Note
Go to the end to download the full example code.
Improved Ensemble parameter-efficiency with Packed-Ensembles¶
This tutorial is adapted from a notebook part of a lecture given at the `Helmholtz AI Conference <https://haicon24.de/>`_ by Sebastian Starke, Peter Steinbach, Gianni Franchi, and Olivier Laurent.
In this notebook will work on the MNIST dataset that was introduced by Corinna Cortes, Christopher J.C. Burges, and later modified by Yann LeCun in the foundational paper:
The MNIST dataset consists of 70 000 images of handwritten digits from 0 to 9. The images are grayscale and 28x28-pixel sized. The task is to classify the images into their respective digits. The dataset can be automatically downloaded using the torchvision library.
In this notebook, we will train a model and an ensemble on this task and evaluate their performance. The performance will consist in the following metrics: - Accuracy: the proportion of correctly classified images, - Brier score: a measure of the quality of the predicted probabilities, - Calibration error: a measure of the calibration of the predicted probabilities, - Negative Log-Likelihood: the value of the loss on the test set.
Throughout this notebook, we abstract the training and evaluation process using PyTorch Lightning and TorchUncertainty.
Similarly to keras for tensorflow, PyTorch Lightning is a high-level interface for PyTorch that simplifies the training and evaluation process using a Trainer. TorchUncertainty is partly built on top of PyTorch Lightning and provides tools to train and evaluate models with uncertainty quantification.
TorchUncertainty includes datamodules that handle the data loading and preprocessing. We don’t use them here for tutorial purposes.
1. Download, instantiate and visualize the datasets¶
The dataset is automatically downloaded using torchvision. We then visualize a few images to see a bit what we are working with.
# Create the transforms for the images
import torch
import torchvision.transforms as T
# We set the number of epochs to some low value for the sake of time
max_epochs = 2
train_transform = T.Compose(
[
T.ToTensor(),
# We perform random cropping as data augmentation
T.RandomCrop(28, padding=4),
# As for the MNIST1d dataset, we normalize the data
T.Normalize((0.1307,), (0.3081,)),
]
)
test_transform = T.Compose(
[
T.Grayscale(num_output_channels=1),
T.ToTensor(),
T.CenterCrop(28),
T.Normalize((0.1307,), (0.3081,)),
]
)
# Download and instantiate the dataset
from torch.utils.data import Subset
from torchvision.datasets import MNIST, FashionMNIST
train_data = MNIST(
root="./data/", download=True, train=True, transform=train_transform
)
test_data = MNIST(root="./data/", train=False, transform=test_transform)
# We only take the first 10k images to have the same number of samples as the test set using torch Subsets
ood_data = Subset(
FashionMNIST(root="./data/", download=True, transform=test_transform),
indices=range(10000),
)
# Create the corresponding dataloaders
from torch.utils.data import DataLoader
train_dl = DataLoader(train_data, batch_size=32, shuffle=True)
test_dl = DataLoader(test_data, batch_size=32, shuffle=False)
ood_dl = DataLoader(ood_data, batch_size=32, shuffle=False)
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
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Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
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Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
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Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
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Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
You could replace all this cell by simply loading the MNIST datamodule from TorchUncertainty. Now, let’s visualize a few images from the dataset. For this task, we use the viz_data dataset that applies no transformation to the images.
# Datasets without transformation to visualize the unchanged data
viz_data = MNIST(root="./data/", train=False)
ood_viz_data = FashionMNIST(root="./data/", download=True)
print("In distribution data:")
viz_data[0][0]
In distribution data:
<PIL.Image.Image image mode=L size=28x28 at 0x7F210831A230>
print("Out of distribution data:")
ood_viz_data[0][0]
Out of distribution data:
<PIL.Image.Image image mode=L size=28x28 at 0x7F210840BC40>
2. Create & train the model¶
We will create a simple convolutional neural network (CNN): the LeNet model (also introduced by LeCun).
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(
self,
in_channels: int,
num_classes: int,
) -> None:
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 6, (5, 5))
self.conv2 = nn.Conv2d(6, 16, (5, 5))
self.pooling = nn.AdaptiveAvgPool2d((4, 4))
self.fc1 = nn.Linear(256, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = F.relu(self.conv1(x))
out = F.max_pool2d(out, 2)
out = F.relu(self.conv2(out))
out = F.max_pool2d(out, 2)
out = torch.flatten(out, 1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
return self.fc3(out) # No softmax in the model!
# Instantiate the model, the images are in grayscale so the number of channels is 1
model = LeNet(in_channels=1, num_classes=10)
We now need to define the optimization recipe: - the optimizer, here the standard stochastic gradient descent (SGD) with a learning rate of 0.05 - the scheduler, here cosine annealing.
def optim_recipe(model, lr_mult: float = 1.0):
optimizer = torch.optim.SGD(model.parameters(), lr=0.05 * lr_mult)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
return {"optimizer": optimizer, "scheduler": scheduler}
To train the model, we use TorchUncertainty, a library that we have developed to ease the training and evaluation of models with uncertainty.
Note: To train supervised classification models we most often use the cross-entropy loss. With weight-decay, minimizing this loss amounts to finding a Maximum a posteriori (MAP) estimate of the model parameters. This means that the model is trained to predict the most likely class for each input.
from torch_uncertainty.routines import ClassificationRoutine
from torch_uncertainty import TUTrainer
# Create the trainer that will handle the training
trainer = TUTrainer(accelerator="cpu", max_epochs=max_epochs)
# The routine is a wrapper of the model that contains the training logic with the metrics, etc
routine = ClassificationRoutine(
num_classes=10,
model=model,
loss=nn.CrossEntropyLoss(),
optim_recipe=optim_recipe(model),
eval_ood=True,
)
# In practice, avoid performing the validation on the test set (if you do model selection)
trainer.fit(routine, train_dataloaders=train_dl, val_dataloaders=test_dl)
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: Metric `FPR95` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
warnings.warn(*args, **kwargs) # noqa: B028
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/lightning/pytorch/core/optimizer.py:377: Found unsupported keys in the optimizer configuration: {'scheduler'}
Sanity Checking: | | 0/? [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_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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/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.
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Epoch 0: 3%|▎ | 48/1875 [00:00<00:23, 78.04it/s, v_num=5, train_loss=2.290]
Epoch 0: 3%|▎ | 49/1875 [00:00<00:23, 78.08it/s, v_num=5, train_loss=2.290]
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Epoch 0: 3%|▎ | 50/1875 [00:00<00:23, 78.09it/s, v_num=5, train_loss=2.320]
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Epoch 0: 4%|▍ | 77/1875 [00:00<00:23, 77.74it/s, v_num=5, train_loss=2.260]
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Epoch 0: 4%|▍ | 84/1875 [00:01<00:23, 77.79it/s, v_num=5, train_loss=2.270]
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Epoch 0: 5%|▍ | 89/1875 [00:01<00:22, 77.82it/s, v_num=5, train_loss=2.270]
Epoch 0: 5%|▍ | 89/1875 [00:01<00:22, 77.80it/s, v_num=5, train_loss=2.240]
Epoch 0: 5%|▍ | 90/1875 [00:01<00:22, 77.82it/s, v_num=5, train_loss=2.240]
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Epoch 0: 5%|▍ | 91/1875 [00:01<00:22, 77.83it/s, v_num=5, train_loss=2.230]
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Epoch 0: 5%|▍ | 92/1875 [00:01<00:22, 77.84it/s, v_num=5, train_loss=2.270]
Epoch 0: 5%|▍ | 92/1875 [00:01<00:22, 77.83it/s, v_num=5, train_loss=2.200]
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Epoch 0: 5%|▌ | 102/1875 [00:01<00:22, 77.89it/s, v_num=5, train_loss=2.190]
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Epoch 1: 2%|▏ | 30/1875 [00:00<00:23, 78.30it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 2%|▏ | 31/1875 [00:00<00:23, 78.34it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 2%|▏ | 31/1875 [00:00<00:23, 78.30it/s, v_num=5, train_loss=0.433, Acc%=95.60]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:23, 78.34it/s, v_num=5, train_loss=0.433, Acc%=95.60]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:23, 78.30it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:23, 78.37it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.076, Acc%=95.60]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:23, 78.37it/s, v_num=5, train_loss=0.076, Acc%=95.60]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:23, 78.33it/s, v_num=5, train_loss=0.0851, Acc%=95.60]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:23, 78.37it/s, v_num=5, train_loss=0.0851, Acc%=95.60]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:23, 78.33it/s, v_num=5, train_loss=0.252, Acc%=95.60]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.252, Acc%=95.60]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:23, 78.31it/s, v_num=5, train_loss=0.115, Acc%=95.60]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:23, 78.34it/s, v_num=5, train_loss=0.115, Acc%=95.60]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:23, 78.31it/s, v_num=5, train_loss=0.223, Acc%=95.60]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.223, Acc%=95.60]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:23, 78.37it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:23, 78.34it/s, v_num=5, train_loss=0.0383, Acc%=95.60]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:23, 78.39it/s, v_num=5, train_loss=0.0383, Acc%=95.60]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:23, 78.36it/s, v_num=5, train_loss=0.0667, Acc%=95.60]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.0667, Acc%=95.60]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:23, 78.28it/s, v_num=5, train_loss=0.0911, Acc%=95.60]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:23, 78.24it/s, v_num=5, train_loss=0.0911, Acc%=95.60]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:23, 78.20it/s, v_num=5, train_loss=0.190, Acc%=95.60]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:23, 78.24it/s, v_num=5, train_loss=0.190, Acc%=95.60]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:23, 78.21it/s, v_num=5, train_loss=0.129, Acc%=95.60]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:23, 78.26it/s, v_num=5, train_loss=0.129, Acc%=95.60]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:23, 78.23it/s, v_num=5, train_loss=0.235, Acc%=95.60]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:23, 78.27it/s, v_num=5, train_loss=0.235, Acc%=95.60]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:23, 78.24it/s, v_num=5, train_loss=0.126, Acc%=95.60]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:23, 78.29it/s, v_num=5, train_loss=0.126, Acc%=95.60]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:23, 78.26it/s, v_num=5, train_loss=0.478, Acc%=95.60]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:23, 78.31it/s, v_num=5, train_loss=0.478, Acc%=95.60]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:23, 78.28it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:23, 78.29it/s, v_num=5, train_loss=0.0279, Acc%=95.60]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:23, 78.33it/s, v_num=5, train_loss=0.0279, Acc%=95.60]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:23, 78.30it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:23, 78.38it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.064, Acc%=95.60]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.064, Acc%=95.60]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:23, 78.32it/s, v_num=5, train_loss=0.357, Acc%=95.60]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:23, 78.36it/s, v_num=5, train_loss=0.357, Acc%=95.60]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:23, 78.33it/s, v_num=5, train_loss=0.247, Acc%=95.60]
Epoch 1: 3%|▎ | 54/1875 [00:00<00:23, 78.37it/s, v_num=5, train_loss=0.247, Acc%=95.60]
Epoch 1: 3%|▎ | 54/1875 [00:00<00:23, 78.35it/s, v_num=5, train_loss=0.306, Acc%=95.60]
Epoch 1: 3%|▎ | 55/1875 [00:00<00:23, 78.38it/s, v_num=5, train_loss=0.306, Acc%=95.60]
Epoch 1: 3%|▎ | 55/1875 [00:00<00:23, 78.36it/s, v_num=5, train_loss=0.117, Acc%=95.60]
Epoch 1: 3%|▎ | 56/1875 [00:00<00:23, 78.40it/s, v_num=5, train_loss=0.117, Acc%=95.60]
Epoch 1: 3%|▎ | 56/1875 [00:00<00:23, 78.38it/s, v_num=5, train_loss=0.136, Acc%=95.60]
Epoch 1: 3%|▎ | 57/1875 [00:00<00:23, 78.42it/s, v_num=5, train_loss=0.136, Acc%=95.60]
Epoch 1: 3%|▎ | 57/1875 [00:00<00:23, 78.39it/s, v_num=5, train_loss=0.079, Acc%=95.60]
Epoch 1: 3%|▎ | 58/1875 [00:00<00:23, 78.43it/s, v_num=5, train_loss=0.079, Acc%=95.60]
Epoch 1: 3%|▎ | 58/1875 [00:00<00:23, 78.41it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 3%|▎ | 59/1875 [00:00<00:23, 78.45it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 3%|▎ | 59/1875 [00:00<00:23, 78.43it/s, v_num=5, train_loss=0.0389, Acc%=95.60]
Epoch 1: 3%|▎ | 60/1875 [00:00<00:23, 78.46it/s, v_num=5, train_loss=0.0389, Acc%=95.60]
Epoch 1: 3%|▎ | 60/1875 [00:00<00:23, 78.44it/s, v_num=5, train_loss=0.211, Acc%=95.60]
Epoch 1: 3%|▎ | 61/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.211, Acc%=95.60]
Epoch 1: 3%|▎ | 61/1875 [00:00<00:23, 78.45it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 3%|▎ | 62/1875 [00:00<00:23, 78.48it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 3%|▎ | 62/1875 [00:00<00:23, 78.46it/s, v_num=5, train_loss=0.0744, Acc%=95.60]
Epoch 1: 3%|▎ | 63/1875 [00:00<00:23, 78.48it/s, v_num=5, train_loss=0.0744, Acc%=95.60]
Epoch 1: 3%|▎ | 63/1875 [00:00<00:23, 78.46it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 3%|▎ | 64/1875 [00:00<00:23, 78.49it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 3%|▎ | 64/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.0629, Acc%=95.60]
Epoch 1: 3%|▎ | 65/1875 [00:00<00:23, 78.50it/s, v_num=5, train_loss=0.0629, Acc%=95.60]
Epoch 1: 3%|▎ | 65/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 4%|▎ | 66/1875 [00:00<00:23, 78.49it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 4%|▎ | 66/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.074, Acc%=95.60]
Epoch 1: 4%|▎ | 67/1875 [00:00<00:23, 78.49it/s, v_num=5, train_loss=0.074, Acc%=95.60]
Epoch 1: 4%|▎ | 67/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 4%|▎ | 68/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 4%|▎ | 68/1875 [00:00<00:23, 78.45it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 4%|▎ | 69/1875 [00:00<00:23, 78.47it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 4%|▎ | 69/1875 [00:00<00:23, 78.45it/s, v_num=5, train_loss=0.215, Acc%=95.60]
Epoch 1: 4%|▎ | 70/1875 [00:00<00:22, 78.48it/s, v_num=5, train_loss=0.215, Acc%=95.60]
Epoch 1: 4%|▎ | 70/1875 [00:00<00:23, 78.46it/s, v_num=5, train_loss=0.302, Acc%=95.60]
Epoch 1: 4%|▍ | 71/1875 [00:00<00:22, 78.49it/s, v_num=5, train_loss=0.302, Acc%=95.60]
Epoch 1: 4%|▍ | 71/1875 [00:00<00:22, 78.47it/s, v_num=5, train_loss=0.123, Acc%=95.60]
Epoch 1: 4%|▍ | 72/1875 [00:00<00:22, 78.50it/s, v_num=5, train_loss=0.123, Acc%=95.60]
Epoch 1: 4%|▍ | 72/1875 [00:00<00:22, 78.48it/s, v_num=5, train_loss=0.250, Acc%=95.60]
Epoch 1: 4%|▍ | 73/1875 [00:00<00:22, 78.50it/s, v_num=5, train_loss=0.250, Acc%=95.60]
Epoch 1: 4%|▍ | 73/1875 [00:00<00:22, 78.48it/s, v_num=5, train_loss=0.0594, Acc%=95.60]
Epoch 1: 4%|▍ | 74/1875 [00:00<00:22, 78.50it/s, v_num=5, train_loss=0.0594, Acc%=95.60]
Epoch 1: 4%|▍ | 74/1875 [00:00<00:22, 78.48it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 4%|▍ | 75/1875 [00:00<00:22, 78.50it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 4%|▍ | 75/1875 [00:00<00:22, 78.48it/s, v_num=5, train_loss=0.0607, Acc%=95.60]
Epoch 1: 4%|▍ | 76/1875 [00:00<00:22, 78.46it/s, v_num=5, train_loss=0.0607, Acc%=95.60]
Epoch 1: 4%|▍ | 76/1875 [00:00<00:22, 78.45it/s, v_num=5, train_loss=0.0395, Acc%=95.60]
Epoch 1: 4%|▍ | 77/1875 [00:00<00:22, 78.47it/s, v_num=5, train_loss=0.0395, Acc%=95.60]
Epoch 1: 4%|▍ | 77/1875 [00:00<00:22, 78.45it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 4%|▍ | 78/1875 [00:00<00:22, 78.47it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 4%|▍ | 78/1875 [00:00<00:22, 78.46it/s, v_num=5, train_loss=0.0757, Acc%=95.60]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:22, 78.48it/s, v_num=5, train_loss=0.0757, Acc%=95.60]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:22, 78.47it/s, v_num=5, train_loss=0.0525, Acc%=95.60]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.0525, Acc%=95.60]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:22, 78.48it/s, v_num=5, train_loss=0.00298, Acc%=95.60]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.00298, Acc%=95.60]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.164, Acc%=95.60]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.164, Acc%=95.60]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.115, Acc%=95.60]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.115, Acc%=95.60]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:22, 78.48it/s, v_num=5, train_loss=0.138, Acc%=95.60]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.138, Acc%=95.60]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.174, Acc%=95.60]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.174, Acc%=95.60]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.162, Acc%=95.60]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.162, Acc%=95.60]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.216, Acc%=95.60]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.216, Acc%=95.60]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.0521, Acc%=95.60]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0521, Acc%=95.60]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.0857, Acc%=95.60]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.0857, Acc%=95.60]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.237, Acc%=95.60]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.237, Acc%=95.60]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.233, Acc%=95.60]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.233, Acc%=95.60]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.683, Acc%=95.60]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.683, Acc%=95.60]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.436, Acc%=95.60]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.436, Acc%=95.60]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.240, Acc%=95.60]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.240, Acc%=95.60]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.227, Acc%=95.60]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.227, Acc%=95.60]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.688, Acc%=95.60]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.688, Acc%=95.60]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.138, Acc%=95.60]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.138, Acc%=95.60]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.300, Acc%=95.60]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.300, Acc%=95.60]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.258, Acc%=95.60]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.258, Acc%=95.60]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.085, Acc%=95.60]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.085, Acc%=95.60]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.222, Acc%=95.60]
Epoch 1: 6%|▌ | 107/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.222, Acc%=95.60]
Epoch 1: 6%|▌ | 107/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.140, Acc%=95.60]
Epoch 1: 6%|▌ | 108/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.140, Acc%=95.60]
Epoch 1: 6%|▌ | 108/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.283, Acc%=95.60]
Epoch 1: 6%|▌ | 109/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.283, Acc%=95.60]
Epoch 1: 6%|▌ | 109/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.144, Acc%=95.60]
Epoch 1: 6%|▌ | 110/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.144, Acc%=95.60]
Epoch 1: 6%|▌ | 110/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.239, Acc%=95.60]
Epoch 1: 6%|▌ | 111/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.239, Acc%=95.60]
Epoch 1: 6%|▌ | 111/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.232, Acc%=95.60]
Epoch 1: 6%|▌ | 112/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.232, Acc%=95.60]
Epoch 1: 6%|▌ | 112/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.385, Acc%=95.60]
Epoch 1: 6%|▌ | 113/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.385, Acc%=95.60]
Epoch 1: 6%|▌ | 113/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.143, Acc%=95.60]
Epoch 1: 6%|▌ | 114/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.143, Acc%=95.60]
Epoch 1: 6%|▌ | 114/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.228, Acc%=95.60]
Epoch 1: 6%|▌ | 115/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.228, Acc%=95.60]
Epoch 1: 6%|▌ | 115/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.0748, Acc%=95.60]
Epoch 1: 6%|▌ | 116/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.0748, Acc%=95.60]
Epoch 1: 6%|▌ | 116/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 6%|▌ | 117/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 6%|▌ | 117/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.0218, Acc%=95.60]
Epoch 1: 6%|▋ | 118/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.0218, Acc%=95.60]
Epoch 1: 6%|▋ | 118/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.0103, Acc%=95.60]
Epoch 1: 6%|▋ | 119/1875 [00:01<00:22, 78.58it/s, v_num=5, train_loss=0.0103, Acc%=95.60]
Epoch 1: 6%|▋ | 119/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.249, Acc%=95.60]
Epoch 1: 6%|▋ | 120/1875 [00:01<00:22, 78.58it/s, v_num=5, train_loss=0.249, Acc%=95.60]
Epoch 1: 6%|▋ | 120/1875 [00:01<00:22, 78.57it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 6%|▋ | 121/1875 [00:01<00:22, 78.56it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 6%|▋ | 121/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.232, Acc%=95.60]
Epoch 1: 7%|▋ | 122/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.232, Acc%=95.60]
Epoch 1: 7%|▋ | 122/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.308, Acc%=95.60]
Epoch 1: 7%|▋ | 123/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.308, Acc%=95.60]
Epoch 1: 7%|▋ | 123/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0566, Acc%=95.60]
Epoch 1: 7%|▋ | 124/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0566, Acc%=95.60]
Epoch 1: 7%|▋ | 124/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.0431, Acc%=95.60]
Epoch 1: 7%|▋ | 125/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0431, Acc%=95.60]
Epoch 1: 7%|▋ | 125/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.154, Acc%=95.60]
Epoch 1: 7%|▋ | 126/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.154, Acc%=95.60]
Epoch 1: 7%|▋ | 126/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.0755, Acc%=95.60]
Epoch 1: 7%|▋ | 127/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.0755, Acc%=95.60]
Epoch 1: 7%|▋ | 127/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.297, Acc%=95.60]
Epoch 1: 7%|▋ | 128/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.297, Acc%=95.60]
Epoch 1: 7%|▋ | 128/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.120, Acc%=95.60]
Epoch 1: 7%|▋ | 129/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.120, Acc%=95.60]
Epoch 1: 7%|▋ | 129/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.166, Acc%=95.60]
Epoch 1: 7%|▋ | 130/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.166, Acc%=95.60]
Epoch 1: 7%|▋ | 130/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.367, Acc%=95.60]
Epoch 1: 7%|▋ | 131/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.367, Acc%=95.60]
Epoch 1: 7%|▋ | 131/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.288, Acc%=95.60]
Epoch 1: 7%|▋ | 132/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.288, Acc%=95.60]
Epoch 1: 7%|▋ | 132/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.220, Acc%=95.60]
Epoch 1: 7%|▋ | 133/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.220, Acc%=95.60]
Epoch 1: 7%|▋ | 133/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.192, Acc%=95.60]
Epoch 1: 7%|▋ | 134/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.192, Acc%=95.60]
Epoch 1: 7%|▋ | 134/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.0437, Acc%=95.60]
Epoch 1: 7%|▋ | 135/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.0437, Acc%=95.60]
Epoch 1: 7%|▋ | 135/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 7%|▋ | 136/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 7%|▋ | 136/1875 [00:01<00:22, 78.49it/s, v_num=5, train_loss=0.0487, Acc%=95.60]
Epoch 1: 7%|▋ | 137/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.0487, Acc%=95.60]
Epoch 1: 7%|▋ | 137/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.0549, Acc%=95.60]
Epoch 1: 7%|▋ | 138/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.0549, Acc%=95.60]
Epoch 1: 7%|▋ | 138/1875 [00:01<00:22, 78.50it/s, v_num=5, train_loss=0.0826, Acc%=95.60]
Epoch 1: 7%|▋ | 139/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.0826, Acc%=95.60]
Epoch 1: 7%|▋ | 139/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.0712, Acc%=95.60]
Epoch 1: 7%|▋ | 140/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.0712, Acc%=95.60]
Epoch 1: 7%|▋ | 140/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.145, Acc%=95.60]
Epoch 1: 8%|▊ | 141/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.145, Acc%=95.60]
Epoch 1: 8%|▊ | 141/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 8%|▊ | 142/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 8%|▊ | 142/1875 [00:01<00:22, 78.51it/s, v_num=5, train_loss=0.285, Acc%=95.60]
Epoch 1: 8%|▊ | 143/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.285, Acc%=95.60]
Epoch 1: 8%|▊ | 143/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.0951, Acc%=95.60]
Epoch 1: 8%|▊ | 144/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0951, Acc%=95.60]
Epoch 1: 8%|▊ | 144/1875 [00:01<00:22, 78.52it/s, v_num=5, train_loss=0.0323, Acc%=95.60]
Epoch 1: 8%|▊ | 145/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.0323, Acc%=95.60]
Epoch 1: 8%|▊ | 145/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.221, Acc%=95.60]
Epoch 1: 8%|▊ | 146/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.221, Acc%=95.60]
Epoch 1: 8%|▊ | 146/1875 [00:01<00:22, 78.53it/s, v_num=5, train_loss=0.0944, Acc%=95.60]
Epoch 1: 8%|▊ | 147/1875 [00:01<00:22, 78.55it/s, v_num=5, train_loss=0.0944, Acc%=95.60]
Epoch 1: 8%|▊ | 147/1875 [00:01<00:22, 78.54it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 8%|▊ | 148/1875 [00:01<00:21, 78.55it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 8%|▊ | 148/1875 [00:01<00:21, 78.54it/s, v_num=5, train_loss=0.0887, Acc%=95.60]
Epoch 1: 8%|▊ | 149/1875 [00:01<00:21, 78.55it/s, v_num=5, train_loss=0.0887, Acc%=95.60]
Epoch 1: 8%|▊ | 149/1875 [00:01<00:21, 78.55it/s, v_num=5, train_loss=0.268, Acc%=95.60]
Epoch 1: 8%|▊ | 150/1875 [00:01<00:21, 78.56it/s, v_num=5, train_loss=0.268, Acc%=95.60]
Epoch 1: 8%|▊ | 150/1875 [00:01<00:21, 78.55it/s, v_num=5, train_loss=0.151, Acc%=95.60]
Epoch 1: 8%|▊ | 151/1875 [00:01<00:21, 78.56it/s, v_num=5, train_loss=0.151, Acc%=95.60]
Epoch 1: 8%|▊ | 151/1875 [00:01<00:21, 78.55it/s, v_num=5, train_loss=0.347, Acc%=95.60]
Epoch 1: 8%|▊ | 152/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.347, Acc%=95.60]
Epoch 1: 8%|▊ | 152/1875 [00:01<00:21, 78.56it/s, v_num=5, train_loss=0.0698, Acc%=95.60]
Epoch 1: 8%|▊ | 153/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.0698, Acc%=95.60]
Epoch 1: 8%|▊ | 153/1875 [00:01<00:21, 78.56it/s, v_num=5, train_loss=0.215, Acc%=95.60]
Epoch 1: 8%|▊ | 154/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.215, Acc%=95.60]
Epoch 1: 8%|▊ | 154/1875 [00:01<00:21, 78.56it/s, v_num=5, train_loss=0.0925, Acc%=95.60]
Epoch 1: 8%|▊ | 155/1875 [00:01<00:21, 78.58it/s, v_num=5, train_loss=0.0925, Acc%=95.60]
Epoch 1: 8%|▊ | 155/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.441, Acc%=95.60]
Epoch 1: 8%|▊ | 156/1875 [00:01<00:21, 78.58it/s, v_num=5, train_loss=0.441, Acc%=95.60]
Epoch 1: 8%|▊ | 156/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.0398, Acc%=95.60]
Epoch 1: 8%|▊ | 157/1875 [00:01<00:21, 78.58it/s, v_num=5, train_loss=0.0398, Acc%=95.60]
Epoch 1: 8%|▊ | 157/1875 [00:01<00:21, 78.57it/s, v_num=5, train_loss=0.097, Acc%=95.60]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:21, 78.58it/s, v_num=5, train_loss=0.097, Acc%=95.60]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.224, Acc%=95.60]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:21, 78.58it/s, v_num=5, train_loss=0.224, Acc%=95.60]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.130, Acc%=95.60]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.130, Acc%=95.60]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.189, Acc%=95.60]
Epoch 1: 9%|▊ | 161/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.189, Acc%=95.60]
Epoch 1: 9%|▊ | 161/1875 [00:02<00:21, 78.56it/s, v_num=5, train_loss=0.207, Acc%=95.60]
Epoch 1: 9%|▊ | 162/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.207, Acc%=95.60]
Epoch 1: 9%|▊ | 162/1875 [00:02<00:21, 78.56it/s, v_num=5, train_loss=0.546, Acc%=95.60]
Epoch 1: 9%|▊ | 163/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.546, Acc%=95.60]
Epoch 1: 9%|▊ | 163/1875 [00:02<00:21, 78.56it/s, v_num=5, train_loss=0.0568, Acc%=95.60]
Epoch 1: 9%|▊ | 164/1875 [00:02<00:21, 78.57it/s, v_num=5, train_loss=0.0568, Acc%=95.60]
Epoch 1: 9%|▊ | 164/1875 [00:02<00:21, 78.56it/s, v_num=5, train_loss=0.0441, Acc%=95.60]
Epoch 1: 9%|▉ | 165/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.0441, Acc%=95.60]
Epoch 1: 9%|▉ | 165/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0881, Acc%=95.60]
Epoch 1: 9%|▉ | 166/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.0881, Acc%=95.60]
Epoch 1: 9%|▉ | 166/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0817, Acc%=95.60]
Epoch 1: 9%|▉ | 167/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0817, Acc%=95.60]
Epoch 1: 9%|▉ | 167/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0901, Acc%=95.60]
Epoch 1: 9%|▉ | 168/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0901, Acc%=95.60]
Epoch 1: 9%|▉ | 168/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.448, Acc%=95.60]
Epoch 1: 9%|▉ | 169/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.448, Acc%=95.60]
Epoch 1: 9%|▉ | 169/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.445, Acc%=95.60]
Epoch 1: 9%|▉ | 170/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.445, Acc%=95.60]
Epoch 1: 9%|▉ | 170/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 9%|▉ | 171/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 9%|▉ | 171/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.178, Acc%=95.60]
Epoch 1: 9%|▉ | 172/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.178, Acc%=95.60]
Epoch 1: 9%|▉ | 172/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.240, Acc%=95.60]
Epoch 1: 9%|▉ | 173/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.240, Acc%=95.60]
Epoch 1: 9%|▉ | 173/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.0681, Acc%=95.60]
Epoch 1: 9%|▉ | 174/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0681, Acc%=95.60]
Epoch 1: 9%|▉ | 174/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.231, Acc%=95.60]
Epoch 1: 9%|▉ | 175/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.231, Acc%=95.60]
Epoch 1: 9%|▉ | 175/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.365, Acc%=95.60]
Epoch 1: 9%|▉ | 176/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.365, Acc%=95.60]
Epoch 1: 9%|▉ | 176/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 9%|▉ | 177/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 9%|▉ | 177/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.0741, Acc%=95.60]
Epoch 1: 9%|▉ | 178/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0741, Acc%=95.60]
Epoch 1: 9%|▉ | 178/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.360, Acc%=95.60]
Epoch 1: 10%|▉ | 179/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.360, Acc%=95.60]
Epoch 1: 10%|▉ | 179/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 10%|▉ | 180/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 10%|▉ | 180/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.0652, Acc%=95.60]
Epoch 1: 10%|▉ | 181/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0652, Acc%=95.60]
Epoch 1: 10%|▉ | 181/1875 [00:02<00:21, 78.53it/s, v_num=5, train_loss=0.0916, Acc%=95.60]
Epoch 1: 10%|▉ | 182/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0916, Acc%=95.60]
Epoch 1: 10%|▉ | 182/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.0741, Acc%=95.60]
Epoch 1: 10%|▉ | 183/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.0741, Acc%=95.60]
Epoch 1: 10%|▉ | 183/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.270, Acc%=95.60]
Epoch 1: 10%|▉ | 184/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.270, Acc%=95.60]
Epoch 1: 10%|▉ | 184/1875 [00:02<00:21, 78.54it/s, v_num=5, train_loss=0.279, Acc%=95.60]
Epoch 1: 10%|▉ | 185/1875 [00:02<00:21, 78.55it/s, v_num=5, train_loss=0.279, Acc%=95.60]
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Epoch 1: 14%|█▍ | 270/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.305, Acc%=95.60]
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Epoch 1: 15%|█▍ | 272/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.374, Acc%=95.60]
Epoch 1: 15%|█▍ | 272/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.0596, Acc%=95.60]
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Epoch 1: 15%|█▍ | 274/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.557, Acc%=95.60]
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Epoch 1: 15%|█▍ | 277/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.143, Acc%=95.60]
Epoch 1: 15%|█▍ | 277/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.113, Acc%=95.60]
Epoch 1: 15%|█▍ | 278/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.113, Acc%=95.60]
Epoch 1: 15%|█▍ | 278/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.0315, Acc%=95.60]
Epoch 1: 15%|█▍ | 279/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.0315, Acc%=95.60]
Epoch 1: 15%|█▍ | 279/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 15%|█▍ | 280/1875 [00:03<00:20, 78.57it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 15%|█▍ | 280/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 15%|█▍ | 281/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.267, Acc%=95.60]
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Epoch 1: 15%|█▌ | 282/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 15%|█▌ | 282/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.248, Acc%=95.60]
Epoch 1: 15%|█▌ | 283/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.248, Acc%=95.60]
Epoch 1: 15%|█▌ | 283/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 15%|█▌ | 284/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 15%|█▌ | 284/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.0148, Acc%=95.60]
Epoch 1: 15%|█▌ | 285/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0148, Acc%=95.60]
Epoch 1: 15%|█▌ | 285/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 15%|█▌ | 286/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 15%|█▌ | 286/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.352, Acc%=95.60]
Epoch 1: 15%|█▌ | 287/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.352, Acc%=95.60]
Epoch 1: 15%|█▌ | 287/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 15%|█▌ | 288/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 15%|█▌ | 288/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 15%|█▌ | 289/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.172, Acc%=95.60]
Epoch 1: 15%|█▌ | 289/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.0806, Acc%=95.60]
Epoch 1: 15%|█▌ | 290/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0806, Acc%=95.60]
Epoch 1: 15%|█▌ | 290/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.0607, Acc%=95.60]
Epoch 1: 16%|█▌ | 291/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0607, Acc%=95.60]
Epoch 1: 16%|█▌ | 291/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.0632, Acc%=95.60]
Epoch 1: 16%|█▌ | 292/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0632, Acc%=95.60]
Epoch 1: 16%|█▌ | 292/1875 [00:03<00:20, 78.53it/s, v_num=5, train_loss=0.0366, Acc%=95.60]
Epoch 1: 16%|█▌ | 293/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0366, Acc%=95.60]
Epoch 1: 16%|█▌ | 293/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0956, Acc%=95.60]
Epoch 1: 16%|█▌ | 294/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0956, Acc%=95.60]
Epoch 1: 16%|█▌ | 294/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.282, Acc%=95.60]
Epoch 1: 16%|█▌ | 295/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.282, Acc%=95.60]
Epoch 1: 16%|█▌ | 295/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 16%|█▌ | 296/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 16%|█▌ | 296/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.0624, Acc%=95.60]
Epoch 1: 16%|█▌ | 297/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.0624, Acc%=95.60]
Epoch 1: 16%|█▌ | 297/1875 [00:03<00:20, 78.54it/s, v_num=5, train_loss=0.261, Acc%=95.60]
Epoch 1: 16%|█▌ | 298/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.261, Acc%=95.60]
Epoch 1: 16%|█▌ | 298/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 16%|█▌ | 299/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 16%|█▌ | 299/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 16%|█▌ | 300/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 16%|█▌ | 300/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.119, Acc%=95.60]
Epoch 1: 16%|█▌ | 301/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.119, Acc%=95.60]
Epoch 1: 16%|█▌ | 301/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 16%|█▌ | 302/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 16%|█▌ | 302/1875 [00:03<00:20, 78.55it/s, v_num=5, train_loss=0.200, Acc%=95.60]
Epoch 1: 16%|█▌ | 303/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.200, Acc%=95.60]
Epoch 1: 16%|█▌ | 303/1875 [00:03<00:20, 78.56it/s, v_num=5, train_loss=0.0995, Acc%=95.60]
Epoch 1: 16%|█▌ | 304/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.0995, Acc%=95.60]
Epoch 1: 16%|█▌ | 304/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.0876, Acc%=95.60]
Epoch 1: 16%|█▋ | 305/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.0876, Acc%=95.60]
Epoch 1: 16%|█▋ | 305/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.313, Acc%=95.60]
Epoch 1: 16%|█▋ | 306/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.313, Acc%=95.60]
Epoch 1: 16%|█▋ | 306/1875 [00:03<00:19, 78.56it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 16%|█▋ | 307/1875 [00:03<00:19, 78.55it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 16%|█▋ | 307/1875 [00:03<00:19, 78.55it/s, v_num=5, train_loss=0.0863, Acc%=95.60]
Epoch 1: 16%|█▋ | 308/1875 [00:03<00:19, 78.54it/s, v_num=5, train_loss=0.0863, Acc%=95.60]
Epoch 1: 16%|█▋ | 308/1875 [00:03<00:19, 78.53it/s, v_num=5, train_loss=0.0544, Acc%=95.60]
Epoch 1: 16%|█▋ | 309/1875 [00:03<00:19, 78.53it/s, v_num=5, train_loss=0.0544, Acc%=95.60]
Epoch 1: 16%|█▋ | 309/1875 [00:03<00:19, 78.52it/s, v_num=5, train_loss=0.437, Acc%=95.60]
Epoch 1: 17%|█▋ | 310/1875 [00:03<00:19, 78.52it/s, v_num=5, train_loss=0.437, Acc%=95.60]
Epoch 1: 17%|█▋ | 310/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.0748, Acc%=95.60]
Epoch 1: 17%|█▋ | 311/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.0748, Acc%=95.60]
Epoch 1: 17%|█▋ | 311/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.0435, Acc%=95.60]
Epoch 1: 17%|█▋ | 312/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.0435, Acc%=95.60]
Epoch 1: 17%|█▋ | 312/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.464, Acc%=95.60]
Epoch 1: 17%|█▋ | 313/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.464, Acc%=95.60]
Epoch 1: 17%|█▋ | 313/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 17%|█▋ | 314/1875 [00:03<00:19, 78.51it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 17%|█▋ | 314/1875 [00:03<00:19, 78.50it/s, v_num=5, train_loss=0.364, Acc%=95.60]
Epoch 1: 17%|█▋ | 315/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.364, Acc%=95.60]
Epoch 1: 17%|█▋ | 315/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.532, Acc%=95.60]
Epoch 1: 17%|█▋ | 316/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.532, Acc%=95.60]
Epoch 1: 17%|█▋ | 316/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.136, Acc%=95.60]
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Epoch 1: 17%|█▋ | 318/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.0547, Acc%=95.60]
Epoch 1: 17%|█▋ | 319/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.0547, Acc%=95.60]
Epoch 1: 17%|█▋ | 319/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.0921, Acc%=95.60]
Epoch 1: 17%|█▋ | 320/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.0921, Acc%=95.60]
Epoch 1: 17%|█▋ | 320/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.136, Acc%=95.60]
Epoch 1: 17%|█▋ | 321/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.136, Acc%=95.60]
Epoch 1: 17%|█▋ | 321/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.600, Acc%=95.60]
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Epoch 1: 17%|█▋ | 322/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.249, Acc%=95.60]
Epoch 1: 17%|█▋ | 323/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.249, Acc%=95.60]
Epoch 1: 17%|█▋ | 323/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.261, Acc%=95.60]
Epoch 1: 17%|█▋ | 324/1875 [00:04<00:19, 78.50it/s, v_num=5, train_loss=0.261, Acc%=95.60]
Epoch 1: 17%|█▋ | 324/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.112, Acc%=95.60]
Epoch 1: 17%|█▋ | 325/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.112, Acc%=95.60]
Epoch 1: 17%|█▋ | 325/1875 [00:04<00:19, 78.49it/s, v_num=5, train_loss=0.132, Acc%=95.60]
Epoch 1: 17%|█▋ | 326/1875 [00:04<00:19, 78.48it/s, v_num=5, train_loss=0.132, Acc%=95.60]
Epoch 1: 17%|█▋ | 326/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 17%|█▋ | 327/1875 [00:04<00:19, 78.48it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 17%|█▋ | 327/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.0721, Acc%=95.60]
Epoch 1: 17%|█▋ | 328/1875 [00:04<00:19, 78.48it/s, v_num=5, train_loss=0.0721, Acc%=95.60]
Epoch 1: 17%|█▋ | 328/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.187, Acc%=95.60]
Epoch 1: 18%|█▊ | 329/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.187, Acc%=95.60]
Epoch 1: 18%|█▊ | 329/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.101, Acc%=95.60]
Epoch 1: 18%|█▊ | 330/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.101, Acc%=95.60]
Epoch 1: 18%|█▊ | 330/1875 [00:04<00:19, 78.46it/s, v_num=5, train_loss=0.093, Acc%=95.60]
Epoch 1: 18%|█▊ | 331/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.093, Acc%=95.60]
Epoch 1: 18%|█▊ | 331/1875 [00:04<00:19, 78.46it/s, v_num=5, train_loss=0.113, Acc%=95.60]
Epoch 1: 18%|█▊ | 332/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.113, Acc%=95.60]
Epoch 1: 18%|█▊ | 332/1875 [00:04<00:19, 78.46it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 18%|█▊ | 333/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 18%|█▊ | 333/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 18%|█▊ | 334/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 18%|█▊ | 334/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 18%|█▊ | 335/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 18%|█▊ | 335/1875 [00:04<00:19, 78.46it/s, v_num=5, train_loss=0.126, Acc%=95.60]
Epoch 1: 18%|█▊ | 336/1875 [00:04<00:19, 78.47it/s, v_num=5, train_loss=0.126, Acc%=95.60]
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Epoch 1: 57%|█████▋ | 1076/1875 [00:13<00:10, 78.61it/s, v_num=5, train_loss=0.0361, Acc%=95.60]
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Epoch 1: 58%|█████▊ | 1079/1875 [00:13<00:10, 78.61it/s, v_num=5, train_loss=0.0308, Acc%=95.60]
Epoch 1: 58%|█████▊ | 1080/1875 [00:13<00:10, 78.61it/s, v_num=5, train_loss=0.0308, Acc%=95.60]
Epoch 1: 58%|█████▊ | 1080/1875 [00:13<00:10, 78.61it/s, v_num=5, train_loss=0.0832, Acc%=95.60]
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Epoch 1: 58%|█████▊ | 1081/1875 [00:13<00:10, 78.61it/s, v_num=5, train_loss=0.197, Acc%=95.60]
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Epoch 1: 58%|█████▊ | 1091/1875 [00:13<00:09, 78.61it/s, v_num=5, train_loss=0.108, Acc%=95.60]
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Epoch 1: 59%|█████▉ | 1102/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.161, Acc%=95.60]
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Epoch 1: 59%|█████▉ | 1111/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.211, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1112/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.211, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1112/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0474, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1113/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0474, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1113/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0275, Acc%=95.60]
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Epoch 1: 59%|█████▉ | 1114/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0423, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1115/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0423, Acc%=95.60]
Epoch 1: 59%|█████▉ | 1115/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.00878, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1116/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.188, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1117/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.109, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1118/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.205, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1119/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0579, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1120/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0631, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1121/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.0635, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1122/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.214, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1123/1875 [00:14<00:09, 78.61it/s, v_num=5, train_loss=0.121, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1184/1875 [00:15<00:08, 78.60it/s, v_num=5, train_loss=0.177, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1186/1875 [00:15<00:08, 78.60it/s, v_num=5, train_loss=0.124, Acc%=95.60]
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Epoch 1: 69%|██████▉ | 1294/1875 [00:16<00:07, 78.60it/s, v_num=5, train_loss=0.123, Acc%=95.60]
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Epoch 1: 69%|██████▉ | 1297/1875 [00:16<00:07, 78.60it/s, v_num=5, train_loss=0.0359, Acc%=95.60]
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Epoch 1: 69%|██████▉ | 1299/1875 [00:16<00:07, 78.60it/s, v_num=5, train_loss=0.553, Acc%=95.60]
Epoch 1: 69%|██████▉ | 1300/1875 [00:16<00:07, 78.60it/s, v_num=5, train_loss=0.553, Acc%=95.60]
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Epoch 1: 69%|██████▉ | 1302/1875 [00:16<00:07, 78.60it/s, v_num=5, train_loss=0.237, Acc%=95.60]
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Epoch 1: 71%|███████ | 1325/1875 [00:16<00:07, 78.57it/s, v_num=5, train_loss=0.178, Acc%=95.60]
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Epoch 1: 71%|███████ | 1326/1875 [00:16<00:06, 78.56it/s, v_num=5, train_loss=0.0405, Acc%=95.60]
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Epoch 1: 71%|███████ | 1327/1875 [00:16<00:06, 78.56it/s, v_num=5, train_loss=0.0858, Acc%=95.60]
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Epoch 1: 71%|███████ | 1328/1875 [00:16<00:06, 78.56it/s, v_num=5, train_loss=0.0623, Acc%=95.60]
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Epoch 1: 71%|███████ | 1329/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.0642, Acc%=95.60]
Epoch 1: 71%|███████ | 1330/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.0642, Acc%=95.60]
Epoch 1: 71%|███████ | 1330/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.0735, Acc%=95.60]
Epoch 1: 71%|███████ | 1331/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.0735, Acc%=95.60]
Epoch 1: 71%|███████ | 1331/1875 [00:16<00:06, 78.56it/s, v_num=5, train_loss=0.162, Acc%=95.60]
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Epoch 1: 71%|███████ | 1332/1875 [00:16<00:06, 78.56it/s, v_num=5, train_loss=0.290, Acc%=95.60]
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Epoch 1: 71%|███████ | 1333/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 71%|███████ | 1334/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 71%|███████ | 1334/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.207, Acc%=95.60]
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Epoch 1: 71%|███████ | 1335/1875 [00:16<00:06, 78.57it/s, v_num=5, train_loss=0.0585, Acc%=95.60]
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Epoch 1: 71%|███████▏ | 1336/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0407, Acc%=95.60]
Epoch 1: 71%|███████▏ | 1337/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0407, Acc%=95.60]
Epoch 1: 71%|███████▏ | 1337/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.200, Acc%=95.60]
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Epoch 1: 71%|███████▏ | 1338/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.116, Acc%=95.60]
Epoch 1: 71%|███████▏ | 1339/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.116, Acc%=95.60]
Epoch 1: 71%|███████▏ | 1339/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.504, Acc%=95.60]
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Epoch 1: 71%|███████▏ | 1340/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.091, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1363/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.139, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1364/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0855, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1365/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0455, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1366/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.637, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1367/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.637, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1367/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0575, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1368/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0575, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1368/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0347, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1369/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0347, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1369/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0505, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1370/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0505, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1370/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1371/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1371/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.299, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1372/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.299, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1372/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.425, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1373/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0839, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1374/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.184, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1375/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.223, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1376/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1377/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1377/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.149, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1378/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.149, Acc%=95.60]
Epoch 1: 73%|███████▎ | 1378/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.225, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1379/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.225, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1379/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1380/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1380/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0798, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1381/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0798, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1381/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0722, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1382/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0722, Acc%=95.60]
Epoch 1: 74%|███████▎ | 1382/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.282, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1383/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0182, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1384/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.048, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1385/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.00974, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1386/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0982, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1387/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0982, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1387/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.148, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1388/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.148, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1388/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0273, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1389/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0273, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1389/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.023, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1390/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.023, Acc%=95.60]
Epoch 1: 74%|███████▍ | 1390/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.0644, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1391/1875 [00:17<00:06, 78.57it/s, v_num=5, train_loss=0.185, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1392/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.111, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1393/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.069, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1394/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.217, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1395/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.227, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1396/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0956, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1397/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0956, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1397/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.250, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1398/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.250, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1398/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1399/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1399/1875 [00:17<00:06, 78.58it/s, v_num=5, train_loss=0.0666, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1400/1875 [00:17<00:06, 78.56it/s, v_num=5, train_loss=0.0666, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1400/1875 [00:17<00:06, 78.56it/s, v_num=5, train_loss=0.0722, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1401/1875 [00:17<00:06, 78.55it/s, v_num=5, train_loss=0.0722, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1401/1875 [00:17<00:06, 78.55it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1402/1875 [00:17<00:06, 78.55it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1402/1875 [00:17<00:06, 78.55it/s, v_num=5, train_loss=0.111, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1403/1875 [00:17<00:06, 78.55it/s, v_num=5, train_loss=0.125, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1404/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.229, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1405/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.344, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1406/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.242, Acc%=95.60]
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Epoch 1: 75%|███████▌ | 1407/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.376, Acc%=95.60]
Epoch 1: 75%|███████▌ | 1408/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.376, Acc%=95.60]
Epoch 1: 75%|███████▌ | 1408/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.205, Acc%=95.60]
Epoch 1: 75%|███████▌ | 1409/1875 [00:17<00:05, 78.56it/s, v_num=5, train_loss=0.205, Acc%=95.60]
Epoch 1: 75%|███████▌ | 1409/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.0743, Acc%=95.60]
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Epoch 1: 75%|███████▌ | 1410/1875 [00:17<00:05, 78.55it/s, v_num=5, train_loss=0.166, Acc%=95.60]
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Epoch 1: 76%|███████▌ | 1421/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.331, Acc%=95.60]
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Epoch 1: 76%|███████▋ | 1433/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.167, Acc%=95.60]
Epoch 1: 76%|███████▋ | 1434/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.167, Acc%=95.60]
Epoch 1: 76%|███████▋ | 1434/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.332, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1435/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.332, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1435/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.140, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1436/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.140, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1436/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.218, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1437/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0952, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1438/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0944, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1439/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.229, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1440/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.108, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1441/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.108, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1441/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0685, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1442/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.256, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1443/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0818, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1444/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0818, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1444/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.00677, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1445/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.102, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1446/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0816, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1447/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0476, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1448/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0476, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1448/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0597, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1449/1875 [00:18<00:05, 78.56it/s, v_num=5, train_loss=0.0154, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1450/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0154, Acc%=95.60]
Epoch 1: 77%|███████▋ | 1450/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0948, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1451/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.00631, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1452/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.114, Acc%=95.60]
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Epoch 1: 77%|███████▋ | 1453/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.125, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1454/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.125, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1454/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.107, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1455/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.107, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1455/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1456/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1456/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0842, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1457/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0842, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1457/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.195, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1458/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.195, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1458/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0612, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1459/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0612, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1459/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.163, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1460/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.163, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1460/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.120, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1461/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.120, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1461/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.255, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1462/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.255, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1462/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1463/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1463/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0546, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1464/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0546, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1464/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0572, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1465/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0572, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1465/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0966, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1466/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0966, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1466/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0746, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1467/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0746, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1467/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.258, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1468/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.258, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1468/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1469/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.103, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1469/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.410, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1470/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.410, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1470/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.490, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1471/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.490, Acc%=95.60]
Epoch 1: 78%|███████▊ | 1471/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0503, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1472/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0503, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1472/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1473/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1473/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.047, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1474/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.047, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1474/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.100, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1475/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.100, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1475/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0375, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1476/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0375, Acc%=95.60]
Epoch 1: 79%|███████▊ | 1476/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0111, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1477/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0111, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1477/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.631, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1478/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.631, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1478/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.182, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1479/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.182, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1479/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.075, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1480/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.075, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1480/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.0501, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1482/1875 [00:18<00:05, 78.57it/s, v_num=5, train_loss=0.101, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1483/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1484/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 79%|███████▉ | 1484/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.0306, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1486/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.048, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1487/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.370, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1488/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.193, Acc%=95.60]
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Epoch 1: 79%|███████▉ | 1489/1875 [00:18<00:04, 78.57it/s, v_num=5, train_loss=0.272, Acc%=95.60]
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Epoch 1: 80%|████████ | 1500/1875 [00:19<00:04, 78.58it/s, v_num=5, train_loss=0.110, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1577/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.102, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1582/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.152, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1583/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.134, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1584/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.348, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1585/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.295, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1589/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.114, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1591/1875 [00:20<00:03, 78.57it/s, v_num=5, train_loss=0.224, Acc%=95.60]
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Epoch 1: 85%|████████▌ | 1600/1875 [00:20<00:03, 78.58it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 85%|████████▌ | 1600/1875 [00:20<00:03, 78.58it/s, v_num=5, train_loss=0.129, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1610/1875 [00:20<00:03, 78.58it/s, v_num=5, train_loss=0.312, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1645/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.298, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1646/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.086, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1647/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.086, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1647/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1648/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1648/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.0759, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1649/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.0759, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1649/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.0239, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1650/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.0239, Acc%=95.60]
Epoch 1: 88%|████████▊ | 1650/1875 [00:20<00:02, 78.58it/s, v_num=5, train_loss=0.159, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1651/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.128, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1652/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.146, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1653/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0378, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1654/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.222, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1655/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0709, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1656/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0656, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1658/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0797, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1659/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0481, Acc%=95.60]
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Epoch 1: 89%|████████▊ | 1660/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0789, Acc%=95.60]
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Epoch 1: 89%|████████▊ | 1661/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0361, Acc%=95.60]
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Epoch 1: 89%|████████▊ | 1662/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0715, Acc%=95.60]
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Epoch 1: 89%|████████▉ | 1666/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.245, Acc%=95.60]
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Epoch 1: 89%|████████▉ | 1668/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.115, Acc%=95.60]
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Epoch 1: 89%|████████▉ | 1669/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.209, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1670/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.209, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1670/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.342, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1671/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.342, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1671/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.134, Acc%=95.60]
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Epoch 1: 89%|████████▉ | 1672/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.117, Acc%=95.60]
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Epoch 1: 89%|████████▉ | 1674/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1675/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 89%|████████▉ | 1675/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0696, Acc%=95.60]
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Epoch 1: 90%|████████▉ | 1679/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.157, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1680/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.157, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1680/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.148, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1681/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.148, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1681/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.0984, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1682/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.0984, Acc%=95.60]
Epoch 1: 90%|████████▉ | 1682/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.0691, Acc%=95.60]
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Epoch 1: 90%|████████▉ | 1684/1875 [00:21<00:02, 78.58it/s, v_num=5, train_loss=0.0411, Acc%=95.60]
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Epoch 1: 90%|████████▉ | 1686/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.202, Acc%=95.60]
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Epoch 1: 90%|█████████ | 1689/1875 [00:21<00:02, 78.57it/s, v_num=5, train_loss=0.0673, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1800/1875 [00:22<00:00, 78.59it/s, v_num=5, train_loss=0.0991, Acc%=95.60]
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Evaluate the trained model on the test set - pay attention to the cls/Acc metric
perf = trainer.test(routine, dataloaders=[test_dl, ood_dl])
/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 ┃ Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Acc │ 97.14% │
│ Brier │ 0.04530 │
│ Entropy │ 0.09925 │
│ NLL │ 0.09610 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Calibration ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ECE │ 0.00486 │
│ aECE │ 0.00494 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ OOD Detection ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUPR │ 91.98% │
│ AUROC │ 93.23% │
│ Entropy │ 0.94508 │
│ FPR95 │ 25.75% │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Selective Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUGRC │ 0.20% │
│ AURC │ 0.23% │
│ Cov@5Risk │ 100.00% │
│ Risk@80Cov │ 0.28% │
└──────────────┴───────────────────────────┘
This table provides a lot of information:
OOD Detection: Binary Classification MNIST vs. FashionMNIST - AUPR/AUROC/FPR95: Measures the quality of the OOD detection. The higher the better for AUPR and AUROC, the lower the better for FPR95.
Calibration: Reliability of the Predictions - ECE: Expected Calibration Error. The lower the better. - aECE: Adaptive Expected Calibration Error. The lower the better. (~More precise version of the ECE)
Classification Performance - Accuracy: The ratio of correctly classified images. The higher the better. - Brier: The quality of the predicted probabilities (Mean Squared Error of the predictions vs. ground-truth). The lower the better. - Negative Log-Likelihood: The value of the loss on the test set. The lower the better.
Selective Classification & Grouping Loss - We talk about these points later in the “To go further” section.
3. Training an ensemble of models with TorchUncertainty¶
You have two options here, you can either train the ensemble directly if you have enough memory, otherwise, you can train independent models and do the ensembling during the evaluation (sometimes called inference).
In this case, we will do it sequentially. In this tutorial, you have the choice between training multiple models, which will take time if you have no GPU, or downloading the pre-trained models that we have prepared for you.
Training the ensemble
To train the ensemble, you will have to use the “deep_ensembles” function from TorchUncertainty, which will replicate and change the initialization of your networks to ensure diversity.
from torch_uncertainty.models import deep_ensembles
from torch_uncertainty.transforms import RepeatTarget
# Create the ensemble model
ensemble = deep_ensembles(
LeNet(in_channels=1, num_classes=10),
num_estimators=2,
task="classification",
reset_model_parameters=True,
)
trainer = TUTrainer(accelerator="cpu", max_epochs=1)
ens_routine = ClassificationRoutine(
is_ensemble=True,
num_classes=10,
model=ensemble,
loss=nn.CrossEntropyLoss(), # The loss for the training
format_batch_fn=RepeatTarget(
2
), # How to handle the targets when comparing the predictions
optim_recipe=optim_recipe(
ensemble, 2.0
), # The optimization scheme with the optimizer and the scheduler as a dictionnary
eval_ood=True, # We want to evaluate the OOD-related metrics
)
trainer.fit(ens_routine, train_dataloaders=train_dl, val_dataloaders=test_dl)
ens_perf = trainer.test(ens_routine, dataloaders=[test_dl, ood_dl])
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Epoch 0: 4%|▍ | 81/1875 [00:01<00:29, 61.56it/s, v_num=6, train_loss=2.190]
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