=3.800, RMSE=22.50]
Epoch 8: 97%|█████████▋| 31/32 [00:00<00:00, 317.73it/s, v_num=2, train_loss=3.840, RMSE=22.50]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 318.87it/s, v_num=2, train_loss=3.840, RMSE=22.50]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 318.20it/s, v_num=2, train_loss=3.500, RMSE=22.50]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 611.65it/s]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 260.52it/s, v_num=2, train_loss=3.500, RMSE=22.00]
Epoch 8: 100%|██████████| 32/32 [00:00<00:00, 259.36it/s, v_num=2, train_loss=3.500, RMSE=22.00]
Epoch 8: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.500, RMSE=22.00]
Epoch 9: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.500, RMSE=22.00]
Epoch 9: 3%|▎ | 1/32 [00:00<00:00, 273.30it/s, v_num=2, train_loss=3.500, RMSE=22.00]
Epoch 9: 3%|▎ | 1/32 [00:00<00:00, 257.15it/s, v_num=2, train_loss=4.150, RMSE=22.00]
Epoch 9: 6%|▋ | 2/32 [00:00<00:00, 290.56it/s, v_num=2, train_loss=4.150, RMSE=22.00]
Epoch 9: 6%|▋ | 2/32 [00:00<00:00, 281.57it/s, v_num=2, train_loss=4.370, RMSE=22.00]
Epoch 9: 9%|▉ | 3/32 [00:00<00:00, 300.48it/s, v_num=2, train_loss=4.370, RMSE=22.00]
Epoch 9: 9%|▉ | 3/32 [00:00<00:00, 294.23it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 12%|█▎ | 4/32 [00:00<00:00, 305.92it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 12%|█▎ | 4/32 [00:00<00:00, 301.01it/s, v_num=2, train_loss=3.970, RMSE=22.00]
Epoch 9: 16%|█▌ | 5/32 [00:00<00:00, 309.08it/s, v_num=2, train_loss=3.970, RMSE=22.00]
Epoch 9: 16%|█▌ | 5/32 [00:00<00:00, 305.00it/s, v_num=2, train_loss=4.140, RMSE=22.00]
Epoch 9: 19%|█▉ | 6/32 [00:00<00:00, 310.95it/s, v_num=2, train_loss=4.140, RMSE=22.00]
Epoch 9: 19%|█▉ | 6/32 [00:00<00:00, 307.54it/s, v_num=2, train_loss=3.620, RMSE=22.00]
Epoch 9: 22%|██▏ | 7/32 [00:00<00:00, 313.35it/s, v_num=2, train_loss=3.620, RMSE=22.00]
Epoch 9: 22%|██▏ | 7/32 [00:00<00:00, 309.93it/s, v_num=2, train_loss=3.880, RMSE=22.00]
Epoch 9: 25%|██▌ | 8/32 [00:00<00:00, 314.58it/s, v_num=2, train_loss=3.880, RMSE=22.00]
Epoch 9: 25%|██▌ | 8/32 [00:00<00:00, 311.96it/s, v_num=2, train_loss=3.870, RMSE=22.00]
Epoch 9: 28%|██▊ | 9/32 [00:00<00:00, 314.55it/s, v_num=2, train_loss=3.870, RMSE=22.00]
Epoch 9: 28%|██▊ | 9/32 [00:00<00:00, 312.24it/s, v_num=2, train_loss=4.060, RMSE=22.00]
Epoch 9: 31%|███▏ | 10/32 [00:00<00:00, 315.52it/s, v_num=2, train_loss=4.060, RMSE=22.00]
Epoch 9: 31%|███▏ | 10/32 [00:00<00:00, 313.42it/s, v_num=2, train_loss=3.610, RMSE=22.00]
Epoch 9: 34%|███▍ | 11/32 [00:00<00:00, 316.16it/s, v_num=2, train_loss=3.610, RMSE=22.00]
Epoch 9: 34%|███▍ | 11/32 [00:00<00:00, 314.21it/s, v_num=2, train_loss=3.870, RMSE=22.00]
Epoch 9: 38%|███▊ | 12/32 [00:00<00:00, 315.99it/s, v_num=2, train_loss=3.870, RMSE=22.00]
Epoch 9: 38%|███▊ | 12/32 [00:00<00:00, 314.22it/s, v_num=2, train_loss=3.650, RMSE=22.00]
Epoch 9: 41%|████ | 13/32 [00:00<00:00, 311.93it/s, v_num=2, train_loss=3.650, RMSE=22.00]
Epoch 9: 41%|████ | 13/32 [00:00<00:00, 310.27it/s, v_num=2, train_loss=4.220, RMSE=22.00]
Epoch 9: 44%|████▍ | 14/32 [00:00<00:00, 312.43it/s, v_num=2, train_loss=4.220, RMSE=22.00]
Epoch 9: 44%|████▍ | 14/32 [00:00<00:00, 310.95it/s, v_num=2, train_loss=3.610, RMSE=22.00]
Epoch 9: 47%|████▋ | 15/32 [00:00<00:00, 312.96it/s, v_num=2, train_loss=3.610, RMSE=22.00]
Epoch 9: 47%|████▋ | 15/32 [00:00<00:00, 311.58it/s, v_num=2, train_loss=3.920, RMSE=22.00]
Epoch 9: 50%|█████ | 16/32 [00:00<00:00, 313.53it/s, v_num=2, train_loss=3.920, RMSE=22.00]
Epoch 9: 50%|█████ | 16/32 [00:00<00:00, 312.22it/s, v_num=2, train_loss=3.540, RMSE=22.00]
Epoch 9: 53%|█████▎ | 17/32 [00:00<00:00, 314.27it/s, v_num=2, train_loss=3.540, RMSE=22.00]
Epoch 9: 53%|█████▎ | 17/32 [00:00<00:00, 313.04it/s, v_num=2, train_loss=4.130, RMSE=22.00]
Epoch 9: 56%|█████▋ | 18/32 [00:00<00:00, 314.66it/s, v_num=2, train_loss=4.130, RMSE=22.00]
Epoch 9: 56%|█████▋ | 18/32 [00:00<00:00, 313.48it/s, v_num=2, train_loss=3.460, RMSE=22.00]
Epoch 9: 59%|█████▉ | 19/32 [00:00<00:00, 314.96it/s, v_num=2, train_loss=3.460, RMSE=22.00]
Epoch 9: 59%|█████▉ | 19/32 [00:00<00:00, 313.84it/s, v_num=2, train_loss=4.120, RMSE=22.00]
Epoch 9: 62%|██████▎ | 20/32 [00:00<00:00, 315.22it/s, v_num=2, train_loss=4.120, RMSE=22.00]
Epoch 9: 62%|██████▎ | 20/32 [00:00<00:00, 314.15it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 66%|██████▌ | 21/32 [00:00<00:00, 315.53it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 66%|██████▌ | 21/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=4.240, RMSE=22.00]
Epoch 9: 69%|██████▉ | 22/32 [00:00<00:00, 316.17it/s, v_num=2, train_loss=4.240, RMSE=22.00]
Epoch 9: 69%|██████▉ | 22/32 [00:00<00:00, 315.21it/s, v_num=2, train_loss=4.420, RMSE=22.00]
Epoch 9: 72%|███████▏ | 23/32 [00:00<00:00, 316.35it/s, v_num=2, train_loss=4.420, RMSE=22.00]
Epoch 9: 72%|███████▏ | 23/32 [00:00<00:00, 315.40it/s, v_num=2, train_loss=3.640, RMSE=22.00]
Epoch 9: 75%|███████▌ | 24/32 [00:00<00:00, 316.38it/s, v_num=2, train_loss=3.640, RMSE=22.00]
Epoch 9: 75%|███████▌ | 24/32 [00:00<00:00, 315.49it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 78%|███████▊ | 25/32 [00:00<00:00, 316.41it/s, v_num=2, train_loss=3.700, RMSE=22.00]
Epoch 9: 78%|███████▊ | 25/32 [00:00<00:00, 315.51it/s, v_num=2, train_loss=3.770, RMSE=22.00]
Epoch 9: 81%|████████▏ | 26/32 [00:00<00:00, 316.69it/s, v_num=2, train_loss=3.770, RMSE=22.00]
Epoch 9: 81%|████████▏ | 26/32 [00:00<00:00, 315.87it/s, v_num=2, train_loss=3.900, RMSE=22.00]
Epoch 9: 84%|████████▍ | 27/32 [00:00<00:00, 316.95it/s, v_num=2, train_loss=3.900, RMSE=22.00]
Epoch 9: 84%|████████▍ | 27/32 [00:00<00:00, 316.16it/s, v_num=2, train_loss=4.110, RMSE=22.00]
Epoch 9: 88%|████████▊ | 28/32 [00:00<00:00, 317.14it/s, v_num=2, train_loss=4.110, RMSE=22.00]
Epoch 9: 88%|████████▊ | 28/32 [00:00<00:00, 316.37it/s, v_num=2, train_loss=3.590, RMSE=22.00]
Epoch 9: 91%|█████████ | 29/32 [00:00<00:00, 317.38it/s, v_num=2, train_loss=3.590, RMSE=22.00]
Epoch 9: 91%|█████████ | 29/32 [00:00<00:00, 316.64it/s, v_num=2, train_loss=3.890, RMSE=22.00]
Epoch 9: 94%|█████████▍| 30/32 [00:00<00:00, 317.57it/s, v_num=2, train_loss=3.890, RMSE=22.00]
Epoch 9: 94%|█████████▍| 30/32 [00:00<00:00, 316.77it/s, v_num=2, train_loss=3.590, RMSE=22.00]
Epoch 9: 97%|█████████▋| 31/32 [00:00<00:00, 317.79it/s, v_num=2, train_loss=3.590, RMSE=22.00]
Epoch 9: 97%|█████████▋| 31/32 [00:00<00:00, 317.10it/s, v_num=2, train_loss=3.890, RMSE=22.00]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.890, RMSE=22.00]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=4.140, RMSE=22.00]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 613.32it/s]
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Validation DataLoader 0: 50%|█████ | 5/10 [00:00<00:00, 607.24it/s]
Validation DataLoader 0: 60%|██████ | 6/10 [00:00<00:00, 608.19it/s]
Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 608.85it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 607.79it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 608.26it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 611.37it/s]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 260.02it/s, v_num=2, train_loss=4.140, RMSE=21.40]
Epoch 9: 100%|██████████| 32/32 [00:00<00:00, 258.90it/s, v_num=2, train_loss=4.140, RMSE=21.40]
Epoch 9: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.140, RMSE=21.40]
Epoch 10: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.140, RMSE=21.40]
Epoch 10: 3%|▎ | 1/32 [00:00<00:00, 285.73it/s, v_num=2, train_loss=4.140, RMSE=21.40]
Epoch 10: 3%|▎ | 1/32 [00:00<00:00, 266.42it/s, v_num=2, train_loss=3.800, RMSE=21.40]
Epoch 10: 6%|▋ | 2/32 [00:00<00:00, 299.75it/s, v_num=2, train_loss=3.800, RMSE=21.40]
Epoch 10: 6%|▋ | 2/32 [00:00<00:00, 290.55it/s, v_num=2, train_loss=3.680, RMSE=21.40]
Epoch 10: 9%|▉ | 3/32 [00:00<00:00, 306.56it/s, v_num=2, train_loss=3.680, RMSE=21.40]
Epoch 10: 9%|▉ | 3/32 [00:00<00:00, 300.01it/s, v_num=2, train_loss=3.710, RMSE=21.40]
Epoch 10: 12%|█▎ | 4/32 [00:00<00:00, 309.86it/s, v_num=2, train_loss=3.710, RMSE=21.40]
Epoch 10: 12%|█▎ | 4/32 [00:00<00:00, 304.81it/s, v_num=2, train_loss=3.690, RMSE=21.40]
Epoch 10: 16%|█▌ | 5/32 [00:00<00:00, 312.21it/s, v_num=2, train_loss=3.690, RMSE=21.40]
Epoch 10: 16%|█▌ | 5/32 [00:00<00:00, 307.95it/s, v_num=2, train_loss=3.640, RMSE=21.40]
Epoch 10: 19%|█▉ | 6/32 [00:00<00:00, 314.22it/s, v_num=2, train_loss=3.640, RMSE=21.40]
Epoch 10: 19%|█▉ | 6/32 [00:00<00:00, 310.75it/s, v_num=2, train_loss=3.760, RMSE=21.40]
Epoch 10: 22%|██▏ | 7/32 [00:00<00:00, 315.38it/s, v_num=2, train_loss=3.760, RMSE=21.40]
Epoch 10: 22%|██▏ | 7/32 [00:00<00:00, 312.37it/s, v_num=2, train_loss=4.180, RMSE=21.40]
Epoch 10: 25%|██▌ | 8/32 [00:00<00:00, 316.18it/s, v_num=2, train_loss=4.180, RMSE=21.40]
Epoch 10: 25%|██▌ | 8/32 [00:00<00:00, 313.52it/s, v_num=2, train_loss=3.750, RMSE=21.40]
Epoch 10: 28%|██▊ | 9/32 [00:00<00:00, 316.56it/s, v_num=2, train_loss=3.750, RMSE=21.40]
Epoch 10: 28%|██▊ | 9/32 [00:00<00:00, 314.21it/s, v_num=2, train_loss=3.820, RMSE=21.40]
Epoch 10: 31%|███▏ | 10/32 [00:00<00:00, 317.59it/s, v_num=2, train_loss=3.820, RMSE=21.40]
Epoch 10: 31%|███▏ | 10/32 [00:00<00:00, 315.07it/s, v_num=2, train_loss=3.700, RMSE=21.40]
Epoch 10: 34%|███▍ | 11/32 [00:00<00:00, 318.07it/s, v_num=2, train_loss=3.700, RMSE=21.40]
Epoch 10: 34%|███▍ | 11/32 [00:00<00:00, 316.09it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 38%|███▊ | 12/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 38%|███▊ | 12/32 [00:00<00:00, 316.26it/s, v_num=2, train_loss=4.420, RMSE=21.40]
Epoch 10: 41%|████ | 13/32 [00:00<00:00, 318.16it/s, v_num=2, train_loss=4.420, RMSE=21.40]
Epoch 10: 41%|████ | 13/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=3.990, RMSE=21.40]
Epoch 10: 44%|████▍ | 14/32 [00:00<00:00, 318.48it/s, v_num=2, train_loss=3.990, RMSE=21.40]
Epoch 10: 44%|████▍ | 14/32 [00:00<00:00, 316.95it/s, v_num=2, train_loss=3.970, RMSE=21.40]
Epoch 10: 47%|████▋ | 15/32 [00:00<00:00, 318.90it/s, v_num=2, train_loss=3.970, RMSE=21.40]
Epoch 10: 47%|████▋ | 15/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=3.530, RMSE=21.40]
Epoch 10: 50%|█████ | 16/32 [00:00<00:00, 319.23it/s, v_num=2, train_loss=3.530, RMSE=21.40]
Epoch 10: 50%|█████ | 16/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.860, RMSE=21.40]
Epoch 10: 53%|█████▎ | 17/32 [00:00<00:00, 319.54it/s, v_num=2, train_loss=3.860, RMSE=21.40]
Epoch 10: 53%|█████▎ | 17/32 [00:00<00:00, 318.26it/s, v_num=2, train_loss=3.840, RMSE=21.40]
Epoch 10: 56%|█████▋ | 18/32 [00:00<00:00, 319.66it/s, v_num=2, train_loss=3.840, RMSE=21.40]
Epoch 10: 56%|█████▋ | 18/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 59%|█████▉ | 19/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 59%|█████▉ | 19/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 62%|██████▎ | 20/32 [00:00<00:00, 320.03it/s, v_num=2, train_loss=3.950, RMSE=21.40]
Epoch 10: 62%|██████▎ | 20/32 [00:00<00:00, 318.93it/s, v_num=2, train_loss=4.110, RMSE=21.40]
Epoch 10: 66%|██████▌ | 21/32 [00:00<00:00, 320.18it/s, v_num=2, train_loss=4.110, RMSE=21.40]
Epoch 10: 66%|██████▌ | 21/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=3.870, RMSE=21.40]
Epoch 10: 69%|██████▉ | 22/32 [00:00<00:00, 320.20it/s, v_num=2, train_loss=3.870, RMSE=21.40]
Epoch 10: 69%|██████▉ | 22/32 [00:00<00:00, 319.21it/s, v_num=2, train_loss=3.810, RMSE=21.40]
Epoch 10: 72%|███████▏ | 23/32 [00:00<00:00, 320.29it/s, v_num=2, train_loss=3.810, RMSE=21.40]
Epoch 10: 72%|███████▏ | 23/32 [00:00<00:00, 319.35it/s, v_num=2, train_loss=3.930, RMSE=21.40]
Epoch 10: 75%|███████▌ | 24/32 [00:00<00:00, 320.60it/s, v_num=2, train_loss=3.930, RMSE=21.40]
Epoch 10: 75%|███████▌ | 24/32 [00:00<00:00, 319.69it/s, v_num=2, train_loss=3.910, RMSE=21.40]
Epoch 10: 78%|███████▊ | 25/32 [00:00<00:00, 320.52it/s, v_num=2, train_loss=3.910, RMSE=21.40]
Epoch 10: 78%|███████▊ | 25/32 [00:00<00:00, 319.65it/s, v_num=2, train_loss=3.520, RMSE=21.40]
Epoch 10: 81%|████████▏ | 26/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.520, RMSE=21.40]
Epoch 10: 81%|████████▏ | 26/32 [00:00<00:00, 319.80it/s, v_num=2, train_loss=3.860, RMSE=21.40]
Epoch 10: 84%|████████▍ | 27/32 [00:00<00:00, 320.71it/s, v_num=2, train_loss=3.860, RMSE=21.40]
Epoch 10: 84%|████████▍ | 27/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=3.620, RMSE=21.40]
Epoch 10: 88%|████████▊ | 28/32 [00:00<00:00, 320.70it/s, v_num=2, train_loss=3.620, RMSE=21.40]
Epoch 10: 88%|████████▊ | 28/32 [00:00<00:00, 319.92it/s, v_num=2, train_loss=3.880, RMSE=21.40]
Epoch 10: 91%|█████████ | 29/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.880, RMSE=21.40]
Epoch 10: 91%|█████████ | 29/32 [00:00<00:00, 320.07it/s, v_num=2, train_loss=3.560, RMSE=21.40]
Epoch 10: 94%|█████████▍| 30/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.560, RMSE=21.40]
Epoch 10: 94%|█████████▍| 30/32 [00:00<00:00, 320.11it/s, v_num=2, train_loss=3.980, RMSE=21.40]
Epoch 10: 97%|█████████▋| 31/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=3.980, RMSE=21.40]
Epoch 10: 97%|█████████▋| 31/32 [00:00<00:00, 318.44it/s, v_num=2, train_loss=4.130, RMSE=21.40]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 319.43it/s, v_num=2, train_loss=4.130, RMSE=21.40]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 318.77it/s, v_num=2, train_loss=3.980, RMSE=21.40]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 611.29it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 615.63it/s]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 261.46it/s, v_num=2, train_loss=3.980, RMSE=20.70]
Epoch 10: 100%|██████████| 32/32 [00:00<00:00, 260.31it/s, v_num=2, train_loss=3.980, RMSE=20.70]
Epoch 10: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.980, RMSE=20.70]
Epoch 11: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.980, RMSE=20.70]
Epoch 11: 3%|▎ | 1/32 [00:00<00:00, 292.14it/s, v_num=2, train_loss=3.980, RMSE=20.70]
Epoch 11: 3%|▎ | 1/32 [00:00<00:00, 274.78it/s, v_num=2, train_loss=3.750, RMSE=20.70]
Epoch 11: 6%|▋ | 2/32 [00:00<00:00, 304.10it/s, v_num=2, train_loss=3.750, RMSE=20.70]
Epoch 11: 6%|▋ | 2/32 [00:00<00:00, 294.55it/s, v_num=2, train_loss=4.040, RMSE=20.70]
Epoch 11: 9%|▉ | 3/32 [00:00<00:00, 309.37it/s, v_num=2, train_loss=4.040, RMSE=20.70]
Epoch 11: 9%|▉ | 3/32 [00:00<00:00, 302.74it/s, v_num=2, train_loss=3.930, RMSE=20.70]
Epoch 11: 12%|█▎ | 4/32 [00:00<00:00, 313.42it/s, v_num=2, train_loss=3.930, RMSE=20.70]
Epoch 11: 12%|█▎ | 4/32 [00:00<00:00, 308.08it/s, v_num=2, train_loss=3.850, RMSE=20.70]
Epoch 11: 16%|█▌ | 5/32 [00:00<00:00, 314.59it/s, v_num=2, train_loss=3.850, RMSE=20.70]
Epoch 11: 16%|█▌ | 5/32 [00:00<00:00, 310.51it/s, v_num=2, train_loss=3.770, RMSE=20.70]
Epoch 11: 19%|█▉ | 6/32 [00:00<00:00, 315.87it/s, v_num=2, train_loss=3.770, RMSE=20.70]
Epoch 11: 19%|█▉ | 6/32 [00:00<00:00, 312.40it/s, v_num=2, train_loss=3.850, RMSE=20.70]
Epoch 11: 22%|██▏ | 7/32 [00:00<00:00, 316.97it/s, v_num=2, train_loss=3.850, RMSE=20.70]
Epoch 11: 22%|██▏ | 7/32 [00:00<00:00, 313.88it/s, v_num=2, train_loss=4.040, RMSE=20.70]
Epoch 11: 25%|██▌ | 8/32 [00:00<00:00, 318.19it/s, v_num=2, train_loss=4.040, RMSE=20.70]
Epoch 11: 25%|██▌ | 8/32 [00:00<00:00, 315.55it/s, v_num=2, train_loss=3.500, RMSE=20.70]
Epoch 11: 28%|██▊ | 9/32 [00:00<00:00, 318.33it/s, v_num=2, train_loss=3.500, RMSE=20.70]
Epoch 11: 28%|██▊ | 9/32 [00:00<00:00, 315.98it/s, v_num=2, train_loss=3.550, RMSE=20.70]
Epoch 11: 31%|███▏ | 10/32 [00:00<00:00, 318.71it/s, v_num=2, train_loss=3.550, RMSE=20.70]
Epoch 11: 31%|███▏ | 10/32 [00:00<00:00, 316.58it/s, v_num=2, train_loss=4.090, RMSE=20.70]
Epoch 11: 34%|███▍ | 11/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=4.090, RMSE=20.70]
Epoch 11: 34%|███▍ | 11/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=3.580, RMSE=20.70]
Epoch 11: 38%|███▊ | 12/32 [00:00<00:00, 319.53it/s, v_num=2, train_loss=3.580, RMSE=20.70]
Epoch 11: 38%|███▊ | 12/32 [00:00<00:00, 317.69it/s, v_num=2, train_loss=3.530, RMSE=20.70]
Epoch 11: 41%|████ | 13/32 [00:00<00:00, 320.09it/s, v_num=2, train_loss=3.530, RMSE=20.70]
Epoch 11: 41%|████ | 13/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=3.650, RMSE=20.70]
Epoch 11: 44%|████▍ | 14/32 [00:00<00:00, 320.28it/s, v_num=2, train_loss=3.650, RMSE=20.70]
Epoch 11: 44%|████▍ | 14/32 [00:00<00:00, 318.72it/s, v_num=2, train_loss=4.240, RMSE=20.70]
Epoch 11: 47%|████▋ | 15/32 [00:00<00:00, 320.37it/s, v_num=2, train_loss=4.240, RMSE=20.70]
Epoch 11: 47%|████▋ | 15/32 [00:00<00:00, 318.94it/s, v_num=2, train_loss=3.720, RMSE=20.70]
Epoch 11: 50%|█████ | 16/32 [00:00<00:00, 320.55it/s, v_num=2, train_loss=3.720, RMSE=20.70]
Epoch 11: 50%|█████ | 16/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.900, RMSE=20.70]
Epoch 11: 53%|█████▎ | 17/32 [00:00<00:00, 320.84it/s, v_num=2, train_loss=3.900, RMSE=20.70]
Epoch 11: 53%|█████▎ | 17/32 [00:00<00:00, 319.56it/s, v_num=2, train_loss=3.700, RMSE=20.70]
Epoch 11: 56%|█████▋ | 18/32 [00:00<00:00, 320.97it/s, v_num=2, train_loss=3.700, RMSE=20.70]
Epoch 11: 56%|█████▋ | 18/32 [00:00<00:00, 319.77it/s, v_num=2, train_loss=3.870, RMSE=20.70]
Epoch 11: 59%|█████▉ | 19/32 [00:00<00:00, 321.09it/s, v_num=2, train_loss=3.870, RMSE=20.70]
Epoch 11: 59%|█████▉ | 19/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=3.200, RMSE=20.70]
Epoch 11: 62%|██████▎ | 20/32 [00:00<00:00, 321.13it/s, v_num=2, train_loss=3.200, RMSE=20.70]
Epoch 11: 62%|██████▎ | 20/32 [00:00<00:00, 320.03it/s, v_num=2, train_loss=4.330, RMSE=20.70]
Epoch 11: 66%|██████▌ | 21/32 [00:00<00:00, 321.53it/s, v_num=2, train_loss=4.330, RMSE=20.70]
Epoch 11: 66%|██████▌ | 21/32 [00:00<00:00, 320.50it/s, v_num=2, train_loss=3.890, RMSE=20.70]
Epoch 11: 69%|██████▉ | 22/32 [00:00<00:00, 321.63it/s, v_num=2, train_loss=3.890, RMSE=20.70]
Epoch 11: 69%|██████▉ | 22/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=3.610, RMSE=20.70]
Epoch 11: 72%|███████▏ | 23/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.610, RMSE=20.70]
Epoch 11: 72%|███████▏ | 23/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=4.060, RMSE=20.70]
Epoch 11: 75%|███████▌ | 24/32 [00:00<00:00, 321.30it/s, v_num=2, train_loss=4.060, RMSE=20.70]
Epoch 11: 75%|███████▌ | 24/32 [00:00<00:00, 320.40it/s, v_num=2, train_loss=3.960, RMSE=20.70]
Epoch 11: 78%|███████▊ | 25/32 [00:00<00:00, 321.37it/s, v_num=2, train_loss=3.960, RMSE=20.70]
Epoch 11: 78%|███████▊ | 25/32 [00:00<00:00, 320.50it/s, v_num=2, train_loss=4.070, RMSE=20.70]
Epoch 11: 81%|████████▏ | 26/32 [00:00<00:00, 321.62it/s, v_num=2, train_loss=4.070, RMSE=20.70]
Epoch 11: 81%|████████▏ | 26/32 [00:00<00:00, 320.78it/s, v_num=2, train_loss=4.100, RMSE=20.70]
Epoch 11: 84%|████████▍ | 27/32 [00:00<00:00, 321.78it/s, v_num=2, train_loss=4.100, RMSE=20.70]
Epoch 11: 84%|████████▍ | 27/32 [00:00<00:00, 320.98it/s, v_num=2, train_loss=3.450, RMSE=20.70]
Epoch 11: 88%|████████▊ | 28/32 [00:00<00:00, 321.94it/s, v_num=2, train_loss=3.450, RMSE=20.70]
Epoch 11: 88%|████████▊ | 28/32 [00:00<00:00, 321.17it/s, v_num=2, train_loss=3.930, RMSE=20.70]
Epoch 11: 91%|█████████ | 29/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=3.930, RMSE=20.70]
Epoch 11: 91%|█████████ | 29/32 [00:00<00:00, 319.57it/s, v_num=2, train_loss=3.500, RMSE=20.70]
Epoch 11: 94%|█████████▍| 30/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.500, RMSE=20.70]
Epoch 11: 94%|█████████▍| 30/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=3.560, RMSE=20.70]
Epoch 11: 97%|█████████▋| 31/32 [00:00<00:00, 319.45it/s, v_num=2, train_loss=3.560, RMSE=20.70]
Epoch 11: 97%|█████████▋| 31/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=4.320, RMSE=20.70]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 319.64it/s, v_num=2, train_loss=4.320, RMSE=20.70]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 318.96it/s, v_num=2, train_loss=2.920, RMSE=20.70]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 618.90it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 616.42it/s]
Validation DataLoader 0: 50%|█████ | 5/10 [00:00<00:00, 599.82it/s]
Validation DataLoader 0: 60%|██████ | 6/10 [00:00<00:00, 600.92it/s]
Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 601.20it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 601.71it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 603.83it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 605.54it/s]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 260.67it/s, v_num=2, train_loss=2.920, RMSE=20.00]
Epoch 11: 100%|██████████| 32/32 [00:00<00:00, 259.49it/s, v_num=2, train_loss=2.920, RMSE=20.00]
Epoch 11: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.920, RMSE=20.00]
Epoch 12: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.920, RMSE=20.00]
Epoch 12: 3%|▎ | 1/32 [00:00<00:00, 281.91it/s, v_num=2, train_loss=2.920, RMSE=20.00]
Epoch 12: 3%|▎ | 1/32 [00:00<00:00, 265.29it/s, v_num=2, train_loss=3.720, RMSE=20.00]
Epoch 12: 6%|▋ | 2/32 [00:00<00:00, 299.74it/s, v_num=2, train_loss=3.720, RMSE=20.00]
Epoch 12: 6%|▋ | 2/32 [00:00<00:00, 290.38it/s, v_num=2, train_loss=3.700, RMSE=20.00]
Epoch 12: 9%|▉ | 3/32 [00:00<00:00, 306.89it/s, v_num=2, train_loss=3.700, RMSE=20.00]
Epoch 12: 9%|▉ | 3/32 [00:00<00:00, 300.39it/s, v_num=2, train_loss=3.870, RMSE=20.00]
Epoch 12: 12%|█▎ | 4/32 [00:00<00:00, 309.50it/s, v_num=2, train_loss=3.870, RMSE=20.00]
Epoch 12: 12%|█▎ | 4/32 [00:00<00:00, 304.52it/s, v_num=2, train_loss=3.940, RMSE=20.00]
Epoch 12: 16%|█▌ | 5/32 [00:00<00:00, 312.09it/s, v_num=2, train_loss=3.940, RMSE=20.00]
Epoch 12: 16%|█▌ | 5/32 [00:00<00:00, 308.07it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 19%|█▉ | 6/32 [00:00<00:00, 314.29it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 19%|█▉ | 6/32 [00:00<00:00, 310.82it/s, v_num=2, train_loss=3.820, RMSE=20.00]
Epoch 12: 22%|██▏ | 7/32 [00:00<00:00, 316.29it/s, v_num=2, train_loss=3.820, RMSE=20.00]
Epoch 12: 22%|██▏ | 7/32 [00:00<00:00, 313.34it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 25%|██▌ | 8/32 [00:00<00:00, 317.50it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 25%|██▌ | 8/32 [00:00<00:00, 314.84it/s, v_num=2, train_loss=3.560, RMSE=20.00]
Epoch 12: 28%|██▊ | 9/32 [00:00<00:00, 317.66it/s, v_num=2, train_loss=3.560, RMSE=20.00]
Epoch 12: 28%|██▊ | 9/32 [00:00<00:00, 315.28it/s, v_num=2, train_loss=4.040, RMSE=20.00]
Epoch 12: 31%|███▏ | 10/32 [00:00<00:00, 317.96it/s, v_num=2, train_loss=4.040, RMSE=20.00]
Epoch 12: 31%|███▏ | 10/32 [00:00<00:00, 315.84it/s, v_num=2, train_loss=3.910, RMSE=20.00]
Epoch 12: 34%|███▍ | 11/32 [00:00<00:00, 318.84it/s, v_num=2, train_loss=3.910, RMSE=20.00]
Epoch 12: 34%|███▍ | 11/32 [00:00<00:00, 316.92it/s, v_num=2, train_loss=4.090, RMSE=20.00]
Epoch 12: 38%|███▊ | 12/32 [00:00<00:00, 319.40it/s, v_num=2, train_loss=4.090, RMSE=20.00]
Epoch 12: 38%|███▊ | 12/32 [00:00<00:00, 317.59it/s, v_num=2, train_loss=3.840, RMSE=20.00]
Epoch 12: 41%|████ | 13/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=3.840, RMSE=20.00]
Epoch 12: 41%|████ | 13/32 [00:00<00:00, 318.25it/s, v_num=2, train_loss=3.610, RMSE=20.00]
Epoch 12: 44%|████▍ | 14/32 [00:00<00:00, 320.12it/s, v_num=2, train_loss=3.610, RMSE=20.00]
Epoch 12: 44%|████▍ | 14/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=3.720, RMSE=20.00]
Epoch 12: 47%|████▋ | 15/32 [00:00<00:00, 320.80it/s, v_num=2, train_loss=3.720, RMSE=20.00]
Epoch 12: 47%|████▋ | 15/32 [00:00<00:00, 319.37it/s, v_num=2, train_loss=3.830, RMSE=20.00]
Epoch 12: 50%|█████ | 16/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.830, RMSE=20.00]
Epoch 12: 50%|█████ | 16/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=3.820, RMSE=20.00]
Epoch 12: 53%|█████▎ | 17/32 [00:00<00:00, 318.14it/s, v_num=2, train_loss=3.820, RMSE=20.00]
Epoch 12: 53%|█████▎ | 17/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=3.680, RMSE=20.00]
Epoch 12: 56%|█████▋ | 18/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.680, RMSE=20.00]
Epoch 12: 56%|█████▋ | 18/32 [00:00<00:00, 317.01it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 59%|█████▉ | 19/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 59%|█████▉ | 19/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=3.680, RMSE=20.00]
Epoch 12: 62%|██████▎ | 20/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.680, RMSE=20.00]
Epoch 12: 62%|██████▎ | 20/32 [00:00<00:00, 317.33it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 66%|██████▌ | 21/32 [00:00<00:00, 318.52it/s, v_num=2, train_loss=4.080, RMSE=20.00]
Epoch 12: 66%|██████▌ | 21/32 [00:00<00:00, 317.49it/s, v_num=2, train_loss=3.830, RMSE=20.00]
Epoch 12: 69%|██████▉ | 22/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=3.830, RMSE=20.00]
Epoch 12: 69%|██████▉ | 22/32 [00:00<00:00, 317.78it/s, v_num=2, train_loss=4.020, RMSE=20.00]
Epoch 12: 72%|███████▏ | 23/32 [00:00<00:00, 318.98it/s, v_num=2, train_loss=4.020, RMSE=20.00]
Epoch 12: 72%|███████▏ | 23/32 [00:00<00:00, 318.03it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 75%|███████▌ | 24/32 [00:00<00:00, 318.90it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 75%|███████▌ | 24/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=3.250, RMSE=20.00]
Epoch 12: 78%|███████▊ | 25/32 [00:00<00:00, 319.19it/s, v_num=2, train_loss=3.250, RMSE=20.00]
Epoch 12: 78%|███████▊ | 25/32 [00:00<00:00, 318.34it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 81%|████████▏ | 26/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.620, RMSE=20.00]
Epoch 12: 81%|████████▏ | 26/32 [00:00<00:00, 318.52it/s, v_num=2, train_loss=3.630, RMSE=20.00]
Epoch 12: 84%|████████▍ | 27/32 [00:00<00:00, 319.51it/s, v_num=2, train_loss=3.630, RMSE=20.00]
Epoch 12: 84%|████████▍ | 27/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=3.780, RMSE=20.00]
Epoch 12: 88%|████████▊ | 28/32 [00:00<00:00, 319.72it/s, v_num=2, train_loss=3.780, RMSE=20.00]
Epoch 12: 88%|████████▊ | 28/32 [00:00<00:00, 318.96it/s, v_num=2, train_loss=3.900, RMSE=20.00]
Epoch 12: 91%|█████████ | 29/32 [00:00<00:00, 319.93it/s, v_num=2, train_loss=3.900, RMSE=20.00]
Epoch 12: 91%|█████████ | 29/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.580, RMSE=20.00]
Epoch 12: 94%|█████████▍| 30/32 [00:00<00:00, 320.08it/s, v_num=2, train_loss=3.580, RMSE=20.00]
Epoch 12: 94%|█████████▍| 30/32 [00:00<00:00, 319.35it/s, v_num=2, train_loss=3.580, RMSE=20.00]
Epoch 12: 97%|█████████▋| 31/32 [00:00<00:00, 320.18it/s, v_num=2, train_loss=3.580, RMSE=20.00]
Epoch 12: 97%|█████████▋| 31/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=3.730, RMSE=20.00]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.730, RMSE=20.00]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 319.65it/s, v_num=2, train_loss=4.130, RMSE=20.00]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 624.29it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 613.71it/s]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 612.28it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 616.43it/s]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 261.92it/s, v_num=2, train_loss=4.130, RMSE=19.30]
Epoch 12: 100%|██████████| 32/32 [00:00<00:00, 260.80it/s, v_num=2, train_loss=4.130, RMSE=19.30]
Epoch 12: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.130, RMSE=19.30]
Epoch 13: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.130, RMSE=19.30]
Epoch 13: 3%|▎ | 1/32 [00:00<00:00, 278.14it/s, v_num=2, train_loss=4.130, RMSE=19.30]
Epoch 13: 3%|▎ | 1/32 [00:00<00:00, 262.41it/s, v_num=2, train_loss=3.960, RMSE=19.30]
Epoch 13: 6%|▋ | 2/32 [00:00<00:00, 295.96it/s, v_num=2, train_loss=3.960, RMSE=19.30]
Epoch 13: 6%|▋ | 2/32 [00:00<00:00, 287.05it/s, v_num=2, train_loss=3.810, RMSE=19.30]
Epoch 13: 9%|▉ | 3/32 [00:00<00:00, 303.35it/s, v_num=2, train_loss=3.810, RMSE=19.30]
Epoch 13: 9%|▉ | 3/32 [00:00<00:00, 296.71it/s, v_num=2, train_loss=3.510, RMSE=19.30]
Epoch 13: 12%|█▎ | 4/32 [00:00<00:00, 307.63it/s, v_num=2, train_loss=3.510, RMSE=19.30]
Epoch 13: 12%|█▎ | 4/32 [00:00<00:00, 302.05it/s, v_num=2, train_loss=3.560, RMSE=19.30]
Epoch 13: 16%|█▌ | 5/32 [00:00<00:00, 311.03it/s, v_num=2, train_loss=3.560, RMSE=19.30]
Epoch 13: 16%|█▌ | 5/32 [00:00<00:00, 307.05it/s, v_num=2, train_loss=3.570, RMSE=19.30]
Epoch 13: 19%|█▉ | 6/32 [00:00<00:00, 312.03it/s, v_num=2, train_loss=3.570, RMSE=19.30]
Epoch 13: 19%|█▉ | 6/32 [00:00<00:00, 308.66it/s, v_num=2, train_loss=4.170, RMSE=19.30]
Epoch 13: 22%|██▏ | 7/32 [00:00<00:00, 311.52it/s, v_num=2, train_loss=4.170, RMSE=19.30]
Epoch 13: 22%|██▏ | 7/32 [00:00<00:00, 308.63it/s, v_num=2, train_loss=3.660, RMSE=19.30]
Epoch 13: 25%|██▌ | 8/32 [00:00<00:00, 312.73it/s, v_num=2, train_loss=3.660, RMSE=19.30]
Epoch 13: 25%|██▌ | 8/32 [00:00<00:00, 309.95it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 28%|██▊ | 9/32 [00:00<00:00, 313.81it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 28%|██▊ | 9/32 [00:00<00:00, 311.48it/s, v_num=2, train_loss=3.440, RMSE=19.30]
Epoch 13: 31%|███▏ | 10/32 [00:00<00:00, 313.67it/s, v_num=2, train_loss=3.440, RMSE=19.30]
Epoch 13: 31%|███▏ | 10/32 [00:00<00:00, 311.63it/s, v_num=2, train_loss=3.620, RMSE=19.30]
Epoch 13: 34%|███▍ | 11/32 [00:00<00:00, 314.68it/s, v_num=2, train_loss=3.620, RMSE=19.30]
Epoch 13: 34%|███▍ | 11/32 [00:00<00:00, 312.80it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 38%|███▊ | 12/32 [00:00<00:00, 315.38it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 38%|███▊ | 12/32 [00:00<00:00, 313.66it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 41%|████ | 13/32 [00:00<00:00, 316.47it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 41%|████ | 13/32 [00:00<00:00, 314.45it/s, v_num=2, train_loss=3.840, RMSE=19.30]
Epoch 13: 44%|████▍ | 14/32 [00:00<00:00, 316.89it/s, v_num=2, train_loss=3.840, RMSE=19.30]
Epoch 13: 44%|████▍ | 14/32 [00:00<00:00, 315.41it/s, v_num=2, train_loss=3.430, RMSE=19.30]
Epoch 13: 47%|████▋ | 15/32 [00:00<00:00, 317.37it/s, v_num=2, train_loss=3.430, RMSE=19.30]
Epoch 13: 47%|████▋ | 15/32 [00:00<00:00, 315.99it/s, v_num=2, train_loss=3.650, RMSE=19.30]
Epoch 13: 50%|█████ | 16/32 [00:00<00:00, 317.77it/s, v_num=2, train_loss=3.650, RMSE=19.30]
Epoch 13: 50%|█████ | 16/32 [00:00<00:00, 316.45it/s, v_num=2, train_loss=3.820, RMSE=19.30]
Epoch 13: 53%|█████▎ | 17/32 [00:00<00:00, 318.07it/s, v_num=2, train_loss=3.820, RMSE=19.30]
Epoch 13: 53%|█████▎ | 17/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=3.720, RMSE=19.30]
Epoch 13: 56%|█████▋ | 18/32 [00:00<00:00, 318.78it/s, v_num=2, train_loss=3.720, RMSE=19.30]
Epoch 13: 56%|█████▋ | 18/32 [00:00<00:00, 317.61it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 59%|█████▉ | 19/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 59%|█████▉ | 19/32 [00:00<00:00, 317.78it/s, v_num=2, train_loss=3.350, RMSE=19.30]
Epoch 13: 62%|██████▎ | 20/32 [00:00<00:00, 319.25it/s, v_num=2, train_loss=3.350, RMSE=19.30]
Epoch 13: 62%|██████▎ | 20/32 [00:00<00:00, 318.17it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 66%|██████▌ | 21/32 [00:00<00:00, 319.39it/s, v_num=2, train_loss=3.980, RMSE=19.30]
Epoch 13: 66%|██████▌ | 21/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.880, RMSE=19.30]
Epoch 13: 69%|██████▉ | 22/32 [00:00<00:00, 319.78it/s, v_num=2, train_loss=3.880, RMSE=19.30]
Epoch 13: 69%|██████▉ | 22/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=3.910, RMSE=19.30]
Epoch 13: 72%|███████▏ | 23/32 [00:00<00:00, 319.87it/s, v_num=2, train_loss=3.910, RMSE=19.30]
Epoch 13: 72%|███████▏ | 23/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=4.080, RMSE=19.30]
Epoch 13: 75%|███████▌ | 24/32 [00:00<00:00, 319.97it/s, v_num=2, train_loss=4.080, RMSE=19.30]
Epoch 13: 75%|███████▌ | 24/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.440, RMSE=19.30]
Epoch 13: 78%|███████▊ | 25/32 [00:00<00:00, 320.17it/s, v_num=2, train_loss=3.440, RMSE=19.30]
Epoch 13: 78%|███████▊ | 25/32 [00:00<00:00, 319.33it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 81%|████████▏ | 26/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=3.850, RMSE=19.30]
Epoch 13: 81%|████████▏ | 26/32 [00:00<00:00, 319.60it/s, v_num=2, train_loss=3.680, RMSE=19.30]
Epoch 13: 84%|████████▍ | 27/32 [00:00<00:00, 320.77it/s, v_num=2, train_loss=3.680, RMSE=19.30]
Epoch 13: 84%|████████▍ | 27/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.790, RMSE=19.30]
Epoch 13: 88%|████████▊ | 28/32 [00:00<00:00, 320.95it/s, v_num=2, train_loss=3.790, RMSE=19.30]
Epoch 13: 88%|████████▊ | 28/32 [00:00<00:00, 320.19it/s, v_num=2, train_loss=3.670, RMSE=19.30]
Epoch 13: 91%|█████████ | 29/32 [00:00<00:00, 320.91it/s, v_num=2, train_loss=3.670, RMSE=19.30]
Epoch 13: 91%|█████████ | 29/32 [00:00<00:00, 320.18it/s, v_num=2, train_loss=3.540, RMSE=19.30]
Epoch 13: 94%|█████████▍| 30/32 [00:00<00:00, 321.07it/s, v_num=2, train_loss=3.540, RMSE=19.30]
Epoch 13: 94%|█████████▍| 30/32 [00:00<00:00, 320.35it/s, v_num=2, train_loss=3.890, RMSE=19.30]
Epoch 13: 97%|█████████▋| 31/32 [00:00<00:00, 321.29it/s, v_num=2, train_loss=3.890, RMSE=19.30]
Epoch 13: 97%|█████████▋| 31/32 [00:00<00:00, 320.60it/s, v_num=2, train_loss=3.790, RMSE=19.30]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 321.55it/s, v_num=2, train_loss=3.790, RMSE=19.30]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 320.89it/s, v_num=2, train_loss=4.050, RMSE=19.30]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 608.22it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 611.03it/s]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 262.44it/s, v_num=2, train_loss=4.050, RMSE=18.50]
Epoch 13: 100%|██████████| 32/32 [00:00<00:00, 261.31it/s, v_num=2, train_loss=4.050, RMSE=18.50]
Epoch 13: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.050, RMSE=18.50]
Epoch 14: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.050, RMSE=18.50]
Epoch 14: 3%|▎ | 1/32 [00:00<00:00, 286.22it/s, v_num=2, train_loss=4.050, RMSE=18.50]
Epoch 14: 3%|▎ | 1/32 [00:00<00:00, 269.73it/s, v_num=2, train_loss=3.490, RMSE=18.50]
Epoch 14: 6%|▋ | 2/32 [00:00<00:00, 303.30it/s, v_num=2, train_loss=3.490, RMSE=18.50]
Epoch 14: 6%|▋ | 2/32 [00:00<00:00, 293.91it/s, v_num=2, train_loss=3.850, RMSE=18.50]
Epoch 14: 9%|▉ | 3/32 [00:00<00:00, 296.38it/s, v_num=2, train_loss=3.850, RMSE=18.50]
Epoch 14: 9%|▉ | 3/32 [00:00<00:00, 289.88it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 14: 12%|█▎ | 4/32 [00:00<00:00, 301.80it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 14: 12%|█▎ | 4/32 [00:00<00:00, 297.09it/s, v_num=2, train_loss=3.520, RMSE=18.50]
Epoch 14: 16%|█▌ | 5/32 [00:00<00:00, 305.60it/s, v_num=2, train_loss=3.520, RMSE=18.50]
Epoch 14: 16%|█▌ | 5/32 [00:00<00:00, 301.76it/s, v_num=2, train_loss=3.840, RMSE=18.50]
Epoch 14: 19%|█▉ | 6/32 [00:00<00:00, 307.93it/s, v_num=2, train_loss=3.840, RMSE=18.50]
Epoch 14: 19%|█▉ | 6/32 [00:00<00:00, 304.65it/s, v_num=2, train_loss=3.540, RMSE=18.50]
Epoch 14: 22%|██▏ | 7/32 [00:00<00:00, 310.38it/s, v_num=2, train_loss=3.540, RMSE=18.50]
Epoch 14: 22%|██▏ | 7/32 [00:00<00:00, 307.52it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 14: 25%|██▌ | 8/32 [00:00<00:00, 311.75it/s, v_num=2, train_loss=3.700, RMSE=18.50]
Epoch 14: 25%|██▌ | 8/32 [00:00<00:00, 309.22it/s, v_num=2, train_loss=3.810, RMSE=18.50]
Epoch 14: 28%|██▊ | 9/32 [00:00<00:00, 312.57it/s, v_num=2, train_loss=3.810, RMSE=18.50]
Epoch 14: 28%|██▊ | 9/32 [00:00<00:00, 310.30it/s, v_num=2, train_loss=3.650, RMSE=18.50]
Epoch 14: 31%|███▏ | 10/32 [00:00<00:00, 313.45it/s, v_num=2, train_loss=3.650, RMSE=18.50]
Epoch 14: 31%|███▏ | 10/32 [00:00<00:00, 311.33it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 14: 34%|███▍ | 11/32 [00:00<00:00, 314.38it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 14: 34%|███▍ | 11/32 [00:00<00:00, 312.51it/s, v_num=2, train_loss=3.630, RMSE=18.50]
Epoch 14: 38%|███▊ | 12/32 [00:00<00:00, 315.11it/s, v_num=2, train_loss=3.630, RMSE=18.50]
Epoch 14: 38%|███▊ | 12/32 [00:00<00:00, 313.39it/s, v_num=2, train_loss=3.720, RMSE=18.50]
Epoch 14: 41%|████ | 13/32 [00:00<00:00, 315.72it/s, v_num=2, train_loss=3.720, RMSE=18.50]
Epoch 14: 41%|████ | 13/32 [00:00<00:00, 314.11it/s, v_num=2, train_loss=3.660, RMSE=18.50]
Epoch 14: 44%|████▍ | 14/32 [00:00<00:00, 315.88it/s, v_num=2, train_loss=3.660, RMSE=18.50]
Epoch 14: 44%|████▍ | 14/32 [00:00<00:00, 314.41it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 47%|████▋ | 15/32 [00:00<00:00, 316.32it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 47%|████▋ | 15/32 [00:00<00:00, 314.93it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 50%|█████ | 16/32 [00:00<00:00, 316.94it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 50%|█████ | 16/32 [00:00<00:00, 315.63it/s, v_num=2, train_loss=3.860, RMSE=18.50]
Epoch 14: 53%|█████▎ | 17/32 [00:00<00:00, 317.38it/s, v_num=2, train_loss=3.860, RMSE=18.50]
Epoch 14: 53%|█████▎ | 17/32 [00:00<00:00, 316.13it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 56%|█████▋ | 18/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.730, RMSE=18.50]
Epoch 14: 56%|█████▋ | 18/32 [00:00<00:00, 316.50it/s, v_num=2, train_loss=3.280, RMSE=18.50]
Epoch 14: 59%|█████▉ | 19/32 [00:00<00:00, 317.82it/s, v_num=2, train_loss=3.280, RMSE=18.50]
Epoch 14: 59%|█████▉ | 19/32 [00:00<00:00, 316.71it/s, v_num=2, train_loss=3.580, RMSE=18.50]
Epoch 14: 62%|██████▎ | 20/32 [00:00<00:00, 318.16it/s, v_num=2, train_loss=3.580, RMSE=18.50]
Epoch 14: 62%|██████▎ | 20/32 [00:00<00:00, 317.10it/s, v_num=2, train_loss=3.650, RMSE=18.50]
Epoch 14: 66%|██████▌ | 21/32 [00:00<00:00, 318.50it/s, v_num=2, train_loss=3.650, RMSE=18.50]
Epoch 14: 66%|██████▌ | 21/32 [00:00<00:00, 317.49it/s, v_num=2, train_loss=3.770, RMSE=18.50]
Epoch 14: 69%|██████▉ | 22/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.770, RMSE=18.50]
Epoch 14: 69%|██████▉ | 22/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=3.800, RMSE=18.50]
Epoch 14: 72%|███████▏ | 23/32 [00:00<00:00, 318.39it/s, v_num=2, train_loss=3.800, RMSE=18.50]
Epoch 14: 72%|███████▏ | 23/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=3.660, RMSE=18.50]
Epoch 14: 75%|███████▌ | 24/32 [00:00<00:00, 318.34it/s, v_num=2, train_loss=3.660, RMSE=18.50]
Epoch 14: 75%|███████▌ | 24/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.520, RMSE=18.50]
Epoch 14: 78%|███████▊ | 25/32 [00:00<00:00, 318.70it/s, v_num=2, train_loss=3.520, RMSE=18.50]
Epoch 14: 78%|███████▊ | 25/32 [00:00<00:00, 317.85it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 14: 81%|████████▏ | 26/32 [00:00<00:00, 318.66it/s, v_num=2, train_loss=3.900, RMSE=18.50]
Epoch 14: 81%|████████▏ | 26/32 [00:00<00:00, 317.84it/s, v_num=2, train_loss=3.600, RMSE=18.50]
Epoch 14: 84%|████████▍ | 27/32 [00:00<00:00, 318.70it/s, v_num=2, train_loss=3.600, RMSE=18.50]
Epoch 14: 84%|████████▍ | 27/32 [00:00<00:00, 317.90it/s, v_num=2, train_loss=3.830, RMSE=18.50]
Epoch 14: 88%|████████▊ | 28/32 [00:00<00:00, 318.80it/s, v_num=2, train_loss=3.830, RMSE=18.50]
Epoch 14: 88%|████████▊ | 28/32 [00:00<00:00, 318.03it/s, v_num=2, train_loss=3.640, RMSE=18.50]
Epoch 14: 91%|█████████ | 29/32 [00:00<00:00, 318.99it/s, v_num=2, train_loss=3.640, RMSE=18.50]
Epoch 14: 91%|█████████ | 29/32 [00:00<00:00, 318.26it/s, v_num=2, train_loss=4.380, RMSE=18.50]
Epoch 14: 94%|█████████▍| 30/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=4.380, RMSE=18.50]
Epoch 14: 94%|█████████▍| 30/32 [00:00<00:00, 318.29it/s, v_num=2, train_loss=3.670, RMSE=18.50]
Epoch 14: 97%|█████████▋| 31/32 [00:00<00:00, 319.12it/s, v_num=2, train_loss=3.670, RMSE=18.50]
Epoch 14: 97%|█████████▋| 31/32 [00:00<00:00, 318.44it/s, v_num=2, train_loss=3.880, RMSE=18.50]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 319.37it/s, v_num=2, train_loss=3.880, RMSE=18.50]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 318.71it/s, v_num=2, train_loss=4.120, RMSE=18.50]
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Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 260.83it/s, v_num=2, train_loss=4.120, RMSE=17.80]
Epoch 14: 100%|██████████| 32/32 [00:00<00:00, 259.60it/s, v_num=2, train_loss=4.120, RMSE=17.80]
Epoch 14: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.120, RMSE=17.80]
Epoch 15: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=4.120, RMSE=17.80]
Epoch 15: 3%|▎ | 1/32 [00:00<00:00, 282.29it/s, v_num=2, train_loss=4.120, RMSE=17.80]
Epoch 15: 3%|▎ | 1/32 [00:00<00:00, 266.39it/s, v_num=2, train_loss=3.850, RMSE=17.80]
Epoch 15: 6%|▋ | 2/32 [00:00<00:00, 297.66it/s, v_num=2, train_loss=3.850, RMSE=17.80]
Epoch 15: 6%|▋ | 2/32 [00:00<00:00, 288.63it/s, v_num=2, train_loss=3.290, RMSE=17.80]
Epoch 15: 9%|▉ | 3/32 [00:00<00:00, 304.68it/s, v_num=2, train_loss=3.290, RMSE=17.80]
Epoch 15: 9%|▉ | 3/32 [00:00<00:00, 298.06it/s, v_num=2, train_loss=3.480, RMSE=17.80]
Epoch 15: 12%|█▎ | 4/32 [00:00<00:00, 308.72it/s, v_num=2, train_loss=3.480, RMSE=17.80]
Epoch 15: 12%|█▎ | 4/32 [00:00<00:00, 303.70it/s, v_num=2, train_loss=3.850, RMSE=17.80]
Epoch 15: 16%|█▌ | 5/32 [00:00<00:00, 311.86it/s, v_num=2, train_loss=3.850, RMSE=17.80]
Epoch 15: 16%|█▌ | 5/32 [00:00<00:00, 307.85it/s, v_num=2, train_loss=3.540, RMSE=17.80]
Epoch 15: 19%|█▉ | 6/32 [00:00<00:00, 313.32it/s, v_num=2, train_loss=3.540, RMSE=17.80]
Epoch 15: 19%|█▉ | 6/32 [00:00<00:00, 309.94it/s, v_num=2, train_loss=3.920, RMSE=17.80]
Epoch 15: 22%|██▏ | 7/32 [00:00<00:00, 314.16it/s, v_num=2, train_loss=3.920, RMSE=17.80]
Epoch 15: 22%|██▏ | 7/32 [00:00<00:00, 311.22it/s, v_num=2, train_loss=3.390, RMSE=17.80]
Epoch 15: 25%|██▌ | 8/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=3.390, RMSE=17.80]
Epoch 15: 25%|██▌ | 8/32 [00:00<00:00, 312.75it/s, v_num=2, train_loss=3.880, RMSE=17.80]
Epoch 15: 28%|██▊ | 9/32 [00:00<00:00, 316.50it/s, v_num=2, train_loss=3.880, RMSE=17.80]
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Epoch 15: 31%|███▏ | 10/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=3.840, RMSE=17.80]
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Epoch 15: 34%|███▍ | 11/32 [00:00<00:00, 317.81it/s, v_num=2, train_loss=3.860, RMSE=17.80]
Epoch 15: 34%|███▍ | 11/32 [00:00<00:00, 315.78it/s, v_num=2, train_loss=3.840, RMSE=17.80]
Epoch 15: 38%|███▊ | 12/32 [00:00<00:00, 318.13it/s, v_num=2, train_loss=3.840, RMSE=17.80]
Epoch 15: 38%|███▊ | 12/32 [00:00<00:00, 316.36it/s, v_num=2, train_loss=3.530, RMSE=17.80]
Epoch 15: 41%|████ | 13/32 [00:00<00:00, 318.56it/s, v_num=2, train_loss=3.530, RMSE=17.80]
Epoch 15: 41%|████ | 13/32 [00:00<00:00, 316.68it/s, v_num=2, train_loss=3.610, RMSE=17.80]
Epoch 15: 44%|████▍ | 14/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.610, RMSE=17.80]
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Epoch 15: 47%|████▋ | 15/32 [00:00<00:00, 319.03it/s, v_num=2, train_loss=3.350, RMSE=17.80]
Epoch 15: 47%|████▋ | 15/32 [00:00<00:00, 317.56it/s, v_num=2, train_loss=3.870, RMSE=17.80]
Epoch 15: 50%|█████ | 16/32 [00:00<00:00, 319.21it/s, v_num=2, train_loss=3.870, RMSE=17.80]
Epoch 15: 50%|█████ | 16/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.900, RMSE=17.80]
Epoch 15: 53%|█████▎ | 17/32 [00:00<00:00, 319.52it/s, v_num=2, train_loss=3.900, RMSE=17.80]
Epoch 15: 53%|█████▎ | 17/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.780, RMSE=17.80]
Epoch 15: 56%|█████▋ | 18/32 [00:00<00:00, 319.76it/s, v_num=2, train_loss=3.780, RMSE=17.80]
Epoch 15: 56%|█████▋ | 18/32 [00:00<00:00, 318.56it/s, v_num=2, train_loss=3.310, RMSE=17.80]
Epoch 15: 59%|█████▉ | 19/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.310, RMSE=17.80]
Epoch 15: 59%|█████▉ | 19/32 [00:00<00:00, 318.51it/s, v_num=2, train_loss=3.930, RMSE=17.80]
Epoch 15: 62%|██████▎ | 20/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.930, RMSE=17.80]
Epoch 15: 62%|██████▎ | 20/32 [00:00<00:00, 318.77it/s, v_num=2, train_loss=3.550, RMSE=17.80]
Epoch 15: 66%|██████▌ | 21/32 [00:00<00:00, 317.40it/s, v_num=2, train_loss=3.550, RMSE=17.80]
Epoch 15: 66%|██████▌ | 21/32 [00:00<00:00, 316.40it/s, v_num=2, train_loss=4.200, RMSE=17.80]
Epoch 15: 69%|██████▉ | 22/32 [00:00<00:00, 317.53it/s, v_num=2, train_loss=4.200, RMSE=17.80]
Epoch 15: 69%|██████▉ | 22/32 [00:00<00:00, 316.58it/s, v_num=2, train_loss=3.650, RMSE=17.80]
Epoch 15: 72%|███████▏ | 23/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=3.650, RMSE=17.80]
Epoch 15: 72%|███████▏ | 23/32 [00:00<00:00, 316.99it/s, v_num=2, train_loss=3.590, RMSE=17.80]
Epoch 15: 75%|███████▌ | 24/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.590, RMSE=17.80]
Epoch 15: 75%|███████▌ | 24/32 [00:00<00:00, 317.20it/s, v_num=2, train_loss=3.500, RMSE=17.80]
Epoch 15: 78%|███████▊ | 25/32 [00:00<00:00, 318.33it/s, v_num=2, train_loss=3.500, RMSE=17.80]
Epoch 15: 78%|███████▊ | 25/32 [00:00<00:00, 317.48it/s, v_num=2, train_loss=3.880, RMSE=17.80]
Epoch 15: 81%|████████▏ | 26/32 [00:00<00:00, 318.53it/s, v_num=2, train_loss=3.880, RMSE=17.80]
Epoch 15: 81%|████████▏ | 26/32 [00:00<00:00, 317.72it/s, v_num=2, train_loss=3.430, RMSE=17.80]
Epoch 15: 84%|████████▍ | 27/32 [00:00<00:00, 318.97it/s, v_num=2, train_loss=3.430, RMSE=17.80]
Epoch 15: 84%|████████▍ | 27/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.860, RMSE=17.80]
Epoch 15: 88%|████████▊ | 28/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=3.860, RMSE=17.80]
Epoch 15: 88%|████████▊ | 28/32 [00:00<00:00, 318.37it/s, v_num=2, train_loss=3.620, RMSE=17.80]
Epoch 15: 91%|█████████ | 29/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.620, RMSE=17.80]
Epoch 15: 91%|█████████ | 29/32 [00:00<00:00, 318.37it/s, v_num=2, train_loss=3.710, RMSE=17.80]
Epoch 15: 94%|█████████▍| 30/32 [00:00<00:00, 319.18it/s, v_num=2, train_loss=3.710, RMSE=17.80]
Epoch 15: 94%|█████████▍| 30/32 [00:00<00:00, 318.47it/s, v_num=2, train_loss=3.790, RMSE=17.80]
Epoch 15: 97%|█████████▋| 31/32 [00:00<00:00, 319.22it/s, v_num=2, train_loss=3.790, RMSE=17.80]
Epoch 15: 97%|█████████▋| 31/32 [00:00<00:00, 318.53it/s, v_num=2, train_loss=3.610, RMSE=17.80]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 319.61it/s, v_num=2, train_loss=3.610, RMSE=17.80]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=3.520, RMSE=17.80]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 609.49it/s]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 607.38it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 606.74it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 610.21it/s]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 260.99it/s, v_num=2, train_loss=3.520, RMSE=17.10]
Epoch 15: 100%|██████████| 32/32 [00:00<00:00, 259.84it/s, v_num=2, train_loss=3.520, RMSE=17.10]
Epoch 15: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.520, RMSE=17.10]
Epoch 16: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.520, RMSE=17.10]
Epoch 16: 3%|▎ | 1/32 [00:00<00:00, 282.64it/s, v_num=2, train_loss=3.520, RMSE=17.10]
Epoch 16: 3%|▎ | 1/32 [00:00<00:00, 266.54it/s, v_num=2, train_loss=3.300, RMSE=17.10]
Epoch 16: 6%|▋ | 2/32 [00:00<00:00, 297.17it/s, v_num=2, train_loss=3.300, RMSE=17.10]
Epoch 16: 6%|▋ | 2/32 [00:00<00:00, 287.25it/s, v_num=2, train_loss=3.750, RMSE=17.10]
Epoch 16: 9%|▉ | 3/32 [00:00<00:00, 303.41it/s, v_num=2, train_loss=3.750, RMSE=17.10]
Epoch 16: 9%|▉ | 3/32 [00:00<00:00, 297.14it/s, v_num=2, train_loss=3.670, RMSE=17.10]
Epoch 16: 12%|█▎ | 4/32 [00:00<00:00, 307.55it/s, v_num=2, train_loss=3.670, RMSE=17.10]
Epoch 16: 12%|█▎ | 4/32 [00:00<00:00, 302.72it/s, v_num=2, train_loss=3.500, RMSE=17.10]
Epoch 16: 16%|█▌ | 5/32 [00:00<00:00, 310.55it/s, v_num=2, train_loss=3.500, RMSE=17.10]
Epoch 16: 16%|█▌ | 5/32 [00:00<00:00, 306.47it/s, v_num=2, train_loss=3.720, RMSE=17.10]
Epoch 16: 19%|█▉ | 6/32 [00:00<00:00, 312.50it/s, v_num=2, train_loss=3.720, RMSE=17.10]
Epoch 16: 19%|█▉ | 6/32 [00:00<00:00, 309.07it/s, v_num=2, train_loss=3.400, RMSE=17.10]
Epoch 16: 22%|██▏ | 7/32 [00:00<00:00, 314.55it/s, v_num=2, train_loss=3.400, RMSE=17.10]
Epoch 16: 22%|██▏ | 7/32 [00:00<00:00, 311.65it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 25%|██▌ | 8/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 25%|██▌ | 8/32 [00:00<00:00, 312.86it/s, v_num=2, train_loss=3.800, RMSE=17.10]
Epoch 16: 28%|██▊ | 9/32 [00:00<00:00, 316.23it/s, v_num=2, train_loss=3.800, RMSE=17.10]
Epoch 16: 28%|██▊ | 9/32 [00:00<00:00, 313.93it/s, v_num=2, train_loss=3.750, RMSE=17.10]
Epoch 16: 31%|███▏ | 10/32 [00:00<00:00, 316.98it/s, v_num=2, train_loss=3.750, RMSE=17.10]
Epoch 16: 31%|███▏ | 10/32 [00:00<00:00, 314.89it/s, v_num=2, train_loss=3.600, RMSE=17.10]
Epoch 16: 34%|███▍ | 11/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.600, RMSE=17.10]
Epoch 16: 34%|███▍ | 11/32 [00:00<00:00, 315.69it/s, v_num=2, train_loss=3.450, RMSE=17.10]
Epoch 16: 38%|███▊ | 12/32 [00:00<00:00, 318.48it/s, v_num=2, train_loss=3.450, RMSE=17.10]
Epoch 16: 38%|███▊ | 12/32 [00:00<00:00, 316.73it/s, v_num=2, train_loss=3.710, RMSE=17.10]
Epoch 16: 41%|████ | 13/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.710, RMSE=17.10]
Epoch 16: 41%|████ | 13/32 [00:00<00:00, 316.67it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 44%|████▍ | 14/32 [00:00<00:00, 318.72it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 44%|████▍ | 14/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=3.870, RMSE=17.10]
Epoch 16: 47%|████▋ | 15/32 [00:00<00:00, 319.23it/s, v_num=2, train_loss=3.870, RMSE=17.10]
Epoch 16: 47%|████▋ | 15/32 [00:00<00:00, 317.80it/s, v_num=2, train_loss=3.730, RMSE=17.10]
Epoch 16: 50%|█████ | 16/32 [00:00<00:00, 319.61it/s, v_num=2, train_loss=3.730, RMSE=17.10]
Epoch 16: 50%|█████ | 16/32 [00:00<00:00, 318.29it/s, v_num=2, train_loss=3.500, RMSE=17.10]
Epoch 16: 53%|█████▎ | 17/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.500, RMSE=17.10]
Epoch 16: 53%|█████▎ | 17/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=3.570, RMSE=17.10]
Epoch 16: 56%|█████▋ | 18/32 [00:00<00:00, 320.00it/s, v_num=2, train_loss=3.570, RMSE=17.10]
Epoch 16: 56%|█████▋ | 18/32 [00:00<00:00, 318.82it/s, v_num=2, train_loss=3.480, RMSE=17.10]
Epoch 16: 59%|█████▉ | 19/32 [00:00<00:00, 320.00it/s, v_num=2, train_loss=3.480, RMSE=17.10]
Epoch 16: 59%|█████▉ | 19/32 [00:00<00:00, 318.84it/s, v_num=2, train_loss=3.670, RMSE=17.10]
Epoch 16: 62%|██████▎ | 20/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.670, RMSE=17.10]
Epoch 16: 62%|██████▎ | 20/32 [00:00<00:00, 319.11it/s, v_num=2, train_loss=4.130, RMSE=17.10]
Epoch 16: 66%|██████▌ | 21/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=4.130, RMSE=17.10]
Epoch 16: 66%|██████▌ | 21/32 [00:00<00:00, 319.45it/s, v_num=2, train_loss=3.390, RMSE=17.10]
Epoch 16: 69%|██████▉ | 22/32 [00:00<00:00, 320.61it/s, v_num=2, train_loss=3.390, RMSE=17.10]
Epoch 16: 69%|██████▉ | 22/32 [00:00<00:00, 319.65it/s, v_num=2, train_loss=3.570, RMSE=17.10]
Epoch 16: 72%|███████▏ | 23/32 [00:00<00:00, 320.69it/s, v_num=2, train_loss=3.570, RMSE=17.10]
Epoch 16: 72%|███████▏ | 23/32 [00:00<00:00, 319.76it/s, v_num=2, train_loss=4.080, RMSE=17.10]
Epoch 16: 75%|███████▌ | 24/32 [00:00<00:00, 320.76it/s, v_num=2, train_loss=4.080, RMSE=17.10]
Epoch 16: 75%|███████▌ | 24/32 [00:00<00:00, 319.87it/s, v_num=2, train_loss=3.700, RMSE=17.10]
Epoch 16: 78%|███████▊ | 25/32 [00:00<00:00, 321.05it/s, v_num=2, train_loss=3.700, RMSE=17.10]
Epoch 16: 78%|███████▊ | 25/32 [00:00<00:00, 320.19it/s, v_num=2, train_loss=3.510, RMSE=17.10]
Epoch 16: 81%|████████▏ | 26/32 [00:00<00:00, 321.03it/s, v_num=2, train_loss=3.510, RMSE=17.10]
Epoch 16: 81%|████████▏ | 26/32 [00:00<00:00, 320.20it/s, v_num=2, train_loss=3.720, RMSE=17.10]
Epoch 16: 84%|████████▍ | 27/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.720, RMSE=17.10]
Epoch 16: 84%|████████▍ | 27/32 [00:00<00:00, 320.34it/s, v_num=2, train_loss=3.910, RMSE=17.10]
Epoch 16: 88%|████████▊ | 28/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.910, RMSE=17.10]
Epoch 16: 88%|████████▊ | 28/32 [00:00<00:00, 320.39it/s, v_num=2, train_loss=3.610, RMSE=17.10]
Epoch 16: 91%|█████████ | 29/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=3.610, RMSE=17.10]
Epoch 16: 91%|█████████ | 29/32 [00:00<00:00, 320.36it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 94%|█████████▍| 30/32 [00:00<00:00, 321.25it/s, v_num=2, train_loss=3.850, RMSE=17.10]
Epoch 16: 94%|█████████▍| 30/32 [00:00<00:00, 320.54it/s, v_num=2, train_loss=3.400, RMSE=17.10]
Epoch 16: 97%|█████████▋| 31/32 [00:00<00:00, 321.32it/s, v_num=2, train_loss=3.400, RMSE=17.10]
Epoch 16: 97%|█████████▋| 31/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.450, RMSE=17.10]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 321.49it/s, v_num=2, train_loss=3.450, RMSE=17.10]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 320.81it/s, v_num=2, train_loss=3.380, RMSE=17.10]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 612.41it/s]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 262.41it/s, v_num=2, train_loss=3.380, RMSE=16.30]
Epoch 16: 100%|██████████| 32/32 [00:00<00:00, 261.26it/s, v_num=2, train_loss=3.380, RMSE=16.30]
Epoch 16: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.380, RMSE=16.30]
Epoch 17: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.380, RMSE=16.30]
Epoch 17: 3%|▎ | 1/32 [00:00<00:00, 283.15it/s, v_num=2, train_loss=3.380, RMSE=16.30]
Epoch 17: 3%|▎ | 1/32 [00:00<00:00, 266.93it/s, v_num=2, train_loss=3.960, RMSE=16.30]
Epoch 17: 6%|▋ | 2/32 [00:00<00:00, 297.89it/s, v_num=2, train_loss=3.960, RMSE=16.30]
Epoch 17: 6%|▋ | 2/32 [00:00<00:00, 288.81it/s, v_num=2, train_loss=3.520, RMSE=16.30]
Epoch 17: 9%|▉ | 3/32 [00:00<00:00, 304.56it/s, v_num=2, train_loss=3.520, RMSE=16.30]
Epoch 17: 9%|▉ | 3/32 [00:00<00:00, 298.23it/s, v_num=2, train_loss=3.870, RMSE=16.30]
Epoch 17: 12%|█▎ | 4/32 [00:00<00:00, 309.47it/s, v_num=2, train_loss=3.870, RMSE=16.30]
Epoch 17: 12%|█▎ | 4/32 [00:00<00:00, 303.64it/s, v_num=2, train_loss=3.360, RMSE=16.30]
Epoch 17: 16%|█▌ | 5/32 [00:00<00:00, 312.05it/s, v_num=2, train_loss=3.360, RMSE=16.30]
Epoch 17: 16%|█▌ | 5/32 [00:00<00:00, 308.05it/s, v_num=2, train_loss=3.660, RMSE=16.30]
Epoch 17: 19%|█▉ | 6/32 [00:00<00:00, 312.01it/s, v_num=2, train_loss=3.660, RMSE=16.30]
Epoch 17: 19%|█▉ | 6/32 [00:00<00:00, 308.67it/s, v_num=2, train_loss=3.590, RMSE=16.30]
Epoch 17: 22%|██▏ | 7/32 [00:00<00:00, 307.18it/s, v_num=2, train_loss=3.590, RMSE=16.30]
Epoch 17: 22%|██▏ | 7/32 [00:00<00:00, 304.38it/s, v_num=2, train_loss=3.430, RMSE=16.30]
Epoch 17: 25%|██▌ | 8/32 [00:00<00:00, 309.37it/s, v_num=2, train_loss=3.430, RMSE=16.30]
Epoch 17: 25%|██▌ | 8/32 [00:00<00:00, 306.82it/s, v_num=2, train_loss=3.670, RMSE=16.30]
Epoch 17: 28%|██▊ | 9/32 [00:00<00:00, 311.00it/s, v_num=2, train_loss=3.670, RMSE=16.30]
Epoch 17: 28%|██▊ | 9/32 [00:00<00:00, 308.40it/s, v_num=2, train_loss=3.730, RMSE=16.30]
Epoch 17: 31%|███▏ | 10/32 [00:00<00:00, 312.26it/s, v_num=2, train_loss=3.730, RMSE=16.30]
Epoch 17: 31%|███▏ | 10/32 [00:00<00:00, 310.25it/s, v_num=2, train_loss=3.470, RMSE=16.30]
Epoch 17: 34%|███▍ | 11/32 [00:00<00:00, 313.17it/s, v_num=2, train_loss=3.470, RMSE=16.30]
Epoch 17: 34%|███▍ | 11/32 [00:00<00:00, 311.31it/s, v_num=2, train_loss=3.690, RMSE=16.30]
Epoch 17: 38%|███▊ | 12/32 [00:00<00:00, 313.95it/s, v_num=2, train_loss=3.690, RMSE=16.30]
Epoch 17: 38%|███▊ | 12/32 [00:00<00:00, 312.15it/s, v_num=2, train_loss=3.930, RMSE=16.30]
Epoch 17: 41%|████ | 13/32 [00:00<00:00, 314.63it/s, v_num=2, train_loss=3.930, RMSE=16.30]
Epoch 17: 41%|████ | 13/32 [00:00<00:00, 313.06it/s, v_num=2, train_loss=3.960, RMSE=16.30]
Epoch 17: 44%|████▍ | 14/32 [00:00<00:00, 315.56it/s, v_num=2, train_loss=3.960, RMSE=16.30]
Epoch 17: 44%|████▍ | 14/32 [00:00<00:00, 314.10it/s, v_num=2, train_loss=3.560, RMSE=16.30]
Epoch 17: 47%|████▋ | 15/32 [00:00<00:00, 316.03it/s, v_num=2, train_loss=3.560, RMSE=16.30]
Epoch 17: 47%|████▋ | 15/32 [00:00<00:00, 314.65it/s, v_num=2, train_loss=3.280, RMSE=16.30]
Epoch 17: 50%|█████ | 16/32 [00:00<00:00, 316.35it/s, v_num=2, train_loss=3.280, RMSE=16.30]
Epoch 17: 50%|█████ | 16/32 [00:00<00:00, 315.05it/s, v_num=2, train_loss=3.790, RMSE=16.30]
Epoch 17: 53%|█████▎ | 17/32 [00:00<00:00, 316.67it/s, v_num=2, train_loss=3.790, RMSE=16.30]
Epoch 17: 53%|█████▎ | 17/32 [00:00<00:00, 315.40it/s, v_num=2, train_loss=3.120, RMSE=16.30]
Epoch 17: 56%|█████▋ | 18/32 [00:00<00:00, 317.24it/s, v_num=2, train_loss=3.120, RMSE=16.30]
Epoch 17: 56%|█████▋ | 18/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=3.770, RMSE=16.30]
Epoch 17: 59%|█████▉ | 19/32 [00:00<00:00, 317.26it/s, v_num=2, train_loss=3.770, RMSE=16.30]
Epoch 17: 59%|█████▉ | 19/32 [00:00<00:00, 316.15it/s, v_num=2, train_loss=3.710, RMSE=16.30]
Epoch 17: 62%|██████▎ | 20/32 [00:00<00:00, 317.57it/s, v_num=2, train_loss=3.710, RMSE=16.30]
Epoch 17: 62%|██████▎ | 20/32 [00:00<00:00, 316.53it/s, v_num=2, train_loss=3.450, RMSE=16.30]
Epoch 17: 66%|██████▌ | 21/32 [00:00<00:00, 317.85it/s, v_num=2, train_loss=3.450, RMSE=16.30]
Epoch 17: 66%|██████▌ | 21/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=3.640, RMSE=16.30]
Epoch 17: 69%|██████▉ | 22/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.640, RMSE=16.30]
Epoch 17: 69%|██████▉ | 22/32 [00:00<00:00, 317.13it/s, v_num=2, train_loss=3.610, RMSE=16.30]
Epoch 17: 72%|███████▏ | 23/32 [00:00<00:00, 318.54it/s, v_num=2, train_loss=3.610, RMSE=16.30]
Epoch 17: 72%|███████▏ | 23/32 [00:00<00:00, 317.62it/s, v_num=2, train_loss=3.740, RMSE=16.30]
Epoch 17: 75%|███████▌ | 24/32 [00:00<00:00, 318.77it/s, v_num=2, train_loss=3.740, RMSE=16.30]
Epoch 17: 75%|███████▌ | 24/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.460, RMSE=16.30]
Epoch 17: 78%|███████▊ | 25/32 [00:00<00:00, 318.93it/s, v_num=2, train_loss=3.460, RMSE=16.30]
Epoch 17: 78%|███████▊ | 25/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.800, RMSE=16.30]
Epoch 17: 81%|████████▏ | 26/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.800, RMSE=16.30]
Epoch 17: 81%|████████▏ | 26/32 [00:00<00:00, 318.27it/s, v_num=2, train_loss=3.850, RMSE=16.30]
Epoch 17: 84%|████████▍ | 27/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.850, RMSE=16.30]
Epoch 17: 84%|████████▍ | 27/32 [00:00<00:00, 318.54it/s, v_num=2, train_loss=3.570, RMSE=16.30]
Epoch 17: 88%|████████▊ | 28/32 [00:00<00:00, 319.36it/s, v_num=2, train_loss=3.570, RMSE=16.30]
Epoch 17: 88%|████████▊ | 28/32 [00:00<00:00, 318.61it/s, v_num=2, train_loss=3.560, RMSE=16.30]
Epoch 17: 91%|█████████ | 29/32 [00:00<00:00, 319.51it/s, v_num=2, train_loss=3.560, RMSE=16.30]
Epoch 17: 91%|█████████ | 29/32 [00:00<00:00, 318.78it/s, v_num=2, train_loss=3.750, RMSE=16.30]
Epoch 17: 94%|█████████▍| 30/32 [00:00<00:00, 319.49it/s, v_num=2, train_loss=3.750, RMSE=16.30]
Epoch 17: 94%|█████████▍| 30/32 [00:00<00:00, 318.78it/s, v_num=2, train_loss=3.360, RMSE=16.30]
Epoch 17: 97%|█████████▋| 31/32 [00:00<00:00, 319.56it/s, v_num=2, train_loss=3.360, RMSE=16.30]
Epoch 17: 97%|█████████▋| 31/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.600, RMSE=16.30]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.600, RMSE=16.30]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 319.26it/s, v_num=2, train_loss=3.370, RMSE=16.30]
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Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 623.69it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 622.51it/s]
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Validation DataLoader 0: 60%|██████ | 6/10 [00:00<00:00, 618.61it/s]
Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 616.07it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 615.91it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 614.39it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 616.64it/s]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 261.57it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 17: 100%|██████████| 32/32 [00:00<00:00, 260.47it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 17: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 3%|▎ | 1/32 [00:00<00:00, 290.28it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 3%|▎ | 1/32 [00:00<00:00, 273.51it/s, v_num=2, train_loss=3.980, RMSE=15.70]
Epoch 18: 6%|▋ | 2/32 [00:00<00:00, 305.51it/s, v_num=2, train_loss=3.980, RMSE=15.70]
Epoch 18: 6%|▋ | 2/32 [00:00<00:00, 296.05it/s, v_num=2, train_loss=3.990, RMSE=15.70]
Epoch 18: 9%|▉ | 3/32 [00:00<00:00, 311.34it/s, v_num=2, train_loss=3.990, RMSE=15.70]
Epoch 18: 9%|▉ | 3/32 [00:00<00:00, 304.80it/s, v_num=2, train_loss=3.640, RMSE=15.70]
Epoch 18: 12%|█▎ | 4/32 [00:00<00:00, 314.27it/s, v_num=2, train_loss=3.640, RMSE=15.70]
Epoch 18: 12%|█▎ | 4/32 [00:00<00:00, 309.18it/s, v_num=2, train_loss=3.440, RMSE=15.70]
Epoch 18: 16%|█▌ | 5/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=3.440, RMSE=15.70]
Epoch 18: 16%|█▌ | 5/32 [00:00<00:00, 311.71it/s, v_num=2, train_loss=3.440, RMSE=15.70]
Epoch 18: 19%|█▉ | 6/32 [00:00<00:00, 317.52it/s, v_num=2, train_loss=3.440, RMSE=15.70]
Epoch 18: 19%|█▉ | 6/32 [00:00<00:00, 313.50it/s, v_num=2, train_loss=3.750, RMSE=15.70]
Epoch 18: 22%|██▏ | 7/32 [00:00<00:00, 318.07it/s, v_num=2, train_loss=3.750, RMSE=15.70]
Epoch 18: 22%|██▏ | 7/32 [00:00<00:00, 315.06it/s, v_num=2, train_loss=3.840, RMSE=15.70]
Epoch 18: 25%|██▌ | 8/32 [00:00<00:00, 318.84it/s, v_num=2, train_loss=3.840, RMSE=15.70]
Epoch 18: 25%|██▌ | 8/32 [00:00<00:00, 316.21it/s, v_num=2, train_loss=3.270, RMSE=15.70]
Epoch 18: 28%|██▊ | 9/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.270, RMSE=15.70]
Epoch 18: 28%|██▊ | 9/32 [00:00<00:00, 316.99it/s, v_num=2, train_loss=3.470, RMSE=15.70]
Epoch 18: 31%|███▏ | 10/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.470, RMSE=15.70]
Epoch 18: 31%|███▏ | 10/32 [00:00<00:00, 316.78it/s, v_num=2, train_loss=3.510, RMSE=15.70]
Epoch 18: 34%|███▍ | 11/32 [00:00<00:00, 319.65it/s, v_num=2, train_loss=3.510, RMSE=15.70]
Epoch 18: 34%|███▍ | 11/32 [00:00<00:00, 317.73it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 38%|███▊ | 12/32 [00:00<00:00, 319.86it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 38%|███▊ | 12/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.650, RMSE=15.70]
Epoch 18: 41%|████ | 13/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=3.650, RMSE=15.70]
Epoch 18: 41%|████ | 13/32 [00:00<00:00, 318.59it/s, v_num=2, train_loss=3.470, RMSE=15.70]
Epoch 18: 44%|████▍ | 14/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=3.470, RMSE=15.70]
Epoch 18: 44%|████▍ | 14/32 [00:00<00:00, 319.05it/s, v_num=2, train_loss=3.530, RMSE=15.70]
Epoch 18: 47%|████▋ | 15/32 [00:00<00:00, 321.07it/s, v_num=2, train_loss=3.530, RMSE=15.70]
Epoch 18: 47%|████▋ | 15/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.260, RMSE=15.70]
Epoch 18: 50%|█████ | 16/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.260, RMSE=15.70]
Epoch 18: 50%|█████ | 16/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=3.630, RMSE=15.70]
Epoch 18: 53%|█████▎ | 17/32 [00:00<00:00, 320.93it/s, v_num=2, train_loss=3.630, RMSE=15.70]
Epoch 18: 53%|█████▎ | 17/32 [00:00<00:00, 319.62it/s, v_num=2, train_loss=3.760, RMSE=15.70]
Epoch 18: 56%|█████▋ | 18/32 [00:00<00:00, 321.18it/s, v_num=2, train_loss=3.760, RMSE=15.70]
Epoch 18: 56%|█████▋ | 18/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.020, RMSE=15.70]
Epoch 18: 59%|█████▉ | 19/32 [00:00<00:00, 321.44it/s, v_num=2, train_loss=3.020, RMSE=15.70]
Epoch 18: 59%|█████▉ | 19/32 [00:00<00:00, 320.31it/s, v_num=2, train_loss=3.760, RMSE=15.70]
Epoch 18: 62%|██████▎ | 20/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.760, RMSE=15.70]
Epoch 18: 62%|██████▎ | 20/32 [00:00<00:00, 320.73it/s, v_num=2, train_loss=3.630, RMSE=15.70]
Epoch 18: 66%|██████▌ | 21/32 [00:00<00:00, 321.50it/s, v_num=2, train_loss=3.630, RMSE=15.70]
Epoch 18: 66%|██████▌ | 21/32 [00:00<00:00, 320.47it/s, v_num=2, train_loss=3.500, RMSE=15.70]
Epoch 18: 69%|██████▉ | 22/32 [00:00<00:00, 321.62it/s, v_num=2, train_loss=3.500, RMSE=15.70]
Epoch 18: 69%|██████▉ | 22/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=3.280, RMSE=15.70]
Epoch 18: 72%|███████▏ | 23/32 [00:00<00:00, 321.44it/s, v_num=2, train_loss=3.280, RMSE=15.70]
Epoch 18: 72%|███████▏ | 23/32 [00:00<00:00, 320.50it/s, v_num=2, train_loss=3.770, RMSE=15.70]
Epoch 18: 75%|███████▌ | 24/32 [00:00<00:00, 321.76it/s, v_num=2, train_loss=3.770, RMSE=15.70]
Epoch 18: 75%|███████▌ | 24/32 [00:00<00:00, 320.86it/s, v_num=2, train_loss=4.020, RMSE=15.70]
Epoch 18: 78%|███████▊ | 25/32 [00:00<00:00, 319.71it/s, v_num=2, train_loss=4.020, RMSE=15.70]
Epoch 18: 78%|███████▊ | 25/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.430, RMSE=15.70]
Epoch 18: 81%|████████▏ | 26/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=3.430, RMSE=15.70]
Epoch 18: 81%|████████▏ | 26/32 [00:00<00:00, 319.05it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 84%|████████▍ | 27/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=3.370, RMSE=15.70]
Epoch 18: 84%|████████▍ | 27/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.770, RMSE=15.70]
Epoch 18: 88%|████████▊ | 28/32 [00:00<00:00, 319.77it/s, v_num=2, train_loss=3.770, RMSE=15.70]
Epoch 18: 88%|████████▊ | 28/32 [00:00<00:00, 318.98it/s, v_num=2, train_loss=3.820, RMSE=15.70]
Epoch 18: 91%|█████████ | 29/32 [00:00<00:00, 319.89it/s, v_num=2, train_loss=3.820, RMSE=15.70]
Epoch 18: 91%|█████████ | 29/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=3.380, RMSE=15.70]
Epoch 18: 94%|█████████▍| 30/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.380, RMSE=15.70]
Epoch 18: 94%|█████████▍| 30/32 [00:00<00:00, 319.28it/s, v_num=2, train_loss=3.800, RMSE=15.70]
Epoch 18: 97%|█████████▋| 31/32 [00:00<00:00, 320.10it/s, v_num=2, train_loss=3.800, RMSE=15.70]
Epoch 18: 97%|█████████▋| 31/32 [00:00<00:00, 319.38it/s, v_num=2, train_loss=3.790, RMSE=15.70]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 320.29it/s, v_num=2, train_loss=3.790, RMSE=15.70]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.540, RMSE=15.70]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 610.26it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 613.96it/s]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 261.79it/s, v_num=2, train_loss=3.540, RMSE=15.10]
Epoch 18: 100%|██████████| 32/32 [00:00<00:00, 260.66it/s, v_num=2, train_loss=3.540, RMSE=15.10]
Epoch 18: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.540, RMSE=15.10]
Epoch 19: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.540, RMSE=15.10]
Epoch 19: 3%|▎ | 1/32 [00:00<00:00, 285.68it/s, v_num=2, train_loss=3.540, RMSE=15.10]
Epoch 19: 3%|▎ | 1/32 [00:00<00:00, 269.35it/s, v_num=2, train_loss=3.240, RMSE=15.10]
Epoch 19: 6%|▋ | 2/32 [00:00<00:00, 300.19it/s, v_num=2, train_loss=3.240, RMSE=15.10]
Epoch 19: 6%|▋ | 2/32 [00:00<00:00, 291.02it/s, v_num=2, train_loss=3.320, RMSE=15.10]
Epoch 19: 9%|▉ | 3/32 [00:00<00:00, 306.62it/s, v_num=2, train_loss=3.320, RMSE=15.10]
Epoch 19: 9%|▉ | 3/32 [00:00<00:00, 300.20it/s, v_num=2, train_loss=3.570, RMSE=15.10]
Epoch 19: 12%|█▎ | 4/32 [00:00<00:00, 311.80it/s, v_num=2, train_loss=3.570, RMSE=15.10]
Epoch 19: 12%|█▎ | 4/32 [00:00<00:00, 306.76it/s, v_num=2, train_loss=4.000, RMSE=15.10]
Epoch 19: 16%|█▌ | 5/32 [00:00<00:00, 313.43it/s, v_num=2, train_loss=4.000, RMSE=15.10]
Epoch 19: 16%|█▌ | 5/32 [00:00<00:00, 309.38it/s, v_num=2, train_loss=3.330, RMSE=15.10]
Epoch 19: 19%|█▉ | 6/32 [00:00<00:00, 314.88it/s, v_num=2, train_loss=3.330, RMSE=15.10]
Epoch 19: 19%|█▉ | 6/32 [00:00<00:00, 311.45it/s, v_num=2, train_loss=3.600, RMSE=15.10]
Epoch 19: 22%|██▏ | 7/32 [00:00<00:00, 315.27it/s, v_num=2, train_loss=3.600, RMSE=15.10]
Epoch 19: 22%|██▏ | 7/32 [00:00<00:00, 312.31it/s, v_num=2, train_loss=3.670, RMSE=15.10]
Epoch 19: 25%|██▌ | 8/32 [00:00<00:00, 316.28it/s, v_num=2, train_loss=3.670, RMSE=15.10]
Epoch 19: 25%|██▌ | 8/32 [00:00<00:00, 313.67it/s, v_num=2, train_loss=3.590, RMSE=15.10]
Epoch 19: 28%|██▊ | 9/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=3.590, RMSE=15.10]
Epoch 19: 28%|██▊ | 9/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=3.330, RMSE=15.10]
Epoch 19: 31%|███▏ | 10/32 [00:00<00:00, 318.14it/s, v_num=2, train_loss=3.330, RMSE=15.10]
Epoch 19: 31%|███▏ | 10/32 [00:00<00:00, 316.03it/s, v_num=2, train_loss=3.630, RMSE=15.10]
Epoch 19: 34%|███▍ | 11/32 [00:00<00:00, 318.67it/s, v_num=2, train_loss=3.630, RMSE=15.10]
Epoch 19: 34%|███▍ | 11/32 [00:00<00:00, 316.77it/s, v_num=2, train_loss=3.590, RMSE=15.10]
Epoch 19: 38%|███▊ | 12/32 [00:00<00:00, 319.02it/s, v_num=2, train_loss=3.590, RMSE=15.10]
Epoch 19: 38%|███▊ | 12/32 [00:00<00:00, 317.28it/s, v_num=2, train_loss=3.660, RMSE=15.10]
Epoch 19: 41%|████ | 13/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=3.660, RMSE=15.10]
Epoch 19: 41%|████ | 13/32 [00:00<00:00, 318.18it/s, v_num=2, train_loss=3.480, RMSE=15.10]
Epoch 19: 44%|████▍ | 14/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.480, RMSE=15.10]
Epoch 19: 44%|████▍ | 14/32 [00:00<00:00, 318.42it/s, v_num=2, train_loss=3.760, RMSE=15.10]
Epoch 19: 47%|████▋ | 15/32 [00:00<00:00, 320.19it/s, v_num=2, train_loss=3.760, RMSE=15.10]
Epoch 19: 47%|████▋ | 15/32 [00:00<00:00, 318.75it/s, v_num=2, train_loss=3.630, RMSE=15.10]
Epoch 19: 50%|█████ | 16/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.630, RMSE=15.10]
Epoch 19: 50%|█████ | 16/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=3.680, RMSE=15.10]
Epoch 19: 53%|█████▎ | 17/32 [00:00<00:00, 320.61it/s, v_num=2, train_loss=3.680, RMSE=15.10]
Epoch 19: 53%|█████▎ | 17/32 [00:00<00:00, 319.11it/s, v_num=2, train_loss=3.460, RMSE=15.10]
Epoch 19: 56%|█████▋ | 18/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=3.460, RMSE=15.10]
Epoch 19: 56%|█████▋ | 18/32 [00:00<00:00, 319.54it/s, v_num=2, train_loss=3.690, RMSE=15.10]
Epoch 19: 59%|█████▉ | 19/32 [00:00<00:00, 320.96it/s, v_num=2, train_loss=3.690, RMSE=15.10]
Epoch 19: 59%|█████▉ | 19/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.730, RMSE=15.10]
Epoch 19: 62%|██████▎ | 20/32 [00:00<00:00, 320.92it/s, v_num=2, train_loss=3.730, RMSE=15.10]
Epoch 19: 62%|██████▎ | 20/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.970, RMSE=15.10]
Epoch 19: 66%|██████▌ | 21/32 [00:00<00:00, 320.86it/s, v_num=2, train_loss=3.970, RMSE=15.10]
Epoch 19: 66%|██████▌ | 21/32 [00:00<00:00, 319.84it/s, v_num=2, train_loss=3.310, RMSE=15.10]
Epoch 19: 69%|██████▉ | 22/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.310, RMSE=15.10]
Epoch 19: 69%|██████▉ | 22/32 [00:00<00:00, 320.16it/s, v_num=2, train_loss=3.350, RMSE=15.10]
Epoch 19: 72%|███████▏ | 23/32 [00:00<00:00, 321.33it/s, v_num=2, train_loss=3.350, RMSE=15.10]
Epoch 19: 72%|███████▏ | 23/32 [00:00<00:00, 320.40it/s, v_num=2, train_loss=3.450, RMSE=15.10]
Epoch 19: 75%|███████▌ | 24/32 [00:00<00:00, 321.49it/s, v_num=2, train_loss=3.450, RMSE=15.10]
Epoch 19: 75%|███████▌ | 24/32 [00:00<00:00, 320.59it/s, v_num=2, train_loss=2.990, RMSE=15.10]
Epoch 19: 78%|███████▊ | 25/32 [00:00<00:00, 321.58it/s, v_num=2, train_loss=2.990, RMSE=15.10]
Epoch 19: 78%|███████▊ | 25/32 [00:00<00:00, 320.67it/s, v_num=2, train_loss=3.580, RMSE=15.10]
Epoch 19: 81%|████████▏ | 26/32 [00:00<00:00, 321.81it/s, v_num=2, train_loss=3.580, RMSE=15.10]
Epoch 19: 81%|████████▏ | 26/32 [00:00<00:00, 320.99it/s, v_num=2, train_loss=3.450, RMSE=15.10]
Epoch 19: 84%|████████▍ | 27/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.450, RMSE=15.10]
Epoch 19: 84%|████████▍ | 27/32 [00:00<00:00, 321.00it/s, v_num=2, train_loss=3.380, RMSE=15.10]
Epoch 19: 88%|████████▊ | 28/32 [00:00<00:00, 321.95it/s, v_num=2, train_loss=3.380, RMSE=15.10]
Epoch 19: 88%|████████▊ | 28/32 [00:00<00:00, 321.18it/s, v_num=2, train_loss=3.750, RMSE=15.10]
Epoch 19: 91%|█████████ | 29/32 [00:00<00:00, 322.12it/s, v_num=2, train_loss=3.750, RMSE=15.10]
Epoch 19: 91%|█████████ | 29/32 [00:00<00:00, 321.39it/s, v_num=2, train_loss=3.400, RMSE=15.10]
Epoch 19: 94%|█████████▍| 30/32 [00:00<00:00, 322.18it/s, v_num=2, train_loss=3.400, RMSE=15.10]
Epoch 19: 94%|█████████▍| 30/32 [00:00<00:00, 321.44it/s, v_num=2, train_loss=3.740, RMSE=15.10]
Epoch 19: 97%|█████████▋| 31/32 [00:00<00:00, 322.41it/s, v_num=2, train_loss=3.740, RMSE=15.10]
Epoch 19: 97%|█████████▋| 31/32 [00:00<00:00, 321.71it/s, v_num=2, train_loss=3.790, RMSE=15.10]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 322.57it/s, v_num=2, train_loss=3.790, RMSE=15.10]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=3.700, RMSE=15.10]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 604.78it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 605.50it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 608.20it/s]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 263.02it/s, v_num=2, train_loss=3.700, RMSE=14.40]
Epoch 19: 100%|██████████| 32/32 [00:00<00:00, 261.86it/s, v_num=2, train_loss=3.700, RMSE=14.40]
Epoch 19: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.700, RMSE=14.40]
Epoch 20: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.700, RMSE=14.40]
Epoch 20: 3%|▎ | 1/32 [00:00<00:00, 274.78it/s, v_num=2, train_loss=3.700, RMSE=14.40]
Epoch 20: 3%|▎ | 1/32 [00:00<00:00, 255.28it/s, v_num=2, train_loss=3.630, RMSE=14.40]
Epoch 20: 6%|▋ | 2/32 [00:00<00:00, 290.55it/s, v_num=2, train_loss=3.630, RMSE=14.40]
Epoch 20: 6%|▋ | 2/32 [00:00<00:00, 281.92it/s, v_num=2, train_loss=3.880, RMSE=14.40]
Epoch 20: 9%|▉ | 3/32 [00:00<00:00, 300.46it/s, v_num=2, train_loss=3.880, RMSE=14.40]
Epoch 20: 9%|▉ | 3/32 [00:00<00:00, 294.30it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 12%|█▎ | 4/32 [00:00<00:00, 305.92it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 12%|█▎ | 4/32 [00:00<00:00, 301.08it/s, v_num=2, train_loss=3.790, RMSE=14.40]
Epoch 20: 16%|█▌ | 5/32 [00:00<00:00, 308.75it/s, v_num=2, train_loss=3.790, RMSE=14.40]
Epoch 20: 16%|█▌ | 5/32 [00:00<00:00, 304.82it/s, v_num=2, train_loss=3.590, RMSE=14.40]
Epoch 20: 19%|█▉ | 6/32 [00:00<00:00, 311.64it/s, v_num=2, train_loss=3.590, RMSE=14.40]
Epoch 20: 19%|█▉ | 6/32 [00:00<00:00, 308.28it/s, v_num=2, train_loss=3.640, RMSE=14.40]
Epoch 20: 22%|██▏ | 7/32 [00:00<00:00, 312.18it/s, v_num=2, train_loss=3.640, RMSE=14.40]
Epoch 20: 22%|██▏ | 7/32 [00:00<00:00, 309.27it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 25%|██▌ | 8/32 [00:00<00:00, 313.41it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 25%|██▌ | 8/32 [00:00<00:00, 310.87it/s, v_num=2, train_loss=3.520, RMSE=14.40]
Epoch 20: 28%|██▊ | 9/32 [00:00<00:00, 314.47it/s, v_num=2, train_loss=3.520, RMSE=14.40]
Epoch 20: 28%|██▊ | 9/32 [00:00<00:00, 312.19it/s, v_num=2, train_loss=3.230, RMSE=14.40]
Epoch 20: 31%|███▏ | 10/32 [00:00<00:00, 315.85it/s, v_num=2, train_loss=3.230, RMSE=14.40]
Epoch 20: 31%|███▏ | 10/32 [00:00<00:00, 313.46it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 34%|███▍ | 11/32 [00:00<00:00, 312.06it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 34%|███▍ | 11/32 [00:00<00:00, 310.06it/s, v_num=2, train_loss=3.320, RMSE=14.40]
Epoch 20: 38%|███▊ | 12/32 [00:00<00:00, 312.60it/s, v_num=2, train_loss=3.320, RMSE=14.40]
Epoch 20: 38%|███▊ | 12/32 [00:00<00:00, 310.89it/s, v_num=2, train_loss=3.710, RMSE=14.40]
Epoch 20: 41%|████ | 13/32 [00:00<00:00, 313.42it/s, v_num=2, train_loss=3.710, RMSE=14.40]
Epoch 20: 41%|████ | 13/32 [00:00<00:00, 311.84it/s, v_num=2, train_loss=3.150, RMSE=14.40]
Epoch 20: 44%|████▍ | 14/32 [00:00<00:00, 314.07it/s, v_num=2, train_loss=3.150, RMSE=14.40]
Epoch 20: 44%|████▍ | 14/32 [00:00<00:00, 312.59it/s, v_num=2, train_loss=3.620, RMSE=14.40]
Epoch 20: 47%|████▋ | 15/32 [00:00<00:00, 314.63it/s, v_num=2, train_loss=3.620, RMSE=14.40]
Epoch 20: 47%|████▋ | 15/32 [00:00<00:00, 313.00it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 50%|█████ | 16/32 [00:00<00:00, 315.10it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 50%|█████ | 16/32 [00:00<00:00, 313.79it/s, v_num=2, train_loss=3.860, RMSE=14.40]
Epoch 20: 53%|█████▎ | 17/32 [00:00<00:00, 315.32it/s, v_num=2, train_loss=3.860, RMSE=14.40]
Epoch 20: 53%|█████▎ | 17/32 [00:00<00:00, 314.09it/s, v_num=2, train_loss=3.340, RMSE=14.40]
Epoch 20: 56%|█████▋ | 18/32 [00:00<00:00, 315.60it/s, v_num=2, train_loss=3.340, RMSE=14.40]
Epoch 20: 56%|█████▋ | 18/32 [00:00<00:00, 314.44it/s, v_num=2, train_loss=3.500, RMSE=14.40]
Epoch 20: 59%|█████▉ | 19/32 [00:00<00:00, 315.76it/s, v_num=2, train_loss=3.500, RMSE=14.40]
Epoch 20: 59%|█████▉ | 19/32 [00:00<00:00, 314.65it/s, v_num=2, train_loss=3.430, RMSE=14.40]
Epoch 20: 62%|██████▎ | 20/32 [00:00<00:00, 316.18it/s, v_num=2, train_loss=3.430, RMSE=14.40]
Epoch 20: 62%|██████▎ | 20/32 [00:00<00:00, 315.14it/s, v_num=2, train_loss=2.990, RMSE=14.40]
Epoch 20: 66%|██████▌ | 21/32 [00:00<00:00, 316.50it/s, v_num=2, train_loss=2.990, RMSE=14.40]
Epoch 20: 66%|██████▌ | 21/32 [00:00<00:00, 315.50it/s, v_num=2, train_loss=2.930, RMSE=14.40]
Epoch 20: 69%|██████▉ | 22/32 [00:00<00:00, 316.46it/s, v_num=2, train_loss=2.930, RMSE=14.40]
Epoch 20: 69%|██████▉ | 22/32 [00:00<00:00, 315.50it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 72%|███████▏ | 23/32 [00:00<00:00, 316.67it/s, v_num=2, train_loss=3.560, RMSE=14.40]
Epoch 20: 72%|███████▏ | 23/32 [00:00<00:00, 315.75it/s, v_num=2, train_loss=3.330, RMSE=14.40]
Epoch 20: 75%|███████▌ | 24/32 [00:00<00:00, 317.06it/s, v_num=2, train_loss=3.330, RMSE=14.40]
Epoch 20: 75%|███████▌ | 24/32 [00:00<00:00, 316.01it/s, v_num=2, train_loss=3.410, RMSE=14.40]
Epoch 20: 78%|███████▊ | 25/32 [00:00<00:00, 317.27it/s, v_num=2, train_loss=3.410, RMSE=14.40]
Epoch 20: 78%|███████▊ | 25/32 [00:00<00:00, 316.42it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 81%|████████▏ | 26/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.540, RMSE=14.40]
Epoch 20: 81%|████████▏ | 26/32 [00:00<00:00, 316.63it/s, v_num=2, train_loss=3.950, RMSE=14.40]
Epoch 20: 84%|████████▍ | 27/32 [00:00<00:00, 317.66it/s, v_num=2, train_loss=3.950, RMSE=14.40]
Epoch 20: 84%|████████▍ | 27/32 [00:00<00:00, 316.87it/s, v_num=2, train_loss=3.940, RMSE=14.40]
Epoch 20: 88%|████████▊ | 28/32 [00:00<00:00, 317.88it/s, v_num=2, train_loss=3.940, RMSE=14.40]
Epoch 20: 88%|████████▊ | 28/32 [00:00<00:00, 317.12it/s, v_num=2, train_loss=3.380, RMSE=14.40]
Epoch 20: 91%|█████████ | 29/32 [00:00<00:00, 318.25it/s, v_num=2, train_loss=3.380, RMSE=14.40]
Epoch 20: 91%|█████████ | 29/32 [00:00<00:00, 317.50it/s, v_num=2, train_loss=3.460, RMSE=14.40]
Epoch 20: 94%|█████████▍| 30/32 [00:00<00:00, 318.45it/s, v_num=2, train_loss=3.460, RMSE=14.40]
Epoch 20: 94%|█████████▍| 30/32 [00:00<00:00, 317.74it/s, v_num=2, train_loss=3.550, RMSE=14.40]
Epoch 20: 97%|█████████▋| 31/32 [00:00<00:00, 318.55it/s, v_num=2, train_loss=3.550, RMSE=14.40]
Epoch 20: 97%|█████████▋| 31/32 [00:00<00:00, 317.87it/s, v_num=2, train_loss=3.470, RMSE=14.40]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 318.81it/s, v_num=2, train_loss=3.470, RMSE=14.40]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 318.14it/s, v_num=2, train_loss=3.650, RMSE=14.40]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 575.93it/s]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 258.30it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 20: 100%|██████████| 32/32 [00:00<00:00, 257.15it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 20: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 21: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 21: 3%|▎ | 1/32 [00:00<00:00, 272.55it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 21: 3%|▎ | 1/32 [00:00<00:00, 257.38it/s, v_num=2, train_loss=3.370, RMSE=13.80]
Epoch 21: 6%|▋ | 2/32 [00:00<00:00, 291.28it/s, v_num=2, train_loss=3.370, RMSE=13.80]
Epoch 21: 6%|▋ | 2/32 [00:00<00:00, 282.62it/s, v_num=2, train_loss=3.620, RMSE=13.80]
Epoch 21: 9%|▉ | 3/32 [00:00<00:00, 299.17it/s, v_num=2, train_loss=3.620, RMSE=13.80]
Epoch 21: 9%|▉ | 3/32 [00:00<00:00, 293.06it/s, v_num=2, train_loss=3.420, RMSE=13.80]
Epoch 21: 12%|█▎ | 4/32 [00:00<00:00, 298.85it/s, v_num=2, train_loss=3.420, RMSE=13.80]
Epoch 21: 12%|█▎ | 4/32 [00:00<00:00, 294.21it/s, v_num=2, train_loss=3.550, RMSE=13.80]
Epoch 21: 16%|█▌ | 5/32 [00:00<00:00, 302.12it/s, v_num=2, train_loss=3.550, RMSE=13.80]
Epoch 21: 16%|█▌ | 5/32 [00:00<00:00, 298.33it/s, v_num=2, train_loss=3.580, RMSE=13.80]
Epoch 21: 19%|█▉ | 6/32 [00:00<00:00, 305.75it/s, v_num=2, train_loss=3.580, RMSE=13.80]
Epoch 21: 19%|█▉ | 6/32 [00:00<00:00, 302.50it/s, v_num=2, train_loss=3.390, RMSE=13.80]
Epoch 21: 22%|██▏ | 7/32 [00:00<00:00, 306.88it/s, v_num=2, train_loss=3.390, RMSE=13.80]
Epoch 21: 22%|██▏ | 7/32 [00:00<00:00, 304.08it/s, v_num=2, train_loss=3.610, RMSE=13.80]
Epoch 21: 25%|██▌ | 8/32 [00:00<00:00, 308.69it/s, v_num=2, train_loss=3.610, RMSE=13.80]
Epoch 21: 25%|██▌ | 8/32 [00:00<00:00, 306.22it/s, v_num=2, train_loss=3.560, RMSE=13.80]
Epoch 21: 28%|██▊ | 9/32 [00:00<00:00, 310.13it/s, v_num=2, train_loss=3.560, RMSE=13.80]
Epoch 21: 28%|██▊ | 9/32 [00:00<00:00, 307.71it/s, v_num=2, train_loss=3.450, RMSE=13.80]
Epoch 21: 31%|███▏ | 10/32 [00:00<00:00, 311.09it/s, v_num=2, train_loss=3.450, RMSE=13.80]
Epoch 21: 31%|███▏ | 10/32 [00:00<00:00, 309.02it/s, v_num=2, train_loss=3.440, RMSE=13.80]
Epoch 21: 34%|███▍ | 11/32 [00:00<00:00, 312.01it/s, v_num=2, train_loss=3.440, RMSE=13.80]
Epoch 21: 34%|███▍ | 11/32 [00:00<00:00, 310.14it/s, v_num=2, train_loss=3.460, RMSE=13.80]
Epoch 21: 38%|███▊ | 12/32 [00:00<00:00, 312.39it/s, v_num=2, train_loss=3.460, RMSE=13.80]
Epoch 21: 38%|███▊ | 12/32 [00:00<00:00, 310.69it/s, v_num=2, train_loss=3.620, RMSE=13.80]
Epoch 21: 41%|████ | 13/32 [00:00<00:00, 312.93it/s, v_num=2, train_loss=3.620, RMSE=13.80]
Epoch 21: 41%|████ | 13/32 [00:00<00:00, 311.37it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 21: 44%|████▍ | 14/32 [00:00<00:00, 313.68it/s, v_num=2, train_loss=3.650, RMSE=13.80]
Epoch 21: 44%|████▍ | 14/32 [00:00<00:00, 312.21it/s, v_num=2, train_loss=3.740, RMSE=13.80]
Epoch 21: 47%|████▋ | 15/32 [00:00<00:00, 314.61it/s, v_num=2, train_loss=3.740, RMSE=13.80]
Epoch 21: 47%|████▋ | 15/32 [00:00<00:00, 313.23it/s, v_num=2, train_loss=3.120, RMSE=13.80]
Epoch 21: 50%|█████ | 16/32 [00:00<00:00, 314.89it/s, v_num=2, train_loss=3.120, RMSE=13.80]
Epoch 21: 50%|█████ | 16/32 [00:00<00:00, 313.61it/s, v_num=2, train_loss=3.830, RMSE=13.80]
Epoch 21: 53%|█████▎ | 17/32 [00:00<00:00, 315.35it/s, v_num=2, train_loss=3.830, RMSE=13.80]
Epoch 21: 53%|█████▎ | 17/32 [00:00<00:00, 314.13it/s, v_num=2, train_loss=3.350, RMSE=13.80]
Epoch 21: 56%|█████▋ | 18/32 [00:00<00:00, 315.80it/s, v_num=2, train_loss=3.350, RMSE=13.80]
Epoch 21: 56%|█████▋ | 18/32 [00:00<00:00, 314.64it/s, v_num=2, train_loss=3.440, RMSE=13.80]
Epoch 21: 59%|█████▉ | 19/32 [00:00<00:00, 316.23it/s, v_num=2, train_loss=3.440, RMSE=13.80]
Epoch 21: 59%|█████▉ | 19/32 [00:00<00:00, 314.98it/s, v_num=2, train_loss=3.360, RMSE=13.80]
Epoch 21: 62%|██████▎ | 20/32 [00:00<00:00, 316.66it/s, v_num=2, train_loss=3.360, RMSE=13.80]
Epoch 21: 62%|██████▎ | 20/32 [00:00<00:00, 315.62it/s, v_num=2, train_loss=3.310, RMSE=13.80]
Epoch 21: 66%|██████▌ | 21/32 [00:00<00:00, 311.31it/s, v_num=2, train_loss=3.310, RMSE=13.80]
Epoch 21: 66%|██████▌ | 21/32 [00:00<00:00, 309.80it/s, v_num=2, train_loss=3.610, RMSE=13.80]
Epoch 21: 69%|██████▉ | 22/32 [00:00<00:00, 302.86it/s, v_num=2, train_loss=3.610, RMSE=13.80]
Epoch 21: 69%|██████▉ | 22/32 [00:00<00:00, 301.63it/s, v_num=2, train_loss=3.540, RMSE=13.80]
Epoch 21: 72%|███████▏ | 23/32 [00:00<00:00, 299.66it/s, v_num=2, train_loss=3.540, RMSE=13.80]
Epoch 21: 72%|███████▏ | 23/32 [00:00<00:00, 298.83it/s, v_num=2, train_loss=3.350, RMSE=13.80]
Epoch 21: 75%|███████▌ | 24/32 [00:00<00:00, 300.55it/s, v_num=2, train_loss=3.350, RMSE=13.80]
Epoch 21: 75%|███████▌ | 24/32 [00:00<00:00, 299.77it/s, v_num=2, train_loss=3.450, RMSE=13.80]
Epoch 21: 78%|███████▊ | 25/32 [00:00<00:00, 301.07it/s, v_num=2, train_loss=3.450, RMSE=13.80]
Epoch 21: 78%|███████▊ | 25/32 [00:00<00:00, 300.30it/s, v_num=2, train_loss=3.230, RMSE=13.80]
Epoch 21: 81%|████████▏ | 26/32 [00:00<00:00, 301.87it/s, v_num=2, train_loss=3.230, RMSE=13.80]
Epoch 21: 81%|████████▏ | 26/32 [00:00<00:00, 301.12it/s, v_num=2, train_loss=3.550, RMSE=13.80]
Epoch 21: 84%|████████▍ | 27/32 [00:00<00:00, 302.57it/s, v_num=2, train_loss=3.550, RMSE=13.80]
Epoch 21: 84%|████████▍ | 27/32 [00:00<00:00, 301.86it/s, v_num=2, train_loss=3.580, RMSE=13.80]
Epoch 21: 88%|████████▊ | 28/32 [00:00<00:00, 303.22it/s, v_num=2, train_loss=3.580, RMSE=13.80]
Epoch 21: 88%|████████▊ | 28/32 [00:00<00:00, 302.54it/s, v_num=2, train_loss=3.380, RMSE=13.80]
Epoch 21: 91%|█████████ | 29/32 [00:00<00:00, 302.45it/s, v_num=2, train_loss=3.380, RMSE=13.80]
Epoch 21: 91%|█████████ | 29/32 [00:00<00:00, 301.69it/s, v_num=2, train_loss=3.340, RMSE=13.80]
Epoch 21: 94%|█████████▍| 30/32 [00:00<00:00, 303.04it/s, v_num=2, train_loss=3.340, RMSE=13.80]
Epoch 21: 94%|█████████▍| 30/32 [00:00<00:00, 302.39it/s, v_num=2, train_loss=3.430, RMSE=13.80]
Epoch 21: 97%|█████████▋| 31/32 [00:00<00:00, 303.65it/s, v_num=2, train_loss=3.430, RMSE=13.80]
Epoch 21: 97%|█████████▋| 31/32 [00:00<00:00, 303.03it/s, v_num=2, train_loss=3.270, RMSE=13.80]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 304.21it/s, v_num=2, train_loss=3.270, RMSE=13.80]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 303.61it/s, v_num=2, train_loss=3.560, RMSE=13.80]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 628.08it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 623.97it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 618.01it/s]
Validation DataLoader 0: 50%|█████ | 5/10 [00:00<00:00, 617.65it/s]
Validation DataLoader 0: 60%|██████ | 6/10 [00:00<00:00, 615.80it/s]
Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 616.41it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 616.53it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 615.30it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 619.20it/s]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 251.26it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 21: 100%|██████████| 32/32 [00:00<00:00, 250.20it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 21: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 22: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 22: 3%|▎ | 1/32 [00:00<00:00, 293.47it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 22: 3%|▎ | 1/32 [00:00<00:00, 276.34it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 6%|▋ | 2/32 [00:00<00:00, 304.75it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 6%|▋ | 2/32 [00:00<00:00, 295.28it/s, v_num=2, train_loss=3.380, RMSE=13.10]
Epoch 22: 9%|▉ | 3/32 [00:00<00:00, 310.31it/s, v_num=2, train_loss=3.380, RMSE=13.10]
Epoch 22: 9%|▉ | 3/32 [00:00<00:00, 303.74it/s, v_num=2, train_loss=3.440, RMSE=13.10]
Epoch 22: 12%|█▎ | 4/32 [00:00<00:00, 314.57it/s, v_num=2, train_loss=3.440, RMSE=13.10]
Epoch 22: 12%|█▎ | 4/32 [00:00<00:00, 309.43it/s, v_num=2, train_loss=3.240, RMSE=13.10]
Epoch 22: 16%|█▌ | 5/32 [00:00<00:00, 316.13it/s, v_num=2, train_loss=3.240, RMSE=13.10]
Epoch 22: 16%|█▌ | 5/32 [00:00<00:00, 312.04it/s, v_num=2, train_loss=3.670, RMSE=13.10]
Epoch 22: 19%|█▉ | 6/32 [00:00<00:00, 317.78it/s, v_num=2, train_loss=3.670, RMSE=13.10]
Epoch 22: 19%|█▉ | 6/32 [00:00<00:00, 314.33it/s, v_num=2, train_loss=3.570, RMSE=13.10]
Epoch 22: 22%|██▏ | 7/32 [00:00<00:00, 318.68it/s, v_num=2, train_loss=3.570, RMSE=13.10]
Epoch 22: 22%|██▏ | 7/32 [00:00<00:00, 315.67it/s, v_num=2, train_loss=3.440, RMSE=13.10]
Epoch 22: 25%|██▌ | 8/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=3.440, RMSE=13.10]
Epoch 22: 25%|██▌ | 8/32 [00:00<00:00, 316.72it/s, v_num=2, train_loss=3.840, RMSE=13.10]
Epoch 22: 28%|██▊ | 9/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.840, RMSE=13.10]
Epoch 22: 28%|██▊ | 9/32 [00:00<00:00, 317.54it/s, v_num=2, train_loss=3.260, RMSE=13.10]
Epoch 22: 31%|███▏ | 10/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.260, RMSE=13.10]
Epoch 22: 31%|███▏ | 10/32 [00:00<00:00, 317.89it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 22: 34%|███▍ | 11/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.560, RMSE=13.10]
Epoch 22: 34%|███▍ | 11/32 [00:00<00:00, 318.39it/s, v_num=2, train_loss=3.380, RMSE=13.10]
Epoch 22: 38%|███▊ | 12/32 [00:00<00:00, 316.14it/s, v_num=2, train_loss=3.380, RMSE=13.10]
Epoch 22: 38%|███▊ | 12/32 [00:00<00:00, 313.63it/s, v_num=2, train_loss=3.400, RMSE=13.10]
Epoch 22: 41%|████ | 13/32 [00:00<00:00, 313.04it/s, v_num=2, train_loss=3.400, RMSE=13.10]
Epoch 22: 41%|████ | 13/32 [00:00<00:00, 311.45it/s, v_num=2, train_loss=3.430, RMSE=13.10]
Epoch 22: 44%|████▍ | 14/32 [00:00<00:00, 313.92it/s, v_num=2, train_loss=3.430, RMSE=13.10]
Epoch 22: 44%|████▍ | 14/32 [00:00<00:00, 312.46it/s, v_num=2, train_loss=3.420, RMSE=13.10]
Epoch 22: 47%|████▋ | 15/32 [00:00<00:00, 314.44it/s, v_num=2, train_loss=3.420, RMSE=13.10]
Epoch 22: 47%|████▋ | 15/32 [00:00<00:00, 313.06it/s, v_num=2, train_loss=3.620, RMSE=13.10]
Epoch 22: 50%|█████ | 16/32 [00:00<00:00, 314.88it/s, v_num=2, train_loss=3.620, RMSE=13.10]
Epoch 22: 50%|█████ | 16/32 [00:00<00:00, 313.60it/s, v_num=2, train_loss=3.120, RMSE=13.10]
Epoch 22: 53%|█████▎ | 17/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=3.120, RMSE=13.10]
Epoch 22: 53%|█████▎ | 17/32 [00:00<00:00, 314.24it/s, v_num=2, train_loss=3.110, RMSE=13.10]
Epoch 22: 56%|█████▋ | 18/32 [00:00<00:00, 316.21it/s, v_num=2, train_loss=3.110, RMSE=13.10]
Epoch 22: 56%|█████▋ | 18/32 [00:00<00:00, 314.85it/s, v_num=2, train_loss=3.050, RMSE=13.10]
Epoch 22: 59%|█████▉ | 19/32 [00:00<00:00, 316.48it/s, v_num=2, train_loss=3.050, RMSE=13.10]
Epoch 22: 59%|█████▉ | 19/32 [00:00<00:00, 315.39it/s, v_num=2, train_loss=3.750, RMSE=13.10]
Epoch 22: 62%|██████▎ | 20/32 [00:00<00:00, 316.85it/s, v_num=2, train_loss=3.750, RMSE=13.10]
Epoch 22: 62%|██████▎ | 20/32 [00:00<00:00, 315.81it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 66%|██████▌ | 21/32 [00:00<00:00, 317.18it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 66%|██████▌ | 21/32 [00:00<00:00, 316.19it/s, v_num=2, train_loss=3.740, RMSE=13.10]
Epoch 22: 69%|██████▉ | 22/32 [00:00<00:00, 317.36it/s, v_num=2, train_loss=3.740, RMSE=13.10]
Epoch 22: 69%|██████▉ | 22/32 [00:00<00:00, 316.38it/s, v_num=2, train_loss=3.580, RMSE=13.10]
Epoch 22: 72%|███████▏ | 23/32 [00:00<00:00, 317.79it/s, v_num=2, train_loss=3.580, RMSE=13.10]
Epoch 22: 72%|███████▏ | 23/32 [00:00<00:00, 316.84it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 75%|███████▌ | 24/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=3.530, RMSE=13.10]
Epoch 22: 75%|███████▌ | 24/32 [00:00<00:00, 317.13it/s, v_num=2, train_loss=3.450, RMSE=13.10]
Epoch 22: 78%|███████▊ | 25/32 [00:00<00:00, 318.22it/s, v_num=2, train_loss=3.450, RMSE=13.10]
Epoch 22: 78%|███████▊ | 25/32 [00:00<00:00, 317.37it/s, v_num=2, train_loss=3.360, RMSE=13.10]
Epoch 22: 81%|████████▏ | 26/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.360, RMSE=13.10]
Epoch 22: 81%|████████▏ | 26/32 [00:00<00:00, 317.28it/s, v_num=2, train_loss=3.200, RMSE=13.10]
Epoch 22: 84%|████████▍ | 27/32 [00:00<00:00, 318.45it/s, v_num=2, train_loss=3.200, RMSE=13.10]
Epoch 22: 84%|████████▍ | 27/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=3.690, RMSE=13.10]
Epoch 22: 88%|████████▊ | 28/32 [00:00<00:00, 318.62it/s, v_num=2, train_loss=3.690, RMSE=13.10]
Epoch 22: 88%|████████▊ | 28/32 [00:00<00:00, 317.87it/s, v_num=2, train_loss=3.150, RMSE=13.10]
Epoch 22: 91%|█████████ | 29/32 [00:00<00:00, 318.74it/s, v_num=2, train_loss=3.150, RMSE=13.10]
Epoch 22: 91%|█████████ | 29/32 [00:00<00:00, 318.02it/s, v_num=2, train_loss=3.340, RMSE=13.10]
Epoch 22: 94%|█████████▍| 30/32 [00:00<00:00, 318.91it/s, v_num=2, train_loss=3.340, RMSE=13.10]
Epoch 22: 94%|█████████▍| 30/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.310, RMSE=13.10]
Epoch 22: 97%|█████████▋| 31/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.310, RMSE=13.10]
Epoch 22: 97%|█████████▋| 31/32 [00:00<00:00, 318.44it/s, v_num=2, train_loss=3.310, RMSE=13.10]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 319.50it/s, v_num=2, train_loss=3.310, RMSE=13.10]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=3.120, RMSE=13.10]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 614.27it/s]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 261.18it/s, v_num=2, train_loss=3.120, RMSE=12.50]
Epoch 22: 100%|██████████| 32/32 [00:00<00:00, 260.04it/s, v_num=2, train_loss=3.120, RMSE=12.50]
Epoch 22: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.120, RMSE=12.50]
Epoch 23: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.120, RMSE=12.50]
Epoch 23: 3%|▎ | 1/32 [00:00<00:00, 287.18it/s, v_num=2, train_loss=3.120, RMSE=12.50]
Epoch 23: 3%|▎ | 1/32 [00:00<00:00, 270.81it/s, v_num=2, train_loss=3.740, RMSE=12.50]
Epoch 23: 6%|▋ | 2/32 [00:00<00:00, 303.40it/s, v_num=2, train_loss=3.740, RMSE=12.50]
Epoch 23: 6%|▋ | 2/32 [00:00<00:00, 293.97it/s, v_num=2, train_loss=3.470, RMSE=12.50]
Epoch 23: 9%|▉ | 3/32 [00:00<00:00, 309.40it/s, v_num=2, train_loss=3.470, RMSE=12.50]
Epoch 23: 9%|▉ | 3/32 [00:00<00:00, 302.87it/s, v_num=2, train_loss=3.180, RMSE=12.50]
Epoch 23: 12%|█▎ | 4/32 [00:00<00:00, 311.95it/s, v_num=2, train_loss=3.180, RMSE=12.50]
Epoch 23: 12%|█▎ | 4/32 [00:00<00:00, 306.95it/s, v_num=2, train_loss=3.210, RMSE=12.50]
Epoch 23: 16%|█▌ | 5/32 [00:00<00:00, 314.18it/s, v_num=2, train_loss=3.210, RMSE=12.50]
Epoch 23: 16%|█▌ | 5/32 [00:00<00:00, 310.12it/s, v_num=2, train_loss=3.010, RMSE=12.50]
Epoch 23: 19%|█▉ | 6/32 [00:00<00:00, 315.67it/s, v_num=2, train_loss=3.010, RMSE=12.50]
Epoch 23: 19%|█▉ | 6/32 [00:00<00:00, 312.22it/s, v_num=2, train_loss=3.450, RMSE=12.50]
Epoch 23: 22%|██▏ | 7/32 [00:00<00:00, 317.43it/s, v_num=2, train_loss=3.450, RMSE=12.50]
Epoch 23: 22%|██▏ | 7/32 [00:00<00:00, 314.46it/s, v_num=2, train_loss=3.680, RMSE=12.50]
Epoch 23: 25%|██▌ | 8/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=3.680, RMSE=12.50]
Epoch 23: 25%|██▌ | 8/32 [00:00<00:00, 315.45it/s, v_num=2, train_loss=3.480, RMSE=12.50]
Epoch 23: 28%|██▊ | 9/32 [00:00<00:00, 318.31it/s, v_num=2, train_loss=3.480, RMSE=12.50]
Epoch 23: 28%|██▊ | 9/32 [00:00<00:00, 315.98it/s, v_num=2, train_loss=3.240, RMSE=12.50]
Epoch 23: 31%|███▏ | 10/32 [00:00<00:00, 318.88it/s, v_num=2, train_loss=3.240, RMSE=12.50]
Epoch 23: 31%|███▏ | 10/32 [00:00<00:00, 316.77it/s, v_num=2, train_loss=3.380, RMSE=12.50]
Epoch 23: 34%|███▍ | 11/32 [00:00<00:00, 319.69it/s, v_num=2, train_loss=3.380, RMSE=12.50]
Epoch 23: 34%|███▍ | 11/32 [00:00<00:00, 317.77it/s, v_num=2, train_loss=3.390, RMSE=12.50]
Epoch 23: 38%|███▊ | 12/32 [00:00<00:00, 319.98it/s, v_num=2, train_loss=3.390, RMSE=12.50]
Epoch 23: 38%|███▊ | 12/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.440, RMSE=12.50]
Epoch 23: 41%|████ | 13/32 [00:00<00:00, 319.94it/s, v_num=2, train_loss=3.440, RMSE=12.50]
Epoch 23: 41%|████ | 13/32 [00:00<00:00, 318.30it/s, v_num=2, train_loss=3.320, RMSE=12.50]
Epoch 23: 44%|████▍ | 14/32 [00:00<00:00, 319.96it/s, v_num=2, train_loss=3.320, RMSE=12.50]
Epoch 23: 44%|████▍ | 14/32 [00:00<00:00, 318.46it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 47%|████▋ | 15/32 [00:00<00:00, 316.48it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 47%|████▋ | 15/32 [00:00<00:00, 315.08it/s, v_num=2, train_loss=3.630, RMSE=12.50]
Epoch 23: 50%|█████ | 16/32 [00:00<00:00, 317.12it/s, v_num=2, train_loss=3.630, RMSE=12.50]
Epoch 23: 50%|█████ | 16/32 [00:00<00:00, 315.82it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 53%|█████▎ | 17/32 [00:00<00:00, 317.50it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 53%|█████▎ | 17/32 [00:00<00:00, 316.27it/s, v_num=2, train_loss=3.580, RMSE=12.50]
Epoch 23: 56%|█████▋ | 18/32 [00:00<00:00, 317.77it/s, v_num=2, train_loss=3.580, RMSE=12.50]
Epoch 23: 56%|█████▋ | 18/32 [00:00<00:00, 316.59it/s, v_num=2, train_loss=3.320, RMSE=12.50]
Epoch 23: 59%|█████▉ | 19/32 [00:00<00:00, 317.97it/s, v_num=2, train_loss=3.320, RMSE=12.50]
Epoch 23: 59%|█████▉ | 19/32 [00:00<00:00, 316.86it/s, v_num=2, train_loss=3.530, RMSE=12.50]
Epoch 23: 62%|██████▎ | 20/32 [00:00<00:00, 318.37it/s, v_num=2, train_loss=3.530, RMSE=12.50]
Epoch 23: 62%|██████▎ | 20/32 [00:00<00:00, 317.11it/s, v_num=2, train_loss=3.020, RMSE=12.50]
Epoch 23: 66%|██████▌ | 21/32 [00:00<00:00, 318.56it/s, v_num=2, train_loss=3.020, RMSE=12.50]
Epoch 23: 66%|██████▌ | 21/32 [00:00<00:00, 317.54it/s, v_num=2, train_loss=3.360, RMSE=12.50]
Epoch 23: 69%|██████▉ | 22/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.360, RMSE=12.50]
Epoch 23: 69%|██████▉ | 22/32 [00:00<00:00, 317.90it/s, v_num=2, train_loss=3.520, RMSE=12.50]
Epoch 23: 72%|███████▏ | 23/32 [00:00<00:00, 319.03it/s, v_num=2, train_loss=3.520, RMSE=12.50]
Epoch 23: 72%|███████▏ | 23/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 75%|███████▌ | 24/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 75%|███████▌ | 24/32 [00:00<00:00, 318.19it/s, v_num=2, train_loss=3.580, RMSE=12.50]
Epoch 23: 78%|███████▊ | 25/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.580, RMSE=12.50]
Epoch 23: 78%|███████▊ | 25/32 [00:00<00:00, 318.33it/s, v_num=2, train_loss=3.670, RMSE=12.50]
Epoch 23: 81%|████████▏ | 26/32 [00:00<00:00, 318.87it/s, v_num=2, train_loss=3.670, RMSE=12.50]
Epoch 23: 81%|████████▏ | 26/32 [00:00<00:00, 318.03it/s, v_num=2, train_loss=2.970, RMSE=12.50]
Epoch 23: 84%|████████▍ | 27/32 [00:00<00:00, 318.81it/s, v_num=2, train_loss=2.970, RMSE=12.50]
Epoch 23: 84%|████████▍ | 27/32 [00:00<00:00, 318.01it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 88%|████████▊ | 28/32 [00:00<00:00, 318.88it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 88%|████████▊ | 28/32 [00:00<00:00, 318.11it/s, v_num=2, train_loss=3.440, RMSE=12.50]
Epoch 23: 91%|█████████ | 29/32 [00:00<00:00, 318.98it/s, v_num=2, train_loss=3.440, RMSE=12.50]
Epoch 23: 91%|█████████ | 29/32 [00:00<00:00, 318.24it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 94%|█████████▍| 30/32 [00:00<00:00, 318.92it/s, v_num=2, train_loss=3.220, RMSE=12.50]
Epoch 23: 94%|█████████▍| 30/32 [00:00<00:00, 318.22it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 97%|█████████▋| 31/32 [00:00<00:00, 319.04it/s, v_num=2, train_loss=3.490, RMSE=12.50]
Epoch 23: 97%|█████████▋| 31/32 [00:00<00:00, 318.35it/s, v_num=2, train_loss=3.280, RMSE=12.50]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 319.26it/s, v_num=2, train_loss=3.280, RMSE=12.50]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 318.60it/s, v_num=2, train_loss=2.980, RMSE=12.50]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 623.69it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 616.51it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 613.18it/s]
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Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 612.31it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 610.57it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 610.98it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 615.22it/s]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 261.07it/s, v_num=2, train_loss=2.980, RMSE=11.80]
Epoch 23: 100%|██████████| 32/32 [00:00<00:00, 259.86it/s, v_num=2, train_loss=2.980, RMSE=11.80]
Epoch 23: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.980, RMSE=11.80]
Epoch 24: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.980, RMSE=11.80]
Epoch 24: 3%|▎ | 1/32 [00:00<00:00, 283.57it/s, v_num=2, train_loss=2.980, RMSE=11.80]
Epoch 24: 3%|▎ | 1/32 [00:00<00:00, 266.51it/s, v_num=2, train_loss=3.780, RMSE=11.80]
Epoch 24: 6%|▋ | 2/32 [00:00<00:00, 297.18it/s, v_num=2, train_loss=3.780, RMSE=11.80]
Epoch 24: 6%|▋ | 2/32 [00:00<00:00, 288.17it/s, v_num=2, train_loss=3.690, RMSE=11.80]
Epoch 24: 9%|▉ | 3/32 [00:00<00:00, 304.61it/s, v_num=2, train_loss=3.690, RMSE=11.80]
Epoch 24: 9%|▉ | 3/32 [00:00<00:00, 298.27it/s, v_num=2, train_loss=3.180, RMSE=11.80]
Epoch 24: 12%|█▎ | 4/32 [00:00<00:00, 309.10it/s, v_num=2, train_loss=3.180, RMSE=11.80]
Epoch 24: 12%|█▎ | 4/32 [00:00<00:00, 304.18it/s, v_num=2, train_loss=3.520, RMSE=11.80]
Epoch 24: 16%|█▌ | 5/32 [00:00<00:00, 311.86it/s, v_num=2, train_loss=3.520, RMSE=11.80]
Epoch 24: 16%|█▌ | 5/32 [00:00<00:00, 307.85it/s, v_num=2, train_loss=3.240, RMSE=11.80]
Epoch 24: 19%|█▉ | 6/32 [00:00<00:00, 313.75it/s, v_num=2, train_loss=3.240, RMSE=11.80]
Epoch 24: 19%|█▉ | 6/32 [00:00<00:00, 310.25it/s, v_num=2, train_loss=3.200, RMSE=11.80]
Epoch 24: 22%|██▏ | 7/32 [00:00<00:00, 315.11it/s, v_num=2, train_loss=3.200, RMSE=11.80]
Epoch 24: 22%|██▏ | 7/32 [00:00<00:00, 312.20it/s, v_num=2, train_loss=3.320, RMSE=11.80]
Epoch 24: 25%|██▌ | 8/32 [00:00<00:00, 316.25it/s, v_num=2, train_loss=3.320, RMSE=11.80]
Epoch 24: 25%|██▌ | 8/32 [00:00<00:00, 313.67it/s, v_num=2, train_loss=3.270, RMSE=11.80]
Epoch 24: 28%|██▊ | 9/32 [00:00<00:00, 316.96it/s, v_num=2, train_loss=3.270, RMSE=11.80]
Epoch 24: 28%|██▊ | 9/32 [00:00<00:00, 314.39it/s, v_num=2, train_loss=3.130, RMSE=11.80]
Epoch 24: 31%|███▏ | 10/32 [00:00<00:00, 317.53it/s, v_num=2, train_loss=3.130, RMSE=11.80]
Epoch 24: 31%|███▏ | 10/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=3.390, RMSE=11.80]
Epoch 24: 34%|███▍ | 11/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.390, RMSE=11.80]
Epoch 24: 34%|███▍ | 11/32 [00:00<00:00, 316.17it/s, v_num=2, train_loss=3.800, RMSE=11.80]
Epoch 24: 38%|███▊ | 12/32 [00:00<00:00, 318.30it/s, v_num=2, train_loss=3.800, RMSE=11.80]
Epoch 24: 38%|███▊ | 12/32 [00:00<00:00, 316.54it/s, v_num=2, train_loss=2.990, RMSE=11.80]
Epoch 24: 41%|████ | 13/32 [00:00<00:00, 318.61it/s, v_num=2, train_loss=2.990, RMSE=11.80]
Epoch 24: 41%|████ | 13/32 [00:00<00:00, 316.97it/s, v_num=2, train_loss=3.440, RMSE=11.80]
Epoch 24: 44%|████▍ | 14/32 [00:00<00:00, 319.12it/s, v_num=2, train_loss=3.440, RMSE=11.80]
Epoch 24: 44%|████▍ | 14/32 [00:00<00:00, 317.59it/s, v_num=2, train_loss=3.220, RMSE=11.80]
Epoch 24: 47%|████▋ | 15/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=3.220, RMSE=11.80]
Epoch 24: 47%|████▋ | 15/32 [00:00<00:00, 317.73it/s, v_num=2, train_loss=3.410, RMSE=11.80]
Epoch 24: 50%|█████ | 16/32 [00:00<00:00, 319.31it/s, v_num=2, train_loss=3.410, RMSE=11.80]
Epoch 24: 50%|█████ | 16/32 [00:00<00:00, 317.98it/s, v_num=2, train_loss=3.500, RMSE=11.80]
Epoch 24: 53%|█████▎ | 17/32 [00:00<00:00, 319.67it/s, v_num=2, train_loss=3.500, RMSE=11.80]
Epoch 24: 53%|█████▎ | 17/32 [00:00<00:00, 318.37it/s, v_num=2, train_loss=3.330, RMSE=11.80]
Epoch 24: 56%|█████▋ | 18/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=3.330, RMSE=11.80]
Epoch 24: 56%|█████▋ | 18/32 [00:00<00:00, 318.73it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 59%|█████▉ | 19/32 [00:00<00:00, 320.13it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 59%|█████▉ | 19/32 [00:00<00:00, 319.01it/s, v_num=2, train_loss=3.160, RMSE=11.80]
Epoch 24: 62%|██████▎ | 20/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=3.160, RMSE=11.80]
Epoch 24: 62%|██████▎ | 20/32 [00:00<00:00, 319.30it/s, v_num=2, train_loss=3.180, RMSE=11.80]
Epoch 24: 66%|██████▌ | 21/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=3.180, RMSE=11.80]
Epoch 24: 66%|██████▌ | 21/32 [00:00<00:00, 319.55it/s, v_num=2, train_loss=3.360, RMSE=11.80]
Epoch 24: 69%|██████▉ | 22/32 [00:00<00:00, 321.00it/s, v_num=2, train_loss=3.360, RMSE=11.80]
Epoch 24: 69%|██████▉ | 22/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 72%|███████▏ | 23/32 [00:00<00:00, 321.27it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 72%|███████▏ | 23/32 [00:00<00:00, 320.26it/s, v_num=2, train_loss=3.200, RMSE=11.80]
Epoch 24: 75%|███████▌ | 24/32 [00:00<00:00, 321.41it/s, v_num=2, train_loss=3.200, RMSE=11.80]
Epoch 24: 75%|███████▌ | 24/32 [00:00<00:00, 320.52it/s, v_num=2, train_loss=3.250, RMSE=11.80]
Epoch 24: 78%|███████▊ | 25/32 [00:00<00:00, 321.53it/s, v_num=2, train_loss=3.250, RMSE=11.80]
Epoch 24: 78%|███████▊ | 25/32 [00:00<00:00, 320.68it/s, v_num=2, train_loss=3.400, RMSE=11.80]
Epoch 24: 81%|████████▏ | 26/32 [00:00<00:00, 321.63it/s, v_num=2, train_loss=3.400, RMSE=11.80]
Epoch 24: 81%|████████▏ | 26/32 [00:00<00:00, 320.79it/s, v_num=2, train_loss=3.260, RMSE=11.80]
Epoch 24: 84%|████████▍ | 27/32 [00:00<00:00, 321.96it/s, v_num=2, train_loss=3.260, RMSE=11.80]
Epoch 24: 84%|████████▍ | 27/32 [00:00<00:00, 321.17it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 88%|████████▊ | 28/32 [00:00<00:00, 321.91it/s, v_num=2, train_loss=3.460, RMSE=11.80]
Epoch 24: 88%|████████▊ | 28/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.010, RMSE=11.80]
Epoch 24: 91%|█████████ | 29/32 [00:00<00:00, 321.73it/s, v_num=2, train_loss=3.010, RMSE=11.80]
Epoch 24: 91%|█████████ | 29/32 [00:00<00:00, 320.97it/s, v_num=2, train_loss=3.430, RMSE=11.80]
Epoch 24: 94%|█████████▍| 30/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.430, RMSE=11.80]
Epoch 24: 94%|█████████▍| 30/32 [00:00<00:00, 321.09it/s, v_num=2, train_loss=3.380, RMSE=11.80]
Epoch 24: 97%|█████████▋| 31/32 [00:00<00:00, 322.08it/s, v_num=2, train_loss=3.380, RMSE=11.80]
Epoch 24: 97%|█████████▋| 31/32 [00:00<00:00, 321.39it/s, v_num=2, train_loss=3.240, RMSE=11.80]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 322.20it/s, v_num=2, train_loss=3.240, RMSE=11.80]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 321.53it/s, v_num=2, train_loss=3.260, RMSE=11.80]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 611.41it/s]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 261.60it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 24: 100%|██████████| 32/32 [00:00<00:00, 260.46it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 24: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 3%|▎ | 1/32 [00:00<00:00, 278.34it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 3%|▎ | 1/32 [00:00<00:00, 262.64it/s, v_num=2, train_loss=3.310, RMSE=11.20]
Epoch 25: 6%|▋ | 2/32 [00:00<00:00, 295.48it/s, v_num=2, train_loss=3.310, RMSE=11.20]
Epoch 25: 6%|▋ | 2/32 [00:00<00:00, 286.56it/s, v_num=2, train_loss=3.310, RMSE=11.20]
Epoch 25: 9%|▉ | 3/32 [00:00<00:00, 304.65it/s, v_num=2, train_loss=3.310, RMSE=11.20]
Epoch 25: 9%|▉ | 3/32 [00:00<00:00, 298.32it/s, v_num=2, train_loss=3.330, RMSE=11.20]
Epoch 25: 12%|█▎ | 4/32 [00:00<00:00, 307.73it/s, v_num=2, train_loss=3.330, RMSE=11.20]
Epoch 25: 12%|█▎ | 4/32 [00:00<00:00, 302.79it/s, v_num=2, train_loss=3.690, RMSE=11.20]
Epoch 25: 16%|█▌ | 5/32 [00:00<00:00, 310.09it/s, v_num=2, train_loss=3.690, RMSE=11.20]
Epoch 25: 16%|█▌ | 5/32 [00:00<00:00, 306.12it/s, v_num=2, train_loss=3.100, RMSE=11.20]
Epoch 25: 19%|█▉ | 6/32 [00:00<00:00, 311.71it/s, v_num=2, train_loss=3.100, RMSE=11.20]
Epoch 25: 19%|█▉ | 6/32 [00:00<00:00, 308.39it/s, v_num=2, train_loss=3.200, RMSE=11.20]
Epoch 25: 22%|██▏ | 7/32 [00:00<00:00, 313.95it/s, v_num=2, train_loss=3.200, RMSE=11.20]
Epoch 25: 22%|██▏ | 7/32 [00:00<00:00, 311.08it/s, v_num=2, train_loss=3.250, RMSE=11.20]
Epoch 25: 25%|██▌ | 8/32 [00:00<00:00, 314.93it/s, v_num=2, train_loss=3.250, RMSE=11.20]
Epoch 25: 25%|██▌ | 8/32 [00:00<00:00, 312.38it/s, v_num=2, train_loss=3.590, RMSE=11.20]
Epoch 25: 28%|██▊ | 9/32 [00:00<00:00, 316.01it/s, v_num=2, train_loss=3.590, RMSE=11.20]
Epoch 25: 28%|██▊ | 9/32 [00:00<00:00, 313.73it/s, v_num=2, train_loss=2.780, RMSE=11.20]
Epoch 25: 31%|███▏ | 10/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=2.780, RMSE=11.20]
Epoch 25: 31%|███▏ | 10/32 [00:00<00:00, 314.75it/s, v_num=2, train_loss=3.350, RMSE=11.20]
Epoch 25: 34%|███▍ | 11/32 [00:00<00:00, 318.01it/s, v_num=2, train_loss=3.350, RMSE=11.20]
Epoch 25: 34%|███▍ | 11/32 [00:00<00:00, 315.87it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 38%|███▊ | 12/32 [00:00<00:00, 318.79it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 38%|███▊ | 12/32 [00:00<00:00, 317.04it/s, v_num=2, train_loss=3.420, RMSE=11.20]
Epoch 25: 41%|████ | 13/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=3.420, RMSE=11.20]
Epoch 25: 41%|████ | 13/32 [00:00<00:00, 317.72it/s, v_num=2, train_loss=3.390, RMSE=11.20]
Epoch 25: 44%|████▍ | 14/32 [00:00<00:00, 319.93it/s, v_num=2, train_loss=3.390, RMSE=11.20]
Epoch 25: 44%|████▍ | 14/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.400, RMSE=11.20]
Epoch 25: 47%|████▋ | 15/32 [00:00<00:00, 320.15it/s, v_num=2, train_loss=3.400, RMSE=11.20]
Epoch 25: 47%|████▋ | 15/32 [00:00<00:00, 318.75it/s, v_num=2, train_loss=3.590, RMSE=11.20]
Epoch 25: 50%|█████ | 16/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=3.590, RMSE=11.20]
Epoch 25: 50%|█████ | 16/32 [00:00<00:00, 319.31it/s, v_num=2, train_loss=2.890, RMSE=11.20]
Epoch 25: 53%|█████▎ | 17/32 [00:00<00:00, 320.97it/s, v_num=2, train_loss=2.890, RMSE=11.20]
Epoch 25: 53%|█████▎ | 17/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=3.330, RMSE=11.20]
Epoch 25: 56%|█████▋ | 18/32 [00:00<00:00, 321.21it/s, v_num=2, train_loss=3.330, RMSE=11.20]
Epoch 25: 56%|█████▋ | 18/32 [00:00<00:00, 320.03it/s, v_num=2, train_loss=3.440, RMSE=11.20]
Epoch 25: 59%|█████▉ | 19/32 [00:00<00:00, 321.54it/s, v_num=2, train_loss=3.440, RMSE=11.20]
Epoch 25: 59%|█████▉ | 19/32 [00:00<00:00, 320.43it/s, v_num=2, train_loss=3.540, RMSE=11.20]
Epoch 25: 62%|██████▎ | 20/32 [00:00<00:00, 321.88it/s, v_num=2, train_loss=3.540, RMSE=11.20]
Epoch 25: 62%|██████▎ | 20/32 [00:00<00:00, 320.81it/s, v_num=2, train_loss=3.190, RMSE=11.20]
Epoch 25: 66%|██████▌ | 21/32 [00:00<00:00, 322.06it/s, v_num=2, train_loss=3.190, RMSE=11.20]
Epoch 25: 66%|██████▌ | 21/32 [00:00<00:00, 321.04it/s, v_num=2, train_loss=3.270, RMSE=11.20]
Epoch 25: 69%|██████▉ | 22/32 [00:00<00:00, 322.23it/s, v_num=2, train_loss=3.270, RMSE=11.20]
Epoch 25: 69%|██████▉ | 22/32 [00:00<00:00, 321.25it/s, v_num=2, train_loss=3.120, RMSE=11.20]
Epoch 25: 72%|███████▏ | 23/32 [00:00<00:00, 322.21it/s, v_num=2, train_loss=3.120, RMSE=11.20]
Epoch 25: 72%|███████▏ | 23/32 [00:00<00:00, 321.27it/s, v_num=2, train_loss=3.230, RMSE=11.20]
Epoch 25: 75%|███████▌ | 24/32 [00:00<00:00, 322.49it/s, v_num=2, train_loss=3.230, RMSE=11.20]
Epoch 25: 75%|███████▌ | 24/32 [00:00<00:00, 321.48it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 78%|███████▊ | 25/32 [00:00<00:00, 322.52it/s, v_num=2, train_loss=3.260, RMSE=11.20]
Epoch 25: 78%|███████▊ | 25/32 [00:00<00:00, 321.66it/s, v_num=2, train_loss=3.410, RMSE=11.20]
Epoch 25: 81%|████████▏ | 26/32 [00:00<00:00, 322.68it/s, v_num=2, train_loss=3.410, RMSE=11.20]
Epoch 25: 81%|████████▏ | 26/32 [00:00<00:00, 321.82it/s, v_num=2, train_loss=3.150, RMSE=11.20]
Epoch 25: 84%|████████▍ | 27/32 [00:00<00:00, 322.82it/s, v_num=2, train_loss=3.150, RMSE=11.20]
Epoch 25: 84%|████████▍ | 27/32 [00:00<00:00, 322.02it/s, v_num=2, train_loss=3.460, RMSE=11.20]
Epoch 25: 88%|████████▊ | 28/32 [00:00<00:00, 322.91it/s, v_num=2, train_loss=3.460, RMSE=11.20]
Epoch 25: 88%|████████▊ | 28/32 [00:00<00:00, 322.12it/s, v_num=2, train_loss=3.220, RMSE=11.20]
Epoch 25: 91%|█████████ | 29/32 [00:00<00:00, 323.01it/s, v_num=2, train_loss=3.220, RMSE=11.20]
Epoch 25: 91%|█████████ | 29/32 [00:00<00:00, 322.25it/s, v_num=2, train_loss=3.290, RMSE=11.20]
Epoch 25: 94%|█████████▍| 30/32 [00:00<00:00, 322.88it/s, v_num=2, train_loss=3.290, RMSE=11.20]
Epoch 25: 94%|█████████▍| 30/32 [00:00<00:00, 322.15it/s, v_num=2, train_loss=3.690, RMSE=11.20]
Epoch 25: 97%|█████████▋| 31/32 [00:00<00:00, 322.93it/s, v_num=2, train_loss=3.690, RMSE=11.20]
Epoch 25: 97%|█████████▋| 31/32 [00:00<00:00, 322.23it/s, v_num=2, train_loss=2.980, RMSE=11.20]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 323.13it/s, v_num=2, train_loss=2.980, RMSE=11.20]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 322.44it/s, v_num=2, train_loss=3.410, RMSE=11.20]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 609.11it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 610.17it/s]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 610.24it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 609.72it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 614.12it/s]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 263.47it/s, v_num=2, train_loss=3.410, RMSE=10.60]
Epoch 25: 100%|██████████| 32/32 [00:00<00:00, 262.30it/s, v_num=2, train_loss=3.410, RMSE=10.60]
Epoch 25: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.410, RMSE=10.60]
Epoch 26: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.410, RMSE=10.60]
Epoch 26: 3%|▎ | 1/32 [00:00<00:00, 274.26it/s, v_num=2, train_loss=3.410, RMSE=10.60]
Epoch 26: 3%|▎ | 1/32 [00:00<00:00, 258.84it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 26: 6%|▋ | 2/32 [00:00<00:00, 292.11it/s, v_num=2, train_loss=3.110, RMSE=10.60]
Epoch 26: 6%|▋ | 2/32 [00:00<00:00, 283.44it/s, v_num=2, train_loss=3.620, RMSE=10.60]
Epoch 26: 9%|▉ | 3/32 [00:00<00:00, 301.71it/s, v_num=2, train_loss=3.620, RMSE=10.60]
Epoch 26: 9%|▉ | 3/32 [00:00<00:00, 295.53it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 26: 12%|█▎ | 4/32 [00:00<00:00, 308.02it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 26: 12%|█▎ | 4/32 [00:00<00:00, 303.18it/s, v_num=2, train_loss=2.810, RMSE=10.60]
Epoch 26: 16%|█▌ | 5/32 [00:00<00:00, 309.57it/s, v_num=2, train_loss=2.810, RMSE=10.60]
Epoch 26: 16%|█▌ | 5/32 [00:00<00:00, 305.66it/s, v_num=2, train_loss=3.610, RMSE=10.60]
Epoch 26: 19%|█▉ | 6/32 [00:00<00:00, 311.81it/s, v_num=2, train_loss=3.610, RMSE=10.60]
Epoch 26: 19%|█▉ | 6/32 [00:00<00:00, 308.45it/s, v_num=2, train_loss=3.370, RMSE=10.60]
Epoch 26: 22%|██▏ | 7/32 [00:00<00:00, 313.52it/s, v_num=2, train_loss=3.370, RMSE=10.60]
Epoch 26: 22%|██▏ | 7/32 [00:00<00:00, 310.49it/s, v_num=2, train_loss=3.120, RMSE=10.60]
Epoch 26: 25%|██▌ | 8/32 [00:00<00:00, 315.08it/s, v_num=2, train_loss=3.120, RMSE=10.60]
Epoch 26: 25%|██▌ | 8/32 [00:00<00:00, 312.08it/s, v_num=2, train_loss=3.050, RMSE=10.60]
Epoch 26: 28%|██▊ | 9/32 [00:00<00:00, 315.98it/s, v_num=2, train_loss=3.050, RMSE=10.60]
Epoch 26: 28%|██▊ | 9/32 [00:00<00:00, 313.66it/s, v_num=2, train_loss=3.500, RMSE=10.60]
Epoch 26: 31%|███▏ | 10/32 [00:00<00:00, 316.88it/s, v_num=2, train_loss=3.500, RMSE=10.60]
Epoch 26: 31%|███▏ | 10/32 [00:00<00:00, 314.82it/s, v_num=2, train_loss=3.640, RMSE=10.60]
Epoch 26: 34%|███▍ | 11/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=3.640, RMSE=10.60]
Epoch 26: 34%|███▍ | 11/32 [00:00<00:00, 315.49it/s, v_num=2, train_loss=3.040, RMSE=10.60]
Epoch 26: 38%|███▊ | 12/32 [00:00<00:00, 318.06it/s, v_num=2, train_loss=3.040, RMSE=10.60]
Epoch 26: 38%|███▊ | 12/32 [00:00<00:00, 316.23it/s, v_num=2, train_loss=3.480, RMSE=10.60]
Epoch 26: 41%|████ | 13/32 [00:00<00:00, 318.63it/s, v_num=2, train_loss=3.480, RMSE=10.60]
Epoch 26: 41%|████ | 13/32 [00:00<00:00, 316.99it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 26: 44%|████▍ | 14/32 [00:00<00:00, 318.88it/s, v_num=2, train_loss=3.340, RMSE=10.60]
Epoch 26: 44%|████▍ | 14/32 [00:00<00:00, 317.35it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 26: 47%|████▋ | 15/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 26: 47%|████▋ | 15/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.190, RMSE=10.60]
Epoch 26: 50%|█████ | 16/32 [00:00<00:00, 319.46it/s, v_num=2, train_loss=3.190, RMSE=10.60]
Epoch 26: 50%|█████ | 16/32 [00:00<00:00, 318.14it/s, v_num=2, train_loss=3.210, RMSE=10.60]
Epoch 26: 53%|█████▎ | 17/32 [00:00<00:00, 319.59it/s, v_num=2, train_loss=3.210, RMSE=10.60]
Epoch 26: 53%|█████▎ | 17/32 [00:00<00:00, 318.35it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 26: 56%|█████▋ | 18/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 26: 56%|█████▋ | 18/32 [00:00<00:00, 318.65it/s, v_num=2, train_loss=3.230, RMSE=10.60]
Epoch 26: 59%|█████▉ | 19/32 [00:00<00:00, 317.18it/s, v_num=2, train_loss=3.230, RMSE=10.60]
Epoch 26: 59%|█████▉ | 19/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=3.390, RMSE=10.60]
Epoch 26: 62%|██████▎ | 20/32 [00:00<00:00, 317.34it/s, v_num=2, train_loss=3.390, RMSE=10.60]
Epoch 26: 62%|██████▎ | 20/32 [00:00<00:00, 316.30it/s, v_num=2, train_loss=2.970, RMSE=10.60]
Epoch 26: 66%|██████▌ | 21/32 [00:00<00:00, 317.61it/s, v_num=2, train_loss=2.970, RMSE=10.60]
Epoch 26: 66%|██████▌ | 21/32 [00:00<00:00, 316.62it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 26: 69%|██████▉ | 22/32 [00:00<00:00, 318.09it/s, v_num=2, train_loss=3.260, RMSE=10.60]
Epoch 26: 69%|██████▉ | 22/32 [00:00<00:00, 317.13it/s, v_num=2, train_loss=3.400, RMSE=10.60]
Epoch 26: 72%|███████▏ | 23/32 [00:00<00:00, 318.34it/s, v_num=2, train_loss=3.400, RMSE=10.60]
Epoch 26: 72%|███████▏ | 23/32 [00:00<00:00, 317.42it/s, v_num=2, train_loss=3.380, RMSE=10.60]
Epoch 26: 75%|███████▌ | 24/32 [00:00<00:00, 318.61it/s, v_num=2, train_loss=3.380, RMSE=10.60]
Epoch 26: 75%|███████▌ | 24/32 [00:00<00:00, 317.73it/s, v_num=2, train_loss=2.940, RMSE=10.60]
Epoch 26: 78%|███████▊ | 25/32 [00:00<00:00, 318.88it/s, v_num=2, train_loss=2.940, RMSE=10.60]
Epoch 26: 78%|███████▊ | 25/32 [00:00<00:00, 318.04it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 26: 81%|████████▏ | 26/32 [00:00<00:00, 319.28it/s, v_num=2, train_loss=3.330, RMSE=10.60]
Epoch 26: 81%|████████▏ | 26/32 [00:00<00:00, 318.34it/s, v_num=2, train_loss=3.190, RMSE=10.60]
Epoch 26: 84%|████████▍ | 27/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=3.190, RMSE=10.60]
Epoch 26: 84%|████████▍ | 27/32 [00:00<00:00, 318.67it/s, v_num=2, train_loss=3.380, RMSE=10.60]
Epoch 26: 88%|████████▊ | 28/32 [00:00<00:00, 319.65it/s, v_num=2, train_loss=3.380, RMSE=10.60]
Epoch 26: 88%|████████▊ | 28/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.350, RMSE=10.60]
Epoch 26: 91%|█████████ | 29/32 [00:00<00:00, 319.68it/s, v_num=2, train_loss=3.350, RMSE=10.60]
Epoch 26: 91%|█████████ | 29/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=2.960, RMSE=10.60]
Epoch 26: 94%|█████████▍| 30/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=2.960, RMSE=10.60]
Epoch 26: 94%|█████████▍| 30/32 [00:00<00:00, 319.17it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 26: 97%|█████████▋| 31/32 [00:00<00:00, 320.13it/s, v_num=2, train_loss=3.180, RMSE=10.60]
Epoch 26: 97%|█████████▋| 31/32 [00:00<00:00, 319.44it/s, v_num=2, train_loss=3.290, RMSE=10.60]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=3.290, RMSE=10.60]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=3.040, RMSE=10.60]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 619.33it/s]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 262.15it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 26: 100%|██████████| 32/32 [00:00<00:00, 261.01it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 26: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 27: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 27: 3%|▎ | 1/32 [00:00<00:00, 286.63it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 27: 3%|▎ | 1/32 [00:00<00:00, 267.84it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 6%|▋ | 2/32 [00:00<00:00, 301.20it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 6%|▋ | 2/32 [00:00<00:00, 292.00it/s, v_num=2, train_loss=3.220, RMSE=10.10]
Epoch 27: 9%|▉ | 3/32 [00:00<00:00, 306.27it/s, v_num=2, train_loss=3.220, RMSE=10.10]
Epoch 27: 9%|▉ | 3/32 [00:00<00:00, 299.92it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 12%|█▎ | 4/32 [00:00<00:00, 309.30it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 12%|█▎ | 4/32 [00:00<00:00, 304.38it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 16%|█▌ | 5/32 [00:00<00:00, 311.88it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 16%|█▌ | 5/32 [00:00<00:00, 307.87it/s, v_num=2, train_loss=3.320, RMSE=10.10]
Epoch 27: 19%|█▉ | 6/32 [00:00<00:00, 314.10it/s, v_num=2, train_loss=3.320, RMSE=10.10]
Epoch 27: 19%|█▉ | 6/32 [00:00<00:00, 310.69it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 22%|██▏ | 7/32 [00:00<00:00, 315.57it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 22%|██▏ | 7/32 [00:00<00:00, 312.65it/s, v_num=2, train_loss=3.320, RMSE=10.10]
Epoch 27: 25%|██▌ | 8/32 [00:00<00:00, 316.29it/s, v_num=2, train_loss=3.320, RMSE=10.10]
Epoch 27: 25%|██▌ | 8/32 [00:00<00:00, 313.58it/s, v_num=2, train_loss=3.560, RMSE=10.10]
Epoch 27: 28%|██▊ | 9/32 [00:00<00:00, 316.48it/s, v_num=2, train_loss=3.560, RMSE=10.10]
Epoch 27: 28%|██▊ | 9/32 [00:00<00:00, 314.15it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 31%|███▏ | 10/32 [00:00<00:00, 317.39it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 31%|███▏ | 10/32 [00:00<00:00, 314.94it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 27: 34%|███▍ | 11/32 [00:00<00:00, 317.96it/s, v_num=2, train_loss=3.040, RMSE=10.10]
Epoch 27: 34%|███▍ | 11/32 [00:00<00:00, 316.08it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 38%|███▊ | 12/32 [00:00<00:00, 318.59it/s, v_num=2, train_loss=3.200, RMSE=10.10]
Epoch 27: 38%|███▊ | 12/32 [00:00<00:00, 316.83it/s, v_num=2, train_loss=3.130, RMSE=10.10]
Epoch 27: 41%|████ | 13/32 [00:00<00:00, 318.81it/s, v_num=2, train_loss=3.130, RMSE=10.10]
Epoch 27: 41%|████ | 13/32 [00:00<00:00, 317.19it/s, v_num=2, train_loss=3.000, RMSE=10.10]
Epoch 27: 44%|████▍ | 14/32 [00:00<00:00, 319.16it/s, v_num=2, train_loss=3.000, RMSE=10.10]
Epoch 27: 44%|████▍ | 14/32 [00:00<00:00, 317.64it/s, v_num=2, train_loss=3.130, RMSE=10.10]
Epoch 27: 47%|████▋ | 15/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=3.130, RMSE=10.10]
Epoch 27: 47%|████▋ | 15/32 [00:00<00:00, 318.41it/s, v_num=2, train_loss=3.390, RMSE=10.10]
Epoch 27: 50%|█████ | 16/32 [00:00<00:00, 319.91it/s, v_num=2, train_loss=3.390, RMSE=10.10]
Epoch 27: 50%|█████ | 16/32 [00:00<00:00, 318.59it/s, v_num=2, train_loss=3.270, RMSE=10.10]
Epoch 27: 53%|█████▎ | 17/32 [00:00<00:00, 319.60it/s, v_num=2, train_loss=3.270, RMSE=10.10]
Epoch 27: 53%|█████▎ | 17/32 [00:00<00:00, 318.36it/s, v_num=2, train_loss=2.800, RMSE=10.10]
Epoch 27: 56%|█████▋ | 18/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=2.800, RMSE=10.10]
Epoch 27: 56%|█████▋ | 18/32 [00:00<00:00, 318.64it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 59%|█████▉ | 19/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 59%|█████▉ | 19/32 [00:00<00:00, 318.75it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 62%|██████▎ | 20/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.150, RMSE=10.10]
Epoch 27: 62%|██████▎ | 20/32 [00:00<00:00, 318.96it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 66%|██████▌ | 21/32 [00:00<00:00, 320.12it/s, v_num=2, train_loss=3.190, RMSE=10.10]
Epoch 27: 66%|██████▌ | 21/32 [00:00<00:00, 319.10it/s, v_num=2, train_loss=3.390, RMSE=10.10]
Epoch 27: 69%|██████▉ | 22/32 [00:00<00:00, 320.27it/s, v_num=2, train_loss=3.390, RMSE=10.10]
Epoch 27: 69%|██████▉ | 22/32 [00:00<00:00, 319.29it/s, v_num=2, train_loss=3.000, RMSE=10.10]
Epoch 27: 72%|███████▏ | 23/32 [00:00<00:00, 320.30it/s, v_num=2, train_loss=3.000, RMSE=10.10]
Epoch 27: 72%|███████▏ | 23/32 [00:00<00:00, 319.35it/s, v_num=2, train_loss=3.460, RMSE=10.10]
Epoch 27: 75%|███████▌ | 24/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=3.460, RMSE=10.10]
Epoch 27: 75%|███████▌ | 24/32 [00:00<00:00, 319.43it/s, v_num=2, train_loss=3.180, RMSE=10.10]
Epoch 27: 78%|███████▊ | 25/32 [00:00<00:00, 320.32it/s, v_num=2, train_loss=3.180, RMSE=10.10]
Epoch 27: 78%|███████▊ | 25/32 [00:00<00:00, 319.46it/s, v_num=2, train_loss=3.500, RMSE=10.10]
Epoch 27: 81%|████████▏ | 26/32 [00:00<00:00, 320.36it/s, v_num=2, train_loss=3.500, RMSE=10.10]
Epoch 27: 81%|████████▏ | 26/32 [00:00<00:00, 319.53it/s, v_num=2, train_loss=3.240, RMSE=10.10]
Epoch 27: 84%|████████▍ | 27/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=3.240, RMSE=10.10]
Epoch 27: 84%|████████▍ | 27/32 [00:00<00:00, 319.59it/s, v_num=2, train_loss=3.250, RMSE=10.10]
Epoch 27: 88%|████████▊ | 28/32 [00:00<00:00, 320.60it/s, v_num=2, train_loss=3.250, RMSE=10.10]
Epoch 27: 88%|████████▊ | 28/32 [00:00<00:00, 319.83it/s, v_num=2, train_loss=3.440, RMSE=10.10]
Epoch 27: 91%|█████████ | 29/32 [00:00<00:00, 320.77it/s, v_num=2, train_loss=3.440, RMSE=10.10]
Epoch 27: 91%|█████████ | 29/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.220, RMSE=10.10]
Epoch 27: 94%|█████████▍| 30/32 [00:00<00:00, 320.81it/s, v_num=2, train_loss=3.220, RMSE=10.10]
Epoch 27: 94%|█████████▍| 30/32 [00:00<00:00, 320.09it/s, v_num=2, train_loss=3.240, RMSE=10.10]
Epoch 27: 97%|█████████▋| 31/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.240, RMSE=10.10]
Epoch 27: 97%|█████████▋| 31/32 [00:00<00:00, 320.14it/s, v_num=2, train_loss=3.310, RMSE=10.10]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 321.20it/s, v_num=2, train_loss=3.310, RMSE=10.10]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 320.42it/s, v_num=2, train_loss=3.530, RMSE=10.10]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 569.20it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 552.16it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 547.81it/s]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 255.04it/s, v_num=2, train_loss=3.530, RMSE=9.640]
Epoch 27: 100%|██████████| 32/32 [00:00<00:00, 253.58it/s, v_num=2, train_loss=3.530, RMSE=9.640]
Epoch 27: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.530, RMSE=9.640]
Epoch 28: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.530, RMSE=9.640]
Epoch 28: 3%|▎ | 1/32 [00:00<00:00, 214.27it/s, v_num=2, train_loss=3.530, RMSE=9.640]
Epoch 28: 3%|▎ | 1/32 [00:00<00:00, 202.74it/s, v_num=2, train_loss=3.060, RMSE=9.640]
Epoch 28: 6%|▋ | 2/32 [00:00<00:00, 242.38it/s, v_num=2, train_loss=3.060, RMSE=9.640]
Epoch 28: 6%|▋ | 2/32 [00:00<00:00, 236.33it/s, v_num=2, train_loss=3.210, RMSE=9.640]
Epoch 28: 9%|▉ | 3/32 [00:00<00:00, 263.33it/s, v_num=2, train_loss=3.210, RMSE=9.640]
Epoch 28: 9%|▉ | 3/32 [00:00<00:00, 258.59it/s, v_num=2, train_loss=3.280, RMSE=9.640]
Epoch 28: 12%|█▎ | 4/32 [00:00<00:00, 276.22it/s, v_num=2, train_loss=3.280, RMSE=9.640]
Epoch 28: 12%|█▎ | 4/32 [00:00<00:00, 272.29it/s, v_num=2, train_loss=3.270, RMSE=9.640]
Epoch 28: 16%|█▌ | 5/32 [00:00<00:00, 277.37it/s, v_num=2, train_loss=3.270, RMSE=9.640]
Epoch 28: 16%|█▌ | 5/32 [00:00<00:00, 274.09it/s, v_num=2, train_loss=3.360, RMSE=9.640]
Epoch 28: 19%|█▉ | 6/32 [00:00<00:00, 284.26it/s, v_num=2, train_loss=3.360, RMSE=9.640]
Epoch 28: 19%|█▉ | 6/32 [00:00<00:00, 281.47it/s, v_num=2, train_loss=3.120, RMSE=9.640]
Epoch 28: 22%|██▏ | 7/32 [00:00<00:00, 289.05it/s, v_num=2, train_loss=3.120, RMSE=9.640]
Epoch 28: 22%|██▏ | 7/32 [00:00<00:00, 286.59it/s, v_num=2, train_loss=3.170, RMSE=9.640]
Epoch 28: 25%|██▌ | 8/32 [00:00<00:00, 293.05it/s, v_num=2, train_loss=3.170, RMSE=9.640]
Epoch 28: 25%|██▌ | 8/32 [00:00<00:00, 290.83it/s, v_num=2, train_loss=3.210, RMSE=9.640]
Epoch 28: 28%|██▊ | 9/32 [00:00<00:00, 296.01it/s, v_num=2, train_loss=3.210, RMSE=9.640]
Epoch 28: 28%|██▊ | 9/32 [00:00<00:00, 293.99it/s, v_num=2, train_loss=3.360, RMSE=9.640]
Epoch 28: 31%|███▏ | 10/32 [00:00<00:00, 298.37it/s, v_num=2, train_loss=3.360, RMSE=9.640]
Epoch 28: 31%|███▏ | 10/32 [00:00<00:00, 296.51it/s, v_num=2, train_loss=3.080, RMSE=9.640]
Epoch 28: 34%|███▍ | 11/32 [00:00<00:00, 300.71it/s, v_num=2, train_loss=3.080, RMSE=9.640]
Epoch 28: 34%|███▍ | 11/32 [00:00<00:00, 299.00it/s, v_num=2, train_loss=3.370, RMSE=9.640]
Epoch 28: 38%|███▊ | 12/32 [00:00<00:00, 302.01it/s, v_num=2, train_loss=3.370, RMSE=9.640]
Epoch 28: 38%|███▊ | 12/32 [00:00<00:00, 300.42it/s, v_num=2, train_loss=3.270, RMSE=9.640]
Epoch 28: 41%|████ | 13/32 [00:00<00:00, 303.54it/s, v_num=2, train_loss=3.270, RMSE=9.640]
Epoch 28: 41%|████ | 13/32 [00:00<00:00, 302.06it/s, v_num=2, train_loss=3.090, RMSE=9.640]
Epoch 28: 44%|████▍ | 14/32 [00:00<00:00, 304.85it/s, v_num=2, train_loss=3.090, RMSE=9.640]
Epoch 28: 44%|████▍ | 14/32 [00:00<00:00, 303.46it/s, v_num=2, train_loss=3.190, RMSE=9.640]
Epoch 28: 47%|████▋ | 15/32 [00:00<00:00, 306.20it/s, v_num=2, train_loss=3.190, RMSE=9.640]
Epoch 28: 47%|████▋ | 15/32 [00:00<00:00, 304.88it/s, v_num=2, train_loss=3.280, RMSE=9.640]
Epoch 28: 50%|█████ | 16/32 [00:00<00:00, 307.17it/s, v_num=2, train_loss=3.280, RMSE=9.640]
Epoch 28: 50%|█████ | 16/32 [00:00<00:00, 305.93it/s, v_num=2, train_loss=3.140, RMSE=9.640]
Epoch 28: 53%|█████▎ | 17/32 [00:00<00:00, 307.94it/s, v_num=2, train_loss=3.140, RMSE=9.640]
Epoch 28: 53%|█████▎ | 17/32 [00:00<00:00, 306.75it/s, v_num=2, train_loss=3.180, RMSE=9.640]
Epoch 28: 56%|█████▋ | 18/32 [00:00<00:00, 308.33it/s, v_num=2, train_loss=3.180, RMSE=9.640]
Epoch 28: 56%|█████▋ | 18/32 [00:00<00:00, 307.20it/s, v_num=2, train_loss=3.090, RMSE=9.640]
Epoch 28: 59%|█████▉ | 19/32 [00:00<00:00, 308.32it/s, v_num=2, train_loss=3.090, RMSE=9.640]
Epoch 28: 59%|█████▉ | 19/32 [00:00<00:00, 307.21it/s, v_num=2, train_loss=2.760, RMSE=9.640]
Epoch 28: 62%|██████▎ | 20/32 [00:00<00:00, 308.71it/s, v_num=2, train_loss=2.760, RMSE=9.640]
Epoch 28: 62%|██████▎ | 20/32 [00:00<00:00, 307.66it/s, v_num=2, train_loss=3.120, RMSE=9.640]
Epoch 28: 66%|██████▌ | 21/32 [00:00<00:00, 309.20it/s, v_num=2, train_loss=3.120, RMSE=9.640]
Epoch 28: 66%|██████▌ | 21/32 [00:00<00:00, 308.24it/s, v_num=2, train_loss=3.130, RMSE=9.640]
Epoch 28: 69%|██████▉ | 22/32 [00:00<00:00, 309.75it/s, v_num=2, train_loss=3.130, RMSE=9.640]
Epoch 28: 69%|██████▉ | 22/32 [00:00<00:00, 308.84it/s, v_num=2, train_loss=3.140, RMSE=9.640]
Epoch 28: 72%|███████▏ | 23/32 [00:00<00:00, 310.39it/s, v_num=2, train_loss=3.140, RMSE=9.640]
Epoch 28: 72%|███████▏ | 23/32 [00:00<00:00, 309.52it/s, v_num=2, train_loss=3.390, RMSE=9.640]
Epoch 28: 75%|███████▌ | 24/32 [00:00<00:00, 311.08it/s, v_num=2, train_loss=3.390, RMSE=9.640]
Epoch 28: 75%|███████▌ | 24/32 [00:00<00:00, 310.13it/s, v_num=2, train_loss=3.410, RMSE=9.640]
Epoch 28: 78%|███████▊ | 25/32 [00:00<00:00, 311.58it/s, v_num=2, train_loss=3.410, RMSE=9.640]
Epoch 28: 78%|███████▊ | 25/32 [00:00<00:00, 310.77it/s, v_num=2, train_loss=3.660, RMSE=9.640]
Epoch 28: 81%|████████▏ | 26/32 [00:00<00:00, 311.94it/s, v_num=2, train_loss=3.660, RMSE=9.640]
Epoch 28: 81%|████████▏ | 26/32 [00:00<00:00, 311.15it/s, v_num=2, train_loss=3.030, RMSE=9.640]
Epoch 28: 84%|████████▍ | 27/32 [00:00<00:00, 312.35it/s, v_num=2, train_loss=3.030, RMSE=9.640]
Epoch 28: 84%|████████▍ | 27/32 [00:00<00:00, 311.60it/s, v_num=2, train_loss=3.310, RMSE=9.640]
Epoch 28: 88%|████████▊ | 28/32 [00:00<00:00, 312.67it/s, v_num=2, train_loss=3.310, RMSE=9.640]
Epoch 28: 88%|████████▊ | 28/32 [00:00<00:00, 311.94it/s, v_num=2, train_loss=3.240, RMSE=9.640]
Epoch 28: 91%|█████████ | 29/32 [00:00<00:00, 313.00it/s, v_num=2, train_loss=3.240, RMSE=9.640]
Epoch 28: 91%|█████████ | 29/32 [00:00<00:00, 312.30it/s, v_num=2, train_loss=3.110, RMSE=9.640]
Epoch 28: 94%|█████████▍| 30/32 [00:00<00:00, 313.39it/s, v_num=2, train_loss=3.110, RMSE=9.640]
Epoch 28: 94%|█████████▍| 30/32 [00:00<00:00, 312.71it/s, v_num=2, train_loss=3.220, RMSE=9.640]
Epoch 28: 97%|█████████▋| 31/32 [00:00<00:00, 313.72it/s, v_num=2, train_loss=3.220, RMSE=9.640]
Epoch 28: 97%|█████████▋| 31/32 [00:00<00:00, 313.05it/s, v_num=2, train_loss=3.030, RMSE=9.640]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 314.16it/s, v_num=2, train_loss=3.030, RMSE=9.640]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 313.52it/s, v_num=2, train_loss=3.260, RMSE=9.640]
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Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 257.42it/s, v_num=2, train_loss=3.260, RMSE=9.120]
Epoch 28: 100%|██████████| 32/32 [00:00<00:00, 256.35it/s, v_num=2, train_loss=3.260, RMSE=9.120]
Epoch 28: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.260, RMSE=9.120]
Epoch 29: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.260, RMSE=9.120]
Epoch 29: 3%|▎ | 1/32 [00:00<00:00, 276.74it/s, v_num=2, train_loss=3.260, RMSE=9.120]
Epoch 29: 3%|▎ | 1/32 [00:00<00:00, 261.21it/s, v_num=2, train_loss=3.080, RMSE=9.120]
Epoch 29: 6%|▋ | 2/32 [00:00<00:00, 293.50it/s, v_num=2, train_loss=3.080, RMSE=9.120]
Epoch 29: 6%|▋ | 2/32 [00:00<00:00, 284.58it/s, v_num=2, train_loss=3.270, RMSE=9.120]
Epoch 29: 9%|▉ | 3/32 [00:00<00:00, 302.11it/s, v_num=2, train_loss=3.270, RMSE=9.120]
Epoch 29: 9%|▉ | 3/32 [00:00<00:00, 295.93it/s, v_num=2, train_loss=3.130, RMSE=9.120]
Epoch 29: 12%|█▎ | 4/32 [00:00<00:00, 308.41it/s, v_num=2, train_loss=3.130, RMSE=9.120]
Epoch 29: 12%|█▎ | 4/32 [00:00<00:00, 303.56it/s, v_num=2, train_loss=2.870, RMSE=9.120]
Epoch 29: 16%|█▌ | 5/32 [00:00<00:00, 310.89it/s, v_num=2, train_loss=2.870, RMSE=9.120]
Epoch 29: 16%|█▌ | 5/32 [00:00<00:00, 306.94it/s, v_num=2, train_loss=2.760, RMSE=9.120]
Epoch 29: 19%|█▉ | 6/32 [00:00<00:00, 313.36it/s, v_num=2, train_loss=2.760, RMSE=9.120]
Epoch 29: 19%|█▉ | 6/32 [00:00<00:00, 309.99it/s, v_num=2, train_loss=3.330, RMSE=9.120]
Epoch 29: 22%|██▏ | 7/32 [00:00<00:00, 314.76it/s, v_num=2, train_loss=3.330, RMSE=9.120]
Epoch 29: 22%|██▏ | 7/32 [00:00<00:00, 311.86it/s, v_num=2, train_loss=3.200, RMSE=9.120]
Epoch 29: 25%|██▌ | 8/32 [00:00<00:00, 312.20it/s, v_num=2, train_loss=3.200, RMSE=9.120]
Epoch 29: 25%|██▌ | 8/32 [00:00<00:00, 308.36it/s, v_num=2, train_loss=3.510, RMSE=9.120]
Epoch 29: 28%|██▊ | 9/32 [00:00<00:00, 308.37it/s, v_num=2, train_loss=3.510, RMSE=9.120]
Epoch 29: 28%|██▊ | 9/32 [00:00<00:00, 305.87it/s, v_num=2, train_loss=3.550, RMSE=9.120]
Epoch 29: 31%|███▏ | 10/32 [00:00<00:00, 309.21it/s, v_num=2, train_loss=3.550, RMSE=9.120]
Epoch 29: 31%|███▏ | 10/32 [00:00<00:00, 307.23it/s, v_num=2, train_loss=3.210, RMSE=9.120]
Epoch 29: 34%|███▍ | 11/32 [00:00<00:00, 310.60it/s, v_num=2, train_loss=3.210, RMSE=9.120]
Epoch 29: 34%|███▍ | 11/32 [00:00<00:00, 308.68it/s, v_num=2, train_loss=3.230, RMSE=9.120]
Epoch 29: 38%|███▊ | 12/32 [00:00<00:00, 311.77it/s, v_num=2, train_loss=3.230, RMSE=9.120]
Epoch 29: 38%|███▊ | 12/32 [00:00<00:00, 310.11it/s, v_num=2, train_loss=3.290, RMSE=9.120]
Epoch 29: 41%|████ | 13/32 [00:00<00:00, 312.61it/s, v_num=2, train_loss=3.290, RMSE=9.120]
Epoch 29: 41%|████ | 13/32 [00:00<00:00, 311.02it/s, v_num=2, train_loss=3.140, RMSE=9.120]
Epoch 29: 44%|████▍ | 14/32 [00:00<00:00, 313.27it/s, v_num=2, train_loss=3.140, RMSE=9.120]
Epoch 29: 44%|████▍ | 14/32 [00:00<00:00, 311.82it/s, v_num=2, train_loss=3.010, RMSE=9.120]
Epoch 29: 47%|████▋ | 15/32 [00:00<00:00, 314.07it/s, v_num=2, train_loss=3.010, RMSE=9.120]
Epoch 29: 47%|████▋ | 15/32 [00:00<00:00, 312.70it/s, v_num=2, train_loss=2.960, RMSE=9.120]
Epoch 29: 50%|█████ | 16/32 [00:00<00:00, 314.80it/s, v_num=2, train_loss=2.960, RMSE=9.120]
Epoch 29: 50%|█████ | 16/32 [00:00<00:00, 313.52it/s, v_num=2, train_loss=3.270, RMSE=9.120]
Epoch 29: 53%|█████▎ | 17/32 [00:00<00:00, 315.43it/s, v_num=2, train_loss=3.270, RMSE=9.120]
Epoch 29: 53%|█████▎ | 17/32 [00:00<00:00, 314.21it/s, v_num=2, train_loss=3.400, RMSE=9.120]
Epoch 29: 56%|█████▋ | 18/32 [00:00<00:00, 316.12it/s, v_num=2, train_loss=3.400, RMSE=9.120]
Epoch 29: 56%|█████▋ | 18/32 [00:00<00:00, 314.98it/s, v_num=2, train_loss=3.130, RMSE=9.120]
Epoch 29: 59%|█████▉ | 19/32 [00:00<00:00, 316.64it/s, v_num=2, train_loss=3.130, RMSE=9.120]
Epoch 29: 59%|█████▉ | 19/32 [00:00<00:00, 315.55it/s, v_num=2, train_loss=3.150, RMSE=9.120]
Epoch 29: 62%|██████▎ | 20/32 [00:00<00:00, 316.99it/s, v_num=2, train_loss=3.150, RMSE=9.120]
Epoch 29: 62%|██████▎ | 20/32 [00:00<00:00, 315.95it/s, v_num=2, train_loss=3.080, RMSE=9.120]
Epoch 29: 66%|██████▌ | 21/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=3.080, RMSE=9.120]
Epoch 29: 66%|██████▌ | 21/32 [00:00<00:00, 316.41it/s, v_num=2, train_loss=3.140, RMSE=9.120]
Epoch 29: 69%|██████▉ | 22/32 [00:00<00:00, 317.81it/s, v_num=2, train_loss=3.140, RMSE=9.120]
Epoch 29: 69%|██████▉ | 22/32 [00:00<00:00, 316.72it/s, v_num=2, train_loss=3.160, RMSE=9.120]
Epoch 29: 72%|███████▏ | 23/32 [00:00<00:00, 315.84it/s, v_num=2, train_loss=3.160, RMSE=9.120]
Epoch 29: 72%|███████▏ | 23/32 [00:00<00:00, 314.94it/s, v_num=2, train_loss=3.310, RMSE=9.120]
Epoch 29: 75%|███████▌ | 24/32 [00:00<00:00, 316.20it/s, v_num=2, train_loss=3.310, RMSE=9.120]
Epoch 29: 75%|███████▌ | 24/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=3.210, RMSE=9.120]
Epoch 29: 78%|███████▊ | 25/32 [00:00<00:00, 316.54it/s, v_num=2, train_loss=3.210, RMSE=9.120]
Epoch 29: 78%|███████▊ | 25/32 [00:00<00:00, 315.70it/s, v_num=2, train_loss=3.300, RMSE=9.120]
Epoch 29: 81%|████████▏ | 26/32 [00:00<00:00, 316.85it/s, v_num=2, train_loss=3.300, RMSE=9.120]
Epoch 29: 81%|████████▏ | 26/32 [00:00<00:00, 316.04it/s, v_num=2, train_loss=3.290, RMSE=9.120]
Epoch 29: 84%|████████▍ | 27/32 [00:00<00:00, 316.72it/s, v_num=2, train_loss=3.290, RMSE=9.120]
Epoch 29: 84%|████████▍ | 27/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=2.990, RMSE=9.120]
Epoch 29: 88%|████████▊ | 28/32 [00:00<00:00, 316.69it/s, v_num=2, train_loss=2.990, RMSE=9.120]
Epoch 29: 88%|████████▊ | 28/32 [00:00<00:00, 315.92it/s, v_num=2, train_loss=2.990, RMSE=9.120]
Epoch 29: 91%|█████████ | 29/32 [00:00<00:00, 316.89it/s, v_num=2, train_loss=2.990, RMSE=9.120]
Epoch 29: 91%|█████████ | 29/32 [00:00<00:00, 316.17it/s, v_num=2, train_loss=3.200, RMSE=9.120]
Epoch 29: 94%|█████████▍| 30/32 [00:00<00:00, 316.98it/s, v_num=2, train_loss=3.200, RMSE=9.120]
Epoch 29: 94%|█████████▍| 30/32 [00:00<00:00, 316.25it/s, v_num=2, train_loss=3.190, RMSE=9.120]
Epoch 29: 97%|█████████▋| 31/32 [00:00<00:00, 317.15it/s, v_num=2, train_loss=3.190, RMSE=9.120]
Epoch 29: 97%|█████████▋| 31/32 [00:00<00:00, 316.48it/s, v_num=2, train_loss=3.020, RMSE=9.120]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 317.58it/s, v_num=2, train_loss=3.020, RMSE=9.120]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 316.91it/s, v_num=2, train_loss=3.140, RMSE=9.120]
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Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 259.88it/s, v_num=2, train_loss=3.140, RMSE=8.620]
Epoch 29: 100%|██████████| 32/32 [00:00<00:00, 258.78it/s, v_num=2, train_loss=3.140, RMSE=8.620]
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Epoch 30: 3%|▎ | 1/32 [00:00<00:00, 269.35it/s, v_num=2, train_loss=3.340, RMSE=8.620]
Epoch 30: 6%|▋ | 2/32 [00:00<00:00, 301.11it/s, v_num=2, train_loss=3.340, RMSE=8.620]
Epoch 30: 6%|▋ | 2/32 [00:00<00:00, 291.80it/s, v_num=2, train_loss=3.300, RMSE=8.620]
Epoch 30: 9%|▉ | 3/32 [00:00<00:00, 309.23it/s, v_num=2, train_loss=3.300, RMSE=8.620]
Epoch 30: 9%|▉ | 3/32 [00:00<00:00, 302.73it/s, v_num=2, train_loss=3.330, RMSE=8.620]
Epoch 30: 12%|█▎ | 4/32 [00:00<00:00, 313.24it/s, v_num=2, train_loss=3.330, RMSE=8.620]
Epoch 30: 12%|█▎ | 4/32 [00:00<00:00, 308.18it/s, v_num=2, train_loss=3.200, RMSE=8.620]
Epoch 30: 16%|█▌ | 5/32 [00:00<00:00, 314.51it/s, v_num=2, train_loss=3.200, RMSE=8.620]
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Epoch 30: 19%|█▉ | 6/32 [00:00<00:00, 312.76it/s, v_num=2, train_loss=3.060, RMSE=8.620]
Epoch 30: 22%|██▏ | 7/32 [00:00<00:00, 317.93it/s, v_num=2, train_loss=3.060, RMSE=8.620]
Epoch 30: 22%|██▏ | 7/32 [00:00<00:00, 314.97it/s, v_num=2, train_loss=3.290, RMSE=8.620]
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Epoch 30: 25%|██▌ | 8/32 [00:00<00:00, 316.18it/s, v_num=2, train_loss=3.350, RMSE=8.620]
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Epoch 30: 31%|███▏ | 10/32 [00:00<00:00, 317.82it/s, v_num=2, train_loss=3.250, RMSE=8.620]
Epoch 30: 34%|███▍ | 11/32 [00:00<00:00, 320.43it/s, v_num=2, train_loss=3.250, RMSE=8.620]
Epoch 30: 34%|███▍ | 11/32 [00:00<00:00, 318.19it/s, v_num=2, train_loss=3.060, RMSE=8.620]
Epoch 30: 38%|███▊ | 12/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=3.060, RMSE=8.620]
Epoch 30: 38%|███▊ | 12/32 [00:00<00:00, 318.49it/s, v_num=2, train_loss=3.030, RMSE=8.620]
Epoch 30: 41%|████ | 13/32 [00:00<00:00, 320.28it/s, v_num=2, train_loss=3.030, RMSE=8.620]
Epoch 30: 41%|████ | 13/32 [00:00<00:00, 318.64it/s, v_num=2, train_loss=3.310, RMSE=8.620]
Epoch 30: 44%|████▍ | 14/32 [00:00<00:00, 320.65it/s, v_num=2, train_loss=3.310, RMSE=8.620]
Epoch 30: 44%|████▍ | 14/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.340, RMSE=8.620]
Epoch 30: 47%|████▋ | 15/32 [00:00<00:00, 320.62it/s, v_num=2, train_loss=3.340, RMSE=8.620]
Epoch 30: 47%|████▋ | 15/32 [00:00<00:00, 319.17it/s, v_num=2, train_loss=3.000, RMSE=8.620]
Epoch 30: 50%|█████ | 16/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=3.000, RMSE=8.620]
Epoch 30: 50%|█████ | 16/32 [00:00<00:00, 319.77it/s, v_num=2, train_loss=3.070, RMSE=8.620]
Epoch 30: 53%|█████▎ | 17/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=3.070, RMSE=8.620]
Epoch 30: 53%|█████▎ | 17/32 [00:00<00:00, 317.17it/s, v_num=2, train_loss=3.190, RMSE=8.620]
Epoch 30: 56%|█████▋ | 18/32 [00:00<00:00, 317.18it/s, v_num=2, train_loss=3.190, RMSE=8.620]
Epoch 30: 56%|█████▋ | 18/32 [00:00<00:00, 316.03it/s, v_num=2, train_loss=3.440, RMSE=8.620]
Epoch 30: 59%|█████▉ | 19/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.440, RMSE=8.620]
Epoch 30: 59%|█████▉ | 19/32 [00:00<00:00, 316.34it/s, v_num=2, train_loss=2.930, RMSE=8.620]
Epoch 30: 62%|██████▎ | 20/32 [00:00<00:00, 317.12it/s, v_num=2, train_loss=2.930, RMSE=8.620]
Epoch 30: 62%|██████▎ | 20/32 [00:00<00:00, 315.59it/s, v_num=2, train_loss=3.030, RMSE=8.620]
Epoch 30: 66%|██████▌ | 21/32 [00:00<00:00, 311.75it/s, v_num=2, train_loss=3.030, RMSE=8.620]
Epoch 30: 66%|██████▌ | 21/32 [00:00<00:00, 310.76it/s, v_num=2, train_loss=3.240, RMSE=8.620]
Epoch 30: 69%|██████▉ | 22/32 [00:00<00:00, 312.13it/s, v_num=2, train_loss=3.240, RMSE=8.620]
Epoch 30: 69%|██████▉ | 22/32 [00:00<00:00, 311.07it/s, v_num=2, train_loss=2.860, RMSE=8.620]
Epoch 30: 72%|███████▏ | 23/32 [00:00<00:00, 312.73it/s, v_num=2, train_loss=2.860, RMSE=8.620]
Epoch 30: 72%|███████▏ | 23/32 [00:00<00:00, 311.86it/s, v_num=2, train_loss=2.920, RMSE=8.620]
Epoch 30: 75%|███████▌ | 24/32 [00:00<00:00, 313.19it/s, v_num=2, train_loss=2.920, RMSE=8.620]
Epoch 30: 75%|███████▌ | 24/32 [00:00<00:00, 312.34it/s, v_num=2, train_loss=3.100, RMSE=8.620]
Epoch 30: 78%|███████▊ | 25/32 [00:00<00:00, 313.71it/s, v_num=2, train_loss=3.100, RMSE=8.620]
Epoch 30: 78%|███████▊ | 25/32 [00:00<00:00, 312.90it/s, v_num=2, train_loss=3.020, RMSE=8.620]
Epoch 30: 81%|████████▏ | 26/32 [00:00<00:00, 314.23it/s, v_num=2, train_loss=3.020, RMSE=8.620]
Epoch 30: 81%|████████▏ | 26/32 [00:00<00:00, 313.43it/s, v_num=2, train_loss=3.010, RMSE=8.620]
Epoch 30: 84%|████████▍ | 27/32 [00:00<00:00, 314.59it/s, v_num=2, train_loss=3.010, RMSE=8.620]
Epoch 30: 84%|████████▍ | 27/32 [00:00<00:00, 313.82it/s, v_num=2, train_loss=2.980, RMSE=8.620]
Epoch 30: 88%|████████▊ | 28/32 [00:00<00:00, 314.82it/s, v_num=2, train_loss=2.980, RMSE=8.620]
Epoch 30: 88%|████████▊ | 28/32 [00:00<00:00, 314.08it/s, v_num=2, train_loss=2.960, RMSE=8.620]
Epoch 30: 91%|█████████ | 29/32 [00:00<00:00, 315.06it/s, v_num=2, train_loss=2.960, RMSE=8.620]
Epoch 30: 91%|█████████ | 29/32 [00:00<00:00, 314.33it/s, v_num=2, train_loss=3.100, RMSE=8.620]
Epoch 30: 94%|█████████▍| 30/32 [00:00<00:00, 315.36it/s, v_num=2, train_loss=3.100, RMSE=8.620]
Epoch 30: 94%|█████████▍| 30/32 [00:00<00:00, 314.67it/s, v_num=2, train_loss=3.370, RMSE=8.620]
Epoch 30: 97%|█████████▋| 31/32 [00:00<00:00, 315.83it/s, v_num=2, train_loss=3.370, RMSE=8.620]
Epoch 30: 97%|█████████▋| 31/32 [00:00<00:00, 315.17it/s, v_num=2, train_loss=3.040, RMSE=8.620]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 316.28it/s, v_num=2, train_loss=3.040, RMSE=8.620]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 315.63it/s, v_num=2, train_loss=3.230, RMSE=8.620]
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Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 259.23it/s, v_num=2, train_loss=3.230, RMSE=8.370]
Epoch 30: 100%|██████████| 32/32 [00:00<00:00, 258.10it/s, v_num=2, train_loss=3.230, RMSE=8.370]
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Epoch 31: 9%|▉ | 3/32 [00:00<00:00, 294.35it/s, v_num=2, train_loss=2.910, RMSE=8.370]
Epoch 31: 12%|█▎ | 4/32 [00:00<00:00, 305.59it/s, v_num=2, train_loss=2.910, RMSE=8.370]
Epoch 31: 12%|█▎ | 4/32 [00:00<00:00, 300.82it/s, v_num=2, train_loss=3.460, RMSE=8.370]
Epoch 31: 16%|█▌ | 5/32 [00:00<00:00, 309.41it/s, v_num=2, train_loss=3.460, RMSE=8.370]
Epoch 31: 16%|█▌ | 5/32 [00:00<00:00, 305.51it/s, v_num=2, train_loss=3.320, RMSE=8.370]
Epoch 31: 19%|█▉ | 6/32 [00:00<00:00, 312.65it/s, v_num=2, train_loss=3.320, RMSE=8.370]
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Epoch 31: 22%|██▏ | 7/32 [00:00<00:00, 314.35it/s, v_num=2, train_loss=2.970, RMSE=8.370]
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Epoch 31: 25%|██▌ | 8/32 [00:00<00:00, 315.80it/s, v_num=2, train_loss=3.240, RMSE=8.370]
Epoch 31: 25%|██▌ | 8/32 [00:00<00:00, 313.24it/s, v_num=2, train_loss=3.330, RMSE=8.370]
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Epoch 31: 31%|███▏ | 10/32 [00:00<00:00, 310.68it/s, v_num=2, train_loss=2.960, RMSE=8.370]
Epoch 31: 34%|███▍ | 11/32 [00:00<00:00, 314.24it/s, v_num=2, train_loss=2.960, RMSE=8.370]
Epoch 31: 34%|███▍ | 11/32 [00:00<00:00, 312.41it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 38%|███▊ | 12/32 [00:00<00:00, 315.13it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 38%|███▊ | 12/32 [00:00<00:00, 313.42it/s, v_num=2, train_loss=3.250, RMSE=8.370]
Epoch 31: 41%|████ | 13/32 [00:00<00:00, 315.63it/s, v_num=2, train_loss=3.250, RMSE=8.370]
Epoch 31: 41%|████ | 13/32 [00:00<00:00, 314.04it/s, v_num=2, train_loss=3.090, RMSE=8.370]
Epoch 31: 44%|████▍ | 14/32 [00:00<00:00, 315.83it/s, v_num=2, train_loss=3.090, RMSE=8.370]
Epoch 31: 44%|████▍ | 14/32 [00:00<00:00, 314.33it/s, v_num=2, train_loss=3.160, RMSE=8.370]
Epoch 31: 47%|████▋ | 15/32 [00:00<00:00, 316.27it/s, v_num=2, train_loss=3.160, RMSE=8.370]
Epoch 31: 47%|████▋ | 15/32 [00:00<00:00, 314.68it/s, v_num=2, train_loss=3.160, RMSE=8.370]
Epoch 31: 50%|█████ | 16/32 [00:00<00:00, 316.76it/s, v_num=2, train_loss=3.160, RMSE=8.370]
Epoch 31: 50%|█████ | 16/32 [00:00<00:00, 315.47it/s, v_num=2, train_loss=3.060, RMSE=8.370]
Epoch 31: 53%|█████▎ | 17/32 [00:00<00:00, 317.20it/s, v_num=2, train_loss=3.060, RMSE=8.370]
Epoch 31: 53%|█████▎ | 17/32 [00:00<00:00, 315.98it/s, v_num=2, train_loss=2.990, RMSE=8.370]
Epoch 31: 56%|█████▋ | 18/32 [00:00<00:00, 317.71it/s, v_num=2, train_loss=2.990, RMSE=8.370]
Epoch 31: 56%|█████▋ | 18/32 [00:00<00:00, 316.57it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 59%|█████▉ | 19/32 [00:00<00:00, 317.96it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 59%|█████▉ | 19/32 [00:00<00:00, 316.80it/s, v_num=2, train_loss=3.230, RMSE=8.370]
Epoch 31: 62%|██████▎ | 20/32 [00:00<00:00, 318.16it/s, v_num=2, train_loss=3.230, RMSE=8.370]
Epoch 31: 62%|██████▎ | 20/32 [00:00<00:00, 317.01it/s, v_num=2, train_loss=2.910, RMSE=8.370]
Epoch 31: 66%|██████▌ | 21/32 [00:00<00:00, 318.23it/s, v_num=2, train_loss=2.910, RMSE=8.370]
Epoch 31: 66%|██████▌ | 21/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 69%|██████▉ | 22/32 [00:00<00:00, 318.21it/s, v_num=2, train_loss=3.150, RMSE=8.370]
Epoch 31: 69%|██████▉ | 22/32 [00:00<00:00, 317.25it/s, v_num=2, train_loss=2.770, RMSE=8.370]
Epoch 31: 72%|███████▏ | 23/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=2.770, RMSE=8.370]
Epoch 31: 72%|███████▏ | 23/32 [00:00<00:00, 317.15it/s, v_num=2, train_loss=3.140, RMSE=8.370]
Epoch 31: 75%|███████▌ | 24/32 [00:00<00:00, 318.19it/s, v_num=2, train_loss=3.140, RMSE=8.370]
Epoch 31: 75%|███████▌ | 24/32 [00:00<00:00, 317.15it/s, v_num=2, train_loss=3.130, RMSE=8.370]
Epoch 31: 78%|███████▊ | 25/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.130, RMSE=8.370]
Epoch 31: 78%|███████▊ | 25/32 [00:00<00:00, 317.41it/s, v_num=2, train_loss=3.090, RMSE=8.370]
Epoch 31: 81%|████████▏ | 26/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.090, RMSE=8.370]
Epoch 31: 81%|████████▏ | 26/32 [00:00<00:00, 317.54it/s, v_num=2, train_loss=3.080, RMSE=8.370]
Epoch 31: 84%|████████▍ | 27/32 [00:00<00:00, 318.22it/s, v_num=2, train_loss=3.080, RMSE=8.370]
Epoch 31: 84%|████████▍ | 27/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.370, RMSE=8.370]
Epoch 31: 88%|████████▊ | 28/32 [00:00<00:00, 318.43it/s, v_num=2, train_loss=3.370, RMSE=8.370]
Epoch 31: 88%|████████▊ | 28/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.270, RMSE=8.370]
Epoch 31: 91%|█████████ | 29/32 [00:00<00:00, 318.66it/s, v_num=2, train_loss=3.270, RMSE=8.370]
Epoch 31: 91%|█████████ | 29/32 [00:00<00:00, 317.93it/s, v_num=2, train_loss=3.130, RMSE=8.370]
Epoch 31: 94%|█████████▍| 30/32 [00:00<00:00, 318.88it/s, v_num=2, train_loss=3.130, RMSE=8.370]
Epoch 31: 94%|█████████▍| 30/32 [00:00<00:00, 318.18it/s, v_num=2, train_loss=2.870, RMSE=8.370]
Epoch 31: 97%|█████████▋| 31/32 [00:00<00:00, 318.97it/s, v_num=2, train_loss=2.870, RMSE=8.370]
Epoch 31: 97%|█████████▋| 31/32 [00:00<00:00, 318.28it/s, v_num=2, train_loss=3.310, RMSE=8.370]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.310, RMSE=8.370]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 318.55it/s, v_num=2, train_loss=3.060, RMSE=8.370]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 618.05it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 622.05it/s]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 261.48it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 31: 100%|██████████| 32/32 [00:00<00:00, 260.35it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 31: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 3%|▎ | 1/32 [00:00<00:00, 284.94it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 3%|▎ | 1/32 [00:00<00:00, 268.62it/s, v_num=2, train_loss=3.020, RMSE=7.890]
Epoch 32: 6%|▋ | 2/32 [00:00<00:00, 300.02it/s, v_num=2, train_loss=3.020, RMSE=7.890]
Epoch 32: 6%|▋ | 2/32 [00:00<00:00, 290.94it/s, v_num=2, train_loss=3.220, RMSE=7.890]
Epoch 32: 9%|▉ | 3/32 [00:00<00:00, 306.98it/s, v_num=2, train_loss=3.220, RMSE=7.890]
Epoch 32: 9%|▉ | 3/32 [00:00<00:00, 300.57it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 12%|█▎ | 4/32 [00:00<00:00, 311.43it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 12%|█▎ | 4/32 [00:00<00:00, 306.45it/s, v_num=2, train_loss=3.070, RMSE=7.890]
Epoch 32: 16%|█▌ | 5/32 [00:00<00:00, 311.71it/s, v_num=2, train_loss=3.070, RMSE=7.890]
Epoch 32: 16%|█▌ | 5/32 [00:00<00:00, 307.70it/s, v_num=2, train_loss=2.910, RMSE=7.890]
Epoch 32: 19%|█▉ | 6/32 [00:00<00:00, 313.16it/s, v_num=2, train_loss=2.910, RMSE=7.890]
Epoch 32: 19%|█▉ | 6/32 [00:00<00:00, 309.76it/s, v_num=2, train_loss=3.170, RMSE=7.890]
Epoch 32: 22%|██▏ | 7/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=3.170, RMSE=7.890]
Epoch 32: 22%|██▏ | 7/32 [00:00<00:00, 311.60it/s, v_num=2, train_loss=3.560, RMSE=7.890]
Epoch 32: 25%|██▌ | 8/32 [00:00<00:00, 316.16it/s, v_num=2, train_loss=3.560, RMSE=7.890]
Epoch 32: 25%|██▌ | 8/32 [00:00<00:00, 313.17it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 28%|██▊ | 9/32 [00:00<00:00, 316.80it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 28%|██▊ | 9/32 [00:00<00:00, 314.48it/s, v_num=2, train_loss=2.950, RMSE=7.890]
Epoch 32: 31%|███▏ | 10/32 [00:00<00:00, 317.43it/s, v_num=2, train_loss=2.950, RMSE=7.890]
Epoch 32: 31%|███▏ | 10/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=3.270, RMSE=7.890]
Epoch 32: 34%|███▍ | 11/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=3.270, RMSE=7.890]
Epoch 32: 34%|███▍ | 11/32 [00:00<00:00, 315.99it/s, v_num=2, train_loss=3.010, RMSE=7.890]
Epoch 32: 38%|███▊ | 12/32 [00:00<00:00, 318.19it/s, v_num=2, train_loss=3.010, RMSE=7.890]
Epoch 32: 38%|███▊ | 12/32 [00:00<00:00, 316.43it/s, v_num=2, train_loss=3.470, RMSE=7.890]
Epoch 32: 41%|████ | 13/32 [00:00<00:00, 318.95it/s, v_num=2, train_loss=3.470, RMSE=7.890]
Epoch 32: 41%|████ | 13/32 [00:00<00:00, 317.34it/s, v_num=2, train_loss=2.860, RMSE=7.890]
Epoch 32: 44%|████▍ | 14/32 [00:00<00:00, 319.29it/s, v_num=2, train_loss=2.860, RMSE=7.890]
Epoch 32: 44%|████▍ | 14/32 [00:00<00:00, 317.80it/s, v_num=2, train_loss=3.230, RMSE=7.890]
Epoch 32: 47%|████▋ | 15/32 [00:00<00:00, 319.62it/s, v_num=2, train_loss=3.230, RMSE=7.890]
Epoch 32: 47%|████▋ | 15/32 [00:00<00:00, 318.13it/s, v_num=2, train_loss=3.220, RMSE=7.890]
Epoch 32: 50%|█████ | 16/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=3.220, RMSE=7.890]
Epoch 32: 50%|█████ | 16/32 [00:00<00:00, 318.47it/s, v_num=2, train_loss=3.260, RMSE=7.890]
Epoch 32: 53%|█████▎ | 17/32 [00:00<00:00, 320.21it/s, v_num=2, train_loss=3.260, RMSE=7.890]
Epoch 32: 53%|█████▎ | 17/32 [00:00<00:00, 318.96it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 56%|█████▋ | 18/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 56%|█████▋ | 18/32 [00:00<00:00, 319.27it/s, v_num=2, train_loss=2.870, RMSE=7.890]
Epoch 32: 59%|█████▉ | 19/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=2.870, RMSE=7.890]
Epoch 32: 59%|█████▉ | 19/32 [00:00<00:00, 319.46it/s, v_num=2, train_loss=3.020, RMSE=7.890]
Epoch 32: 62%|██████▎ | 20/32 [00:00<00:00, 320.78it/s, v_num=2, train_loss=3.020, RMSE=7.890]
Epoch 32: 62%|██████▎ | 20/32 [00:00<00:00, 319.67it/s, v_num=2, train_loss=3.130, RMSE=7.890]
Epoch 32: 66%|██████▌ | 21/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=3.130, RMSE=7.890]
Epoch 32: 66%|██████▌ | 21/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=2.870, RMSE=7.890]
Epoch 32: 69%|██████▉ | 22/32 [00:00<00:00, 321.20it/s, v_num=2, train_loss=2.870, RMSE=7.890]
Epoch 32: 69%|██████▉ | 22/32 [00:00<00:00, 320.23it/s, v_num=2, train_loss=2.980, RMSE=7.890]
Epoch 32: 72%|███████▏ | 23/32 [00:00<00:00, 321.39it/s, v_num=2, train_loss=2.980, RMSE=7.890]
Epoch 32: 72%|███████▏ | 23/32 [00:00<00:00, 320.44it/s, v_num=2, train_loss=3.180, RMSE=7.890]
Epoch 32: 75%|███████▌ | 24/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=3.180, RMSE=7.890]
Epoch 32: 75%|███████▌ | 24/32 [00:00<00:00, 320.17it/s, v_num=2, train_loss=3.030, RMSE=7.890]
Epoch 32: 78%|███████▊ | 25/32 [00:00<00:00, 321.11it/s, v_num=2, train_loss=3.030, RMSE=7.890]
Epoch 32: 78%|███████▊ | 25/32 [00:00<00:00, 320.21it/s, v_num=2, train_loss=2.970, RMSE=7.890]
Epoch 32: 81%|████████▏ | 26/32 [00:00<00:00, 321.33it/s, v_num=2, train_loss=2.970, RMSE=7.890]
Epoch 32: 81%|████████▏ | 26/32 [00:00<00:00, 320.52it/s, v_num=2, train_loss=3.390, RMSE=7.890]
Epoch 32: 84%|████████▍ | 27/32 [00:00<00:00, 319.42it/s, v_num=2, train_loss=3.390, RMSE=7.890]
Epoch 32: 84%|████████▍ | 27/32 [00:00<00:00, 318.63it/s, v_num=2, train_loss=3.320, RMSE=7.890]
Epoch 32: 88%|████████▊ | 28/32 [00:00<00:00, 319.47it/s, v_num=2, train_loss=3.320, RMSE=7.890]
Epoch 32: 88%|████████▊ | 28/32 [00:00<00:00, 318.71it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 91%|█████████ | 29/32 [00:00<00:00, 319.58it/s, v_num=2, train_loss=3.060, RMSE=7.890]
Epoch 32: 91%|█████████ | 29/32 [00:00<00:00, 318.85it/s, v_num=2, train_loss=3.150, RMSE=7.890]
Epoch 32: 94%|█████████▍| 30/32 [00:00<00:00, 319.63it/s, v_num=2, train_loss=3.150, RMSE=7.890]
Epoch 32: 94%|█████████▍| 30/32 [00:00<00:00, 318.93it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 97%|█████████▋| 31/32 [00:00<00:00, 319.89it/s, v_num=2, train_loss=2.900, RMSE=7.890]
Epoch 32: 97%|█████████▋| 31/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=3.190, RMSE=7.890]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 320.16it/s, v_num=2, train_loss=3.190, RMSE=7.890]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 319.49it/s, v_num=2, train_loss=3.240, RMSE=7.890]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 607.32it/s]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 261.08it/s, v_num=2, train_loss=3.240, RMSE=7.710]
Epoch 32: 100%|██████████| 32/32 [00:00<00:00, 259.94it/s, v_num=2, train_loss=3.240, RMSE=7.710]
Epoch 32: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.240, RMSE=7.710]
Epoch 33: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.240, RMSE=7.710]
Epoch 33: 3%|▎ | 1/32 [00:00<00:00, 274.96it/s, v_num=2, train_loss=3.240, RMSE=7.710]
Epoch 33: 3%|▎ | 1/32 [00:00<00:00, 259.71it/s, v_num=2, train_loss=2.910, RMSE=7.710]
Epoch 33: 6%|▋ | 2/32 [00:00<00:00, 294.36it/s, v_num=2, train_loss=2.910, RMSE=7.710]
Epoch 33: 6%|▋ | 2/32 [00:00<00:00, 285.58it/s, v_num=2, train_loss=3.190, RMSE=7.710]
Epoch 33: 9%|▉ | 3/32 [00:00<00:00, 303.52it/s, v_num=2, train_loss=3.190, RMSE=7.710]
Epoch 33: 9%|▉ | 3/32 [00:00<00:00, 297.32it/s, v_num=2, train_loss=2.710, RMSE=7.710]
Epoch 33: 12%|█▎ | 4/32 [00:00<00:00, 308.30it/s, v_num=2, train_loss=2.710, RMSE=7.710]
Epoch 33: 12%|█▎ | 4/32 [00:00<00:00, 303.41it/s, v_num=2, train_loss=3.260, RMSE=7.710]
Epoch 33: 16%|█▌ | 5/32 [00:00<00:00, 311.07it/s, v_num=2, train_loss=3.260, RMSE=7.710]
Epoch 33: 16%|█▌ | 5/32 [00:00<00:00, 306.83it/s, v_num=2, train_loss=3.340, RMSE=7.710]
Epoch 33: 19%|█▉ | 6/32 [00:00<00:00, 312.86it/s, v_num=2, train_loss=3.340, RMSE=7.710]
Epoch 33: 19%|█▉ | 6/32 [00:00<00:00, 309.44it/s, v_num=2, train_loss=2.820, RMSE=7.710]
Epoch 33: 22%|██▏ | 7/32 [00:00<00:00, 314.96it/s, v_num=2, train_loss=2.820, RMSE=7.710]
Epoch 33: 22%|██▏ | 7/32 [00:00<00:00, 312.05it/s, v_num=2, train_loss=3.150, RMSE=7.710]
Epoch 33: 25%|██▌ | 8/32 [00:00<00:00, 315.79it/s, v_num=2, train_loss=3.150, RMSE=7.710]
Epoch 33: 25%|██▌ | 8/32 [00:00<00:00, 313.20it/s, v_num=2, train_loss=3.030, RMSE=7.710]
Epoch 33: 28%|██▊ | 9/32 [00:00<00:00, 316.63it/s, v_num=2, train_loss=3.030, RMSE=7.710]
Epoch 33: 28%|██▊ | 9/32 [00:00<00:00, 314.33it/s, v_num=2, train_loss=2.860, RMSE=7.710]
Epoch 33: 31%|███▏ | 10/32 [00:00<00:00, 317.02it/s, v_num=2, train_loss=2.860, RMSE=7.710]
Epoch 33: 31%|███▏ | 10/32 [00:00<00:00, 314.93it/s, v_num=2, train_loss=3.380, RMSE=7.710]
Epoch 33: 34%|███▍ | 11/32 [00:00<00:00, 317.67it/s, v_num=2, train_loss=3.380, RMSE=7.710]
Epoch 33: 34%|███▍ | 11/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=2.970, RMSE=7.710]
Epoch 33: 38%|███▊ | 12/32 [00:00<00:00, 318.25it/s, v_num=2, train_loss=2.970, RMSE=7.710]
Epoch 33: 38%|███▊ | 12/32 [00:00<00:00, 316.51it/s, v_num=2, train_loss=3.480, RMSE=7.710]
Epoch 33: 41%|████ | 13/32 [00:00<00:00, 318.63it/s, v_num=2, train_loss=3.480, RMSE=7.710]
Epoch 33: 41%|████ | 13/32 [00:00<00:00, 316.99it/s, v_num=2, train_loss=3.420, RMSE=7.710]
Epoch 33: 44%|████▍ | 14/32 [00:00<00:00, 319.02it/s, v_num=2, train_loss=3.420, RMSE=7.710]
Epoch 33: 44%|████▍ | 14/32 [00:00<00:00, 317.51it/s, v_num=2, train_loss=2.920, RMSE=7.710]
Epoch 33: 47%|████▋ | 15/32 [00:00<00:00, 319.48it/s, v_num=2, train_loss=2.920, RMSE=7.710]
Epoch 33: 47%|████▋ | 15/32 [00:00<00:00, 317.81it/s, v_num=2, train_loss=3.230, RMSE=7.710]
Epoch 33: 50%|█████ | 16/32 [00:00<00:00, 319.50it/s, v_num=2, train_loss=3.230, RMSE=7.710]
Epoch 33: 50%|█████ | 16/32 [00:00<00:00, 318.13it/s, v_num=2, train_loss=2.950, RMSE=7.710]
Epoch 33: 53%|█████▎ | 17/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=2.950, RMSE=7.710]
Epoch 33: 53%|█████▎ | 17/32 [00:00<00:00, 318.50it/s, v_num=2, train_loss=3.180, RMSE=7.710]
Epoch 33: 56%|█████▋ | 18/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=3.180, RMSE=7.710]
Epoch 33: 56%|█████▋ | 18/32 [00:00<00:00, 318.63it/s, v_num=2, train_loss=3.290, RMSE=7.710]
Epoch 33: 59%|█████▉ | 19/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=3.290, RMSE=7.710]
Epoch 33: 59%|█████▉ | 19/32 [00:00<00:00, 318.83it/s, v_num=2, train_loss=3.280, RMSE=7.710]
Epoch 33: 62%|██████▎ | 20/32 [00:00<00:00, 320.25it/s, v_num=2, train_loss=3.280, RMSE=7.710]
Epoch 33: 62%|██████▎ | 20/32 [00:00<00:00, 319.13it/s, v_num=2, train_loss=3.370, RMSE=7.710]
Epoch 33: 66%|██████▌ | 21/32 [00:00<00:00, 320.15it/s, v_num=2, train_loss=3.370, RMSE=7.710]
Epoch 33: 66%|██████▌ | 21/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=2.940, RMSE=7.710]
Epoch 33: 69%|██████▉ | 22/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=2.940, RMSE=7.710]
Epoch 33: 69%|██████▉ | 22/32 [00:00<00:00, 319.27it/s, v_num=2, train_loss=2.940, RMSE=7.710]
Epoch 33: 72%|███████▏ | 23/32 [00:00<00:00, 320.33it/s, v_num=2, train_loss=2.940, RMSE=7.710]
Epoch 33: 72%|███████▏ | 23/32 [00:00<00:00, 319.40it/s, v_num=2, train_loss=2.840, RMSE=7.710]
Epoch 33: 75%|███████▌ | 24/32 [00:00<00:00, 320.56it/s, v_num=2, train_loss=2.840, RMSE=7.710]
Epoch 33: 75%|███████▌ | 24/32 [00:00<00:00, 319.66it/s, v_num=2, train_loss=2.960, RMSE=7.710]
Epoch 33: 78%|███████▊ | 25/32 [00:00<00:00, 320.54it/s, v_num=2, train_loss=2.960, RMSE=7.710]
Epoch 33: 78%|███████▊ | 25/32 [00:00<00:00, 319.68it/s, v_num=2, train_loss=2.880, RMSE=7.710]
Epoch 33: 81%|████████▏ | 26/32 [00:00<00:00, 320.59it/s, v_num=2, train_loss=2.880, RMSE=7.710]
Epoch 33: 81%|████████▏ | 26/32 [00:00<00:00, 319.77it/s, v_num=2, train_loss=2.990, RMSE=7.710]
Epoch 33: 84%|████████▍ | 27/32 [00:00<00:00, 320.70it/s, v_num=2, train_loss=2.990, RMSE=7.710]
Epoch 33: 84%|████████▍ | 27/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=3.010, RMSE=7.710]
Epoch 33: 88%|████████▊ | 28/32 [00:00<00:00, 320.52it/s, v_num=2, train_loss=3.010, RMSE=7.710]
Epoch 33: 88%|████████▊ | 28/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=3.050, RMSE=7.710]
Epoch 33: 91%|█████████ | 29/32 [00:00<00:00, 320.35it/s, v_num=2, train_loss=3.050, RMSE=7.710]
Epoch 33: 91%|█████████ | 29/32 [00:00<00:00, 319.61it/s, v_num=2, train_loss=3.000, RMSE=7.710]
Epoch 33: 94%|█████████▍| 30/32 [00:00<00:00, 320.43it/s, v_num=2, train_loss=3.000, RMSE=7.710]
Epoch 33: 94%|█████████▍| 30/32 [00:00<00:00, 319.72it/s, v_num=2, train_loss=2.980, RMSE=7.710]
Epoch 33: 97%|█████████▋| 31/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=2.980, RMSE=7.710]
Epoch 33: 97%|█████████▋| 31/32 [00:00<00:00, 319.76it/s, v_num=2, train_loss=3.180, RMSE=7.710]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.180, RMSE=7.710]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 319.96it/s, v_num=2, train_loss=3.180, RMSE=7.710]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 615.84it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 613.92it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 617.75it/s]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 262.14it/s, v_num=2, train_loss=3.180, RMSE=7.380]
Epoch 33: 100%|██████████| 32/32 [00:00<00:00, 261.02it/s, v_num=2, train_loss=3.180, RMSE=7.380]
Epoch 33: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.180, RMSE=7.380]
Epoch 34: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.180, RMSE=7.380]
Epoch 34: 3%|▎ | 1/32 [00:00<00:00, 291.27it/s, v_num=2, train_loss=3.180, RMSE=7.380]
Epoch 34: 3%|▎ | 1/32 [00:00<00:00, 274.44it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 6%|▋ | 2/32 [00:00<00:00, 302.86it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 6%|▋ | 2/32 [00:00<00:00, 293.55it/s, v_num=2, train_loss=3.340, RMSE=7.380]
Epoch 34: 9%|▉ | 3/32 [00:00<00:00, 308.90it/s, v_num=2, train_loss=3.340, RMSE=7.380]
Epoch 34: 9%|▉ | 3/32 [00:00<00:00, 302.46it/s, v_num=2, train_loss=2.860, RMSE=7.380]
Epoch 34: 12%|█▎ | 4/32 [00:00<00:00, 313.49it/s, v_num=2, train_loss=2.860, RMSE=7.380]
Epoch 34: 12%|█▎ | 4/32 [00:00<00:00, 308.19it/s, v_num=2, train_loss=3.140, RMSE=7.380]
Epoch 34: 16%|█▌ | 5/32 [00:00<00:00, 315.32it/s, v_num=2, train_loss=3.140, RMSE=7.380]
Epoch 34: 16%|█▌ | 5/32 [00:00<00:00, 311.25it/s, v_num=2, train_loss=3.050, RMSE=7.380]
Epoch 34: 19%|█▉ | 6/32 [00:00<00:00, 316.54it/s, v_num=2, train_loss=3.050, RMSE=7.380]
Epoch 34: 19%|█▉ | 6/32 [00:00<00:00, 313.11it/s, v_num=2, train_loss=3.140, RMSE=7.380]
Epoch 34: 22%|██▏ | 7/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=3.140, RMSE=7.380]
Epoch 34: 22%|██▏ | 7/32 [00:00<00:00, 314.50it/s, v_num=2, train_loss=3.080, RMSE=7.380]
Epoch 34: 25%|██▌ | 8/32 [00:00<00:00, 318.60it/s, v_num=2, train_loss=3.080, RMSE=7.380]
Epoch 34: 25%|██▌ | 8/32 [00:00<00:00, 315.60it/s, v_num=2, train_loss=3.250, RMSE=7.380]
Epoch 34: 28%|██▊ | 9/32 [00:00<00:00, 319.27it/s, v_num=2, train_loss=3.250, RMSE=7.380]
Epoch 34: 28%|██▊ | 9/32 [00:00<00:00, 316.94it/s, v_num=2, train_loss=3.070, RMSE=7.380]
Epoch 34: 31%|███▏ | 10/32 [00:00<00:00, 319.44it/s, v_num=2, train_loss=3.070, RMSE=7.380]
Epoch 34: 31%|███▏ | 10/32 [00:00<00:00, 317.34it/s, v_num=2, train_loss=2.920, RMSE=7.380]
Epoch 34: 34%|███▍ | 11/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=2.920, RMSE=7.380]
Epoch 34: 34%|███▍ | 11/32 [00:00<00:00, 317.96it/s, v_num=2, train_loss=3.110, RMSE=7.380]
Epoch 34: 38%|███▊ | 12/32 [00:00<00:00, 320.17it/s, v_num=2, train_loss=3.110, RMSE=7.380]
Epoch 34: 38%|███▊ | 12/32 [00:00<00:00, 318.40it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 41%|████ | 13/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 41%|████ | 13/32 [00:00<00:00, 314.63it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 44%|████▍ | 14/32 [00:00<00:00, 316.45it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 44%|████▍ | 14/32 [00:00<00:00, 314.90it/s, v_num=2, train_loss=3.170, RMSE=7.380]
Epoch 34: 47%|████▋ | 15/32 [00:00<00:00, 316.87it/s, v_num=2, train_loss=3.170, RMSE=7.380]
Epoch 34: 47%|████▋ | 15/32 [00:00<00:00, 315.50it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 50%|█████ | 16/32 [00:00<00:00, 317.25it/s, v_num=2, train_loss=3.090, RMSE=7.380]
Epoch 34: 50%|█████ | 16/32 [00:00<00:00, 315.94it/s, v_num=2, train_loss=3.120, RMSE=7.380]
Epoch 34: 53%|█████▎ | 17/32 [00:00<00:00, 317.56it/s, v_num=2, train_loss=3.120, RMSE=7.380]
Epoch 34: 53%|█████▎ | 17/32 [00:00<00:00, 316.31it/s, v_num=2, train_loss=3.010, RMSE=7.380]
Epoch 34: 56%|█████▋ | 18/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=3.010, RMSE=7.380]
Epoch 34: 56%|█████▋ | 18/32 [00:00<00:00, 316.94it/s, v_num=2, train_loss=3.110, RMSE=7.380]
Epoch 34: 59%|█████▉ | 19/32 [00:00<00:00, 318.50it/s, v_num=2, train_loss=3.110, RMSE=7.380]
Epoch 34: 59%|█████▉ | 19/32 [00:00<00:00, 317.39it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 62%|██████▎ | 20/32 [00:00<00:00, 318.50it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 62%|██████▎ | 20/32 [00:00<00:00, 317.45it/s, v_num=2, train_loss=2.960, RMSE=7.380]
Epoch 34: 66%|██████▌ | 21/32 [00:00<00:00, 318.74it/s, v_num=2, train_loss=2.960, RMSE=7.380]
Epoch 34: 66%|██████▌ | 21/32 [00:00<00:00, 317.75it/s, v_num=2, train_loss=3.240, RMSE=7.380]
Epoch 34: 69%|██████▉ | 22/32 [00:00<00:00, 319.19it/s, v_num=2, train_loss=3.240, RMSE=7.380]
Epoch 34: 69%|██████▉ | 22/32 [00:00<00:00, 318.24it/s, v_num=2, train_loss=2.770, RMSE=7.380]
Epoch 34: 72%|███████▏ | 23/32 [00:00<00:00, 319.50it/s, v_num=2, train_loss=2.770, RMSE=7.380]
Epoch 34: 72%|███████▏ | 23/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=2.900, RMSE=7.380]
Epoch 34: 75%|███████▌ | 24/32 [00:00<00:00, 319.73it/s, v_num=2, train_loss=2.900, RMSE=7.380]
Epoch 34: 75%|███████▌ | 24/32 [00:00<00:00, 318.81it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 78%|███████▊ | 25/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=3.030, RMSE=7.380]
Epoch 34: 78%|███████▊ | 25/32 [00:00<00:00, 318.91it/s, v_num=2, train_loss=3.060, RMSE=7.380]
Epoch 34: 81%|████████▏ | 26/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.060, RMSE=7.380]
Epoch 34: 81%|████████▏ | 26/32 [00:00<00:00, 319.09it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 84%|████████▍ | 27/32 [00:00<00:00, 319.98it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 84%|████████▍ | 27/32 [00:00<00:00, 319.17it/s, v_num=2, train_loss=2.890, RMSE=7.380]
Epoch 34: 88%|████████▊ | 28/32 [00:00<00:00, 320.11it/s, v_num=2, train_loss=2.890, RMSE=7.380]
Epoch 34: 88%|████████▊ | 28/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=2.860, RMSE=7.380]
Epoch 34: 91%|█████████ | 29/32 [00:00<00:00, 320.26it/s, v_num=2, train_loss=2.860, RMSE=7.380]
Epoch 34: 91%|█████████ | 29/32 [00:00<00:00, 319.53it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 94%|█████████▍| 30/32 [00:00<00:00, 320.41it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 94%|█████████▍| 30/32 [00:00<00:00, 319.70it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 97%|█████████▋| 31/32 [00:00<00:00, 320.66it/s, v_num=2, train_loss=3.190, RMSE=7.380]
Epoch 34: 97%|█████████▋| 31/32 [00:00<00:00, 319.98it/s, v_num=2, train_loss=2.960, RMSE=7.380]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 320.82it/s, v_num=2, train_loss=2.960, RMSE=7.380]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 320.14it/s, v_num=2, train_loss=3.060, RMSE=7.380]
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Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 262.02it/s, v_num=2, train_loss=3.060, RMSE=7.180]
Epoch 34: 100%|██████████| 32/32 [00:00<00:00, 260.87it/s, v_num=2, train_loss=3.060, RMSE=7.180]
Epoch 34: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.060, RMSE=7.180]
Epoch 35: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.060, RMSE=7.180]
Epoch 35: 3%|▎ | 1/32 [00:00<00:00, 283.80it/s, v_num=2, train_loss=3.060, RMSE=7.180]
Epoch 35: 3%|▎ | 1/32 [00:00<00:00, 267.49it/s, v_num=2, train_loss=3.050, RMSE=7.180]
Epoch 35: 6%|▋ | 2/32 [00:00<00:00, 299.80it/s, v_num=2, train_loss=3.050, RMSE=7.180]
Epoch 35: 6%|▋ | 2/32 [00:00<00:00, 290.70it/s, v_num=2, train_loss=3.110, RMSE=7.180]
Epoch 35: 9%|▉ | 3/32 [00:00<00:00, 306.47it/s, v_num=2, train_loss=3.110, RMSE=7.180]
Epoch 35: 9%|▉ | 3/32 [00:00<00:00, 300.15it/s, v_num=2, train_loss=3.030, RMSE=7.180]
Epoch 35: 12%|█▎ | 4/32 [00:00<00:00, 309.56it/s, v_num=2, train_loss=3.030, RMSE=7.180]
Epoch 35: 12%|█▎ | 4/32 [00:00<00:00, 304.58it/s, v_num=2, train_loss=2.940, RMSE=7.180]
Epoch 35: 16%|█▌ | 5/32 [00:00<00:00, 309.27it/s, v_num=2, train_loss=2.940, RMSE=7.180]
Epoch 35: 16%|█▌ | 5/32 [00:00<00:00, 305.18it/s, v_num=2, train_loss=2.950, RMSE=7.180]
Epoch 35: 19%|█▉ | 6/32 [00:00<00:00, 311.48it/s, v_num=2, train_loss=2.950, RMSE=7.180]
Epoch 35: 19%|█▉ | 6/32 [00:00<00:00, 307.36it/s, v_num=2, train_loss=2.820, RMSE=7.180]
Epoch 35: 22%|██▏ | 7/32 [00:00<00:00, 312.95it/s, v_num=2, train_loss=2.820, RMSE=7.180]
Epoch 35: 22%|██▏ | 7/32 [00:00<00:00, 310.10it/s, v_num=2, train_loss=2.940, RMSE=7.180]
Epoch 35: 25%|██▌ | 8/32 [00:00<00:00, 314.42it/s, v_num=2, train_loss=2.940, RMSE=7.180]
Epoch 35: 25%|██▌ | 8/32 [00:00<00:00, 311.91it/s, v_num=2, train_loss=2.950, RMSE=7.180]
Epoch 35: 28%|██▊ | 9/32 [00:00<00:00, 315.66it/s, v_num=2, train_loss=2.950, RMSE=7.180]
Epoch 35: 28%|██▊ | 9/32 [00:00<00:00, 313.41it/s, v_num=2, train_loss=3.080, RMSE=7.180]
Epoch 35: 31%|███▏ | 10/32 [00:00<00:00, 316.84it/s, v_num=2, train_loss=3.080, RMSE=7.180]
Epoch 35: 31%|███▏ | 10/32 [00:00<00:00, 314.77it/s, v_num=2, train_loss=2.920, RMSE=7.180]
Epoch 35: 34%|███▍ | 11/32 [00:00<00:00, 317.88it/s, v_num=2, train_loss=2.920, RMSE=7.180]
Epoch 35: 34%|███▍ | 11/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=3.250, RMSE=7.180]
Epoch 35: 38%|███▊ | 12/32 [00:00<00:00, 317.39it/s, v_num=2, train_loss=3.250, RMSE=7.180]
Epoch 35: 38%|███▊ | 12/32 [00:00<00:00, 315.64it/s, v_num=2, train_loss=3.340, RMSE=7.180]
Epoch 35: 41%|████ | 13/32 [00:00<00:00, 317.82it/s, v_num=2, train_loss=3.340, RMSE=7.180]
Epoch 35: 41%|████ | 13/32 [00:00<00:00, 316.21it/s, v_num=2, train_loss=3.030, RMSE=7.180]
Epoch 35: 44%|████▍ | 14/32 [00:00<00:00, 318.35it/s, v_num=2, train_loss=3.030, RMSE=7.180]
Epoch 35: 44%|████▍ | 14/32 [00:00<00:00, 316.80it/s, v_num=2, train_loss=2.990, RMSE=7.180]
Epoch 35: 47%|████▋ | 15/32 [00:00<00:00, 318.94it/s, v_num=2, train_loss=2.990, RMSE=7.180]
Epoch 35: 47%|████▋ | 15/32 [00:00<00:00, 317.55it/s, v_num=2, train_loss=2.750, RMSE=7.180]
Epoch 35: 50%|█████ | 16/32 [00:00<00:00, 319.20it/s, v_num=2, train_loss=2.750, RMSE=7.180]
Epoch 35: 50%|█████ | 16/32 [00:00<00:00, 317.87it/s, v_num=2, train_loss=2.960, RMSE=7.180]
Epoch 35: 53%|█████▎ | 17/32 [00:00<00:00, 319.40it/s, v_num=2, train_loss=2.960, RMSE=7.180]
Epoch 35: 53%|█████▎ | 17/32 [00:00<00:00, 318.16it/s, v_num=2, train_loss=2.920, RMSE=7.180]
Epoch 35: 56%|█████▋ | 18/32 [00:00<00:00, 319.69it/s, v_num=2, train_loss=2.920, RMSE=7.180]
Epoch 35: 56%|█████▋ | 18/32 [00:00<00:00, 318.52it/s, v_num=2, train_loss=3.260, RMSE=7.180]
Epoch 35: 59%|█████▉ | 19/32 [00:00<00:00, 320.19it/s, v_num=2, train_loss=3.260, RMSE=7.180]
Epoch 35: 59%|█████▉ | 19/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=3.310, RMSE=7.180]
Epoch 35: 62%|██████▎ | 20/32 [00:00<00:00, 320.38it/s, v_num=2, train_loss=3.310, RMSE=7.180]
Epoch 35: 62%|██████▎ | 20/32 [00:00<00:00, 319.30it/s, v_num=2, train_loss=3.050, RMSE=7.180]
Epoch 35: 66%|██████▌ | 21/32 [00:00<00:00, 320.56it/s, v_num=2, train_loss=3.050, RMSE=7.180]
Epoch 35: 66%|██████▌ | 21/32 [00:00<00:00, 319.54it/s, v_num=2, train_loss=3.020, RMSE=7.180]
Epoch 35: 69%|██████▉ | 22/32 [00:00<00:00, 320.77it/s, v_num=2, train_loss=3.020, RMSE=7.180]
Epoch 35: 69%|██████▉ | 22/32 [00:00<00:00, 319.81it/s, v_num=2, train_loss=3.200, RMSE=7.180]
Epoch 35: 72%|███████▏ | 23/32 [00:00<00:00, 320.91it/s, v_num=2, train_loss=3.200, RMSE=7.180]
Epoch 35: 72%|███████▏ | 23/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=3.150, RMSE=7.180]
Epoch 35: 75%|███████▌ | 24/32 [00:00<00:00, 321.25it/s, v_num=2, train_loss=3.150, RMSE=7.180]
Epoch 35: 75%|███████▌ | 24/32 [00:00<00:00, 320.37it/s, v_num=2, train_loss=3.090, RMSE=7.180]
Epoch 35: 78%|███████▊ | 25/32 [00:00<00:00, 321.43it/s, v_num=2, train_loss=3.090, RMSE=7.180]
Epoch 35: 78%|███████▊ | 25/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=3.040, RMSE=7.180]
Epoch 35: 81%|████████▏ | 26/32 [00:00<00:00, 321.45it/s, v_num=2, train_loss=3.040, RMSE=7.180]
Epoch 35: 81%|████████▏ | 26/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=3.010, RMSE=7.180]
Epoch 35: 84%|████████▍ | 27/32 [00:00<00:00, 321.58it/s, v_num=2, train_loss=3.010, RMSE=7.180]
Epoch 35: 84%|████████▍ | 27/32 [00:00<00:00, 320.79it/s, v_num=2, train_loss=3.120, RMSE=7.180]
Epoch 35: 88%|████████▊ | 28/32 [00:00<00:00, 321.79it/s, v_num=2, train_loss=3.120, RMSE=7.180]
Epoch 35: 88%|████████▊ | 28/32 [00:00<00:00, 320.99it/s, v_num=2, train_loss=3.040, RMSE=7.180]
Epoch 35: 91%|█████████ | 29/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=3.040, RMSE=7.180]
Epoch 35: 91%|█████████ | 29/32 [00:00<00:00, 321.06it/s, v_num=2, train_loss=2.970, RMSE=7.180]
Epoch 35: 94%|█████████▍| 30/32 [00:00<00:00, 321.77it/s, v_num=2, train_loss=2.970, RMSE=7.180]
Epoch 35: 94%|█████████▍| 30/32 [00:00<00:00, 321.05it/s, v_num=2, train_loss=3.020, RMSE=7.180]
Epoch 35: 97%|█████████▋| 31/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.020, RMSE=7.180]
Epoch 35: 97%|█████████▋| 31/32 [00:00<00:00, 319.30it/s, v_num=2, train_loss=3.070, RMSE=7.180]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=3.070, RMSE=7.180]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 319.56it/s, v_num=2, train_loss=3.050, RMSE=7.180]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 612.93it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 615.77it/s]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 260.72it/s, v_num=2, train_loss=3.050, RMSE=6.670]
Epoch 35: 100%|██████████| 32/32 [00:00<00:00, 259.34it/s, v_num=2, train_loss=3.050, RMSE=6.670]
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Epoch 36: 3%|▎ | 1/32 [00:00<00:00, 264.89it/s, v_num=2, train_loss=3.050, RMSE=6.670]
Epoch 36: 3%|▎ | 1/32 [00:00<00:00, 250.09it/s, v_num=2, train_loss=3.030, RMSE=6.670]
Epoch 36: 6%|▋ | 2/32 [00:00<00:00, 283.10it/s, v_num=2, train_loss=3.030, RMSE=6.670]
Epoch 36: 6%|▋ | 2/32 [00:00<00:00, 274.97it/s, v_num=2, train_loss=3.080, RMSE=6.670]
Epoch 36: 9%|▉ | 3/32 [00:00<00:00, 293.11it/s, v_num=2, train_loss=3.080, RMSE=6.670]
Epoch 36: 9%|▉ | 3/32 [00:00<00:00, 287.33it/s, v_num=2, train_loss=2.940, RMSE=6.670]
Epoch 36: 12%|█▎ | 4/32 [00:00<00:00, 299.64it/s, v_num=2, train_loss=2.940, RMSE=6.670]
Epoch 36: 12%|█▎ | 4/32 [00:00<00:00, 295.04it/s, v_num=2, train_loss=2.900, RMSE=6.670]
Epoch 36: 16%|█▌ | 5/32 [00:00<00:00, 302.97it/s, v_num=2, train_loss=2.900, RMSE=6.670]
Epoch 36: 16%|█▌ | 5/32 [00:00<00:00, 299.18it/s, v_num=2, train_loss=2.880, RMSE=6.670]
Epoch 36: 19%|█▉ | 6/32 [00:00<00:00, 306.67it/s, v_num=2, train_loss=2.880, RMSE=6.670]
Epoch 36: 19%|█▉ | 6/32 [00:00<00:00, 303.48it/s, v_num=2, train_loss=3.000, RMSE=6.670]
Epoch 36: 22%|██▏ | 7/32 [00:00<00:00, 308.79it/s, v_num=2, train_loss=3.000, RMSE=6.670]
Epoch 36: 22%|██▏ | 7/32 [00:00<00:00, 305.97it/s, v_num=2, train_loss=3.110, RMSE=6.670]
Epoch 36: 25%|██▌ | 8/32 [00:00<00:00, 310.38it/s, v_num=2, train_loss=3.110, RMSE=6.670]
Epoch 36: 25%|██▌ | 8/32 [00:00<00:00, 307.86it/s, v_num=2, train_loss=2.780, RMSE=6.670]
Epoch 36: 28%|██▊ | 9/32 [00:00<00:00, 311.68it/s, v_num=2, train_loss=2.780, RMSE=6.670]
Epoch 36: 28%|██▊ | 9/32 [00:00<00:00, 309.44it/s, v_num=2, train_loss=2.930, RMSE=6.670]
Epoch 36: 31%|███▏ | 10/32 [00:00<00:00, 313.08it/s, v_num=2, train_loss=2.930, RMSE=6.670]
Epoch 36: 31%|███▏ | 10/32 [00:00<00:00, 311.06it/s, v_num=2, train_loss=3.150, RMSE=6.670]
Epoch 36: 34%|███▍ | 11/32 [00:00<00:00, 314.07it/s, v_num=2, train_loss=3.150, RMSE=6.670]
Epoch 36: 34%|███▍ | 11/32 [00:00<00:00, 312.19it/s, v_num=2, train_loss=2.980, RMSE=6.670]
Epoch 36: 38%|███▊ | 12/32 [00:00<00:00, 314.92it/s, v_num=2, train_loss=2.980, RMSE=6.670]
Epoch 36: 38%|███▊ | 12/32 [00:00<00:00, 313.12it/s, v_num=2, train_loss=3.420, RMSE=6.670]
Epoch 36: 41%|████ | 13/32 [00:00<00:00, 315.47it/s, v_num=2, train_loss=3.420, RMSE=6.670]
Epoch 36: 41%|████ | 13/32 [00:00<00:00, 313.84it/s, v_num=2, train_loss=3.260, RMSE=6.670]
Epoch 36: 44%|████▍ | 14/32 [00:00<00:00, 316.05it/s, v_num=2, train_loss=3.260, RMSE=6.670]
Epoch 36: 44%|████▍ | 14/32 [00:00<00:00, 314.31it/s, v_num=2, train_loss=3.210, RMSE=6.670]
Epoch 36: 47%|████▋ | 15/32 [00:00<00:00, 316.33it/s, v_num=2, train_loss=3.210, RMSE=6.670]
Epoch 36: 47%|████▋ | 15/32 [00:00<00:00, 314.90it/s, v_num=2, train_loss=2.860, RMSE=6.670]
Epoch 36: 50%|█████ | 16/32 [00:00<00:00, 316.76it/s, v_num=2, train_loss=2.860, RMSE=6.670]
Epoch 36: 50%|█████ | 16/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=2.920, RMSE=6.670]
Epoch 36: 53%|█████▎ | 17/32 [00:00<00:00, 317.10it/s, v_num=2, train_loss=2.920, RMSE=6.670]
Epoch 36: 53%|█████▎ | 17/32 [00:00<00:00, 315.87it/s, v_num=2, train_loss=2.830, RMSE=6.670]
Epoch 36: 56%|█████▋ | 18/32 [00:00<00:00, 317.57it/s, v_num=2, train_loss=2.830, RMSE=6.670]
Epoch 36: 56%|█████▋ | 18/32 [00:00<00:00, 316.39it/s, v_num=2, train_loss=3.300, RMSE=6.670]
Epoch 36: 59%|█████▉ | 19/32 [00:00<00:00, 317.93it/s, v_num=2, train_loss=3.300, RMSE=6.670]
Epoch 36: 59%|█████▉ | 19/32 [00:00<00:00, 316.82it/s, v_num=2, train_loss=2.990, RMSE=6.670]
Epoch 36: 62%|██████▎ | 20/32 [00:00<00:00, 318.06it/s, v_num=2, train_loss=2.990, RMSE=6.670]
Epoch 36: 62%|██████▎ | 20/32 [00:00<00:00, 317.01it/s, v_num=2, train_loss=2.810, RMSE=6.670]
Epoch 36: 66%|██████▌ | 21/32 [00:00<00:00, 318.48it/s, v_num=2, train_loss=2.810, RMSE=6.670]
Epoch 36: 66%|██████▌ | 21/32 [00:00<00:00, 317.48it/s, v_num=2, train_loss=2.740, RMSE=6.670]
Epoch 36: 69%|██████▉ | 22/32 [00:00<00:00, 318.82it/s, v_num=2, train_loss=2.740, RMSE=6.670]
Epoch 36: 69%|██████▉ | 22/32 [00:00<00:00, 317.86it/s, v_num=2, train_loss=3.110, RMSE=6.670]
Epoch 36: 72%|███████▏ | 23/32 [00:00<00:00, 319.15it/s, v_num=2, train_loss=3.110, RMSE=6.670]
Epoch 36: 72%|███████▏ | 23/32 [00:00<00:00, 318.24it/s, v_num=2, train_loss=2.950, RMSE=6.670]
Epoch 36: 75%|███████▌ | 24/32 [00:00<00:00, 319.25it/s, v_num=2, train_loss=2.950, RMSE=6.670]
Epoch 36: 75%|███████▌ | 24/32 [00:00<00:00, 318.36it/s, v_num=2, train_loss=2.760, RMSE=6.670]
Epoch 36: 78%|███████▊ | 25/32 [00:00<00:00, 319.34it/s, v_num=2, train_loss=2.760, RMSE=6.670]
Epoch 36: 78%|███████▊ | 25/32 [00:00<00:00, 318.49it/s, v_num=2, train_loss=3.370, RMSE=6.670]
Epoch 36: 81%|████████▏ | 26/32 [00:00<00:00, 319.58it/s, v_num=2, train_loss=3.370, RMSE=6.670]
Epoch 36: 81%|████████▏ | 26/32 [00:00<00:00, 318.77it/s, v_num=2, train_loss=3.030, RMSE=6.670]
Epoch 36: 84%|████████▍ | 27/32 [00:00<00:00, 319.78it/s, v_num=2, train_loss=3.030, RMSE=6.670]
Epoch 36: 84%|████████▍ | 27/32 [00:00<00:00, 319.00it/s, v_num=2, train_loss=2.840, RMSE=6.670]
Epoch 36: 88%|████████▊ | 28/32 [00:00<00:00, 320.18it/s, v_num=2, train_loss=2.840, RMSE=6.670]
Epoch 36: 88%|████████▊ | 28/32 [00:00<00:00, 319.40it/s, v_num=2, train_loss=3.250, RMSE=6.670]
Epoch 36: 91%|█████████ | 29/32 [00:00<00:00, 320.29it/s, v_num=2, train_loss=3.250, RMSE=6.670]
Epoch 36: 91%|█████████ | 29/32 [00:00<00:00, 319.56it/s, v_num=2, train_loss=2.960, RMSE=6.670]
Epoch 36: 94%|█████████▍| 30/32 [00:00<00:00, 320.46it/s, v_num=2, train_loss=2.960, RMSE=6.670]
Epoch 36: 94%|█████████▍| 30/32 [00:00<00:00, 319.75it/s, v_num=2, train_loss=2.980, RMSE=6.670]
Epoch 36: 97%|█████████▋| 31/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=2.980, RMSE=6.670]
Epoch 36: 97%|█████████▋| 31/32 [00:00<00:00, 319.89it/s, v_num=2, train_loss=3.460, RMSE=6.670]
Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 320.97it/s, v_num=2, train_loss=3.460, RMSE=6.670]
Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 320.31it/s, v_num=2, train_loss=3.360, RMSE=6.670]
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Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 261.02it/s, v_num=2, train_loss=3.360, RMSE=6.710]
Epoch 36: 100%|██████████| 32/32 [00:00<00:00, 259.90it/s, v_num=2, train_loss=3.360, RMSE=6.710]
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Epoch 37: 3%|▎ | 1/32 [00:00<00:00, 281.88it/s, v_num=2, train_loss=3.360, RMSE=6.710]
Epoch 37: 3%|▎ | 1/32 [00:00<00:00, 265.98it/s, v_num=2, train_loss=3.230, RMSE=6.710]
Epoch 37: 6%|▋ | 2/32 [00:00<00:00, 297.66it/s, v_num=2, train_loss=3.230, RMSE=6.710]
Epoch 37: 6%|▋ | 2/32 [00:00<00:00, 288.67it/s, v_num=2, train_loss=2.930, RMSE=6.710]
Epoch 37: 9%|▉ | 3/32 [00:00<00:00, 306.56it/s, v_num=2, train_loss=2.930, RMSE=6.710]
Epoch 37: 9%|▉ | 3/32 [00:00<00:00, 300.22it/s, v_num=2, train_loss=2.890, RMSE=6.710]
Epoch 37: 12%|█▎ | 4/32 [00:00<00:00, 310.51it/s, v_num=2, train_loss=2.890, RMSE=6.710]
Epoch 37: 12%|█▎ | 4/32 [00:00<00:00, 305.51it/s, v_num=2, train_loss=2.950, RMSE=6.710]
Epoch 37: 16%|█▌ | 5/32 [00:00<00:00, 312.55it/s, v_num=2, train_loss=2.950, RMSE=6.710]
Epoch 37: 16%|█▌ | 5/32 [00:00<00:00, 308.50it/s, v_num=2, train_loss=3.340, RMSE=6.710]
Epoch 37: 19%|█▉ | 6/32 [00:00<00:00, 314.53it/s, v_num=2, train_loss=3.340, RMSE=6.710]
Epoch 37: 19%|█▉ | 6/32 [00:00<00:00, 311.15it/s, v_num=2, train_loss=3.020, RMSE=6.710]
Epoch 37: 22%|██▏ | 7/32 [00:00<00:00, 315.71it/s, v_num=2, train_loss=3.020, RMSE=6.710]
Epoch 37: 22%|██▏ | 7/32 [00:00<00:00, 312.75it/s, v_num=2, train_loss=3.060, RMSE=6.710]
Epoch 37: 25%|██▌ | 8/32 [00:00<00:00, 316.97it/s, v_num=2, train_loss=3.060, RMSE=6.710]
Epoch 37: 25%|██▌ | 8/32 [00:00<00:00, 314.27it/s, v_num=2, train_loss=2.980, RMSE=6.710]
Epoch 37: 28%|██▊ | 9/32 [00:00<00:00, 317.75it/s, v_num=2, train_loss=2.980, RMSE=6.710]
Epoch 37: 28%|██▊ | 9/32 [00:00<00:00, 315.45it/s, v_num=2, train_loss=2.910, RMSE=6.710]
Epoch 37: 31%|███▏ | 10/32 [00:00<00:00, 318.39it/s, v_num=2, train_loss=2.910, RMSE=6.710]
Epoch 37: 31%|███▏ | 10/32 [00:00<00:00, 316.30it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 34%|███▍ | 11/32 [00:00<00:00, 319.08it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 34%|███▍ | 11/32 [00:00<00:00, 317.16it/s, v_num=2, train_loss=2.860, RMSE=6.710]
Epoch 37: 38%|███▊ | 12/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=2.860, RMSE=6.710]
Epoch 37: 38%|███▊ | 12/32 [00:00<00:00, 317.98it/s, v_num=2, train_loss=2.750, RMSE=6.710]
Epoch 37: 41%|████ | 13/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=2.750, RMSE=6.710]
Epoch 37: 41%|████ | 13/32 [00:00<00:00, 318.12it/s, v_num=2, train_loss=2.920, RMSE=6.710]
Epoch 37: 44%|████▍ | 14/32 [00:00<00:00, 319.59it/s, v_num=2, train_loss=2.920, RMSE=6.710]
Epoch 37: 44%|████▍ | 14/32 [00:00<00:00, 318.10it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 47%|████▋ | 15/32 [00:00<00:00, 319.82it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 47%|████▋ | 15/32 [00:00<00:00, 318.40it/s, v_num=2, train_loss=3.010, RMSE=6.710]
Epoch 37: 50%|█████ | 16/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=3.010, RMSE=6.710]
Epoch 37: 50%|█████ | 16/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=2.920, RMSE=6.710]
Epoch 37: 53%|█████▎ | 17/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=2.920, RMSE=6.710]
Epoch 37: 53%|█████▎ | 17/32 [00:00<00:00, 315.99it/s, v_num=2, train_loss=3.050, RMSE=6.710]
Epoch 37: 56%|█████▋ | 18/32 [00:00<00:00, 317.33it/s, v_num=2, train_loss=3.050, RMSE=6.710]
Epoch 37: 56%|█████▋ | 18/32 [00:00<00:00, 316.15it/s, v_num=2, train_loss=3.140, RMSE=6.710]
Epoch 37: 59%|█████▉ | 19/32 [00:00<00:00, 317.62it/s, v_num=2, train_loss=3.140, RMSE=6.710]
Epoch 37: 59%|█████▉ | 19/32 [00:00<00:00, 316.52it/s, v_num=2, train_loss=3.140, RMSE=6.710]
Epoch 37: 62%|██████▎ | 20/32 [00:00<00:00, 317.83it/s, v_num=2, train_loss=3.140, RMSE=6.710]
Epoch 37: 62%|██████▎ | 20/32 [00:00<00:00, 316.78it/s, v_num=2, train_loss=3.130, RMSE=6.710]
Epoch 37: 66%|██████▌ | 21/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=3.130, RMSE=6.710]
Epoch 37: 66%|██████▌ | 21/32 [00:00<00:00, 317.22it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 69%|██████▉ | 22/32 [00:00<00:00, 318.64it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 69%|██████▉ | 22/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 72%|███████▏ | 23/32 [00:00<00:00, 318.46it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 72%|███████▏ | 23/32 [00:00<00:00, 317.54it/s, v_num=2, train_loss=3.000, RMSE=6.710]
Epoch 37: 75%|███████▌ | 24/32 [00:00<00:00, 318.56it/s, v_num=2, train_loss=3.000, RMSE=6.710]
Epoch 37: 75%|███████▌ | 24/32 [00:00<00:00, 317.68it/s, v_num=2, train_loss=3.080, RMSE=6.710]
Epoch 37: 78%|███████▊ | 25/32 [00:00<00:00, 318.79it/s, v_num=2, train_loss=3.080, RMSE=6.710]
Epoch 37: 78%|███████▊ | 25/32 [00:00<00:00, 317.94it/s, v_num=2, train_loss=2.980, RMSE=6.710]
Epoch 37: 81%|████████▏ | 26/32 [00:00<00:00, 319.16it/s, v_num=2, train_loss=2.980, RMSE=6.710]
Epoch 37: 81%|████████▏ | 26/32 [00:00<00:00, 318.36it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 84%|████████▍ | 27/32 [00:00<00:00, 319.30it/s, v_num=2, train_loss=2.990, RMSE=6.710]
Epoch 37: 84%|████████▍ | 27/32 [00:00<00:00, 318.52it/s, v_num=2, train_loss=3.020, RMSE=6.710]
Epoch 37: 88%|████████▊ | 28/32 [00:00<00:00, 319.52it/s, v_num=2, train_loss=3.020, RMSE=6.710]
Epoch 37: 88%|████████▊ | 28/32 [00:00<00:00, 318.76it/s, v_num=2, train_loss=3.360, RMSE=6.710]
Epoch 37: 91%|█████████ | 29/32 [00:00<00:00, 319.77it/s, v_num=2, train_loss=3.360, RMSE=6.710]
Epoch 37: 91%|█████████ | 29/32 [00:00<00:00, 319.04it/s, v_num=2, train_loss=3.000, RMSE=6.710]
Epoch 37: 94%|█████████▍| 30/32 [00:00<00:00, 320.07it/s, v_num=2, train_loss=3.000, RMSE=6.710]
Epoch 37: 94%|█████████▍| 30/32 [00:00<00:00, 319.36it/s, v_num=2, train_loss=2.840, RMSE=6.710]
Epoch 37: 97%|█████████▋| 31/32 [00:00<00:00, 320.22it/s, v_num=2, train_loss=2.840, RMSE=6.710]
Epoch 37: 97%|█████████▋| 31/32 [00:00<00:00, 319.55it/s, v_num=2, train_loss=2.850, RMSE=6.710]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 320.53it/s, v_num=2, train_loss=2.850, RMSE=6.710]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 319.87it/s, v_num=2, train_loss=3.350, RMSE=6.710]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 617.23it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 621.04it/s]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 262.37it/s, v_num=2, train_loss=3.350, RMSE=6.430]
Epoch 37: 100%|██████████| 32/32 [00:00<00:00, 261.14it/s, v_num=2, train_loss=3.350, RMSE=6.430]
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Epoch 38: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=3.350, RMSE=6.430]
Epoch 38: 3%|▎ | 1/32 [00:00<00:00, 287.71it/s, v_num=2, train_loss=3.350, RMSE=6.430]
Epoch 38: 3%|▎ | 1/32 [00:00<00:00, 270.91it/s, v_num=2, train_loss=2.850, RMSE=6.430]
Epoch 38: 6%|▋ | 2/32 [00:00<00:00, 300.30it/s, v_num=2, train_loss=2.850, RMSE=6.430]
Epoch 38: 6%|▋ | 2/32 [00:00<00:00, 291.16it/s, v_num=2, train_loss=3.390, RMSE=6.430]
Epoch 38: 9%|▉ | 3/32 [00:00<00:00, 306.47it/s, v_num=2, train_loss=3.390, RMSE=6.430]
Epoch 38: 9%|▉ | 3/32 [00:00<00:00, 300.12it/s, v_num=2, train_loss=3.260, RMSE=6.430]
Epoch 38: 12%|█▎ | 4/32 [00:00<00:00, 310.75it/s, v_num=2, train_loss=3.260, RMSE=6.430]
Epoch 38: 12%|█▎ | 4/32 [00:00<00:00, 305.79it/s, v_num=2, train_loss=2.920, RMSE=6.430]
Epoch 38: 16%|█▌ | 5/32 [00:00<00:00, 313.94it/s, v_num=2, train_loss=2.920, RMSE=6.430]
Epoch 38: 16%|█▌ | 5/32 [00:00<00:00, 309.83it/s, v_num=2, train_loss=3.050, RMSE=6.430]
Epoch 38: 19%|█▉ | 6/32 [00:00<00:00, 315.74it/s, v_num=2, train_loss=3.050, RMSE=6.430]
Epoch 38: 19%|█▉ | 6/32 [00:00<00:00, 312.22it/s, v_num=2, train_loss=2.750, RMSE=6.430]
Epoch 38: 22%|██▏ | 7/32 [00:00<00:00, 316.72it/s, v_num=2, train_loss=2.750, RMSE=6.430]
Epoch 38: 22%|██▏ | 7/32 [00:00<00:00, 313.77it/s, v_num=2, train_loss=2.820, RMSE=6.430]
Epoch 38: 25%|██▌ | 8/32 [00:00<00:00, 317.61it/s, v_num=2, train_loss=2.820, RMSE=6.430]
Epoch 38: 25%|██▌ | 8/32 [00:00<00:00, 315.02it/s, v_num=2, train_loss=2.790, RMSE=6.430]
Epoch 38: 28%|██▊ | 9/32 [00:00<00:00, 318.61it/s, v_num=2, train_loss=2.790, RMSE=6.430]
Epoch 38: 28%|██▊ | 9/32 [00:00<00:00, 315.75it/s, v_num=2, train_loss=2.940, RMSE=6.430]
Epoch 38: 31%|███▏ | 10/32 [00:00<00:00, 318.84it/s, v_num=2, train_loss=2.940, RMSE=6.430]
Epoch 38: 31%|███▏ | 10/32 [00:00<00:00, 316.73it/s, v_num=2, train_loss=3.230, RMSE=6.430]
Epoch 38: 34%|███▍ | 11/32 [00:00<00:00, 319.41it/s, v_num=2, train_loss=3.230, RMSE=6.430]
Epoch 38: 34%|███▍ | 11/32 [00:00<00:00, 317.47it/s, v_num=2, train_loss=2.800, RMSE=6.430]
Epoch 38: 38%|███▊ | 12/32 [00:00<00:00, 319.52it/s, v_num=2, train_loss=2.800, RMSE=6.430]
Epoch 38: 38%|███▊ | 12/32 [00:00<00:00, 317.77it/s, v_num=2, train_loss=2.790, RMSE=6.430]
Epoch 38: 41%|████ | 13/32 [00:00<00:00, 319.58it/s, v_num=2, train_loss=2.790, RMSE=6.430]
Epoch 38: 41%|████ | 13/32 [00:00<00:00, 317.92it/s, v_num=2, train_loss=2.990, RMSE=6.430]
Epoch 38: 44%|████▍ | 14/32 [00:00<00:00, 320.09it/s, v_num=2, train_loss=2.990, RMSE=6.430]
Epoch 38: 44%|████▍ | 14/32 [00:00<00:00, 318.58it/s, v_num=2, train_loss=2.610, RMSE=6.430]
Epoch 38: 47%|████▋ | 15/32 [00:00<00:00, 320.30it/s, v_num=2, train_loss=2.610, RMSE=6.430]
Epoch 38: 47%|████▋ | 15/32 [00:00<00:00, 318.89it/s, v_num=2, train_loss=3.250, RMSE=6.430]
Epoch 38: 50%|█████ | 16/32 [00:00<00:00, 320.62it/s, v_num=2, train_loss=3.250, RMSE=6.430]
Epoch 38: 50%|█████ | 16/32 [00:00<00:00, 319.29it/s, v_num=2, train_loss=2.870, RMSE=6.430]
Epoch 38: 53%|█████▎ | 17/32 [00:00<00:00, 320.58it/s, v_num=2, train_loss=2.870, RMSE=6.430]
Epoch 38: 53%|█████▎ | 17/32 [00:00<00:00, 319.28it/s, v_num=2, train_loss=3.020, RMSE=6.430]
Epoch 38: 56%|█████▋ | 18/32 [00:00<00:00, 320.69it/s, v_num=2, train_loss=3.020, RMSE=6.430]
Epoch 38: 56%|█████▋ | 18/32 [00:00<00:00, 319.43it/s, v_num=2, train_loss=3.070, RMSE=6.430]
Epoch 38: 59%|█████▉ | 19/32 [00:00<00:00, 320.66it/s, v_num=2, train_loss=3.070, RMSE=6.430]
Epoch 38: 59%|█████▉ | 19/32 [00:00<00:00, 319.53it/s, v_num=2, train_loss=3.290, RMSE=6.430]
Epoch 38: 62%|██████▎ | 20/32 [00:00<00:00, 320.62it/s, v_num=2, train_loss=3.290, RMSE=6.430]
Epoch 38: 62%|██████▎ | 20/32 [00:00<00:00, 319.55it/s, v_num=2, train_loss=2.890, RMSE=6.430]
Epoch 38: 66%|██████▌ | 21/32 [00:00<00:00, 320.74it/s, v_num=2, train_loss=2.890, RMSE=6.430]
Epoch 38: 66%|██████▌ | 21/32 [00:00<00:00, 319.73it/s, v_num=2, train_loss=3.380, RMSE=6.430]
Epoch 38: 69%|██████▉ | 22/32 [00:00<00:00, 320.76it/s, v_num=2, train_loss=3.380, RMSE=6.430]
Epoch 38: 69%|██████▉ | 22/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=2.580, RMSE=6.430]
Epoch 38: 72%|███████▏ | 23/32 [00:00<00:00, 320.95it/s, v_num=2, train_loss=2.580, RMSE=6.430]
Epoch 38: 72%|███████▏ | 23/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=3.360, RMSE=6.430]
Epoch 38: 75%|███████▌ | 24/32 [00:00<00:00, 320.88it/s, v_num=2, train_loss=3.360, RMSE=6.430]
Epoch 38: 75%|███████▌ | 24/32 [00:00<00:00, 319.99it/s, v_num=2, train_loss=3.070, RMSE=6.430]
Epoch 38: 78%|███████▊ | 25/32 [00:00<00:00, 321.04it/s, v_num=2, train_loss=3.070, RMSE=6.430]
Epoch 38: 78%|███████▊ | 25/32 [00:00<00:00, 320.19it/s, v_num=2, train_loss=2.850, RMSE=6.430]
Epoch 38: 81%|████████▏ | 26/32 [00:00<00:00, 321.16it/s, v_num=2, train_loss=2.850, RMSE=6.430]
Epoch 38: 81%|████████▏ | 26/32 [00:00<00:00, 320.31it/s, v_num=2, train_loss=3.000, RMSE=6.430]
Epoch 38: 84%|████████▍ | 27/32 [00:00<00:00, 321.37it/s, v_num=2, train_loss=3.000, RMSE=6.430]
Epoch 38: 84%|████████▍ | 27/32 [00:00<00:00, 320.57it/s, v_num=2, train_loss=2.950, RMSE=6.430]
Epoch 38: 88%|████████▊ | 28/32 [00:00<00:00, 321.28it/s, v_num=2, train_loss=2.950, RMSE=6.430]
Epoch 38: 88%|████████▊ | 28/32 [00:00<00:00, 320.51it/s, v_num=2, train_loss=3.100, RMSE=6.430]
Epoch 38: 91%|█████████ | 29/32 [00:00<00:00, 321.33it/s, v_num=2, train_loss=3.100, RMSE=6.430]
Epoch 38: 91%|█████████ | 29/32 [00:00<00:00, 320.59it/s, v_num=2, train_loss=2.990, RMSE=6.430]
Epoch 38: 94%|█████████▍| 30/32 [00:00<00:00, 321.43it/s, v_num=2, train_loss=2.990, RMSE=6.430]
Epoch 38: 94%|█████████▍| 30/32 [00:00<00:00, 320.71it/s, v_num=2, train_loss=3.280, RMSE=6.430]
Epoch 38: 97%|█████████▋| 31/32 [00:00<00:00, 321.56it/s, v_num=2, train_loss=3.280, RMSE=6.430]
Epoch 38: 97%|█████████▋| 31/32 [00:00<00:00, 320.83it/s, v_num=2, train_loss=3.080, RMSE=6.430]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=3.080, RMSE=6.430]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 321.23it/s, v_num=2, train_loss=2.810, RMSE=6.430]
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Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 262.76it/s, v_num=2, train_loss=2.810, RMSE=6.450]
Epoch 38: 100%|██████████| 32/32 [00:00<00:00, 261.61it/s, v_num=2, train_loss=2.810, RMSE=6.450]
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Epoch 39: 3%|▎ | 1/32 [00:00<00:00, 279.34it/s, v_num=2, train_loss=2.810, RMSE=6.450]
Epoch 39: 3%|▎ | 1/32 [00:00<00:00, 263.46it/s, v_num=2, train_loss=3.090, RMSE=6.450]
Epoch 39: 6%|▋ | 2/32 [00:00<00:00, 297.06it/s, v_num=2, train_loss=3.090, RMSE=6.450]
Epoch 39: 6%|▋ | 2/32 [00:00<00:00, 286.59it/s, v_num=2, train_loss=2.770, RMSE=6.450]
Epoch 39: 9%|▉ | 3/32 [00:00<00:00, 289.74it/s, v_num=2, train_loss=2.770, RMSE=6.450]
Epoch 39: 9%|▉ | 3/32 [00:00<00:00, 283.80it/s, v_num=2, train_loss=3.160, RMSE=6.450]
Epoch 39: 12%|█▎ | 4/32 [00:00<00:00, 296.72it/s, v_num=2, train_loss=3.160, RMSE=6.450]
Epoch 39: 12%|█▎ | 4/32 [00:00<00:00, 292.22it/s, v_num=2, train_loss=3.270, RMSE=6.450]
Epoch 39: 16%|█▌ | 5/32 [00:00<00:00, 301.82it/s, v_num=2, train_loss=3.270, RMSE=6.450]
Epoch 39: 16%|█▌ | 5/32 [00:00<00:00, 298.06it/s, v_num=2, train_loss=2.600, RMSE=6.450]
Epoch 39: 19%|█▉ | 6/32 [00:00<00:00, 304.93it/s, v_num=2, train_loss=2.600, RMSE=6.450]
Epoch 39: 19%|█▉ | 6/32 [00:00<00:00, 301.61it/s, v_num=2, train_loss=3.030, RMSE=6.450]
Epoch 39: 22%|██▏ | 7/32 [00:00<00:00, 307.96it/s, v_num=2, train_loss=3.030, RMSE=6.450]
Epoch 39: 22%|██▏ | 7/32 [00:00<00:00, 304.74it/s, v_num=2, train_loss=3.030, RMSE=6.450]
Epoch 39: 25%|██▌ | 8/32 [00:00<00:00, 309.99it/s, v_num=2, train_loss=3.030, RMSE=6.450]
Epoch 39: 25%|██▌ | 8/32 [00:00<00:00, 307.48it/s, v_num=2, train_loss=3.040, RMSE=6.450]
Epoch 39: 28%|██▊ | 9/32 [00:00<00:00, 311.49it/s, v_num=2, train_loss=3.040, RMSE=6.450]
Epoch 39: 28%|██▊ | 9/32 [00:00<00:00, 309.25it/s, v_num=2, train_loss=2.840, RMSE=6.450]
Epoch 39: 31%|███▏ | 10/32 [00:00<00:00, 312.45it/s, v_num=2, train_loss=2.840, RMSE=6.450]
Epoch 39: 31%|███▏ | 10/32 [00:00<00:00, 310.41it/s, v_num=2, train_loss=3.000, RMSE=6.450]
Epoch 39: 34%|███▍ | 11/32 [00:00<00:00, 313.08it/s, v_num=2, train_loss=3.000, RMSE=6.450]
Epoch 39: 34%|███▍ | 11/32 [00:00<00:00, 311.23it/s, v_num=2, train_loss=3.010, RMSE=6.450]
Epoch 39: 38%|███▊ | 12/32 [00:00<00:00, 313.93it/s, v_num=2, train_loss=3.010, RMSE=6.450]
Epoch 39: 38%|███▊ | 12/32 [00:00<00:00, 312.20it/s, v_num=2, train_loss=3.140, RMSE=6.450]
Epoch 39: 41%|████ | 13/32 [00:00<00:00, 314.03it/s, v_num=2, train_loss=3.140, RMSE=6.450]
Epoch 39: 41%|████ | 13/32 [00:00<00:00, 312.42it/s, v_num=2, train_loss=3.160, RMSE=6.450]
Epoch 39: 44%|████▍ | 14/32 [00:00<00:00, 314.63it/s, v_num=2, train_loss=3.160, RMSE=6.450]
Epoch 39: 44%|████▍ | 14/32 [00:00<00:00, 313.16it/s, v_num=2, train_loss=2.990, RMSE=6.450]
Epoch 39: 47%|████▋ | 15/32 [00:00<00:00, 315.21it/s, v_num=2, train_loss=2.990, RMSE=6.450]
Epoch 39: 47%|████▋ | 15/32 [00:00<00:00, 313.82it/s, v_num=2, train_loss=2.940, RMSE=6.450]
Epoch 39: 50%|█████ | 16/32 [00:00<00:00, 315.90it/s, v_num=2, train_loss=2.940, RMSE=6.450]
Epoch 39: 50%|█████ | 16/32 [00:00<00:00, 314.59it/s, v_num=2, train_loss=3.330, RMSE=6.450]
Epoch 39: 53%|█████▎ | 17/32 [00:00<00:00, 315.89it/s, v_num=2, train_loss=3.330, RMSE=6.450]
Epoch 39: 53%|█████▎ | 17/32 [00:00<00:00, 314.65it/s, v_num=2, train_loss=2.930, RMSE=6.450]
Epoch 39: 56%|█████▋ | 18/32 [00:00<00:00, 315.67it/s, v_num=2, train_loss=2.930, RMSE=6.450]
Epoch 39: 56%|█████▋ | 18/32 [00:00<00:00, 314.45it/s, v_num=2, train_loss=2.870, RMSE=6.450]
Epoch 39: 59%|█████▉ | 19/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=2.870, RMSE=6.450]
Epoch 39: 59%|█████▉ | 19/32 [00:00<00:00, 314.64it/s, v_num=2, train_loss=2.970, RMSE=6.450]
Epoch 39: 62%|██████▎ | 20/32 [00:00<00:00, 316.07it/s, v_num=2, train_loss=2.970, RMSE=6.450]
Epoch 39: 62%|██████▎ | 20/32 [00:00<00:00, 315.02it/s, v_num=2, train_loss=2.600, RMSE=6.450]
Epoch 39: 66%|██████▌ | 21/32 [00:00<00:00, 316.75it/s, v_num=2, train_loss=2.600, RMSE=6.450]
Epoch 39: 66%|██████▌ | 21/32 [00:00<00:00, 315.77it/s, v_num=2, train_loss=2.850, RMSE=6.450]
Epoch 39: 69%|██████▉ | 22/32 [00:00<00:00, 317.05it/s, v_num=2, train_loss=2.850, RMSE=6.450]
Epoch 39: 69%|██████▉ | 22/32 [00:00<00:00, 316.10it/s, v_num=2, train_loss=2.970, RMSE=6.450]
Epoch 39: 72%|███████▏ | 23/32 [00:00<00:00, 317.35it/s, v_num=2, train_loss=2.970, RMSE=6.450]
Epoch 39: 72%|███████▏ | 23/32 [00:00<00:00, 316.44it/s, v_num=2, train_loss=3.010, RMSE=6.450]
Epoch 39: 75%|███████▌ | 24/32 [00:00<00:00, 317.53it/s, v_num=2, train_loss=3.010, RMSE=6.450]
Epoch 39: 75%|███████▌ | 24/32 [00:00<00:00, 316.66it/s, v_num=2, train_loss=2.960, RMSE=6.450]
Epoch 39: 78%|███████▊ | 25/32 [00:00<00:00, 317.91it/s, v_num=2, train_loss=2.960, RMSE=6.450]
Epoch 39: 78%|███████▊ | 25/32 [00:00<00:00, 317.07it/s, v_num=2, train_loss=2.790, RMSE=6.450]
Epoch 39: 81%|████████▏ | 26/32 [00:00<00:00, 318.09it/s, v_num=2, train_loss=2.790, RMSE=6.450]
Epoch 39: 81%|████████▏ | 26/32 [00:00<00:00, 317.28it/s, v_num=2, train_loss=3.100, RMSE=6.450]
Epoch 39: 84%|████████▍ | 27/32 [00:00<00:00, 318.36it/s, v_num=2, train_loss=3.100, RMSE=6.450]
Epoch 39: 84%|████████▍ | 27/32 [00:00<00:00, 317.57it/s, v_num=2, train_loss=3.090, RMSE=6.450]
Epoch 39: 88%|████████▊ | 28/32 [00:00<00:00, 318.49it/s, v_num=2, train_loss=3.090, RMSE=6.450]
Epoch 39: 88%|████████▊ | 28/32 [00:00<00:00, 317.74it/s, v_num=2, train_loss=2.880, RMSE=6.450]
Epoch 39: 91%|█████████ | 29/32 [00:00<00:00, 318.69it/s, v_num=2, train_loss=2.880, RMSE=6.450]
Epoch 39: 91%|█████████ | 29/32 [00:00<00:00, 317.96it/s, v_num=2, train_loss=3.180, RMSE=6.450]
Epoch 39: 94%|█████████▍| 30/32 [00:00<00:00, 318.98it/s, v_num=2, train_loss=3.180, RMSE=6.450]
Epoch 39: 94%|█████████▍| 30/32 [00:00<00:00, 318.27it/s, v_num=2, train_loss=3.230, RMSE=6.450]
Epoch 39: 97%|█████████▋| 31/32 [00:00<00:00, 319.17it/s, v_num=2, train_loss=3.230, RMSE=6.450]
Epoch 39: 97%|█████████▋| 31/32 [00:00<00:00, 318.49it/s, v_num=2, train_loss=2.880, RMSE=6.450]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 319.42it/s, v_num=2, train_loss=2.880, RMSE=6.450]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 318.72it/s, v_num=2, train_loss=2.480, RMSE=6.450]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 612.44it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 612.94it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 616.68it/s]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 260.20it/s, v_num=2, train_loss=2.480, RMSE=6.070]
Epoch 39: 100%|██████████| 32/32 [00:00<00:00, 258.76it/s, v_num=2, train_loss=2.480, RMSE=6.070]
Epoch 39: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.480, RMSE=6.070]
Epoch 40: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.480, RMSE=6.070]
Epoch 40: 3%|▎ | 1/32 [00:00<00:00, 238.65it/s, v_num=2, train_loss=2.480, RMSE=6.070]
Epoch 40: 3%|▎ | 1/32 [00:00<00:00, 224.57it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 6%|▋ | 2/32 [00:00<00:00, 242.52it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 6%|▋ | 2/32 [00:00<00:00, 235.25it/s, v_num=2, train_loss=2.870, RMSE=6.070]
Epoch 40: 9%|▉ | 3/32 [00:00<00:00, 245.29it/s, v_num=2, train_loss=2.870, RMSE=6.070]
Epoch 40: 9%|▉ | 3/32 [00:00<00:00, 240.19it/s, v_num=2, train_loss=2.770, RMSE=6.070]
Epoch 40: 12%|█▎ | 4/32 [00:00<00:00, 247.80it/s, v_num=2, train_loss=2.770, RMSE=6.070]
Epoch 40: 12%|█▎ | 4/32 [00:00<00:00, 244.58it/s, v_num=2, train_loss=3.060, RMSE=6.070]
Epoch 40: 16%|█▌ | 5/32 [00:00<00:00, 259.89it/s, v_num=2, train_loss=3.060, RMSE=6.070]
Epoch 40: 16%|█▌ | 5/32 [00:00<00:00, 257.08it/s, v_num=2, train_loss=2.960, RMSE=6.070]
Epoch 40: 19%|█▉ | 6/32 [00:00<00:00, 268.67it/s, v_num=2, train_loss=2.960, RMSE=6.070]
Epoch 40: 19%|█▉ | 6/32 [00:00<00:00, 266.20it/s, v_num=2, train_loss=2.850, RMSE=6.070]
Epoch 40: 22%|██▏ | 7/32 [00:00<00:00, 275.46it/s, v_num=2, train_loss=2.850, RMSE=6.070]
Epoch 40: 22%|██▏ | 7/32 [00:00<00:00, 273.22it/s, v_num=2, train_loss=3.120, RMSE=6.070]
Epoch 40: 25%|██▌ | 8/32 [00:00<00:00, 280.87it/s, v_num=2, train_loss=3.120, RMSE=6.070]
Epoch 40: 25%|██▌ | 8/32 [00:00<00:00, 278.86it/s, v_num=2, train_loss=2.800, RMSE=6.070]
Epoch 40: 28%|██▊ | 9/32 [00:00<00:00, 285.60it/s, v_num=2, train_loss=2.800, RMSE=6.070]
Epoch 40: 28%|██▊ | 9/32 [00:00<00:00, 283.74it/s, v_num=2, train_loss=2.940, RMSE=6.070]
Epoch 40: 31%|███▏ | 10/32 [00:00<00:00, 289.12it/s, v_num=2, train_loss=2.940, RMSE=6.070]
Epoch 40: 31%|███▏ | 10/32 [00:00<00:00, 287.41it/s, v_num=2, train_loss=3.040, RMSE=6.070]
Epoch 40: 34%|███▍ | 11/32 [00:00<00:00, 292.12it/s, v_num=2, train_loss=3.040, RMSE=6.070]
Epoch 40: 34%|███▍ | 11/32 [00:00<00:00, 290.44it/s, v_num=2, train_loss=2.820, RMSE=6.070]
Epoch 40: 38%|███▊ | 12/32 [00:00<00:00, 294.01it/s, v_num=2, train_loss=2.820, RMSE=6.070]
Epoch 40: 38%|███▊ | 12/32 [00:00<00:00, 292.51it/s, v_num=2, train_loss=2.880, RMSE=6.070]
Epoch 40: 41%|████ | 13/32 [00:00<00:00, 296.36it/s, v_num=2, train_loss=2.880, RMSE=6.070]
Epoch 40: 41%|████ | 13/32 [00:00<00:00, 294.97it/s, v_num=2, train_loss=3.340, RMSE=6.070]
Epoch 40: 44%|████▍ | 14/32 [00:00<00:00, 298.43it/s, v_num=2, train_loss=3.340, RMSE=6.070]
Epoch 40: 44%|████▍ | 14/32 [00:00<00:00, 297.11it/s, v_num=2, train_loss=3.230, RMSE=6.070]
Epoch 40: 47%|████▋ | 15/32 [00:00<00:00, 300.09it/s, v_num=2, train_loss=3.230, RMSE=6.070]
Epoch 40: 47%|████▋ | 15/32 [00:00<00:00, 298.86it/s, v_num=2, train_loss=3.140, RMSE=6.070]
Epoch 40: 50%|█████ | 16/32 [00:00<00:00, 301.49it/s, v_num=2, train_loss=3.140, RMSE=6.070]
Epoch 40: 50%|█████ | 16/32 [00:00<00:00, 300.31it/s, v_num=2, train_loss=3.070, RMSE=6.070]
Epoch 40: 53%|█████▎ | 17/32 [00:00<00:00, 302.84it/s, v_num=2, train_loss=3.070, RMSE=6.070]
Epoch 40: 53%|█████▎ | 17/32 [00:00<00:00, 301.73it/s, v_num=2, train_loss=3.370, RMSE=6.070]
Epoch 40: 56%|█████▋ | 18/32 [00:00<00:00, 304.25it/s, v_num=2, train_loss=3.370, RMSE=6.070]
Epoch 40: 56%|█████▋ | 18/32 [00:00<00:00, 303.19it/s, v_num=2, train_loss=2.870, RMSE=6.070]
Epoch 40: 59%|█████▉ | 19/32 [00:00<00:00, 305.46it/s, v_num=2, train_loss=2.870, RMSE=6.070]
Epoch 40: 59%|█████▉ | 19/32 [00:00<00:00, 304.45it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 62%|██████▎ | 20/32 [00:00<00:00, 306.34it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 62%|██████▎ | 20/32 [00:00<00:00, 305.34it/s, v_num=2, train_loss=2.990, RMSE=6.070]
Epoch 40: 66%|██████▌ | 21/32 [00:00<00:00, 304.92it/s, v_num=2, train_loss=2.990, RMSE=6.070]
Epoch 40: 66%|██████▌ | 21/32 [00:00<00:00, 304.00it/s, v_num=2, train_loss=2.930, RMSE=6.070]
Epoch 40: 69%|██████▉ | 22/32 [00:00<00:00, 305.80it/s, v_num=2, train_loss=2.930, RMSE=6.070]
Epoch 40: 69%|██████▉ | 22/32 [00:00<00:00, 304.92it/s, v_num=2, train_loss=2.690, RMSE=6.070]
Epoch 40: 72%|███████▏ | 23/32 [00:00<00:00, 306.78it/s, v_num=2, train_loss=2.690, RMSE=6.070]
Epoch 40: 72%|███████▏ | 23/32 [00:00<00:00, 305.94it/s, v_num=2, train_loss=2.850, RMSE=6.070]
Epoch 40: 75%|███████▌ | 24/32 [00:00<00:00, 307.44it/s, v_num=2, train_loss=2.850, RMSE=6.070]
Epoch 40: 75%|███████▌ | 24/32 [00:00<00:00, 306.63it/s, v_num=2, train_loss=2.910, RMSE=6.070]
Epoch 40: 78%|███████▊ | 25/32 [00:00<00:00, 307.53it/s, v_num=2, train_loss=2.910, RMSE=6.070]
Epoch 40: 78%|███████▊ | 25/32 [00:00<00:00, 306.74it/s, v_num=2, train_loss=3.230, RMSE=6.070]
Epoch 40: 81%|████████▏ | 26/32 [00:00<00:00, 308.15it/s, v_num=2, train_loss=3.230, RMSE=6.070]
Epoch 40: 81%|████████▏ | 26/32 [00:00<00:00, 307.39it/s, v_num=2, train_loss=2.890, RMSE=6.070]
Epoch 40: 84%|████████▍ | 27/32 [00:00<00:00, 308.91it/s, v_num=2, train_loss=2.890, RMSE=6.070]
Epoch 40: 84%|████████▍ | 27/32 [00:00<00:00, 308.18it/s, v_num=2, train_loss=2.930, RMSE=6.070]
Epoch 40: 88%|████████▊ | 28/32 [00:00<00:00, 309.56it/s, v_num=2, train_loss=2.930, RMSE=6.070]
Epoch 40: 88%|████████▊ | 28/32 [00:00<00:00, 308.78it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 91%|█████████ | 29/32 [00:00<00:00, 310.09it/s, v_num=2, train_loss=2.920, RMSE=6.070]
Epoch 40: 91%|█████████ | 29/32 [00:00<00:00, 309.39it/s, v_num=2, train_loss=2.740, RMSE=6.070]
Epoch 40: 94%|█████████▍| 30/32 [00:00<00:00, 310.58it/s, v_num=2, train_loss=2.740, RMSE=6.070]
Epoch 40: 94%|█████████▍| 30/32 [00:00<00:00, 309.92it/s, v_num=2, train_loss=3.100, RMSE=6.070]
Epoch 40: 97%|█████████▋| 31/32 [00:00<00:00, 311.16it/s, v_num=2, train_loss=3.100, RMSE=6.070]
Epoch 40: 97%|█████████▋| 31/32 [00:00<00:00, 310.45it/s, v_num=2, train_loss=3.090, RMSE=6.070]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 311.81it/s, v_num=2, train_loss=3.090, RMSE=6.070]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 311.19it/s, v_num=2, train_loss=2.440, RMSE=6.070]
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Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 256.11it/s, v_num=2, train_loss=2.440, RMSE=5.940]
Epoch 40: 100%|██████████| 32/32 [00:00<00:00, 255.04it/s, v_num=2, train_loss=2.440, RMSE=5.940]
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Epoch 41: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.440, RMSE=5.940]
Epoch 41: 3%|▎ | 1/32 [00:00<00:00, 281.16it/s, v_num=2, train_loss=2.440, RMSE=5.940]
Epoch 41: 3%|▎ | 1/32 [00:00<00:00, 265.23it/s, v_num=2, train_loss=2.750, RMSE=5.940]
Epoch 41: 6%|▋ | 2/32 [00:00<00:00, 299.67it/s, v_num=2, train_loss=2.750, RMSE=5.940]
Epoch 41: 6%|▋ | 2/32 [00:00<00:00, 289.41it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 9%|▉ | 3/32 [00:00<00:00, 305.54it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 9%|▉ | 3/32 [00:00<00:00, 299.31it/s, v_num=2, train_loss=2.980, RMSE=5.940]
Epoch 41: 12%|█▎ | 4/32 [00:00<00:00, 310.27it/s, v_num=2, train_loss=2.980, RMSE=5.940]
Epoch 41: 12%|█▎ | 4/32 [00:00<00:00, 305.37it/s, v_num=2, train_loss=2.770, RMSE=5.940]
Epoch 41: 16%|█▌ | 5/32 [00:00<00:00, 313.20it/s, v_num=2, train_loss=2.770, RMSE=5.940]
Epoch 41: 16%|█▌ | 5/32 [00:00<00:00, 309.22it/s, v_num=2, train_loss=2.770, RMSE=5.940]
Epoch 41: 19%|█▉ | 6/32 [00:00<00:00, 315.34it/s, v_num=2, train_loss=2.770, RMSE=5.940]
Epoch 41: 19%|█▉ | 6/32 [00:00<00:00, 311.63it/s, v_num=2, train_loss=2.870, RMSE=5.940]
Epoch 41: 22%|██▏ | 7/32 [00:00<00:00, 316.81it/s, v_num=2, train_loss=2.870, RMSE=5.940]
Epoch 41: 22%|██▏ | 7/32 [00:00<00:00, 313.88it/s, v_num=2, train_loss=2.570, RMSE=5.940]
Epoch 41: 25%|██▌ | 8/32 [00:00<00:00, 318.02it/s, v_num=2, train_loss=2.570, RMSE=5.940]
Epoch 41: 25%|██▌ | 8/32 [00:00<00:00, 315.42it/s, v_num=2, train_loss=3.010, RMSE=5.940]
Epoch 41: 28%|██▊ | 9/32 [00:00<00:00, 319.03it/s, v_num=2, train_loss=3.010, RMSE=5.940]
Epoch 41: 28%|██▊ | 9/32 [00:00<00:00, 316.72it/s, v_num=2, train_loss=3.300, RMSE=5.940]
Epoch 41: 31%|███▏ | 10/32 [00:00<00:00, 319.42it/s, v_num=2, train_loss=3.300, RMSE=5.940]
Epoch 41: 31%|███▏ | 10/32 [00:00<00:00, 317.24it/s, v_num=2, train_loss=2.790, RMSE=5.940]
Epoch 41: 34%|███▍ | 11/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=2.790, RMSE=5.940]
Epoch 41: 34%|███▍ | 11/32 [00:00<00:00, 317.94it/s, v_num=2, train_loss=3.040, RMSE=5.940]
Epoch 41: 38%|███▊ | 12/32 [00:00<00:00, 320.06it/s, v_num=2, train_loss=3.040, RMSE=5.940]
Epoch 41: 38%|███▊ | 12/32 [00:00<00:00, 318.31it/s, v_num=2, train_loss=2.800, RMSE=5.940]
Epoch 41: 41%|████ | 13/32 [00:00<00:00, 320.69it/s, v_num=2, train_loss=2.800, RMSE=5.940]
Epoch 41: 41%|████ | 13/32 [00:00<00:00, 319.06it/s, v_num=2, train_loss=3.000, RMSE=5.940]
Epoch 41: 44%|████▍ | 14/32 [00:00<00:00, 321.14it/s, v_num=2, train_loss=3.000, RMSE=5.940]
Epoch 41: 44%|████▍ | 14/32 [00:00<00:00, 319.64it/s, v_num=2, train_loss=2.930, RMSE=5.940]
Epoch 41: 47%|████▋ | 15/32 [00:00<00:00, 321.80it/s, v_num=2, train_loss=2.930, RMSE=5.940]
Epoch 41: 47%|████▋ | 15/32 [00:00<00:00, 320.39it/s, v_num=2, train_loss=2.990, RMSE=5.940]
Epoch 41: 50%|█████ | 16/32 [00:00<00:00, 322.12it/s, v_num=2, train_loss=2.990, RMSE=5.940]
Epoch 41: 50%|█████ | 16/32 [00:00<00:00, 320.78it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 53%|█████▎ | 17/32 [00:00<00:00, 322.28it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 53%|█████▎ | 17/32 [00:00<00:00, 321.00it/s, v_num=2, train_loss=3.100, RMSE=5.940]
Epoch 41: 56%|█████▋ | 18/32 [00:00<00:00, 321.98it/s, v_num=2, train_loss=3.100, RMSE=5.940]
Epoch 41: 56%|█████▋ | 18/32 [00:00<00:00, 320.76it/s, v_num=2, train_loss=3.060, RMSE=5.940]
Epoch 41: 59%|█████▉ | 19/32 [00:00<00:00, 322.21it/s, v_num=2, train_loss=3.060, RMSE=5.940]
Epoch 41: 59%|█████▉ | 19/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=3.120, RMSE=5.940]
Epoch 41: 62%|██████▎ | 20/32 [00:00<00:00, 322.23it/s, v_num=2, train_loss=3.120, RMSE=5.940]
Epoch 41: 62%|██████▎ | 20/32 [00:00<00:00, 321.14it/s, v_num=2, train_loss=2.880, RMSE=5.940]
Epoch 41: 66%|██████▌ | 21/32 [00:00<00:00, 322.37it/s, v_num=2, train_loss=2.880, RMSE=5.940]
Epoch 41: 66%|██████▌ | 21/32 [00:00<00:00, 321.35it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 69%|██████▉ | 22/32 [00:00<00:00, 322.53it/s, v_num=2, train_loss=2.970, RMSE=5.940]
Epoch 41: 69%|██████▉ | 22/32 [00:00<00:00, 321.57it/s, v_num=2, train_loss=3.170, RMSE=5.940]
Epoch 41: 72%|███████▏ | 23/32 [00:00<00:00, 322.71it/s, v_num=2, train_loss=3.170, RMSE=5.940]
Epoch 41: 72%|███████▏ | 23/32 [00:00<00:00, 321.76it/s, v_num=2, train_loss=3.170, RMSE=5.940]
Epoch 41: 75%|███████▌ | 24/32 [00:00<00:00, 323.09it/s, v_num=2, train_loss=3.170, RMSE=5.940]
Epoch 41: 75%|███████▌ | 24/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=3.110, RMSE=5.940]
Epoch 41: 78%|███████▊ | 25/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=3.110, RMSE=5.940]
Epoch 41: 78%|███████▊ | 25/32 [00:00<00:00, 322.46it/s, v_num=2, train_loss=2.810, RMSE=5.940]
Epoch 41: 81%|████████▏ | 26/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=2.810, RMSE=5.940]
Epoch 41: 81%|████████▏ | 26/32 [00:00<00:00, 322.47it/s, v_num=2, train_loss=2.930, RMSE=5.940]
Epoch 41: 84%|████████▍ | 27/32 [00:00<00:00, 323.39it/s, v_num=2, train_loss=2.930, RMSE=5.940]
Epoch 41: 84%|████████▍ | 27/32 [00:00<00:00, 322.58it/s, v_num=2, train_loss=2.940, RMSE=5.940]
Epoch 41: 88%|████████▊ | 28/32 [00:00<00:00, 323.62it/s, v_num=2, train_loss=2.940, RMSE=5.940]
Epoch 41: 88%|████████▊ | 28/32 [00:00<00:00, 322.84it/s, v_num=2, train_loss=2.960, RMSE=5.940]
Epoch 41: 91%|█████████ | 29/32 [00:00<00:00, 323.72it/s, v_num=2, train_loss=2.960, RMSE=5.940]
Epoch 41: 91%|█████████ | 29/32 [00:00<00:00, 322.97it/s, v_num=2, train_loss=3.000, RMSE=5.940]
Epoch 41: 94%|█████████▍| 30/32 [00:00<00:00, 323.81it/s, v_num=2, train_loss=3.000, RMSE=5.940]
Epoch 41: 94%|█████████▍| 30/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=3.040, RMSE=5.940]
Epoch 41: 97%|█████████▋| 31/32 [00:00<00:00, 323.84it/s, v_num=2, train_loss=3.040, RMSE=5.940]
Epoch 41: 97%|█████████▋| 31/32 [00:00<00:00, 323.15it/s, v_num=2, train_loss=2.910, RMSE=5.940]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 324.10it/s, v_num=2, train_loss=2.910, RMSE=5.940]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 323.43it/s, v_num=2, train_loss=3.220, RMSE=5.940]
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Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 619.51it/s]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 621.82it/s]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 264.82it/s, v_num=2, train_loss=3.220, RMSE=5.730]
Epoch 41: 100%|██████████| 32/32 [00:00<00:00, 263.68it/s, v_num=2, train_loss=3.220, RMSE=5.730]
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Epoch 42: 3%|▎ | 1/32 [00:00<00:00, 278.93it/s, v_num=2, train_loss=3.220, RMSE=5.730]
Epoch 42: 3%|▎ | 1/32 [00:00<00:00, 263.31it/s, v_num=2, train_loss=3.230, RMSE=5.730]
Epoch 42: 6%|▋ | 2/32 [00:00<00:00, 299.60it/s, v_num=2, train_loss=3.230, RMSE=5.730]
Epoch 42: 6%|▋ | 2/32 [00:00<00:00, 289.65it/s, v_num=2, train_loss=2.920, RMSE=5.730]
Epoch 42: 9%|▉ | 3/32 [00:00<00:00, 307.68it/s, v_num=2, train_loss=2.920, RMSE=5.730]
Epoch 42: 9%|▉ | 3/32 [00:00<00:00, 301.36it/s, v_num=2, train_loss=2.470, RMSE=5.730]
Epoch 42: 12%|█▎ | 4/32 [00:00<00:00, 312.65it/s, v_num=2, train_loss=2.470, RMSE=5.730]
Epoch 42: 12%|█▎ | 4/32 [00:00<00:00, 307.68it/s, v_num=2, train_loss=2.880, RMSE=5.730]
Epoch 42: 16%|█▌ | 5/32 [00:00<00:00, 315.66it/s, v_num=2, train_loss=2.880, RMSE=5.730]
Epoch 42: 16%|█▌ | 5/32 [00:00<00:00, 311.59it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 19%|█▉ | 6/32 [00:00<00:00, 316.97it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 19%|█▉ | 6/32 [00:00<00:00, 313.45it/s, v_num=2, train_loss=2.840, RMSE=5.730]
Epoch 42: 22%|██▏ | 7/32 [00:00<00:00, 311.14it/s, v_num=2, train_loss=2.840, RMSE=5.730]
Epoch 42: 22%|██▏ | 7/32 [00:00<00:00, 307.94it/s, v_num=2, train_loss=3.360, RMSE=5.730]
Epoch 42: 25%|██▌ | 8/32 [00:00<00:00, 312.84it/s, v_num=2, train_loss=3.360, RMSE=5.730]
Epoch 42: 25%|██▌ | 8/32 [00:00<00:00, 310.34it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 28%|██▊ | 9/32 [00:00<00:00, 314.24it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 28%|██▊ | 9/32 [00:00<00:00, 311.99it/s, v_num=2, train_loss=2.850, RMSE=5.730]
Epoch 42: 31%|███▏ | 10/32 [00:00<00:00, 315.46it/s, v_num=2, train_loss=2.850, RMSE=5.730]
Epoch 42: 31%|███▏ | 10/32 [00:00<00:00, 313.39it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 34%|███▍ | 11/32 [00:00<00:00, 316.04it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 34%|███▍ | 11/32 [00:00<00:00, 314.16it/s, v_num=2, train_loss=3.050, RMSE=5.730]
Epoch 42: 38%|███▊ | 12/32 [00:00<00:00, 316.63it/s, v_num=2, train_loss=3.050, RMSE=5.730]
Epoch 42: 38%|███▊ | 12/32 [00:00<00:00, 314.92it/s, v_num=2, train_loss=2.960, RMSE=5.730]
Epoch 42: 41%|████ | 13/32 [00:00<00:00, 317.29it/s, v_num=2, train_loss=2.960, RMSE=5.730]
Epoch 42: 41%|████ | 13/32 [00:00<00:00, 315.69it/s, v_num=2, train_loss=2.970, RMSE=5.730]
Epoch 42: 44%|████▍ | 14/32 [00:00<00:00, 317.78it/s, v_num=2, train_loss=2.970, RMSE=5.730]
Epoch 42: 44%|████▍ | 14/32 [00:00<00:00, 316.27it/s, v_num=2, train_loss=2.910, RMSE=5.730]
Epoch 42: 47%|████▋ | 15/32 [00:00<00:00, 318.38it/s, v_num=2, train_loss=2.910, RMSE=5.730]
Epoch 42: 47%|████▋ | 15/32 [00:00<00:00, 317.00it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 50%|█████ | 16/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 50%|█████ | 16/32 [00:00<00:00, 317.78it/s, v_num=2, train_loss=3.070, RMSE=5.730]
Epoch 42: 53%|█████▎ | 17/32 [00:00<00:00, 319.58it/s, v_num=2, train_loss=3.070, RMSE=5.730]
Epoch 42: 53%|█████▎ | 17/32 [00:00<00:00, 318.35it/s, v_num=2, train_loss=2.760, RMSE=5.730]
Epoch 42: 56%|█████▋ | 18/32 [00:00<00:00, 319.89it/s, v_num=2, train_loss=2.760, RMSE=5.730]
Epoch 42: 56%|█████▋ | 18/32 [00:00<00:00, 318.72it/s, v_num=2, train_loss=2.990, RMSE=5.730]
Epoch 42: 59%|█████▉ | 19/32 [00:00<00:00, 320.28it/s, v_num=2, train_loss=2.990, RMSE=5.730]
Epoch 42: 59%|█████▉ | 19/32 [00:00<00:00, 319.18it/s, v_num=2, train_loss=2.790, RMSE=5.730]
Epoch 42: 62%|██████▎ | 20/32 [00:00<00:00, 320.85it/s, v_num=2, train_loss=2.790, RMSE=5.730]
Epoch 42: 62%|██████▎ | 20/32 [00:00<00:00, 319.79it/s, v_num=2, train_loss=3.050, RMSE=5.730]
Epoch 42: 66%|██████▌ | 21/32 [00:00<00:00, 321.13it/s, v_num=2, train_loss=3.050, RMSE=5.730]
Epoch 42: 66%|██████▌ | 21/32 [00:00<00:00, 320.07it/s, v_num=2, train_loss=2.820, RMSE=5.730]
Epoch 42: 69%|██████▉ | 22/32 [00:00<00:00, 321.37it/s, v_num=2, train_loss=2.820, RMSE=5.730]
Epoch 42: 69%|██████▉ | 22/32 [00:00<00:00, 320.41it/s, v_num=2, train_loss=2.790, RMSE=5.730]
Epoch 42: 72%|███████▏ | 23/32 [00:00<00:00, 321.34it/s, v_num=2, train_loss=2.790, RMSE=5.730]
Epoch 42: 72%|███████▏ | 23/32 [00:00<00:00, 320.40it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 75%|███████▌ | 24/32 [00:00<00:00, 321.15it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 75%|███████▌ | 24/32 [00:00<00:00, 320.24it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 78%|███████▊ | 25/32 [00:00<00:00, 321.47it/s, v_num=2, train_loss=3.140, RMSE=5.730]
Epoch 42: 78%|███████▊ | 25/32 [00:00<00:00, 320.61it/s, v_num=2, train_loss=2.900, RMSE=5.730]
Epoch 42: 81%|████████▏ | 26/32 [00:00<00:00, 321.62it/s, v_num=2, train_loss=2.900, RMSE=5.730]
Epoch 42: 81%|████████▏ | 26/32 [00:00<00:00, 320.72it/s, v_num=2, train_loss=2.960, RMSE=5.730]
Epoch 42: 84%|████████▍ | 27/32 [00:00<00:00, 321.66it/s, v_num=2, train_loss=2.960, RMSE=5.730]
Epoch 42: 84%|████████▍ | 27/32 [00:00<00:00, 320.87it/s, v_num=2, train_loss=3.060, RMSE=5.730]
Epoch 42: 88%|████████▊ | 28/32 [00:00<00:00, 321.87it/s, v_num=2, train_loss=3.060, RMSE=5.730]
Epoch 42: 88%|████████▊ | 28/32 [00:00<00:00, 321.10it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 91%|█████████ | 29/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=2.940, RMSE=5.730]
Epoch 42: 91%|█████████ | 29/32 [00:00<00:00, 321.46it/s, v_num=2, train_loss=2.870, RMSE=5.730]
Epoch 42: 94%|█████████▍| 30/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=2.870, RMSE=5.730]
Epoch 42: 94%|█████████▍| 30/32 [00:00<00:00, 321.68it/s, v_num=2, train_loss=2.870, RMSE=5.730]
Epoch 42: 97%|█████████▋| 31/32 [00:00<00:00, 322.47it/s, v_num=2, train_loss=2.870, RMSE=5.730]
Epoch 42: 97%|█████████▋| 31/32 [00:00<00:00, 321.77it/s, v_num=2, train_loss=2.930, RMSE=5.730]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 322.68it/s, v_num=2, train_loss=2.930, RMSE=5.730]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 322.00it/s, v_num=2, train_loss=2.380, RMSE=5.730]
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Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 264.19it/s, v_num=2, train_loss=2.380, RMSE=5.620]
Epoch 42: 100%|██████████| 32/32 [00:00<00:00, 263.08it/s, v_num=2, train_loss=2.380, RMSE=5.620]
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Epoch 43: 3%|▎ | 1/32 [00:00<00:00, 289.18it/s, v_num=2, train_loss=2.380, RMSE=5.620]
Epoch 43: 3%|▎ | 1/32 [00:00<00:00, 272.64it/s, v_num=2, train_loss=2.720, RMSE=5.620]
Epoch 43: 6%|▋ | 2/32 [00:00<00:00, 304.58it/s, v_num=2, train_loss=2.720, RMSE=5.620]
Epoch 43: 6%|▋ | 2/32 [00:00<00:00, 295.21it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 43: 9%|▉ | 3/32 [00:00<00:00, 312.27it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 43: 9%|▉ | 3/32 [00:00<00:00, 305.70it/s, v_num=2, train_loss=2.870, RMSE=5.620]
Epoch 43: 12%|█▎ | 4/32 [00:00<00:00, 315.89it/s, v_num=2, train_loss=2.870, RMSE=5.620]
Epoch 43: 12%|█▎ | 4/32 [00:00<00:00, 310.81it/s, v_num=2, train_loss=2.970, RMSE=5.620]
Epoch 43: 16%|█▌ | 5/32 [00:00<00:00, 318.03it/s, v_num=2, train_loss=2.970, RMSE=5.620]
Epoch 43: 16%|█▌ | 5/32 [00:00<00:00, 313.95it/s, v_num=2, train_loss=3.140, RMSE=5.620]
Epoch 43: 19%|█▉ | 6/32 [00:00<00:00, 319.01it/s, v_num=2, train_loss=3.140, RMSE=5.620]
Epoch 43: 19%|█▉ | 6/32 [00:00<00:00, 315.33it/s, v_num=2, train_loss=2.880, RMSE=5.620]
Epoch 43: 22%|██▏ | 7/32 [00:00<00:00, 320.36it/s, v_num=2, train_loss=2.880, RMSE=5.620]
Epoch 43: 22%|██▏ | 7/32 [00:00<00:00, 317.07it/s, v_num=2, train_loss=2.920, RMSE=5.620]
Epoch 43: 25%|██▌ | 8/32 [00:00<00:00, 321.28it/s, v_num=2, train_loss=2.920, RMSE=5.620]
Epoch 43: 25%|██▌ | 8/32 [00:00<00:00, 318.66it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 43: 28%|██▊ | 9/32 [00:00<00:00, 322.26it/s, v_num=2, train_loss=3.010, RMSE=5.620]
Epoch 43: 28%|██▊ | 9/32 [00:00<00:00, 319.91it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 43: 31%|███▏ | 10/32 [00:00<00:00, 321.41it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 43: 31%|███▏ | 10/32 [00:00<00:00, 319.29it/s, v_num=2, train_loss=2.970, RMSE=5.620]
Epoch 43: 34%|███▍ | 11/32 [00:00<00:00, 321.83it/s, v_num=2, train_loss=2.970, RMSE=5.620]
Epoch 43: 34%|███▍ | 11/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=2.750, RMSE=5.620]
Epoch 43: 38%|███▊ | 12/32 [00:00<00:00, 322.46it/s, v_num=2, train_loss=2.750, RMSE=5.620]
Epoch 43: 38%|███▊ | 12/32 [00:00<00:00, 320.68it/s, v_num=2, train_loss=3.000, RMSE=5.620]
Epoch 43: 41%|████ | 13/32 [00:00<00:00, 322.83it/s, v_num=2, train_loss=3.000, RMSE=5.620]
Epoch 43: 41%|████ | 13/32 [00:00<00:00, 321.17it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 44%|████▍ | 14/32 [00:00<00:00, 323.24it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 44%|████▍ | 14/32 [00:00<00:00, 321.70it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 47%|████▋ | 15/32 [00:00<00:00, 323.65it/s, v_num=2, train_loss=2.830, RMSE=5.620]
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Epoch 43: 50%|█████ | 16/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 50%|█████ | 16/32 [00:00<00:00, 322.64it/s, v_num=2, train_loss=2.810, RMSE=5.620]
Epoch 43: 53%|█████▎ | 17/32 [00:00<00:00, 324.05it/s, v_num=2, train_loss=2.810, RMSE=5.620]
Epoch 43: 53%|█████▎ | 17/32 [00:00<00:00, 322.77it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 56%|█████▋ | 18/32 [00:00<00:00, 324.31it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 56%|█████▋ | 18/32 [00:00<00:00, 323.11it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 43: 59%|█████▉ | 19/32 [00:00<00:00, 324.50it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 43: 59%|█████▉ | 19/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 43: 62%|██████▎ | 20/32 [00:00<00:00, 324.84it/s, v_num=2, train_loss=2.980, RMSE=5.620]
Epoch 43: 62%|██████▎ | 20/32 [00:00<00:00, 323.75it/s, v_num=2, train_loss=3.070, RMSE=5.620]
Epoch 43: 66%|██████▌ | 21/32 [00:00<00:00, 324.48it/s, v_num=2, train_loss=3.070, RMSE=5.620]
Epoch 43: 66%|██████▌ | 21/32 [00:00<00:00, 323.43it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 43: 69%|██████▉ | 22/32 [00:00<00:00, 324.54it/s, v_num=2, train_loss=2.860, RMSE=5.620]
Epoch 43: 69%|██████▉ | 22/32 [00:00<00:00, 323.56it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 72%|███████▏ | 23/32 [00:00<00:00, 324.67it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 72%|███████▏ | 23/32 [00:00<00:00, 323.73it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 75%|███████▌ | 24/32 [00:00<00:00, 324.93it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 75%|███████▌ | 24/32 [00:00<00:00, 323.94it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 78%|███████▊ | 25/32 [00:00<00:00, 322.95it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 78%|███████▊ | 25/32 [00:00<00:00, 322.09it/s, v_num=2, train_loss=3.020, RMSE=5.620]
Epoch 43: 81%|████████▏ | 26/32 [00:00<00:00, 323.10it/s, v_num=2, train_loss=3.020, RMSE=5.620]
Epoch 43: 81%|████████▏ | 26/32 [00:00<00:00, 322.27it/s, v_num=2, train_loss=3.300, RMSE=5.620]
Epoch 43: 84%|████████▍ | 27/32 [00:00<00:00, 323.27it/s, v_num=2, train_loss=3.300, RMSE=5.620]
Epoch 43: 84%|████████▍ | 27/32 [00:00<00:00, 322.46it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 88%|████████▊ | 28/32 [00:00<00:00, 323.40it/s, v_num=2, train_loss=2.850, RMSE=5.620]
Epoch 43: 88%|████████▊ | 28/32 [00:00<00:00, 322.59it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 91%|█████████ | 29/32 [00:00<00:00, 323.59it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 91%|█████████ | 29/32 [00:00<00:00, 322.85it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 94%|█████████▍| 30/32 [00:00<00:00, 323.80it/s, v_num=2, train_loss=2.910, RMSE=5.620]
Epoch 43: 94%|█████████▍| 30/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=3.110, RMSE=5.620]
Epoch 43: 97%|█████████▋| 31/32 [00:00<00:00, 323.92it/s, v_num=2, train_loss=3.110, RMSE=5.620]
Epoch 43: 97%|█████████▋| 31/32 [00:00<00:00, 323.23it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 324.09it/s, v_num=2, train_loss=2.830, RMSE=5.620]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 323.41it/s, v_num=2, train_loss=2.990, RMSE=5.620]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 622.62it/s]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 264.86it/s, v_num=2, train_loss=2.990, RMSE=5.270]
Epoch 43: 100%|██████████| 32/32 [00:00<00:00, 263.72it/s, v_num=2, train_loss=2.990, RMSE=5.270]
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Epoch 44: 3%|▎ | 1/32 [00:00<00:00, 288.25it/s, v_num=2, train_loss=2.990, RMSE=5.270]
Epoch 44: 3%|▎ | 1/32 [00:00<00:00, 271.76it/s, v_num=2, train_loss=2.960, RMSE=5.270]
Epoch 44: 6%|▋ | 2/32 [00:00<00:00, 303.50it/s, v_num=2, train_loss=2.960, RMSE=5.270]
Epoch 44: 6%|▋ | 2/32 [00:00<00:00, 294.21it/s, v_num=2, train_loss=2.880, RMSE=5.270]
Epoch 44: 9%|▉ | 3/32 [00:00<00:00, 292.48it/s, v_num=2, train_loss=2.880, RMSE=5.270]
Epoch 44: 9%|▉ | 3/32 [00:00<00:00, 284.98it/s, v_num=2, train_loss=3.080, RMSE=5.270]
Epoch 44: 12%|█▎ | 4/32 [00:00<00:00, 277.42it/s, v_num=2, train_loss=3.080, RMSE=5.270]
Epoch 44: 12%|█▎ | 4/32 [00:00<00:00, 272.65it/s, v_num=2, train_loss=2.880, RMSE=5.270]
Epoch 44: 16%|█▌ | 5/32 [00:00<00:00, 271.05it/s, v_num=2, train_loss=2.880, RMSE=5.270]
Epoch 44: 16%|█▌ | 5/32 [00:00<00:00, 267.32it/s, v_num=2, train_loss=3.020, RMSE=5.270]
Epoch 44: 19%|█▉ | 6/32 [00:00<00:00, 266.90it/s, v_num=2, train_loss=3.020, RMSE=5.270]
Epoch 44: 19%|█▉ | 6/32 [00:00<00:00, 263.90it/s, v_num=2, train_loss=3.130, RMSE=5.270]
Epoch 44: 22%|██▏ | 7/32 [00:00<00:00, 264.34it/s, v_num=2, train_loss=3.130, RMSE=5.270]
Epoch 44: 22%|██▏ | 7/32 [00:00<00:00, 261.91it/s, v_num=2, train_loss=2.820, RMSE=5.270]
Epoch 44: 25%|██▌ | 8/32 [00:00<00:00, 262.75it/s, v_num=2, train_loss=2.820, RMSE=5.270]
Epoch 44: 25%|██▌ | 8/32 [00:00<00:00, 260.61it/s, v_num=2, train_loss=2.990, RMSE=5.270]
Epoch 44: 28%|██▊ | 9/32 [00:00<00:00, 265.30it/s, v_num=2, train_loss=2.990, RMSE=5.270]
Epoch 44: 28%|██▊ | 9/32 [00:00<00:00, 263.65it/s, v_num=2, train_loss=3.060, RMSE=5.270]
Epoch 44: 31%|███▏ | 10/32 [00:00<00:00, 269.96it/s, v_num=2, train_loss=3.060, RMSE=5.270]
Epoch 44: 31%|███▏ | 10/32 [00:00<00:00, 268.22it/s, v_num=2, train_loss=3.010, RMSE=5.270]
Epoch 44: 34%|███▍ | 11/32 [00:00<00:00, 273.75it/s, v_num=2, train_loss=3.010, RMSE=5.270]
Epoch 44: 34%|███▍ | 11/32 [00:00<00:00, 272.35it/s, v_num=2, train_loss=2.840, RMSE=5.270]
Epoch 44: 38%|███▊ | 12/32 [00:00<00:00, 277.49it/s, v_num=2, train_loss=2.840, RMSE=5.270]
Epoch 44: 38%|███▊ | 12/32 [00:00<00:00, 276.09it/s, v_num=2, train_loss=3.040, RMSE=5.270]
Epoch 44: 41%|████ | 13/32 [00:00<00:00, 280.54it/s, v_num=2, train_loss=3.040, RMSE=5.270]
Epoch 44: 41%|████ | 13/32 [00:00<00:00, 279.29it/s, v_num=2, train_loss=2.710, RMSE=5.270]
Epoch 44: 44%|████▍ | 14/32 [00:00<00:00, 283.52it/s, v_num=2, train_loss=2.710, RMSE=5.270]
Epoch 44: 44%|████▍ | 14/32 [00:00<00:00, 282.34it/s, v_num=2, train_loss=2.780, RMSE=5.270]
Epoch 44: 47%|████▋ | 15/32 [00:00<00:00, 286.01it/s, v_num=2, train_loss=2.780, RMSE=5.270]
Epoch 44: 47%|████▋ | 15/32 [00:00<00:00, 284.89it/s, v_num=2, train_loss=2.540, RMSE=5.270]
Epoch 44: 50%|█████ | 16/32 [00:00<00:00, 288.22it/s, v_num=2, train_loss=2.540, RMSE=5.270]
Epoch 44: 50%|█████ | 16/32 [00:00<00:00, 287.14it/s, v_num=2, train_loss=2.900, RMSE=5.270]
Epoch 44: 53%|█████▎ | 17/32 [00:00<00:00, 290.30it/s, v_num=2, train_loss=2.900, RMSE=5.270]
Epoch 44: 53%|█████▎ | 17/32 [00:00<00:00, 289.25it/s, v_num=2, train_loss=3.110, RMSE=5.270]
Epoch 44: 56%|█████▋ | 18/32 [00:00<00:00, 292.30it/s, v_num=2, train_loss=3.110, RMSE=5.270]
Epoch 44: 56%|█████▋ | 18/32 [00:00<00:00, 291.22it/s, v_num=2, train_loss=2.690, RMSE=5.270]
Epoch 44: 59%|█████▉ | 19/32 [00:00<00:00, 293.97it/s, v_num=2, train_loss=2.690, RMSE=5.270]
Epoch 44: 59%|█████▉ | 19/32 [00:00<00:00, 293.02it/s, v_num=2, train_loss=3.060, RMSE=5.270]
Epoch 44: 62%|██████▎ | 20/32 [00:00<00:00, 295.46it/s, v_num=2, train_loss=3.060, RMSE=5.270]
Epoch 44: 62%|██████▎ | 20/32 [00:00<00:00, 294.56it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 66%|██████▌ | 21/32 [00:00<00:00, 296.47it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 66%|██████▌ | 21/32 [00:00<00:00, 295.62it/s, v_num=2, train_loss=2.890, RMSE=5.270]
Epoch 44: 69%|██████▉ | 22/32 [00:00<00:00, 297.82it/s, v_num=2, train_loss=2.890, RMSE=5.270]
Epoch 44: 69%|██████▉ | 22/32 [00:00<00:00, 296.98it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 72%|███████▏ | 23/32 [00:00<00:00, 299.12it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 72%|███████▏ | 23/32 [00:00<00:00, 298.33it/s, v_num=2, train_loss=2.980, RMSE=5.270]
Epoch 44: 75%|███████▌ | 24/32 [00:00<00:00, 300.07it/s, v_num=2, train_loss=2.980, RMSE=5.270]
Epoch 44: 75%|███████▌ | 24/32 [00:00<00:00, 299.29it/s, v_num=2, train_loss=3.230, RMSE=5.270]
Epoch 44: 78%|███████▊ | 25/32 [00:00<00:00, 301.00it/s, v_num=2, train_loss=3.230, RMSE=5.270]
Epoch 44: 78%|███████▊ | 25/32 [00:00<00:00, 300.26it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 81%|████████▏ | 26/32 [00:00<00:00, 301.96it/s, v_num=2, train_loss=2.720, RMSE=5.270]
Epoch 44: 81%|████████▏ | 26/32 [00:00<00:00, 301.24it/s, v_num=2, train_loss=3.030, RMSE=5.270]
Epoch 44: 84%|████████▍ | 27/32 [00:00<00:00, 302.94it/s, v_num=2, train_loss=3.030, RMSE=5.270]
Epoch 44: 84%|████████▍ | 27/32 [00:00<00:00, 302.25it/s, v_num=2, train_loss=2.700, RMSE=5.270]
Epoch 44: 88%|████████▊ | 28/32 [00:00<00:00, 303.83it/s, v_num=2, train_loss=2.700, RMSE=5.270]
Epoch 44: 88%|████████▊ | 28/32 [00:00<00:00, 303.14it/s, v_num=2, train_loss=3.000, RMSE=5.270]
Epoch 44: 91%|█████████ | 29/32 [00:00<00:00, 304.53it/s, v_num=2, train_loss=3.000, RMSE=5.270]
Epoch 44: 91%|█████████ | 29/32 [00:00<00:00, 303.85it/s, v_num=2, train_loss=2.890, RMSE=5.270]
Epoch 44: 94%|█████████▍| 30/32 [00:00<00:00, 305.23it/s, v_num=2, train_loss=2.890, RMSE=5.270]
Epoch 44: 94%|█████████▍| 30/32 [00:00<00:00, 304.59it/s, v_num=2, train_loss=3.000, RMSE=5.270]
Epoch 44: 97%|█████████▋| 31/32 [00:00<00:00, 306.05it/s, v_num=2, train_loss=3.000, RMSE=5.270]
Epoch 44: 97%|█████████▋| 31/32 [00:00<00:00, 305.43it/s, v_num=2, train_loss=2.870, RMSE=5.270]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 306.59it/s, v_num=2, train_loss=2.870, RMSE=5.270]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 305.99it/s, v_num=2, train_loss=3.140, RMSE=5.270]
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Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 252.87it/s, v_num=2, train_loss=3.140, RMSE=5.240]
Epoch 44: 100%|██████████| 32/32 [00:00<00:00, 251.83it/s, v_num=2, train_loss=3.140, RMSE=5.240]
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Epoch 45: 6%|▋ | 2/32 [00:00<00:00, 304.59it/s, v_num=2, train_loss=2.810, RMSE=5.240]
Epoch 45: 6%|▋ | 2/32 [00:00<00:00, 295.19it/s, v_num=2, train_loss=2.960, RMSE=5.240]
Epoch 45: 9%|▉ | 3/32 [00:00<00:00, 311.46it/s, v_num=2, train_loss=2.960, RMSE=5.240]
Epoch 45: 9%|▉ | 3/32 [00:00<00:00, 304.94it/s, v_num=2, train_loss=2.890, RMSE=5.240]
Epoch 45: 12%|█▎ | 4/32 [00:00<00:00, 314.77it/s, v_num=2, train_loss=2.890, RMSE=5.240]
Epoch 45: 12%|█▎ | 4/32 [00:00<00:00, 309.69it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 16%|█▌ | 5/32 [00:00<00:00, 317.03it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 16%|█▌ | 5/32 [00:00<00:00, 312.92it/s, v_num=2, train_loss=3.140, RMSE=5.240]
Epoch 45: 19%|█▉ | 6/32 [00:00<00:00, 319.14it/s, v_num=2, train_loss=3.140, RMSE=5.240]
Epoch 45: 19%|█▉ | 6/32 [00:00<00:00, 315.65it/s, v_num=2, train_loss=2.670, RMSE=5.240]
Epoch 45: 22%|██▏ | 7/32 [00:00<00:00, 319.74it/s, v_num=2, train_loss=2.670, RMSE=5.240]
Epoch 45: 22%|██▏ | 7/32 [00:00<00:00, 316.76it/s, v_num=2, train_loss=2.700, RMSE=5.240]
Epoch 45: 25%|██▌ | 8/32 [00:00<00:00, 320.71it/s, v_num=2, train_loss=2.700, RMSE=5.240]
Epoch 45: 25%|██▌ | 8/32 [00:00<00:00, 318.09it/s, v_num=2, train_loss=3.030, RMSE=5.240]
Epoch 45: 28%|██▊ | 9/32 [00:00<00:00, 321.48it/s, v_num=2, train_loss=3.030, RMSE=5.240]
Epoch 45: 28%|██▊ | 9/32 [00:00<00:00, 319.09it/s, v_num=2, train_loss=2.880, RMSE=5.240]
Epoch 45: 31%|███▏ | 10/32 [00:00<00:00, 322.33it/s, v_num=2, train_loss=2.880, RMSE=5.240]
Epoch 45: 31%|███▏ | 10/32 [00:00<00:00, 320.20it/s, v_num=2, train_loss=3.130, RMSE=5.240]
Epoch 45: 34%|███▍ | 11/32 [00:00<00:00, 318.64it/s, v_num=2, train_loss=3.130, RMSE=5.240]
Epoch 45: 34%|███▍ | 11/32 [00:00<00:00, 316.75it/s, v_num=2, train_loss=3.100, RMSE=5.240]
Epoch 45: 38%|███▊ | 12/32 [00:00<00:00, 319.51it/s, v_num=2, train_loss=3.100, RMSE=5.240]
Epoch 45: 38%|███▊ | 12/32 [00:00<00:00, 317.74it/s, v_num=2, train_loss=3.310, RMSE=5.240]
Epoch 45: 41%|████ | 13/32 [00:00<00:00, 320.14it/s, v_num=2, train_loss=3.310, RMSE=5.240]
Epoch 45: 41%|████ | 13/32 [00:00<00:00, 318.53it/s, v_num=2, train_loss=2.900, RMSE=5.240]
Epoch 45: 44%|████▍ | 14/32 [00:00<00:00, 320.61it/s, v_num=2, train_loss=2.900, RMSE=5.240]
Epoch 45: 44%|████▍ | 14/32 [00:00<00:00, 319.03it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 47%|████▋ | 15/32 [00:00<00:00, 320.94it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 47%|████▋ | 15/32 [00:00<00:00, 319.51it/s, v_num=2, train_loss=2.970, RMSE=5.240]
Epoch 45: 50%|█████ | 16/32 [00:00<00:00, 321.23it/s, v_num=2, train_loss=2.970, RMSE=5.240]
Epoch 45: 50%|█████ | 16/32 [00:00<00:00, 319.90it/s, v_num=2, train_loss=2.740, RMSE=5.240]
Epoch 45: 53%|█████▎ | 17/32 [00:00<00:00, 321.59it/s, v_num=2, train_loss=2.740, RMSE=5.240]
Epoch 45: 53%|█████▎ | 17/32 [00:00<00:00, 320.34it/s, v_num=2, train_loss=2.970, RMSE=5.240]
Epoch 45: 56%|█████▋ | 18/32 [00:00<00:00, 321.97it/s, v_num=2, train_loss=2.970, RMSE=5.240]
Epoch 45: 56%|█████▋ | 18/32 [00:00<00:00, 320.80it/s, v_num=2, train_loss=2.850, RMSE=5.240]
Epoch 45: 59%|█████▉ | 19/32 [00:00<00:00, 322.36it/s, v_num=2, train_loss=2.850, RMSE=5.240]
Epoch 45: 59%|█████▉ | 19/32 [00:00<00:00, 321.23it/s, v_num=2, train_loss=2.680, RMSE=5.240]
Epoch 45: 62%|██████▎ | 20/32 [00:00<00:00, 322.01it/s, v_num=2, train_loss=2.680, RMSE=5.240]
Epoch 45: 62%|██████▎ | 20/32 [00:00<00:00, 320.94it/s, v_num=2, train_loss=2.840, RMSE=5.240]
Epoch 45: 66%|██████▌ | 21/32 [00:00<00:00, 322.31it/s, v_num=2, train_loss=2.840, RMSE=5.240]
Epoch 45: 66%|██████▌ | 21/32 [00:00<00:00, 321.24it/s, v_num=2, train_loss=2.980, RMSE=5.240]
Epoch 45: 69%|██████▉ | 22/32 [00:00<00:00, 322.43it/s, v_num=2, train_loss=2.980, RMSE=5.240]
Epoch 45: 69%|██████▉ | 22/32 [00:00<00:00, 321.46it/s, v_num=2, train_loss=2.840, RMSE=5.240]
Epoch 45: 72%|███████▏ | 23/32 [00:00<00:00, 322.82it/s, v_num=2, train_loss=2.840, RMSE=5.240]
Epoch 45: 72%|███████▏ | 23/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 75%|███████▌ | 24/32 [00:00<00:00, 323.03it/s, v_num=2, train_loss=2.820, RMSE=5.240]
Epoch 45: 75%|███████▌ | 24/32 [00:00<00:00, 322.13it/s, v_num=2, train_loss=2.980, RMSE=5.240]
Epoch 45: 78%|███████▊ | 25/32 [00:00<00:00, 323.11it/s, v_num=2, train_loss=2.980, RMSE=5.240]
Epoch 45: 78%|███████▊ | 25/32 [00:00<00:00, 322.25it/s, v_num=2, train_loss=3.090, RMSE=5.240]
Epoch 45: 81%|████████▏ | 26/32 [00:00<00:00, 323.36it/s, v_num=2, train_loss=3.090, RMSE=5.240]
Epoch 45: 81%|████████▏ | 26/32 [00:00<00:00, 322.52it/s, v_num=2, train_loss=3.020, RMSE=5.240]
Epoch 45: 84%|████████▍ | 27/32 [00:00<00:00, 323.64it/s, v_num=2, train_loss=3.020, RMSE=5.240]
Epoch 45: 84%|████████▍ | 27/32 [00:00<00:00, 322.76it/s, v_num=2, train_loss=2.810, RMSE=5.240]
Epoch 45: 88%|████████▊ | 28/32 [00:00<00:00, 323.71it/s, v_num=2, train_loss=2.810, RMSE=5.240]
Epoch 45: 88%|████████▊ | 28/32 [00:00<00:00, 322.93it/s, v_num=2, train_loss=3.010, RMSE=5.240]
Epoch 45: 91%|█████████ | 29/32 [00:00<00:00, 323.84it/s, v_num=2, train_loss=3.010, RMSE=5.240]
Epoch 45: 91%|█████████ | 29/32 [00:00<00:00, 323.09it/s, v_num=2, train_loss=2.670, RMSE=5.240]
Epoch 45: 94%|█████████▍| 30/32 [00:00<00:00, 323.87it/s, v_num=2, train_loss=2.670, RMSE=5.240]
Epoch 45: 94%|█████████▍| 30/32 [00:00<00:00, 323.15it/s, v_num=2, train_loss=2.800, RMSE=5.240]
Epoch 45: 97%|█████████▋| 31/32 [00:00<00:00, 324.05it/s, v_num=2, train_loss=2.800, RMSE=5.240]
Epoch 45: 97%|█████████▋| 31/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=2.720, RMSE=5.240]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 324.33it/s, v_num=2, train_loss=2.720, RMSE=5.240]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 323.65it/s, v_num=2, train_loss=2.430, RMSE=5.240]
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Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 624.82it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 627.27it/s]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 265.46it/s, v_num=2, train_loss=2.430, RMSE=5.160]
Epoch 45: 100%|██████████| 32/32 [00:00<00:00, 264.31it/s, v_num=2, train_loss=2.430, RMSE=5.160]
Epoch 45: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.430, RMSE=5.160]
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Epoch 46: 3%|▎ | 1/32 [00:00<00:00, 296.46it/s, v_num=2, train_loss=2.430, RMSE=5.160]
Epoch 46: 3%|▎ | 1/32 [00:00<00:00, 278.32it/s, v_num=2, train_loss=3.010, RMSE=5.160]
Epoch 46: 6%|▋ | 2/32 [00:00<00:00, 309.11it/s, v_num=2, train_loss=3.010, RMSE=5.160]
Epoch 46: 6%|▋ | 2/32 [00:00<00:00, 299.39it/s, v_num=2, train_loss=2.840, RMSE=5.160]
Epoch 46: 9%|▉ | 3/32 [00:00<00:00, 315.08it/s, v_num=2, train_loss=2.840, RMSE=5.160]
Epoch 46: 9%|▉ | 3/32 [00:00<00:00, 308.46it/s, v_num=2, train_loss=2.870, RMSE=5.160]
Epoch 46: 12%|█▎ | 4/32 [00:00<00:00, 317.64it/s, v_num=2, train_loss=2.870, RMSE=5.160]
Epoch 46: 12%|█▎ | 4/32 [00:00<00:00, 312.48it/s, v_num=2, train_loss=2.900, RMSE=5.160]
Epoch 46: 16%|█▌ | 5/32 [00:00<00:00, 319.09it/s, v_num=2, train_loss=2.900, RMSE=5.160]
Epoch 46: 16%|█▌ | 5/32 [00:00<00:00, 314.92it/s, v_num=2, train_loss=2.780, RMSE=5.160]
Epoch 46: 19%|█▉ | 6/32 [00:00<00:00, 320.64it/s, v_num=2, train_loss=2.780, RMSE=5.160]
Epoch 46: 19%|█▉ | 6/32 [00:00<00:00, 317.17it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 22%|██▏ | 7/32 [00:00<00:00, 320.15it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 22%|██▏ | 7/32 [00:00<00:00, 317.05it/s, v_num=2, train_loss=2.960, RMSE=5.160]
Epoch 46: 25%|██▌ | 8/32 [00:00<00:00, 321.13it/s, v_num=2, train_loss=2.960, RMSE=5.160]
Epoch 46: 25%|██▌ | 8/32 [00:00<00:00, 318.51it/s, v_num=2, train_loss=3.080, RMSE=5.160]
Epoch 46: 28%|██▊ | 9/32 [00:00<00:00, 321.73it/s, v_num=2, train_loss=3.080, RMSE=5.160]
Epoch 46: 28%|██▊ | 9/32 [00:00<00:00, 319.36it/s, v_num=2, train_loss=2.700, RMSE=5.160]
Epoch 46: 31%|███▏ | 10/32 [00:00<00:00, 322.17it/s, v_num=2, train_loss=2.700, RMSE=5.160]
Epoch 46: 31%|███▏ | 10/32 [00:00<00:00, 320.02it/s, v_num=2, train_loss=2.810, RMSE=5.160]
Epoch 46: 34%|███▍ | 11/32 [00:00<00:00, 322.21it/s, v_num=2, train_loss=2.810, RMSE=5.160]
Epoch 46: 34%|███▍ | 11/32 [00:00<00:00, 320.23it/s, v_num=2, train_loss=3.010, RMSE=5.160]
Epoch 46: 38%|███▊ | 12/32 [00:00<00:00, 322.44it/s, v_num=2, train_loss=3.010, RMSE=5.160]
Epoch 46: 38%|███▊ | 12/32 [00:00<00:00, 320.67it/s, v_num=2, train_loss=2.720, RMSE=5.160]
Epoch 46: 41%|████ | 13/32 [00:00<00:00, 322.87it/s, v_num=2, train_loss=2.720, RMSE=5.160]
Epoch 46: 41%|████ | 13/32 [00:00<00:00, 321.16it/s, v_num=2, train_loss=2.920, RMSE=5.160]
Epoch 46: 44%|████▍ | 14/32 [00:00<00:00, 323.14it/s, v_num=2, train_loss=2.920, RMSE=5.160]
Epoch 46: 44%|████▍ | 14/32 [00:00<00:00, 321.51it/s, v_num=2, train_loss=3.030, RMSE=5.160]
Epoch 46: 47%|████▋ | 15/32 [00:00<00:00, 323.51it/s, v_num=2, train_loss=3.030, RMSE=5.160]
Epoch 46: 47%|████▋ | 15/32 [00:00<00:00, 321.96it/s, v_num=2, train_loss=2.970, RMSE=5.160]
Epoch 46: 50%|█████ | 16/32 [00:00<00:00, 323.75it/s, v_num=2, train_loss=2.970, RMSE=5.160]
Epoch 46: 50%|█████ | 16/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=2.810, RMSE=5.160]
Epoch 46: 53%|█████▎ | 17/32 [00:00<00:00, 323.76it/s, v_num=2, train_loss=2.810, RMSE=5.160]
Epoch 46: 53%|█████▎ | 17/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 56%|█████▋ | 18/32 [00:00<00:00, 323.41it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 56%|█████▋ | 18/32 [00:00<00:00, 322.19it/s, v_num=2, train_loss=2.880, RMSE=5.160]
Epoch 46: 59%|█████▉ | 19/32 [00:00<00:00, 323.78it/s, v_num=2, train_loss=2.880, RMSE=5.160]
Epoch 46: 59%|█████▉ | 19/32 [00:00<00:00, 322.65it/s, v_num=2, train_loss=2.870, RMSE=5.160]
Epoch 46: 62%|██████▎ | 20/32 [00:00<00:00, 323.25it/s, v_num=2, train_loss=2.870, RMSE=5.160]
Epoch 46: 62%|██████▎ | 20/32 [00:00<00:00, 322.00it/s, v_num=2, train_loss=3.000, RMSE=5.160]
Epoch 46: 66%|██████▌ | 21/32 [00:00<00:00, 323.32it/s, v_num=2, train_loss=3.000, RMSE=5.160]
Epoch 46: 66%|██████▌ | 21/32 [00:00<00:00, 322.29it/s, v_num=2, train_loss=2.750, RMSE=5.160]
Epoch 46: 69%|██████▉ | 22/32 [00:00<00:00, 323.57it/s, v_num=2, train_loss=2.750, RMSE=5.160]
Epoch 46: 69%|██████▉ | 22/32 [00:00<00:00, 322.60it/s, v_num=2, train_loss=3.100, RMSE=5.160]
Epoch 46: 72%|███████▏ | 23/32 [00:00<00:00, 323.75it/s, v_num=2, train_loss=3.100, RMSE=5.160]
Epoch 46: 72%|███████▏ | 23/32 [00:00<00:00, 322.80it/s, v_num=2, train_loss=2.960, RMSE=5.160]
Epoch 46: 75%|███████▌ | 24/32 [00:00<00:00, 323.97it/s, v_num=2, train_loss=2.960, RMSE=5.160]
Epoch 46: 75%|███████▌ | 24/32 [00:00<00:00, 323.08it/s, v_num=2, train_loss=2.940, RMSE=5.160]
Epoch 46: 78%|███████▊ | 25/32 [00:00<00:00, 324.02it/s, v_num=2, train_loss=2.940, RMSE=5.160]
Epoch 46: 78%|███████▊ | 25/32 [00:00<00:00, 323.16it/s, v_num=2, train_loss=2.790, RMSE=5.160]
Epoch 46: 81%|████████▏ | 26/32 [00:00<00:00, 323.99it/s, v_num=2, train_loss=2.790, RMSE=5.160]
Epoch 46: 81%|████████▏ | 26/32 [00:00<00:00, 323.14it/s, v_num=2, train_loss=2.790, RMSE=5.160]
Epoch 46: 84%|████████▍ | 27/32 [00:00<00:00, 324.05it/s, v_num=2, train_loss=2.790, RMSE=5.160]
Epoch 46: 84%|████████▍ | 27/32 [00:00<00:00, 323.25it/s, v_num=2, train_loss=2.760, RMSE=5.160]
Epoch 46: 88%|████████▊ | 28/32 [00:00<00:00, 324.29it/s, v_num=2, train_loss=2.760, RMSE=5.160]
Epoch 46: 88%|████████▊ | 28/32 [00:00<00:00, 323.51it/s, v_num=2, train_loss=2.880, RMSE=5.160]
Epoch 46: 91%|█████████ | 29/32 [00:00<00:00, 322.62it/s, v_num=2, train_loss=2.880, RMSE=5.160]
Epoch 46: 91%|█████████ | 29/32 [00:00<00:00, 321.88it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 94%|█████████▍| 30/32 [00:00<00:00, 322.68it/s, v_num=2, train_loss=2.850, RMSE=5.160]
Epoch 46: 94%|█████████▍| 30/32 [00:00<00:00, 321.97it/s, v_num=2, train_loss=2.990, RMSE=5.160]
Epoch 46: 97%|█████████▋| 31/32 [00:00<00:00, 322.86it/s, v_num=2, train_loss=2.990, RMSE=5.160]
Epoch 46: 97%|█████████▋| 31/32 [00:00<00:00, 322.14it/s, v_num=2, train_loss=3.040, RMSE=5.160]
Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 323.23it/s, v_num=2, train_loss=3.040, RMSE=5.160]
Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 322.54it/s, v_num=2, train_loss=2.480, RMSE=5.160]
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Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 264.39it/s, v_num=2, train_loss=2.480, RMSE=5.020]
Epoch 46: 100%|██████████| 32/32 [00:00<00:00, 263.24it/s, v_num=2, train_loss=2.480, RMSE=5.020]
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Epoch 47: 3%|▎ | 1/32 [00:00<00:00, 293.35it/s, v_num=2, train_loss=2.480, RMSE=5.020]
Epoch 47: 3%|▎ | 1/32 [00:00<00:00, 276.29it/s, v_num=2, train_loss=2.840, RMSE=5.020]
Epoch 47: 6%|▋ | 2/32 [00:00<00:00, 307.66it/s, v_num=2, train_loss=2.840, RMSE=5.020]
Epoch 47: 6%|▋ | 2/32 [00:00<00:00, 297.16it/s, v_num=2, train_loss=2.880, RMSE=5.020]
Epoch 47: 9%|▉ | 3/32 [00:00<00:00, 313.41it/s, v_num=2, train_loss=2.880, RMSE=5.020]
Epoch 47: 9%|▉ | 3/32 [00:00<00:00, 306.89it/s, v_num=2, train_loss=2.890, RMSE=5.020]
Epoch 47: 12%|█▎ | 4/32 [00:00<00:00, 316.76it/s, v_num=2, train_loss=2.890, RMSE=5.020]
Epoch 47: 12%|█▎ | 4/32 [00:00<00:00, 311.69it/s, v_num=2, train_loss=3.180, RMSE=5.020]
Epoch 47: 16%|█▌ | 5/32 [00:00<00:00, 318.98it/s, v_num=2, train_loss=3.180, RMSE=5.020]
Epoch 47: 16%|█▌ | 5/32 [00:00<00:00, 314.82it/s, v_num=2, train_loss=2.860, RMSE=5.020]
Epoch 47: 19%|█▉ | 6/32 [00:00<00:00, 320.00it/s, v_num=2, train_loss=2.860, RMSE=5.020]
Epoch 47: 19%|█▉ | 6/32 [00:00<00:00, 316.49it/s, v_num=2, train_loss=2.800, RMSE=5.020]
Epoch 47: 22%|██▏ | 7/32 [00:00<00:00, 321.47it/s, v_num=2, train_loss=2.800, RMSE=5.020]
Epoch 47: 22%|██▏ | 7/32 [00:00<00:00, 318.43it/s, v_num=2, train_loss=3.220, RMSE=5.020]
Epoch 47: 25%|██▌ | 8/32 [00:00<00:00, 322.27it/s, v_num=2, train_loss=3.220, RMSE=5.020]
Epoch 47: 25%|██▌ | 8/32 [00:00<00:00, 319.54it/s, v_num=2, train_loss=2.550, RMSE=5.020]
Epoch 47: 28%|██▊ | 9/32 [00:00<00:00, 322.91it/s, v_num=2, train_loss=2.550, RMSE=5.020]
Epoch 47: 28%|██▊ | 9/32 [00:00<00:00, 320.45it/s, v_num=2, train_loss=2.830, RMSE=5.020]
Epoch 47: 31%|███▏ | 10/32 [00:00<00:00, 323.34it/s, v_num=2, train_loss=2.830, RMSE=5.020]
Epoch 47: 31%|███▏ | 10/32 [00:00<00:00, 321.19it/s, v_num=2, train_loss=2.820, RMSE=5.020]
Epoch 47: 34%|███▍ | 11/32 [00:00<00:00, 323.83it/s, v_num=2, train_loss=2.820, RMSE=5.020]
Epoch 47: 34%|███▍ | 11/32 [00:00<00:00, 321.89it/s, v_num=2, train_loss=2.700, RMSE=5.020]
Epoch 47: 38%|███▊ | 12/32 [00:00<00:00, 324.24it/s, v_num=2, train_loss=2.700, RMSE=5.020]
Epoch 47: 38%|███▊ | 12/32 [00:00<00:00, 322.43it/s, v_num=2, train_loss=3.070, RMSE=5.020]
Epoch 47: 41%|████ | 13/32 [00:00<00:00, 324.42it/s, v_num=2, train_loss=3.070, RMSE=5.020]
Epoch 47: 41%|████ | 13/32 [00:00<00:00, 322.75it/s, v_num=2, train_loss=2.970, RMSE=5.020]
Epoch 47: 44%|████▍ | 14/32 [00:00<00:00, 324.73it/s, v_num=2, train_loss=2.970, RMSE=5.020]
Epoch 47: 44%|████▍ | 14/32 [00:00<00:00, 323.17it/s, v_num=2, train_loss=2.860, RMSE=5.020]
Epoch 47: 47%|████▋ | 15/32 [00:00<00:00, 324.76it/s, v_num=2, train_loss=2.860, RMSE=5.020]
Epoch 47: 47%|████▋ | 15/32 [00:00<00:00, 323.27it/s, v_num=2, train_loss=2.960, RMSE=5.020]
Epoch 47: 50%|█████ | 16/32 [00:00<00:00, 324.50it/s, v_num=2, train_loss=2.960, RMSE=5.020]
Epoch 47: 50%|█████ | 16/32 [00:00<00:00, 323.12it/s, v_num=2, train_loss=3.260, RMSE=5.020]
Epoch 47: 53%|█████▎ | 17/32 [00:00<00:00, 324.62it/s, v_num=2, train_loss=3.260, RMSE=5.020]
Epoch 47: 53%|█████▎ | 17/32 [00:00<00:00, 323.33it/s, v_num=2, train_loss=2.650, RMSE=5.020]
Epoch 47: 56%|█████▋ | 18/32 [00:00<00:00, 324.32it/s, v_num=2, train_loss=2.650, RMSE=5.020]
Epoch 47: 56%|█████▋ | 18/32 [00:00<00:00, 323.09it/s, v_num=2, train_loss=2.760, RMSE=5.020]
Epoch 47: 59%|█████▉ | 19/32 [00:00<00:00, 324.45it/s, v_num=2, train_loss=2.760, RMSE=5.020]
Epoch 47: 59%|█████▉ | 19/32 [00:00<00:00, 323.02it/s, v_num=2, train_loss=2.910, RMSE=5.020]
Epoch 47: 62%|██████▎ | 20/32 [00:00<00:00, 324.34it/s, v_num=2, train_loss=2.910, RMSE=5.020]
Epoch 47: 62%|██████▎ | 20/32 [00:00<00:00, 323.23it/s, v_num=2, train_loss=2.900, RMSE=5.020]
Epoch 47: 66%|██████▌ | 21/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=2.900, RMSE=5.020]
Epoch 47: 66%|██████▌ | 21/32 [00:00<00:00, 323.16it/s, v_num=2, train_loss=2.790, RMSE=5.020]
Epoch 47: 69%|██████▉ | 22/32 [00:00<00:00, 324.20it/s, v_num=2, train_loss=2.790, RMSE=5.020]
Epoch 47: 69%|██████▉ | 22/32 [00:00<00:00, 323.17it/s, v_num=2, train_loss=2.710, RMSE=5.020]
Epoch 47: 72%|███████▏ | 23/32 [00:00<00:00, 324.00it/s, v_num=2, train_loss=2.710, RMSE=5.020]
Epoch 47: 72%|███████▏ | 23/32 [00:00<00:00, 323.05it/s, v_num=2, train_loss=3.000, RMSE=5.020]
Epoch 47: 75%|███████▌ | 24/32 [00:00<00:00, 324.16it/s, v_num=2, train_loss=3.000, RMSE=5.020]
Epoch 47: 75%|███████▌ | 24/32 [00:00<00:00, 323.25it/s, v_num=2, train_loss=2.750, RMSE=5.020]
Epoch 47: 78%|███████▊ | 25/32 [00:00<00:00, 324.35it/s, v_num=2, train_loss=2.750, RMSE=5.020]
Epoch 47: 78%|███████▊ | 25/32 [00:00<00:00, 323.48it/s, v_num=2, train_loss=2.760, RMSE=5.020]
Epoch 47: 81%|████████▏ | 26/32 [00:00<00:00, 324.36it/s, v_num=2, train_loss=2.760, RMSE=5.020]
Epoch 47: 81%|████████▏ | 26/32 [00:00<00:00, 323.49it/s, v_num=2, train_loss=3.100, RMSE=5.020]
Epoch 47: 84%|████████▍ | 27/32 [00:00<00:00, 324.43it/s, v_num=2, train_loss=3.100, RMSE=5.020]
Epoch 47: 84%|████████▍ | 27/32 [00:00<00:00, 323.62it/s, v_num=2, train_loss=3.030, RMSE=5.020]
Epoch 47: 88%|████████▊ | 28/32 [00:00<00:00, 324.63it/s, v_num=2, train_loss=3.030, RMSE=5.020]
Epoch 47: 88%|████████▊ | 28/32 [00:00<00:00, 323.86it/s, v_num=2, train_loss=2.920, RMSE=5.020]
Epoch 47: 91%|█████████ | 29/32 [00:00<00:00, 324.74it/s, v_num=2, train_loss=2.920, RMSE=5.020]
Epoch 47: 91%|█████████ | 29/32 [00:00<00:00, 324.00it/s, v_num=2, train_loss=2.750, RMSE=5.020]
Epoch 47: 94%|█████████▍| 30/32 [00:00<00:00, 324.74it/s, v_num=2, train_loss=2.750, RMSE=5.020]
Epoch 47: 94%|█████████▍| 30/32 [00:00<00:00, 324.02it/s, v_num=2, train_loss=3.050, RMSE=5.020]
Epoch 47: 97%|█████████▋| 31/32 [00:00<00:00, 324.73it/s, v_num=2, train_loss=3.050, RMSE=5.020]
Epoch 47: 97%|█████████▋| 31/32 [00:00<00:00, 324.03it/s, v_num=2, train_loss=2.520, RMSE=5.020]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 325.06it/s, v_num=2, train_loss=2.520, RMSE=5.020]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 324.31it/s, v_num=2, train_loss=2.600, RMSE=5.020]
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Validation DataLoader 0: 20%|██ | 2/10 [00:00<00:00, 638.89it/s]
Validation DataLoader 0: 30%|███ | 3/10 [00:00<00:00, 633.52it/s]
Validation DataLoader 0: 40%|████ | 4/10 [00:00<00:00, 629.09it/s]
Validation DataLoader 0: 50%|█████ | 5/10 [00:00<00:00, 626.11it/s]
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Validation DataLoader 0: 70%|███████ | 7/10 [00:00<00:00, 624.13it/s]
Validation DataLoader 0: 80%|████████ | 8/10 [00:00<00:00, 624.27it/s]
Validation DataLoader 0: 90%|█████████ | 9/10 [00:00<00:00, 622.55it/s]
Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 625.83it/s]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 265.76it/s, v_num=2, train_loss=2.600, RMSE=4.970]
Epoch 47: 100%|██████████| 32/32 [00:00<00:00, 264.64it/s, v_num=2, train_loss=2.600, RMSE=4.970]
Epoch 47: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.600, RMSE=4.970]
Epoch 48: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.600, RMSE=4.970]
Epoch 48: 3%|▎ | 1/32 [00:00<00:00, 291.05it/s, v_num=2, train_loss=2.600, RMSE=4.970]
Epoch 48: 3%|▎ | 1/32 [00:00<00:00, 274.21it/s, v_num=2, train_loss=2.760, RMSE=4.970]
Epoch 48: 6%|▋ | 2/32 [00:00<00:00, 302.72it/s, v_num=2, train_loss=2.760, RMSE=4.970]
Epoch 48: 6%|▋ | 2/32 [00:00<00:00, 292.29it/s, v_num=2, train_loss=2.960, RMSE=4.970]
Epoch 48: 9%|▉ | 3/32 [00:00<00:00, 309.95it/s, v_num=2, train_loss=2.960, RMSE=4.970]
Epoch 48: 9%|▉ | 3/32 [00:00<00:00, 303.41it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 12%|█▎ | 4/32 [00:00<00:00, 314.46it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 12%|█▎ | 4/32 [00:00<00:00, 309.45it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 16%|█▌ | 5/32 [00:00<00:00, 316.75it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 16%|█▌ | 5/32 [00:00<00:00, 312.50it/s, v_num=2, train_loss=2.830, RMSE=4.970]
Epoch 48: 19%|█▉ | 6/32 [00:00<00:00, 318.08it/s, v_num=2, train_loss=2.830, RMSE=4.970]
Epoch 48: 19%|█▉ | 6/32 [00:00<00:00, 314.65it/s, v_num=2, train_loss=3.100, RMSE=4.970]
Epoch 48: 22%|██▏ | 7/32 [00:00<00:00, 319.88it/s, v_num=2, train_loss=3.100, RMSE=4.970]
Epoch 48: 22%|██▏ | 7/32 [00:00<00:00, 316.88it/s, v_num=2, train_loss=2.760, RMSE=4.970]
Epoch 48: 25%|██▌ | 8/32 [00:00<00:00, 320.63it/s, v_num=2, train_loss=2.760, RMSE=4.970]
Epoch 48: 25%|██▌ | 8/32 [00:00<00:00, 318.00it/s, v_num=2, train_loss=3.110, RMSE=4.970]
Epoch 48: 28%|██▊ | 9/32 [00:00<00:00, 321.36it/s, v_num=2, train_loss=3.110, RMSE=4.970]
Epoch 48: 28%|██▊ | 9/32 [00:00<00:00, 318.99it/s, v_num=2, train_loss=2.900, RMSE=4.970]
Epoch 48: 31%|███▏ | 10/32 [00:00<00:00, 321.90it/s, v_num=2, train_loss=2.900, RMSE=4.970]
Epoch 48: 31%|███▏ | 10/32 [00:00<00:00, 319.76it/s, v_num=2, train_loss=2.940, RMSE=4.970]
Epoch 48: 34%|███▍ | 11/32 [00:00<00:00, 322.37it/s, v_num=2, train_loss=2.940, RMSE=4.970]
Epoch 48: 34%|███▍ | 11/32 [00:00<00:00, 320.42it/s, v_num=2, train_loss=2.680, RMSE=4.970]
Epoch 48: 38%|███▊ | 12/32 [00:00<00:00, 322.15it/s, v_num=2, train_loss=2.680, RMSE=4.970]
Epoch 48: 38%|███▊ | 12/32 [00:00<00:00, 320.37it/s, v_num=2, train_loss=3.240, RMSE=4.970]
Epoch 48: 41%|████ | 13/32 [00:00<00:00, 322.54it/s, v_num=2, train_loss=3.240, RMSE=4.970]
Epoch 48: 41%|████ | 13/32 [00:00<00:00, 320.88it/s, v_num=2, train_loss=2.820, RMSE=4.970]
Epoch 48: 44%|████▍ | 14/32 [00:00<00:00, 323.00it/s, v_num=2, train_loss=2.820, RMSE=4.970]
Epoch 48: 44%|████▍ | 14/32 [00:00<00:00, 321.45it/s, v_num=2, train_loss=2.800, RMSE=4.970]
Epoch 48: 47%|████▋ | 15/32 [00:00<00:00, 319.57it/s, v_num=2, train_loss=2.800, RMSE=4.970]
Epoch 48: 47%|████▋ | 15/32 [00:00<00:00, 318.16it/s, v_num=2, train_loss=2.640, RMSE=4.970]
Epoch 48: 50%|█████ | 16/32 [00:00<00:00, 320.17it/s, v_num=2, train_loss=2.640, RMSE=4.970]
Epoch 48: 50%|█████ | 16/32 [00:00<00:00, 318.86it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 53%|█████▎ | 17/32 [00:00<00:00, 320.62it/s, v_num=2, train_loss=2.810, RMSE=4.970]
Epoch 48: 53%|█████▎ | 17/32 [00:00<00:00, 319.37it/s, v_num=2, train_loss=2.950, RMSE=4.970]
Epoch 48: 56%|█████▋ | 18/32 [00:00<00:00, 321.03it/s, v_num=2, train_loss=2.950, RMSE=4.970]
Epoch 48: 56%|█████▋ | 18/32 [00:00<00:00, 319.71it/s, v_num=2, train_loss=2.960, RMSE=4.970]
Epoch 48: 59%|█████▉ | 19/32 [00:00<00:00, 320.78it/s, v_num=2, train_loss=2.960, RMSE=4.970]
Epoch 48: 59%|█████▉ | 19/32 [00:00<00:00, 319.59it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 62%|██████▎ | 20/32 [00:00<00:00, 321.02it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 62%|██████▎ | 20/32 [00:00<00:00, 319.95it/s, v_num=2, train_loss=2.930, RMSE=4.970]
Epoch 48: 66%|██████▌ | 21/32 [00:00<00:00, 321.20it/s, v_num=2, train_loss=2.930, RMSE=4.970]
Epoch 48: 66%|██████▌ | 21/32 [00:00<00:00, 320.16it/s, v_num=2, train_loss=2.900, RMSE=4.970]
Epoch 48: 69%|██████▉ | 22/32 [00:00<00:00, 321.29it/s, v_num=2, train_loss=2.900, RMSE=4.970]
Epoch 48: 69%|██████▉ | 22/32 [00:00<00:00, 320.33it/s, v_num=2, train_loss=2.800, RMSE=4.970]
Epoch 48: 72%|███████▏ | 23/32 [00:00<00:00, 321.54it/s, v_num=2, train_loss=2.800, RMSE=4.970]
Epoch 48: 72%|███████▏ | 23/32 [00:00<00:00, 320.62it/s, v_num=2, train_loss=2.620, RMSE=4.970]
Epoch 48: 75%|███████▌ | 24/32 [00:00<00:00, 321.74it/s, v_num=2, train_loss=2.620, RMSE=4.970]
Epoch 48: 75%|███████▌ | 24/32 [00:00<00:00, 320.73it/s, v_num=2, train_loss=3.010, RMSE=4.970]
Epoch 48: 78%|███████▊ | 25/32 [00:00<00:00, 321.93it/s, v_num=2, train_loss=3.010, RMSE=4.970]
Epoch 48: 78%|███████▊ | 25/32 [00:00<00:00, 321.08it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 81%|████████▏ | 26/32 [00:00<00:00, 321.91it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 81%|████████▏ | 26/32 [00:00<00:00, 321.05it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 84%|████████▍ | 27/32 [00:00<00:00, 322.12it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 84%|████████▍ | 27/32 [00:00<00:00, 321.28it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 88%|████████▊ | 28/32 [00:00<00:00, 322.13it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 88%|████████▊ | 28/32 [00:00<00:00, 321.34it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 91%|█████████ | 29/32 [00:00<00:00, 322.37it/s, v_num=2, train_loss=2.920, RMSE=4.970]
Epoch 48: 91%|█████████ | 29/32 [00:00<00:00, 321.63it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 94%|█████████▍| 30/32 [00:00<00:00, 322.58it/s, v_num=2, train_loss=2.770, RMSE=4.970]
Epoch 48: 94%|█████████▍| 30/32 [00:00<00:00, 321.87it/s, v_num=2, train_loss=2.860, RMSE=4.970]
Epoch 48: 97%|█████████▋| 31/32 [00:00<00:00, 322.73it/s, v_num=2, train_loss=2.860, RMSE=4.970]
Epoch 48: 97%|█████████▋| 31/32 [00:00<00:00, 322.04it/s, v_num=2, train_loss=3.020, RMSE=4.970]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 323.06it/s, v_num=2, train_loss=3.020, RMSE=4.970]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 322.40it/s, v_num=2, train_loss=2.590, RMSE=4.970]
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Validation DataLoader 0: 100%|██████████| 10/10 [00:00<00:00, 621.91it/s]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 264.01it/s, v_num=2, train_loss=2.590, RMSE=4.690]
Epoch 48: 100%|██████████| 32/32 [00:00<00:00, 262.88it/s, v_num=2, train_loss=2.590, RMSE=4.690]
Epoch 48: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.590, RMSE=4.690]
Epoch 49: 0%| | 0/32 [00:00<?, ?it/s, v_num=2, train_loss=2.590, RMSE=4.690]
Epoch 49: 3%|▎ | 1/32 [00:00<00:00, 285.15it/s, v_num=2, train_loss=2.590, RMSE=4.690]
Epoch 49: 3%|▎ | 1/32 [00:00<00:00, 268.87it/s, v_num=2, train_loss=2.910, RMSE=4.690]
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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 │ 3.38949 │
│ MSE │ 23.35014 │
│ NLL │ 2.76722 │
│ RMSE │ 4.83220 │
└──────────────┴───────────────────────────┘
[{'test/reg/MAE': 3.3894922733306885, 'test/reg/MSE': 23.350135803222656, 'test/reg/RMSE': 4.832197666168213, 'test/reg/NLL': 2.7672183513641357}]
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 6.511 seconds)