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 0x7F7F31C00130>
print("Out of distribution data:")
ood_viz_data[0][0]
Out of distribution data:
<PIL.Image.Image image mode=L size=28x28 at 0x7F7F31C01C00>
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%|▎ | 49/1875 [00:00<00:24, 73.34it/s, v_num=5, train_loss=2.300]
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Epoch 0: 3%|▎ | 52/1875 [00:00<00:24, 73.32it/s, v_num=5, train_loss=2.290]
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Epoch 0: 3%|▎ | 59/1875 [00:00<00:24, 73.48it/s, v_num=5, train_loss=2.300]
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Epoch 0: 4%|▍ | 71/1875 [00:00<00:24, 73.73it/s, v_num=5, train_loss=2.280]
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Epoch 0: 4%|▍ | 75/1875 [00:01<00:24, 73.66it/s, v_num=5, train_loss=2.290]
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Epoch 0: 4%|▍ | 76/1875 [00:01<00:24, 73.68it/s, v_num=5, train_loss=2.290]
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Epoch 0: 4%|▍ | 78/1875 [00:01<00:24, 73.56it/s, v_num=5, train_loss=2.270]
Epoch 0: 4%|▍ | 78/1875 [00:01<00:24, 73.54it/s, v_num=5, train_loss=2.310]
Epoch 0: 4%|▍ | 79/1875 [00:01<00:24, 73.53it/s, v_num=5, train_loss=2.310]
Epoch 0: 4%|▍ | 79/1875 [00:01<00:24, 73.51it/s, v_num=5, train_loss=2.310]
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Epoch 0: 5%|▍ | 87/1875 [00:01<00:24, 73.58it/s, v_num=5, train_loss=2.290]
Epoch 0: 5%|▍ | 87/1875 [00:01<00:24, 73.56it/s, v_num=5, train_loss=2.290]
Epoch 0: 5%|▍ | 88/1875 [00:01<00:24, 73.59it/s, v_num=5, train_loss=2.290]
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Epoch 0: 5%|▍ | 89/1875 [00:01<00:24, 73.61it/s, v_num=5, train_loss=2.280]
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Epoch 0: 5%|▍ | 90/1875 [00:01<00:24, 73.62it/s, v_num=5, train_loss=2.280]
Epoch 0: 5%|▍ | 90/1875 [00:01<00:24, 73.60it/s, v_num=5, train_loss=2.300]
Epoch 0: 5%|▍ | 91/1875 [00:01<00:24, 73.63it/s, v_num=5, train_loss=2.300]
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Epoch 0: 5%|▍ | 93/1875 [00:01<00:24, 73.64it/s, v_num=5, train_loss=2.290]
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Epoch 0: 5%|▌ | 94/1875 [00:01<00:24, 73.66it/s, v_num=5, train_loss=2.300]
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Epoch 0: 5%|▌ | 95/1875 [00:01<00:24, 73.45it/s, v_num=5, train_loss=2.270]
Epoch 0: 5%|▌ | 95/1875 [00:01<00:24, 73.43it/s, v_num=5, train_loss=2.290]
Epoch 0: 5%|▌ | 96/1875 [00:01<00:24, 73.36it/s, v_num=5, train_loss=2.290]
Epoch 0: 5%|▌ | 96/1875 [00:01<00:24, 73.34it/s, v_num=5, train_loss=2.300]
Epoch 0: 5%|▌ | 97/1875 [00:01<00:24, 73.37it/s, v_num=5, train_loss=2.300]
Epoch 0: 5%|▌ | 97/1875 [00:01<00:24, 73.35it/s, v_num=5, train_loss=2.280]
Epoch 0: 5%|▌ | 98/1875 [00:01<00:24, 73.38it/s, v_num=5, train_loss=2.280]
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Epoch 0: 5%|▌ | 99/1875 [00:01<00:24, 73.39it/s, v_num=5, train_loss=2.280]
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Epoch 0: 5%|▌ | 100/1875 [00:01<00:24, 73.41it/s, v_num=5, train_loss=2.260]
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Epoch 0: 5%|▌ | 102/1875 [00:01<00:24, 73.41it/s, v_num=5, train_loss=2.300]
Epoch 0: 5%|▌ | 102/1875 [00:01<00:24, 73.40it/s, v_num=5, train_loss=2.280]
Epoch 0: 5%|▌ | 103/1875 [00:01<00:24, 73.44it/s, v_num=5, train_loss=2.280]
Epoch 0: 5%|▌ | 103/1875 [00:01<00:24, 73.42it/s, v_num=5, train_loss=2.270]
Epoch 0: 6%|▌ | 104/1875 [00:01<00:24, 73.46it/s, v_num=5, train_loss=2.270]
Epoch 0: 6%|▌ | 104/1875 [00:01<00:24, 73.45it/s, v_num=5, train_loss=2.270]
Epoch 0: 6%|▌ | 105/1875 [00:01<00:24, 73.49it/s, v_num=5, train_loss=2.270]
Epoch 0: 6%|▌ | 105/1875 [00:01<00:24, 73.47it/s, v_num=5, train_loss=2.250]
Epoch 0: 6%|▌ | 106/1875 [00:01<00:24, 73.51it/s, v_num=5, train_loss=2.250]
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Epoch 0: 6%|▌ | 107/1875 [00:01<00:24, 73.48it/s, v_num=5, train_loss=2.290]
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Epoch 0: 6%|▌ | 108/1875 [00:01<00:24, 73.46it/s, v_num=5, train_loss=2.290]
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Epoch 0: 6%|▌ | 112/1875 [00:01<00:23, 73.46it/s, v_num=5, train_loss=2.280]
Epoch 0: 6%|▌ | 113/1875 [00:01<00:23, 73.49it/s, v_num=5, train_loss=2.280]
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Epoch 0: 6%|▋ | 118/1875 [00:01<00:23, 73.51it/s, v_num=5, train_loss=2.270]
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Epoch 0: 6%|▋ | 119/1875 [00:01<00:23, 73.53it/s, v_num=5, train_loss=2.260]
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Epoch 0: 6%|▋ | 120/1875 [00:01<00:23, 73.55it/s, v_num=5, train_loss=2.270]
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Epoch 0: 7%|▋ | 128/1875 [00:01<00:23, 73.66it/s, v_num=5, train_loss=2.230]
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Epoch 0: 7%|▋ | 131/1875 [00:01<00:23, 73.71it/s, v_num=5, train_loss=2.240]
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Epoch 1: 2%|▏ | 31/1875 [00:00<00:24, 74.56it/s, v_num=5, train_loss=0.209, Acc%=95.60]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:24, 74.62it/s, v_num=5, train_loss=0.209, Acc%=95.60]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:24, 74.57it/s, v_num=5, train_loss=0.254, Acc%=95.60]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:24, 74.61it/s, v_num=5, train_loss=0.254, Acc%=95.60]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:24, 74.57it/s, v_num=5, train_loss=0.217, Acc%=95.60]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:24, 74.63it/s, v_num=5, train_loss=0.217, Acc%=95.60]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:24, 74.58it/s, v_num=5, train_loss=0.218, Acc%=95.60]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:24, 74.62it/s, v_num=5, train_loss=0.218, Acc%=95.60]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:24, 74.58it/s, v_num=5, train_loss=0.118, Acc%=95.60]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:24, 74.63it/s, v_num=5, train_loss=0.118, Acc%=95.60]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:24, 74.59it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:24, 74.64it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:24, 74.59it/s, v_num=5, train_loss=0.245, Acc%=95.60]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:24, 74.57it/s, v_num=5, train_loss=0.245, Acc%=95.60]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:24, 74.52it/s, v_num=5, train_loss=0.559, Acc%=95.60]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:24, 74.53it/s, v_num=5, train_loss=0.559, Acc%=95.60]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:24, 74.49it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:24, 74.51it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:24, 74.47it/s, v_num=5, train_loss=0.125, Acc%=95.60]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:24, 74.49it/s, v_num=5, train_loss=0.125, Acc%=95.60]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:24, 74.45it/s, v_num=5, train_loss=0.0698, Acc%=95.60]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:24, 74.47it/s, v_num=5, train_loss=0.0698, Acc%=95.60]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:24, 74.43it/s, v_num=5, train_loss=0.218, Acc%=95.60]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:24, 74.43it/s, v_num=5, train_loss=0.218, Acc%=95.60]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:24, 74.39it/s, v_num=5, train_loss=0.0712, Acc%=95.60]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:24, 74.41it/s, v_num=5, train_loss=0.0712, Acc%=95.60]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:24, 74.37it/s, v_num=5, train_loss=0.287, Acc%=95.60]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:24, 74.38it/s, v_num=5, train_loss=0.287, Acc%=95.60]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:24, 74.35it/s, v_num=5, train_loss=0.187, Acc%=95.60]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:24, 74.41it/s, v_num=5, train_loss=0.187, Acc%=95.60]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:24, 74.37it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:24, 74.41it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:24, 74.37it/s, v_num=5, train_loss=0.147, Acc%=95.60]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:24, 74.14it/s, v_num=5, train_loss=0.147, Acc%=95.60]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:24, 74.11it/s, v_num=5, train_loss=0.476, Acc%=95.60]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:24, 73.95it/s, v_num=5, train_loss=0.476, Acc%=95.60]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:24, 73.91it/s, v_num=5, train_loss=0.346, Acc%=95.60]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:24, 73.84it/s, v_num=5, train_loss=0.346, Acc%=95.60]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:24, 73.80it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:24, 73.82it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:24, 73.78it/s, v_num=5, train_loss=0.0964, Acc%=95.60]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:24, 73.82it/s, v_num=5, train_loss=0.0964, Acc%=95.60]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:24, 73.79it/s, v_num=5, train_loss=0.253, Acc%=95.60]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:24, 73.82it/s, v_num=5, train_loss=0.253, Acc%=95.60]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:24, 73.79it/s, v_num=5, train_loss=0.145, Acc%=95.60]
Epoch 1: 3%|▎ | 54/1875 [00:00<00:24, 73.83it/s, v_num=5, train_loss=0.145, Acc%=95.60]
Epoch 1: 3%|▎ | 54/1875 [00:00<00:24, 73.80it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 3%|▎ | 55/1875 [00:00<00:24, 73.84it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 3%|▎ | 55/1875 [00:00<00:24, 73.81it/s, v_num=5, train_loss=0.242, Acc%=95.60]
Epoch 1: 3%|▎ | 56/1875 [00:00<00:24, 73.82it/s, v_num=5, train_loss=0.242, Acc%=95.60]
Epoch 1: 3%|▎ | 56/1875 [00:00<00:24, 73.78it/s, v_num=5, train_loss=0.193, Acc%=95.60]
Epoch 1: 3%|▎ | 57/1875 [00:00<00:24, 73.82it/s, v_num=5, train_loss=0.193, Acc%=95.60]
Epoch 1: 3%|▎ | 57/1875 [00:00<00:24, 73.79it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 3%|▎ | 58/1875 [00:00<00:24, 73.79it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 3%|▎ | 58/1875 [00:00<00:24, 73.76it/s, v_num=5, train_loss=0.408, Acc%=95.60]
Epoch 1: 3%|▎ | 59/1875 [00:00<00:24, 73.74it/s, v_num=5, train_loss=0.408, Acc%=95.60]
Epoch 1: 3%|▎ | 59/1875 [00:00<00:24, 73.71it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 3%|▎ | 60/1875 [00:00<00:24, 73.68it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 3%|▎ | 60/1875 [00:00<00:24, 73.65it/s, v_num=5, train_loss=0.196, Acc%=95.60]
Epoch 1: 3%|▎ | 61/1875 [00:00<00:24, 73.65it/s, v_num=5, train_loss=0.196, Acc%=95.60]
Epoch 1: 3%|▎ | 61/1875 [00:00<00:24, 73.63it/s, v_num=5, train_loss=0.0461, Acc%=95.60]
Epoch 1: 3%|▎ | 62/1875 [00:00<00:24, 73.64it/s, v_num=5, train_loss=0.0461, Acc%=95.60]
Epoch 1: 3%|▎ | 62/1875 [00:00<00:24, 73.61it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 3%|▎ | 63/1875 [00:00<00:24, 73.61it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 3%|▎ | 63/1875 [00:00<00:24, 73.58it/s, v_num=5, train_loss=0.455, Acc%=95.60]
Epoch 1: 3%|▎ | 64/1875 [00:00<00:24, 73.59it/s, v_num=5, train_loss=0.455, Acc%=95.60]
Epoch 1: 3%|▎ | 64/1875 [00:00<00:24, 73.56it/s, v_num=5, train_loss=0.348, Acc%=95.60]
Epoch 1: 3%|▎ | 65/1875 [00:00<00:24, 73.59it/s, v_num=5, train_loss=0.348, Acc%=95.60]
Epoch 1: 3%|▎ | 65/1875 [00:00<00:24, 73.56it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 4%|▎ | 66/1875 [00:00<00:24, 73.53it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 4%|▎ | 66/1875 [00:00<00:24, 73.50it/s, v_num=5, train_loss=0.131, Acc%=95.60]
Epoch 1: 4%|▎ | 67/1875 [00:00<00:24, 73.26it/s, v_num=5, train_loss=0.131, Acc%=95.60]
Epoch 1: 4%|▎ | 67/1875 [00:00<00:24, 73.23it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 4%|▎ | 68/1875 [00:00<00:24, 73.15it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 4%|▎ | 68/1875 [00:00<00:24, 73.13it/s, v_num=5, train_loss=0.0546, Acc%=95.60]
Epoch 1: 4%|▎ | 69/1875 [00:00<00:24, 73.16it/s, v_num=5, train_loss=0.0546, Acc%=95.60]
Epoch 1: 4%|▎ | 69/1875 [00:00<00:24, 73.14it/s, v_num=5, train_loss=0.204, Acc%=95.60]
Epoch 1: 4%|▎ | 70/1875 [00:00<00:24, 73.18it/s, v_num=5, train_loss=0.204, Acc%=95.60]
Epoch 1: 4%|▎ | 70/1875 [00:00<00:24, 73.16it/s, v_num=5, train_loss=0.0666, Acc%=95.60]
Epoch 1: 4%|▍ | 71/1875 [00:00<00:24, 73.17it/s, v_num=5, train_loss=0.0666, Acc%=95.60]
Epoch 1: 4%|▍ | 71/1875 [00:00<00:24, 73.15it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 4%|▍ | 72/1875 [00:00<00:24, 73.16it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 4%|▍ | 72/1875 [00:00<00:24, 73.14it/s, v_num=5, train_loss=0.0718, Acc%=95.60]
Epoch 1: 4%|▍ | 73/1875 [00:00<00:24, 73.15it/s, v_num=5, train_loss=0.0718, Acc%=95.60]
Epoch 1: 4%|▍ | 73/1875 [00:00<00:24, 73.13it/s, v_num=5, train_loss=0.347, Acc%=95.60]
Epoch 1: 4%|▍ | 74/1875 [00:01<00:24, 73.14it/s, v_num=5, train_loss=0.347, Acc%=95.60]
Epoch 1: 4%|▍ | 74/1875 [00:01<00:24, 73.12it/s, v_num=5, train_loss=0.225, Acc%=95.60]
Epoch 1: 4%|▍ | 75/1875 [00:01<00:24, 73.16it/s, v_num=5, train_loss=0.225, Acc%=95.60]
Epoch 1: 4%|▍ | 75/1875 [00:01<00:24, 73.13it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 4%|▍ | 76/1875 [00:01<00:24, 73.12it/s, v_num=5, train_loss=0.191, Acc%=95.60]
Epoch 1: 4%|▍ | 76/1875 [00:01<00:24, 73.10it/s, v_num=5, train_loss=0.233, Acc%=95.60]
Epoch 1: 4%|▍ | 77/1875 [00:01<00:24, 73.10it/s, v_num=5, train_loss=0.233, Acc%=95.60]
Epoch 1: 4%|▍ | 77/1875 [00:01<00:24, 73.08it/s, v_num=5, train_loss=0.0204, Acc%=95.60]
Epoch 1: 4%|▍ | 78/1875 [00:01<00:24, 73.10it/s, v_num=5, train_loss=0.0204, Acc%=95.60]
Epoch 1: 4%|▍ | 78/1875 [00:01<00:24, 73.07it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:24, 73.08it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:24, 73.05it/s, v_num=5, train_loss=0.0887, Acc%=95.60]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:24, 73.07it/s, v_num=5, train_loss=0.0887, Acc%=95.60]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:24, 73.05it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:24, 73.04it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:24, 73.02it/s, v_num=5, train_loss=0.352, Acc%=95.60]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:24, 73.02it/s, v_num=5, train_loss=0.352, Acc%=95.60]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:24, 73.00it/s, v_num=5, train_loss=0.0697, Acc%=95.60]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:24, 73.01it/s, v_num=5, train_loss=0.0697, Acc%=95.60]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:24, 72.99it/s, v_num=5, train_loss=0.149, Acc%=95.60]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:24, 73.00it/s, v_num=5, train_loss=0.149, Acc%=95.60]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:24, 72.98it/s, v_num=5, train_loss=0.545, Acc%=95.60]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:24, 72.99it/s, v_num=5, train_loss=0.545, Acc%=95.60]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:24, 72.96it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:24, 72.96it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:24, 72.94it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:24, 72.94it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:24, 72.92it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:24, 72.93it/s, v_num=5, train_loss=0.230, Acc%=95.60]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:24, 72.91it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:24, 72.91it/s, v_num=5, train_loss=0.159, Acc%=95.60]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:24, 72.89it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:24, 72.86it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:24, 72.84it/s, v_num=5, train_loss=0.409, Acc%=95.60]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:24, 72.83it/s, v_num=5, train_loss=0.409, Acc%=95.60]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:24, 72.81it/s, v_num=5, train_loss=0.247, Acc%=95.60]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:24, 72.81it/s, v_num=5, train_loss=0.247, Acc%=95.60]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:24, 72.79it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:24, 72.81it/s, v_num=5, train_loss=0.263, Acc%=95.60]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:24, 72.79it/s, v_num=5, train_loss=0.068, Acc%=95.60]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:24, 72.79it/s, v_num=5, train_loss=0.068, Acc%=95.60]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:24, 72.78it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:24, 72.79it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.155, Acc%=95.60]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:24, 72.79it/s, v_num=5, train_loss=0.155, Acc%=95.60]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.135, Acc%=95.60]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:24, 72.78it/s, v_num=5, train_loss=0.135, Acc%=95.60]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.387, Acc%=95.60]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.387, Acc%=95.60]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.305, Acc%=95.60]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.305, Acc%=95.60]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:24, 72.73it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:24, 72.74it/s, v_num=5, train_loss=0.0765, Acc%=95.60]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:24, 72.76it/s, v_num=5, train_loss=0.0765, Acc%=95.60]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:24, 72.74it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:24, 72.76it/s, v_num=5, train_loss=0.194, Acc%=95.60]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.359, Acc%=95.60]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.359, Acc%=95.60]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.161, Acc%=95.60]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:24, 72.77it/s, v_num=5, train_loss=0.161, Acc%=95.60]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.0538, Acc%=95.60]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:24, 72.74it/s, v_num=5, train_loss=0.0538, Acc%=95.60]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:24, 72.72it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:24, 72.73it/s, v_num=5, train_loss=0.118, Acc%=95.60]
Epoch 1: 6%|▌ | 107/1875 [00:01<00:24, 72.68it/s, v_num=5, train_loss=0.118, Acc%=95.60]
Epoch 1: 6%|▌ | 107/1875 [00:01<00:24, 72.66it/s, v_num=5, train_loss=0.0653, Acc%=95.60]
Epoch 1: 6%|▌ | 108/1875 [00:01<00:24, 72.65it/s, v_num=5, train_loss=0.0653, Acc%=95.60]
Epoch 1: 6%|▌ | 108/1875 [00:01<00:24, 72.63it/s, v_num=5, train_loss=0.257, Acc%=95.60]
Epoch 1: 6%|▌ | 109/1875 [00:01<00:24, 72.60it/s, v_num=5, train_loss=0.257, Acc%=95.60]
Epoch 1: 6%|▌ | 109/1875 [00:01<00:24, 72.58it/s, v_num=5, train_loss=0.0906, Acc%=95.60]
Epoch 1: 6%|▌ | 110/1875 [00:01<00:24, 72.56it/s, v_num=5, train_loss=0.0906, Acc%=95.60]
Epoch 1: 6%|▌ | 110/1875 [00:01<00:24, 72.55it/s, v_num=5, train_loss=0.277, Acc%=95.60]
Epoch 1: 6%|▌ | 111/1875 [00:01<00:24, 72.55it/s, v_num=5, train_loss=0.277, Acc%=95.60]
Epoch 1: 6%|▌ | 111/1875 [00:01<00:24, 72.54it/s, v_num=5, train_loss=0.210, Acc%=95.60]
Epoch 1: 6%|▌ | 112/1875 [00:01<00:24, 72.54it/s, v_num=5, train_loss=0.210, Acc%=95.60]
Epoch 1: 6%|▌ | 112/1875 [00:01<00:24, 72.53it/s, v_num=5, train_loss=0.0463, Acc%=95.60]
Epoch 1: 6%|▌ | 113/1875 [00:01<00:24, 72.54it/s, v_num=5, train_loss=0.0463, Acc%=95.60]
Epoch 1: 6%|▌ | 113/1875 [00:01<00:24, 72.53it/s, v_num=5, train_loss=0.272, Acc%=95.60]
Epoch 1: 6%|▌ | 114/1875 [00:01<00:24, 72.55it/s, v_num=5, train_loss=0.272, Acc%=95.60]
Epoch 1: 6%|▌ | 114/1875 [00:01<00:24, 72.54it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 6%|▌ | 115/1875 [00:01<00:24, 72.57it/s, v_num=5, train_loss=0.150, Acc%=95.60]
Epoch 1: 6%|▌ | 115/1875 [00:01<00:24, 72.56it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 6%|▌ | 116/1875 [00:01<00:24, 72.59it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 6%|▌ | 116/1875 [00:01<00:24, 72.58it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 6%|▌ | 117/1875 [00:01<00:24, 72.60it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 6%|▌ | 117/1875 [00:01<00:24, 72.58it/s, v_num=5, train_loss=0.134, Acc%=95.60]
Epoch 1: 6%|▋ | 118/1875 [00:01<00:24, 72.61it/s, v_num=5, train_loss=0.134, Acc%=95.60]
Epoch 1: 6%|▋ | 118/1875 [00:01<00:24, 72.60it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 6%|▋ | 119/1875 [00:01<00:24, 72.62it/s, v_num=5, train_loss=0.124, Acc%=95.60]
Epoch 1: 6%|▋ | 119/1875 [00:01<00:24, 72.61it/s, v_num=5, train_loss=0.281, Acc%=95.60]
Epoch 1: 6%|▋ | 120/1875 [00:01<00:24, 72.64it/s, v_num=5, train_loss=0.281, Acc%=95.60]
Epoch 1: 6%|▋ | 120/1875 [00:01<00:24, 72.63it/s, v_num=5, train_loss=0.181, Acc%=95.60]
Epoch 1: 6%|▋ | 121/1875 [00:01<00:24, 72.66it/s, v_num=5, train_loss=0.181, Acc%=95.60]
Epoch 1: 6%|▋ | 121/1875 [00:01<00:24, 72.65it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 7%|▋ | 122/1875 [00:01<00:24, 72.68it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 7%|▋ | 122/1875 [00:01<00:24, 72.67it/s, v_num=5, train_loss=0.287, Acc%=95.60]
Epoch 1: 7%|▋ | 123/1875 [00:01<00:24, 72.70it/s, v_num=5, train_loss=0.287, Acc%=95.60]
Epoch 1: 7%|▋ | 123/1875 [00:01<00:24, 72.69it/s, v_num=5, train_loss=0.116, Acc%=95.60]
Epoch 1: 7%|▋ | 124/1875 [00:01<00:24, 72.72it/s, v_num=5, train_loss=0.116, Acc%=95.60]
Epoch 1: 7%|▋ | 124/1875 [00:01<00:24, 72.70it/s, v_num=5, train_loss=0.0516, Acc%=95.60]
Epoch 1: 7%|▋ | 125/1875 [00:01<00:24, 72.72it/s, v_num=5, train_loss=0.0516, Acc%=95.60]
Epoch 1: 7%|▋ | 125/1875 [00:01<00:24, 72.71it/s, v_num=5, train_loss=0.198, Acc%=95.60]
Epoch 1: 7%|▋ | 126/1875 [00:01<00:24, 72.71it/s, v_num=5, train_loss=0.198, Acc%=95.60]
Epoch 1: 7%|▋ | 126/1875 [00:01<00:24, 72.70it/s, v_num=5, train_loss=0.139, Acc%=95.60]
Epoch 1: 7%|▋ | 127/1875 [00:01<00:24, 72.73it/s, v_num=5, train_loss=0.139, Acc%=95.60]
Epoch 1: 7%|▋ | 127/1875 [00:01<00:24, 72.72it/s, v_num=5, train_loss=0.0346, Acc%=95.60]
Epoch 1: 7%|▋ | 128/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.0346, Acc%=95.60]
Epoch 1: 7%|▋ | 128/1875 [00:01<00:24, 72.74it/s, v_num=5, train_loss=0.0695, Acc%=95.60]
Epoch 1: 7%|▋ | 129/1875 [00:01<00:23, 72.76it/s, v_num=5, train_loss=0.0695, Acc%=95.60]
Epoch 1: 7%|▋ | 129/1875 [00:01<00:24, 72.75it/s, v_num=5, train_loss=0.0411, Acc%=95.60]
Epoch 1: 7%|▋ | 130/1875 [00:01<00:23, 72.76it/s, v_num=5, train_loss=0.0411, Acc%=95.60]
Epoch 1: 7%|▋ | 130/1875 [00:01<00:23, 72.75it/s, v_num=5, train_loss=0.173, Acc%=95.60]
Epoch 1: 7%|▋ | 131/1875 [00:01<00:23, 72.78it/s, v_num=5, train_loss=0.173, Acc%=95.60]
Epoch 1: 7%|▋ | 131/1875 [00:01<00:23, 72.77it/s, v_num=5, train_loss=0.0374, Acc%=95.60]
Epoch 1: 7%|▋ | 132/1875 [00:01<00:23, 72.78it/s, v_num=5, train_loss=0.0374, Acc%=95.60]
Epoch 1: 7%|▋ | 132/1875 [00:01<00:23, 72.77it/s, v_num=5, train_loss=0.0334, Acc%=95.60]
Epoch 1: 7%|▋ | 133/1875 [00:01<00:23, 72.78it/s, v_num=5, train_loss=0.0334, Acc%=95.60]
Epoch 1: 7%|▋ | 133/1875 [00:01<00:23, 72.77it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 7%|▋ | 134/1875 [00:01<00:23, 72.80it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 7%|▋ | 134/1875 [00:01<00:23, 72.79it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 7%|▋ | 135/1875 [00:01<00:23, 72.82it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 7%|▋ | 135/1875 [00:01<00:23, 72.81it/s, v_num=5, train_loss=0.723, Acc%=95.60]
Epoch 1: 7%|▋ | 136/1875 [00:01<00:23, 72.83it/s, v_num=5, train_loss=0.723, Acc%=95.60]
Epoch 1: 7%|▋ | 136/1875 [00:01<00:23, 72.82it/s, v_num=5, train_loss=0.355, Acc%=95.60]
Epoch 1: 7%|▋ | 137/1875 [00:01<00:23, 72.84it/s, v_num=5, train_loss=0.355, Acc%=95.60]
Epoch 1: 7%|▋ | 137/1875 [00:01<00:23, 72.83it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 7%|▋ | 138/1875 [00:01<00:23, 72.84it/s, v_num=5, train_loss=0.158, Acc%=95.60]
Epoch 1: 7%|▋ | 138/1875 [00:01<00:23, 72.83it/s, v_num=5, train_loss=0.204, Acc%=95.60]
Epoch 1: 7%|▋ | 139/1875 [00:01<00:23, 72.86it/s, v_num=5, train_loss=0.204, Acc%=95.60]
Epoch 1: 7%|▋ | 139/1875 [00:01<00:23, 72.85it/s, v_num=5, train_loss=0.668, Acc%=95.60]
Epoch 1: 7%|▋ | 140/1875 [00:01<00:23, 72.87it/s, v_num=5, train_loss=0.668, Acc%=95.60]
Epoch 1: 7%|▋ | 140/1875 [00:01<00:23, 72.86it/s, v_num=5, train_loss=0.245, Acc%=95.60]
Epoch 1: 8%|▊ | 141/1875 [00:01<00:23, 72.76it/s, v_num=5, train_loss=0.245, Acc%=95.60]
Epoch 1: 8%|▊ | 141/1875 [00:01<00:23, 72.75it/s, v_num=5, train_loss=0.0766, Acc%=95.60]
Epoch 1: 8%|▊ | 142/1875 [00:01<00:23, 72.74it/s, v_num=5, train_loss=0.0766, Acc%=95.60]
Epoch 1: 8%|▊ | 142/1875 [00:01<00:23, 72.73it/s, v_num=5, train_loss=0.0813, Acc%=95.60]
Epoch 1: 8%|▊ | 143/1875 [00:01<00:23, 72.75it/s, v_num=5, train_loss=0.0813, Acc%=95.60]
Epoch 1: 8%|▊ | 143/1875 [00:01<00:23, 72.74it/s, v_num=5, train_loss=0.0981, Acc%=95.60]
Epoch 1: 8%|▊ | 144/1875 [00:01<00:23, 72.76it/s, v_num=5, train_loss=0.0981, Acc%=95.60]
Epoch 1: 8%|▊ | 144/1875 [00:01<00:23, 72.75it/s, v_num=5, train_loss=0.0884, Acc%=95.60]
Epoch 1: 8%|▊ | 145/1875 [00:01<00:23, 72.78it/s, v_num=5, train_loss=0.0884, Acc%=95.60]
Epoch 1: 8%|▊ | 145/1875 [00:01<00:23, 72.77it/s, v_num=5, train_loss=0.281, Acc%=95.60]
Epoch 1: 8%|▊ | 146/1875 [00:02<00:23, 72.81it/s, v_num=5, train_loss=0.281, Acc%=95.60]
Epoch 1: 8%|▊ | 146/1875 [00:02<00:23, 72.80it/s, v_num=5, train_loss=0.167, Acc%=95.60]
Epoch 1: 8%|▊ | 147/1875 [00:02<00:23, 72.84it/s, v_num=5, train_loss=0.167, Acc%=95.60]
Epoch 1: 8%|▊ | 147/1875 [00:02<00:23, 72.83it/s, v_num=5, train_loss=0.382, Acc%=95.60]
Epoch 1: 8%|▊ | 148/1875 [00:02<00:23, 72.86it/s, v_num=5, train_loss=0.382, Acc%=95.60]
Epoch 1: 8%|▊ | 148/1875 [00:02<00:23, 72.85it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 8%|▊ | 149/1875 [00:02<00:23, 72.88it/s, v_num=5, train_loss=0.104, Acc%=95.60]
Epoch 1: 8%|▊ | 149/1875 [00:02<00:23, 72.87it/s, v_num=5, train_loss=0.0466, Acc%=95.60]
Epoch 1: 8%|▊ | 150/1875 [00:02<00:23, 72.91it/s, v_num=5, train_loss=0.0466, Acc%=95.60]
Epoch 1: 8%|▊ | 150/1875 [00:02<00:23, 72.90it/s, v_num=5, train_loss=0.0916, Acc%=95.60]
Epoch 1: 8%|▊ | 151/1875 [00:02<00:23, 72.92it/s, v_num=5, train_loss=0.0916, Acc%=95.60]
Epoch 1: 8%|▊ | 151/1875 [00:02<00:23, 72.91it/s, v_num=5, train_loss=0.357, Acc%=95.60]
Epoch 1: 8%|▊ | 152/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.357, Acc%=95.60]
Epoch 1: 8%|▊ | 152/1875 [00:02<00:23, 72.93it/s, v_num=5, train_loss=0.274, Acc%=95.60]
Epoch 1: 8%|▊ | 153/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.274, Acc%=95.60]
Epoch 1: 8%|▊ | 153/1875 [00:02<00:23, 72.95it/s, v_num=5, train_loss=0.170, Acc%=95.60]
Epoch 1: 8%|▊ | 154/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.170, Acc%=95.60]
Epoch 1: 8%|▊ | 154/1875 [00:02<00:23, 72.95it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 8%|▊ | 155/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 8%|▊ | 155/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.155, Acc%=95.60]
Epoch 1: 8%|▊ | 156/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.155, Acc%=95.60]
Epoch 1: 8%|▊ | 156/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.0567, Acc%=95.60]
Epoch 1: 8%|▊ | 157/1875 [00:02<00:23, 72.99it/s, v_num=5, train_loss=0.0567, Acc%=95.60]
Epoch 1: 8%|▊ | 157/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:23, 72.99it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.272, Acc%=95.60]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:23, 72.99it/s, v_num=5, train_loss=0.272, Acc%=95.60]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.156, Acc%=95.60]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:23, 73.00it/s, v_num=5, train_loss=0.156, Acc%=95.60]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:23, 72.99it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 9%|▊ | 161/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 9%|▊ | 161/1875 [00:02<00:23, 73.00it/s, v_num=5, train_loss=0.132, Acc%=95.60]
Epoch 1: 9%|▊ | 162/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.132, Acc%=95.60]
Epoch 1: 9%|▊ | 162/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.0377, Acc%=95.60]
Epoch 1: 9%|▊ | 163/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.0377, Acc%=95.60]
Epoch 1: 9%|▊ | 163/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.339, Acc%=95.60]
Epoch 1: 9%|▊ | 164/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.339, Acc%=95.60]
Epoch 1: 9%|▊ | 164/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.325, Acc%=95.60]
Epoch 1: 9%|▉ | 165/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.325, Acc%=95.60]
Epoch 1: 9%|▉ | 165/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.202, Acc%=95.60]
Epoch 1: 9%|▉ | 166/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.202, Acc%=95.60]
Epoch 1: 9%|▉ | 166/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.0832, Acc%=95.60]
Epoch 1: 9%|▉ | 167/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.0832, Acc%=95.60]
Epoch 1: 9%|▉ | 167/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.101, Acc%=95.60]
Epoch 1: 9%|▉ | 168/1875 [00:02<00:23, 73.03it/s, v_num=5, train_loss=0.101, Acc%=95.60]
Epoch 1: 9%|▉ | 168/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.207, Acc%=95.60]
Epoch 1: 9%|▉ | 169/1875 [00:02<00:23, 73.03it/s, v_num=5, train_loss=0.207, Acc%=95.60]
Epoch 1: 9%|▉ | 169/1875 [00:02<00:23, 73.03it/s, v_num=5, train_loss=0.0598, Acc%=95.60]
Epoch 1: 9%|▉ | 170/1875 [00:02<00:23, 73.05it/s, v_num=5, train_loss=0.0598, Acc%=95.60]
Epoch 1: 9%|▉ | 170/1875 [00:02<00:23, 73.04it/s, v_num=5, train_loss=0.370, Acc%=95.60]
Epoch 1: 9%|▉ | 171/1875 [00:02<00:23, 73.04it/s, v_num=5, train_loss=0.370, Acc%=95.60]
Epoch 1: 9%|▉ | 171/1875 [00:02<00:23, 73.02it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 9%|▉ | 172/1875 [00:02<00:23, 73.01it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 9%|▉ | 172/1875 [00:02<00:23, 73.00it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 9%|▉ | 173/1875 [00:02<00:23, 72.99it/s, v_num=5, train_loss=0.267, Acc%=95.60]
Epoch 1: 9%|▉ | 173/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.298, Acc%=95.60]
Epoch 1: 9%|▉ | 174/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.298, Acc%=95.60]
Epoch 1: 9%|▉ | 174/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 9%|▉ | 175/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.292, Acc%=95.60]
Epoch 1: 9%|▉ | 175/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.0468, Acc%=95.60]
Epoch 1: 9%|▉ | 176/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.0468, Acc%=95.60]
Epoch 1: 9%|▉ | 176/1875 [00:02<00:23, 72.93it/s, v_num=5, train_loss=0.147, Acc%=95.60]
Epoch 1: 9%|▉ | 177/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.147, Acc%=95.60]
Epoch 1: 9%|▉ | 177/1875 [00:02<00:23, 72.93it/s, v_num=5, train_loss=0.0215, Acc%=95.60]
Epoch 1: 9%|▉ | 178/1875 [00:02<00:23, 72.95it/s, v_num=5, train_loss=0.0215, Acc%=95.60]
Epoch 1: 9%|▉ | 178/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 10%|▉ | 179/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.176, Acc%=95.60]
Epoch 1: 10%|▉ | 179/1875 [00:02<00:23, 72.95it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 10%|▉ | 180/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.203, Acc%=95.60]
Epoch 1: 10%|▉ | 180/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.023, Acc%=95.60]
Epoch 1: 10%|▉ | 181/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.023, Acc%=95.60]
Epoch 1: 10%|▉ | 181/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.105, Acc%=95.60]
Epoch 1: 10%|▉ | 182/1875 [00:02<00:23, 72.98it/s, v_num=5, train_loss=0.105, Acc%=95.60]
Epoch 1: 10%|▉ | 182/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.419, Acc%=95.60]
Epoch 1: 10%|▉ | 183/1875 [00:02<00:23, 72.97it/s, v_num=5, train_loss=0.419, Acc%=95.60]
Epoch 1: 10%|▉ | 183/1875 [00:02<00:23, 72.96it/s, v_num=5, train_loss=0.0555, Acc%=95.60]
Epoch 1: 10%|▉ | 184/1875 [00:02<00:23, 72.95it/s, v_num=5, train_loss=0.0555, Acc%=95.60]
Epoch 1: 10%|▉ | 184/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.299, Acc%=95.60]
Epoch 1: 10%|▉ | 185/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.299, Acc%=95.60]
Epoch 1: 10%|▉ | 185/1875 [00:02<00:23, 72.93it/s, v_num=5, train_loss=0.153, Acc%=95.60]
Epoch 1: 10%|▉ | 186/1875 [00:02<00:23, 72.94it/s, v_num=5, train_loss=0.153, Acc%=95.60]
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Epoch 1: 12%|█▏ | 225/1875 [00:03<00:22, 72.68it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 12%|█▏ | 226/1875 [00:03<00:22, 72.68it/s, v_num=5, train_loss=0.262, Acc%=95.60]
Epoch 1: 12%|█▏ | 226/1875 [00:03<00:22, 72.67it/s, v_num=5, train_loss=0.509, Acc%=95.60]
Epoch 1: 12%|█▏ | 227/1875 [00:03<00:22, 72.69it/s, v_num=5, train_loss=0.509, Acc%=95.60]
Epoch 1: 12%|█▏ | 227/1875 [00:03<00:22, 72.68it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 12%|█▏ | 228/1875 [00:03<00:22, 72.70it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 12%|█▏ | 228/1875 [00:03<00:22, 72.69it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 12%|█▏ | 229/1875 [00:03<00:22, 72.71it/s, v_num=5, train_loss=0.358, Acc%=95.60]
Epoch 1: 12%|█▏ | 229/1875 [00:03<00:22, 72.70it/s, v_num=5, train_loss=0.139, Acc%=95.60]
Epoch 1: 12%|█▏ | 230/1875 [00:03<00:22, 72.72it/s, v_num=5, train_loss=0.139, Acc%=95.60]
Epoch 1: 12%|█▏ | 230/1875 [00:03<00:22, 72.71it/s, v_num=5, train_loss=0.288, Acc%=95.60]
Epoch 1: 12%|█▏ | 231/1875 [00:03<00:22, 72.73it/s, v_num=5, train_loss=0.288, Acc%=95.60]
Epoch 1: 12%|█▏ | 231/1875 [00:03<00:22, 72.72it/s, v_num=5, train_loss=0.0933, Acc%=95.60]
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Epoch 1: 13%|█▎ | 235/1875 [00:03<00:22, 72.73it/s, v_num=5, train_loss=0.139, Acc%=95.60]
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Epoch 1: 13%|█▎ | 236/1875 [00:03<00:22, 72.75it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 13%|█▎ | 237/1875 [00:03<00:22, 72.77it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 13%|█▎ | 237/1875 [00:03<00:22, 72.76it/s, v_num=5, train_loss=0.0835, Acc%=95.60]
Epoch 1: 13%|█▎ | 238/1875 [00:03<00:22, 72.79it/s, v_num=5, train_loss=0.0835, Acc%=95.60]
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Epoch 1: 13%|█▎ | 239/1875 [00:03<00:22, 72.79it/s, v_num=5, train_loss=0.160, Acc%=95.60]
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Epoch 1: 13%|█▎ | 240/1875 [00:03<00:22, 72.81it/s, v_num=5, train_loss=0.308, Acc%=95.60]
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Epoch 1: 14%|█▎ | 254/1875 [00:03<00:22, 73.03it/s, v_num=5, train_loss=0.313, Acc%=95.60]
Epoch 1: 14%|█▎ | 255/1875 [00:03<00:22, 73.05it/s, v_num=5, train_loss=0.313, Acc%=95.60]
Epoch 1: 14%|█▎ | 255/1875 [00:03<00:22, 73.04it/s, v_num=5, train_loss=0.103, Acc%=95.60]
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Epoch 1: 14%|█▎ | 256/1875 [00:03<00:22, 73.04it/s, v_num=5, train_loss=0.125, Acc%=95.60]
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Epoch 1: 14%|█▎ | 257/1875 [00:03<00:22, 73.05it/s, v_num=5, train_loss=0.407, Acc%=95.60]
Epoch 1: 14%|█▍ | 258/1875 [00:03<00:22, 73.06it/s, v_num=5, train_loss=0.407, Acc%=95.60]
Epoch 1: 14%|█▍ | 258/1875 [00:03<00:22, 73.06it/s, v_num=5, train_loss=0.127, Acc%=95.60]
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Epoch 1: 14%|█▍ | 260/1875 [00:03<00:22, 73.06it/s, v_num=5, train_loss=0.0875, Acc%=95.60]
Epoch 1: 14%|█▍ | 261/1875 [00:03<00:22, 73.08it/s, v_num=5, train_loss=0.0875, Acc%=95.60]
Epoch 1: 14%|█▍ | 261/1875 [00:03<00:22, 73.07it/s, v_num=5, train_loss=0.168, Acc%=95.60]
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Epoch 1: 14%|█▍ | 263/1875 [00:03<00:22, 73.09it/s, v_num=5, train_loss=0.277, Acc%=95.60]
Epoch 1: 14%|█▍ | 263/1875 [00:03<00:22, 73.09it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 14%|█▍ | 264/1875 [00:03<00:22, 73.10it/s, v_num=5, train_loss=0.188, Acc%=95.60]
Epoch 1: 14%|█▍ | 264/1875 [00:03<00:22, 73.10it/s, v_num=5, train_loss=0.102, Acc%=95.60]
Epoch 1: 14%|█▍ | 265/1875 [00:03<00:22, 73.11it/s, v_num=5, train_loss=0.102, Acc%=95.60]
Epoch 1: 14%|█▍ | 265/1875 [00:03<00:22, 73.10it/s, v_num=5, train_loss=0.311, Acc%=95.60]
Epoch 1: 14%|█▍ | 266/1875 [00:03<00:22, 73.12it/s, v_num=5, train_loss=0.311, Acc%=95.60]
Epoch 1: 14%|█▍ | 266/1875 [00:03<00:22, 73.11it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 14%|█▍ | 267/1875 [00:03<00:21, 73.12it/s, v_num=5, train_loss=0.179, Acc%=95.60]
Epoch 1: 14%|█▍ | 267/1875 [00:03<00:21, 73.11it/s, v_num=5, train_loss=0.0437, Acc%=95.60]
Epoch 1: 14%|█▍ | 268/1875 [00:03<00:21, 73.13it/s, v_num=5, train_loss=0.0437, Acc%=95.60]
Epoch 1: 14%|█▍ | 268/1875 [00:03<00:21, 73.12it/s, v_num=5, train_loss=0.377, Acc%=95.60]
Epoch 1: 14%|█▍ | 269/1875 [00:03<00:21, 73.14it/s, v_num=5, train_loss=0.377, Acc%=95.60]
Epoch 1: 14%|█▍ | 269/1875 [00:03<00:21, 73.14it/s, v_num=5, train_loss=0.336, Acc%=95.60]
Epoch 1: 14%|█▍ | 270/1875 [00:03<00:21, 73.16it/s, v_num=5, train_loss=0.336, Acc%=95.60]
Epoch 1: 14%|█▍ | 270/1875 [00:03<00:21, 73.16it/s, v_num=5, train_loss=0.154, Acc%=95.60]
Epoch 1: 14%|█▍ | 271/1875 [00:03<00:21, 73.17it/s, v_num=5, train_loss=0.154, Acc%=95.60]
Epoch 1: 14%|█▍ | 271/1875 [00:03<00:21, 73.17it/s, v_num=5, train_loss=0.0817, Acc%=95.60]
Epoch 1: 15%|█▍ | 272/1875 [00:03<00:21, 73.18it/s, v_num=5, train_loss=0.0817, Acc%=95.60]
Epoch 1: 15%|█▍ | 272/1875 [00:03<00:21, 73.18it/s, v_num=5, train_loss=0.0144, Acc%=95.60]
Epoch 1: 15%|█▍ | 273/1875 [00:03<00:21, 73.20it/s, v_num=5, train_loss=0.0144, Acc%=95.60]
Epoch 1: 15%|█▍ | 273/1875 [00:03<00:21, 73.19it/s, v_num=5, train_loss=0.183, Acc%=95.60]
Epoch 1: 15%|█▍ | 274/1875 [00:03<00:21, 73.21it/s, v_num=5, train_loss=0.183, Acc%=95.60]
Epoch 1: 15%|█▍ | 274/1875 [00:03<00:21, 73.20it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 15%|█▍ | 275/1875 [00:03<00:21, 73.22it/s, v_num=5, train_loss=0.184, Acc%=95.60]
Epoch 1: 15%|█▍ | 275/1875 [00:03<00:21, 73.22it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 15%|█▍ | 276/1875 [00:03<00:21, 73.23it/s, v_num=5, train_loss=0.165, Acc%=95.60]
Epoch 1: 15%|█▍ | 276/1875 [00:03<00:21, 73.22it/s, v_num=5, train_loss=0.0825, Acc%=95.60]
Epoch 1: 15%|█▍ | 277/1875 [00:03<00:21, 73.24it/s, v_num=5, train_loss=0.0825, Acc%=95.60]
Epoch 1: 15%|█▍ | 277/1875 [00:03<00:21, 73.23it/s, v_num=5, train_loss=0.0576, Acc%=95.60]
Epoch 1: 15%|█▍ | 278/1875 [00:03<00:21, 73.25it/s, v_num=5, train_loss=0.0576, Acc%=95.60]
Epoch 1: 15%|█▍ | 278/1875 [00:03<00:21, 73.25it/s, v_num=5, train_loss=0.440, Acc%=95.60]
Epoch 1: 15%|█▍ | 279/1875 [00:03<00:21, 73.27it/s, v_num=5, train_loss=0.440, Acc%=95.60]
Epoch 1: 15%|█▍ | 279/1875 [00:03<00:21, 73.26it/s, v_num=5, train_loss=0.0615, Acc%=95.60]
Epoch 1: 15%|█▍ | 280/1875 [00:03<00:21, 73.28it/s, v_num=5, train_loss=0.0615, Acc%=95.60]
Epoch 1: 15%|█▍ | 280/1875 [00:03<00:21, 73.28it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 15%|█▍ | 281/1875 [00:03<00:21, 73.30it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 15%|█▍ | 281/1875 [00:03<00:21, 73.30it/s, v_num=5, train_loss=0.183, Acc%=95.60]
Epoch 1: 15%|█▌ | 282/1875 [00:03<00:21, 73.31it/s, v_num=5, train_loss=0.183, Acc%=95.60]
Epoch 1: 15%|█▌ | 282/1875 [00:03<00:21, 73.31it/s, v_num=5, train_loss=0.174, Acc%=95.60]
Epoch 1: 15%|█▌ | 283/1875 [00:03<00:21, 73.33it/s, v_num=5, train_loss=0.174, Acc%=95.60]
Epoch 1: 15%|█▌ | 283/1875 [00:03<00:21, 73.33it/s, v_num=5, train_loss=0.385, Acc%=95.60]
Epoch 1: 15%|█▌ | 284/1875 [00:03<00:21, 73.34it/s, v_num=5, train_loss=0.385, Acc%=95.60]
Epoch 1: 15%|█▌ | 284/1875 [00:03<00:21, 73.34it/s, v_num=5, train_loss=0.464, Acc%=95.60]
Epoch 1: 15%|█▌ | 285/1875 [00:03<00:21, 73.35it/s, v_num=5, train_loss=0.464, Acc%=95.60]
Epoch 1: 15%|█▌ | 285/1875 [00:03<00:21, 73.34it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 15%|█▌ | 286/1875 [00:03<00:21, 73.36it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 15%|█▌ | 286/1875 [00:03<00:21, 73.36it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 15%|█▌ | 287/1875 [00:03<00:21, 73.37it/s, v_num=5, train_loss=0.128, Acc%=95.60]
Epoch 1: 15%|█▌ | 287/1875 [00:03<00:21, 73.37it/s, v_num=5, train_loss=0.214, Acc%=95.60]
Epoch 1: 15%|█▌ | 288/1875 [00:03<00:21, 73.38it/s, v_num=5, train_loss=0.214, Acc%=95.60]
Epoch 1: 15%|█▌ | 288/1875 [00:03<00:21, 73.38it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 15%|█▌ | 289/1875 [00:03<00:21, 73.39it/s, v_num=5, train_loss=0.236, Acc%=95.60]
Epoch 1: 15%|█▌ | 289/1875 [00:03<00:21, 73.39it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 15%|█▌ | 290/1875 [00:03<00:21, 73.40it/s, v_num=5, train_loss=0.219, Acc%=95.60]
Epoch 1: 15%|█▌ | 290/1875 [00:03<00:21, 73.39it/s, v_num=5, train_loss=0.0668, Acc%=95.60]
Epoch 1: 16%|█▌ | 291/1875 [00:03<00:21, 73.41it/s, v_num=5, train_loss=0.0668, Acc%=95.60]
Epoch 1: 16%|█▌ | 291/1875 [00:03<00:21, 73.40it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 16%|█▌ | 292/1875 [00:03<00:21, 73.40it/s, v_num=5, train_loss=0.197, Acc%=95.60]
Epoch 1: 16%|█▌ | 292/1875 [00:03<00:21, 73.40it/s, v_num=5, train_loss=0.0429, Acc%=95.60]
Epoch 1: 16%|█▌ | 293/1875 [00:03<00:21, 73.40it/s, v_num=5, train_loss=0.0429, Acc%=95.60]
Epoch 1: 16%|█▌ | 293/1875 [00:03<00:21, 73.39it/s, v_num=5, train_loss=0.202, Acc%=95.60]
Epoch 1: 16%|█▌ | 294/1875 [00:04<00:21, 73.41it/s, v_num=5, train_loss=0.202, Acc%=95.60]
Epoch 1: 16%|█▌ | 294/1875 [00:04<00:21, 73.40it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 16%|█▌ | 295/1875 [00:04<00:21, 73.42it/s, v_num=5, train_loss=0.175, Acc%=95.60]
Epoch 1: 16%|█▌ | 295/1875 [00:04<00:21, 73.41it/s, v_num=5, train_loss=0.244, Acc%=95.60]
Epoch 1: 16%|█▌ | 296/1875 [00:04<00:21, 73.43it/s, v_num=5, train_loss=0.244, Acc%=95.60]
Epoch 1: 16%|█▌ | 296/1875 [00:04<00:21, 73.43it/s, v_num=5, train_loss=0.0327, Acc%=95.60]
Epoch 1: 16%|█▌ | 297/1875 [00:04<00:21, 73.44it/s, v_num=5, train_loss=0.0327, Acc%=95.60]
Epoch 1: 16%|█▌ | 297/1875 [00:04<00:21, 73.44it/s, v_num=5, train_loss=0.0762, Acc%=95.60]
Epoch 1: 16%|█▌ | 298/1875 [00:04<00:21, 73.46it/s, v_num=5, train_loss=0.0762, Acc%=95.60]
Epoch 1: 16%|█▌ | 298/1875 [00:04<00:21, 73.45it/s, v_num=5, train_loss=0.393, Acc%=95.60]
Epoch 1: 16%|█▌ | 299/1875 [00:04<00:21, 73.47it/s, v_num=5, train_loss=0.393, Acc%=95.60]
Epoch 1: 16%|█▌ | 299/1875 [00:04<00:21, 73.47it/s, v_num=5, train_loss=0.141, Acc%=95.60]
Epoch 1: 16%|█▌ | 300/1875 [00:04<00:21, 73.49it/s, v_num=5, train_loss=0.141, Acc%=95.60]
Epoch 1: 16%|█▌ | 300/1875 [00:04<00:21, 73.48it/s, v_num=5, train_loss=0.142, Acc%=95.60]
Epoch 1: 16%|█▌ | 301/1875 [00:04<00:21, 73.50it/s, v_num=5, train_loss=0.142, Acc%=95.60]
Epoch 1: 16%|█▌ | 301/1875 [00:04<00:21, 73.50it/s, v_num=5, train_loss=0.0266, Acc%=95.60]
Epoch 1: 16%|█▌ | 302/1875 [00:04<00:21, 73.52it/s, v_num=5, train_loss=0.0266, Acc%=95.60]
Epoch 1: 16%|█▌ | 302/1875 [00:04<00:21, 73.51it/s, v_num=5, train_loss=0.0834, Acc%=95.60]
Epoch 1: 16%|█▌ | 303/1875 [00:04<00:21, 73.53it/s, v_num=5, train_loss=0.0834, Acc%=95.60]
Epoch 1: 16%|█▌ | 303/1875 [00:04<00:21, 73.53it/s, v_num=5, train_loss=0.0805, Acc%=95.60]
Epoch 1: 16%|█▌ | 304/1875 [00:04<00:21, 73.54it/s, v_num=5, train_loss=0.0805, Acc%=95.60]
Epoch 1: 16%|█▌ | 304/1875 [00:04<00:21, 73.54it/s, v_num=5, train_loss=0.338, Acc%=95.60]
Epoch 1: 16%|█▋ | 305/1875 [00:04<00:21, 73.56it/s, v_num=5, train_loss=0.338, Acc%=95.60]
Epoch 1: 16%|█▋ | 305/1875 [00:04<00:21, 73.55it/s, v_num=5, train_loss=0.598, Acc%=95.60]
Epoch 1: 16%|█▋ | 306/1875 [00:04<00:21, 73.57it/s, v_num=5, train_loss=0.598, Acc%=95.60]
Epoch 1: 16%|█▋ | 306/1875 [00:04<00:21, 73.57it/s, v_num=5, train_loss=0.152, Acc%=95.60]
Epoch 1: 16%|█▋ | 307/1875 [00:04<00:21, 73.58it/s, v_num=5, train_loss=0.152, Acc%=95.60]
Epoch 1: 16%|█▋ | 307/1875 [00:04<00:21, 73.58it/s, v_num=5, train_loss=0.251, Acc%=95.60]
Epoch 1: 16%|█▋ | 308/1875 [00:04<00:21, 73.60it/s, v_num=5, train_loss=0.251, Acc%=95.60]
Epoch 1: 16%|█▋ | 308/1875 [00:04<00:21, 73.60it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 16%|█▋ | 309/1875 [00:04<00:21, 73.61it/s, v_num=5, train_loss=0.106, Acc%=95.60]
Epoch 1: 16%|█▋ | 309/1875 [00:04<00:21, 73.61it/s, v_num=5, train_loss=0.123, Acc%=95.60]
Epoch 1: 17%|█▋ | 310/1875 [00:04<00:21, 73.62it/s, v_num=5, train_loss=0.123, Acc%=95.60]
Epoch 1: 17%|█▋ | 310/1875 [00:04<00:21, 73.62it/s, v_num=5, train_loss=0.192, Acc%=95.60]
Epoch 1: 17%|█▋ | 311/1875 [00:04<00:21, 73.64it/s, v_num=5, train_loss=0.192, Acc%=95.60]
Epoch 1: 17%|█▋ | 311/1875 [00:04<00:21, 73.63it/s, v_num=5, train_loss=0.200, Acc%=95.60]
Epoch 1: 17%|█▋ | 312/1875 [00:04<00:21, 73.64it/s, v_num=5, train_loss=0.200, Acc%=95.60]
Epoch 1: 17%|█▋ | 312/1875 [00:04<00:21, 73.64it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 17%|█▋ | 313/1875 [00:04<00:21, 73.66it/s, v_num=5, train_loss=0.186, Acc%=95.60]
Epoch 1: 17%|█▋ | 313/1875 [00:04<00:21, 73.65it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 17%|█▋ | 314/1875 [00:04<00:21, 73.67it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 17%|█▋ | 314/1875 [00:04<00:21, 73.67it/s, v_num=5, train_loss=0.0497, Acc%=95.60]
Epoch 1: 17%|█▋ | 315/1875 [00:04<00:21, 73.68it/s, v_num=5, train_loss=0.0497, Acc%=95.60]
Epoch 1: 17%|█▋ | 315/1875 [00:04<00:21, 73.68it/s, v_num=5, train_loss=0.384, Acc%=95.60]
Epoch 1: 17%|█▋ | 316/1875 [00:04<00:21, 73.69it/s, v_num=5, train_loss=0.384, Acc%=95.60]
Epoch 1: 17%|█▋ | 316/1875 [00:04<00:21, 73.69it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 17%|█▋ | 317/1875 [00:04<00:21, 73.71it/s, v_num=5, train_loss=0.226, Acc%=95.60]
Epoch 1: 17%|█▋ | 317/1875 [00:04<00:21, 73.70it/s, v_num=5, train_loss=0.419, Acc%=95.60]
Epoch 1: 17%|█▋ | 318/1875 [00:04<00:21, 73.72it/s, v_num=5, train_loss=0.419, Acc%=95.60]
Epoch 1: 17%|█▋ | 318/1875 [00:04<00:21, 73.72it/s, v_num=5, train_loss=0.134, Acc%=95.60]
Epoch 1: 17%|█▋ | 319/1875 [00:04<00:21, 73.74it/s, v_num=5, train_loss=0.134, Acc%=95.60]
Epoch 1: 17%|█▋ | 319/1875 [00:04<00:21, 73.73it/s, v_num=5, train_loss=0.512, Acc%=95.60]
Epoch 1: 17%|█▋ | 320/1875 [00:04<00:21, 73.75it/s, v_num=5, train_loss=0.512, Acc%=95.60]
Epoch 1: 17%|█▋ | 320/1875 [00:04<00:21, 73.75it/s, v_num=5, train_loss=0.041, Acc%=95.60]
Epoch 1: 17%|█▋ | 321/1875 [00:04<00:21, 73.76it/s, v_num=5, train_loss=0.041, Acc%=95.60]
Epoch 1: 17%|█▋ | 321/1875 [00:04<00:21, 73.76it/s, v_num=5, train_loss=0.294, Acc%=95.60]
Epoch 1: 17%|█▋ | 322/1875 [00:04<00:21, 73.77it/s, v_num=5, train_loss=0.294, Acc%=95.60]
Epoch 1: 17%|█▋ | 322/1875 [00:04<00:21, 73.77it/s, v_num=5, train_loss=0.325, Acc%=95.60]
Epoch 1: 17%|█▋ | 323/1875 [00:04<00:21, 73.79it/s, v_num=5, train_loss=0.325, Acc%=95.60]
Epoch 1: 17%|█▋ | 323/1875 [00:04<00:21, 73.78it/s, v_num=5, train_loss=0.270, Acc%=95.60]
Epoch 1: 17%|█▋ | 324/1875 [00:04<00:21, 73.80it/s, v_num=5, train_loss=0.270, Acc%=95.60]
Epoch 1: 17%|█▋ | 324/1875 [00:04<00:21, 73.79it/s, v_num=5, train_loss=0.078, Acc%=95.60]
Epoch 1: 17%|█▋ | 325/1875 [00:04<00:20, 73.81it/s, v_num=5, train_loss=0.078, Acc%=95.60]
Epoch 1: 17%|█▋ | 325/1875 [00:04<00:21, 73.81it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 17%|█▋ | 326/1875 [00:04<00:20, 73.82it/s, v_num=5, train_loss=0.276, Acc%=95.60]
Epoch 1: 17%|█▋ | 326/1875 [00:04<00:20, 73.81it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 17%|█▋ | 327/1875 [00:04<00:20, 73.83it/s, v_num=5, train_loss=0.114, Acc%=95.60]
Epoch 1: 17%|█▋ | 327/1875 [00:04<00:20, 73.82it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 17%|█▋ | 328/1875 [00:04<00:20, 73.84it/s, v_num=5, train_loss=0.146, Acc%=95.60]
Epoch 1: 17%|█▋ | 328/1875 [00:04<00:20, 73.83it/s, v_num=5, train_loss=0.216, Acc%=95.60]
Epoch 1: 18%|█▊ | 329/1875 [00:04<00:20, 73.84it/s, v_num=5, train_loss=0.216, Acc%=95.60]
Epoch 1: 18%|█▊ | 329/1875 [00:04<00:20, 73.84it/s, v_num=5, train_loss=0.0818, Acc%=95.60]
Epoch 1: 18%|█▊ | 330/1875 [00:04<00:20, 73.86it/s, v_num=5, train_loss=0.0818, Acc%=95.60]
Epoch 1: 18%|█▊ | 330/1875 [00:04<00:20, 73.85it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 18%|█▊ | 331/1875 [00:04<00:20, 73.87it/s, v_num=5, train_loss=0.109, Acc%=95.60]
Epoch 1: 18%|█▊ | 331/1875 [00:04<00:20, 73.86it/s, v_num=5, train_loss=0.289, Acc%=95.60]
Epoch 1: 18%|█▊ | 332/1875 [00:04<00:20, 73.87it/s, v_num=5, train_loss=0.289, Acc%=95.60]
Epoch 1: 18%|█▊ | 332/1875 [00:04<00:20, 73.87it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 18%|█▊ | 333/1875 [00:04<00:20, 73.88it/s, v_num=5, train_loss=0.177, Acc%=95.60]
Epoch 1: 18%|█▊ | 333/1875 [00:04<00:20, 73.87it/s, v_num=5, train_loss=0.0844, Acc%=95.60]
Epoch 1: 18%|█▊ | 334/1875 [00:04<00:20, 73.89it/s, v_num=5, train_loss=0.0844, Acc%=95.60]
Epoch 1: 18%|█▊ | 334/1875 [00:04<00:20, 73.88it/s, v_num=5, train_loss=0.220, Acc%=95.60]
Epoch 1: 18%|█▊ | 335/1875 [00:04<00:20, 73.90it/s, v_num=5, train_loss=0.220, Acc%=95.60]
Epoch 1: 18%|█▊ | 335/1875 [00:04<00:20, 73.89it/s, v_num=5, train_loss=0.0979, Acc%=95.60]
Epoch 1: 18%|█▊ | 336/1875 [00:04<00:20, 73.90it/s, v_num=5, train_loss=0.0979, Acc%=95.60]
Epoch 1: 18%|█▊ | 336/1875 [00:04<00:20, 73.90it/s, v_num=5, train_loss=0.133, Acc%=95.60]
Epoch 1: 18%|█▊ | 337/1875 [00:04<00:20, 73.91it/s, v_num=5, train_loss=0.133, Acc%=95.60]
Epoch 1: 18%|█▊ | 337/1875 [00:04<00:20, 73.91it/s, v_num=5, train_loss=0.123, Acc%=95.60]
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Epoch 1: 58%|█████▊ | 1080/1875 [00:14<00:10, 75.68it/s, v_num=5, train_loss=0.0435, Acc%=95.60]
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Epoch 1: 59%|█████▉ | 1114/1875 [00:14<00:10, 75.66it/s, v_num=5, train_loss=0.227, Acc%=95.60]
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Epoch 1: 59%|█████▉ | 1115/1875 [00:14<00:10, 75.66it/s, v_num=5, train_loss=0.192, Acc%=95.60]
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Epoch 1: 60%|█████▉ | 1122/1875 [00:14<00:09, 75.66it/s, v_num=5, train_loss=0.258, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1183/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.174, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1185/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.240, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1186/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.200, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1187/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.324, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1188/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.0701, Acc%=95.60]
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Epoch 1: 63%|██████▎ | 1190/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.338, Acc%=95.60]
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Epoch 1: 64%|██████▎ | 1191/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.0799, Acc%=95.60]
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Epoch 1: 64%|██████▎ | 1192/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.187, Acc%=95.60]
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Epoch 1: 64%|██████▎ | 1193/1875 [00:15<00:09, 75.62it/s, v_num=5, train_loss=0.342, Acc%=95.60]
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Epoch 1: 64%|██████▍ | 1197/1875 [00:15<00:08, 75.62it/s, v_num=5, train_loss=0.0819, Acc%=95.60]
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Epoch 1: 73%|███████▎ | 1378/1875 [00:18<00:06, 75.62it/s, v_num=5, train_loss=0.0645, Acc%=95.60]
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Epoch 1: 74%|███████▎ | 1381/1875 [00:18<00:06, 75.62it/s, v_num=5, train_loss=0.167, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1388/1875 [00:18<00:06, 75.62it/s, v_num=5, train_loss=0.154, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1394/1875 [00:18<00:06, 75.61it/s, v_num=5, train_loss=0.111, Acc%=95.60]
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Epoch 1: 74%|███████▍ | 1395/1875 [00:18<00:06, 75.61it/s, v_num=5, train_loss=0.139, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1399/1875 [00:18<00:06, 75.60it/s, v_num=5, train_loss=0.0588, Acc%=95.60]
Epoch 1: 75%|███████▍ | 1400/1875 [00:18<00:06, 75.61it/s, v_num=5, train_loss=0.0588, Acc%=95.60]
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Epoch 1: 75%|███████▍ | 1401/1875 [00:18<00:06, 75.60it/s, v_num=5, train_loss=0.208, Acc%=95.60]
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Epoch 1: 78%|███████▊ | 1471/1875 [00:19<00:05, 75.65it/s, v_num=5, train_loss=0.275, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1577/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.117, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1578/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.100, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1579/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.116, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1580/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.0364, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1581/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.389, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1582/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.0684, Acc%=95.60]
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Epoch 1: 84%|████████▍ | 1584/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.443, Acc%=95.60]
Epoch 1: 85%|████████▍ | 1585/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.443, Acc%=95.60]
Epoch 1: 85%|████████▍ | 1585/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.284, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1586/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.0333, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1589/1875 [00:20<00:03, 75.70it/s, v_num=5, train_loss=0.091, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1591/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.171, Acc%=95.60]
Epoch 1: 85%|████████▍ | 1592/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.171, Acc%=95.60]
Epoch 1: 85%|████████▍ | 1592/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.120, Acc%=95.60]
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Epoch 1: 85%|████████▍ | 1593/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.219, Acc%=95.60]
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Epoch 1: 85%|████████▌ | 1597/1875 [00:21<00:03, 75.71it/s, v_num=5, train_loss=0.658, Acc%=95.60]
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Epoch 1: 85%|████████▌ | 1599/1875 [00:21<00:03, 75.71it/s, v_num=5, train_loss=0.178, Acc%=95.60]
Epoch 1: 85%|████████▌ | 1600/1875 [00:21<00:03, 75.71it/s, v_num=5, train_loss=0.178, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1610/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.127, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1611/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.112, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1612/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.156, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1613/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.320, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1614/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.250, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1615/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.0737, Acc%=95.60]
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Epoch 1: 86%|████████▌ | 1617/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.117, Acc%=95.60]
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Epoch 1: 86%|████████▋ | 1618/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.273, Acc%=95.60]
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Epoch 1: 86%|████████▋ | 1619/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.0997, Acc%=95.60]
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Epoch 1: 86%|████████▋ | 1620/1875 [00:21<00:03, 75.70it/s, v_num=5, train_loss=0.269, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1641/1875 [00:21<00:03, 75.71it/s, v_num=5, train_loss=0.247, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1642/1875 [00:21<00:03, 75.71it/s, v_num=5, train_loss=0.139, Acc%=95.60]
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Epoch 1: 88%|████████▊ | 1652/1875 [00:21<00:02, 75.71it/s, v_num=5, train_loss=0.138, Acc%=95.60]
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Epoch 1: 90%|████████▉ | 1680/1875 [00:22<00:02, 75.73it/s, v_num=5, train_loss=0.0319, Acc%=95.60]
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Epoch 1: 90%|████████▉ | 1682/1875 [00:22<00:02, 75.74it/s, v_num=5, train_loss=0.652, Acc%=95.60]
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Epoch 1: 95%|█████████▌| 1788/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.367, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1791/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.0918, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1792/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.273, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1793/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.282, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1794/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.105, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1797/1875 [00:23<00:01, 75.76it/s, v_num=5, train_loss=0.196, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1800/1875 [00:23<00:00, 75.77it/s, v_num=5, train_loss=0.0204, Acc%=95.60]
Epoch 1: 96%|█████████▌| 1800/1875 [00:23<00:00, 75.77it/s, v_num=5, train_loss=0.0814, Acc%=95.60]
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Epoch 1: 96%|█████████▌| 1801/1875 [00:23<00:00, 75.77it/s, v_num=5, train_loss=0.110, 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 │ 97.19% │
│ Brier │ 0.04295 │
│ Entropy │ 0.09648 │
│ NLL │ 0.08691 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Calibration ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ECE │ 0.00312 │
│ aECE │ 0.00225 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ OOD Detection ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUPR │ 92.79% │
│ AUROC │ 93.79% │
│ Entropy │ 0.97081 │
│ FPR95 │ 27.31% │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Selective Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUGRC │ 0.16% │
│ AURC │ 0.17% │
│ Cov@5Risk │ 100.00% │
│ Risk@80Cov │ 0.15% │
└──────────────┴───────────────────────────┘
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%|▍ | 82/1875 [00:01<00:30, 59.58it/s, v_num=6, train_loss=2.260]
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Epoch 0: 7%|▋ | 132/1875 [00:02<00:29, 59.64it/s, v_num=6, train_loss=2.110]
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Epoch 0: 74%|███████▍ | 1392/1875 [00:23<00:08, 59.11it/s, v_num=6, train_loss=0.325]
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Epoch 0: 76%|███████▌ | 1426/1875 [00:24<00:07, 59.13it/s, v_num=6, train_loss=0.356]
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Epoch 0: 88%|████████▊ | 1652/1875 [00:27<00:03, 59.08it/s, v_num=6, train_loss=0.296]
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┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Acc │ 96.97% │
│ Brier │ 0.05034 │
│ Entropy │ 0.17028 │
│ NLL │ 0.10820 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Calibration ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ECE │ 0.02250 │
│ aECE │ 0.02251 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ OOD Detection ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUPR │ 91.22% │
│ AUROC │ 93.52% │
│ Entropy │ 1.11220 │
│ FPR95 │ 19.14% │
│ ens_Disagre… │ 0.33280 │
│ ens_Entropy │ 0.98442 │
│ ens_MI │ 0.12778 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Selective Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUGRC │ 0.19% │
│ AURC │ 0.21% │
│ Cov@5Risk │ 100.00% │
│ Risk@80Cov │ 0.25% │
└──────────────┴───────────────────────────┘
The results are not comparable since we only trained the ensemble for one epoch to reduce GitHub’s cpu usage. Feel free to run the notebook on your machine for a longer duration.
We need to multiply the learning rate by 2 to account for the fact that we have 4 models in the ensemble and that we average the loss over all the predictions.
#### Downloading the pre-trained models
We have put the pre-trained models on Hugging Face that you can download with the utility function “hf_hub_download” imported just below. These models are trained for 75 epochs and are therefore not comparable to the all the other models trained in this notebook. The pretrained models can be seen on HuggingFace and TorchUncertainty’s are there.
from torch_uncertainty.utils.hub import hf_hub_download
all_models = []
for i in range(8):
hf_hub_download(
repo_id="ENSTA-U2IS/tutorial-models",
filename=f"version_{i}.ckpt",
local_dir="./models/",
)
model = LeNet(in_channels=1, num_classes=10)
state_dict = torch.load(f"./models/version_{i}.ckpt", map_location="cpu")[
"state_dict"
]
state_dict = {k.replace("model.", ""): v for k, v in state_dict.items()}
model.load_state_dict(state_dict)
all_models.append(model)
from torch_uncertainty.models import deep_ensembles
from torch_uncertainty.transforms import RepeatTarget
ensemble = deep_ensembles(
all_models,
num_estimators=None,
task="classification",
reset_model_parameters=True,
)
ens_routine = ClassificationRoutine(
is_ensemble=True,
num_classes=10,
model=ensemble,
loss=nn.CrossEntropyLoss(), # The loss for the training
format_batch_fn=RepeatTarget(
8
), # How to handle the targets when comparing the predictions
optim_recipe=None, # No optim recipe as the model is already trained
eval_ood=True, # We want to evaluate the OOD-related metrics
)
trainer = TUTrainer(accelerator="cpu", max_epochs=max_epochs)
ens_perf = trainer.test(ens_routine, dataloaders=[test_dl, ood_dl])
/home/runner/work/torch-uncertainty/torch-uncertainty/auto_tutorials_source/tutorial_from_de_to_pe.py:257: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
state_dict = torch.load(f"./models/version_{i}.ckpt", map_location="cpu")[
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┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Acc │ 99.61% │
│ Brier │ 0.00677 │
│ Entropy │ 0.02816 │
│ NLL │ 0.01454 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Calibration ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ECE │ 0.00459 │
│ aECE │ 0.00451 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ OOD Detection ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUPR │ 98.98% │
│ AUROC │ 99.21% │
│ Entropy │ 1.25233 │
│ FPR95 │ 2.63% │
│ ens_Disagre… │ 0.38779 │
│ ens_Entropy │ 1.01787 │
│ ens_MI │ 0.23446 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Selective Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUGRC │ 0.00% │
│ AURC │ 0.00% │
│ Cov@5Risk │ 100.00% │
│ Risk@80Cov │ 0.00% │
└──────────────┴───────────────────────────┘
4. From Deep Ensembles to Packed-Ensembles¶
In the paper Packed-Ensembles for Efficient Uncertainty Quantification published at the International Conference on Learning Representations (ICLR) in 2023, we introduced a modification of Deep Ensembles to make it more computationally-efficient. The idea is to pack the ensemble members into a single model, which allows us to train the ensemble in a single forward pass. This modification is particularly useful when the ensemble size is large, as it is often the case in practice.
We will need to update the model and replace the layers with their Packed equivalents. You can find the documentation of the Packed-Linear layer using this link, and the Packed-Conv2D, here.
import torch
import torch.nn as nn
from einops import rearrange
from torch_uncertainty.layers import PackedConv2d, PackedLinear
class PackedLeNet(nn.Module):
def __init__(
self,
in_channels: int,
num_classes: int,
alpha: int,
num_estimators: int,
) -> None:
super().__init__()
self.num_estimators = num_estimators
self.conv1 = PackedConv2d(
in_channels,
6,
(5, 5),
alpha=alpha,
num_estimators=num_estimators,
first=True,
)
self.conv2 = PackedConv2d(
6,
16,
(5, 5),
alpha=alpha,
num_estimators=num_estimators,
)
self.pooling = nn.AdaptiveAvgPool2d((4, 4))
self.fc1 = PackedLinear(
256, 120, alpha=alpha, num_estimators=num_estimators
)
self.fc2 = PackedLinear(
120, 84, alpha=alpha, num_estimators=num_estimators
)
self.fc3 = PackedLinear(
84,
num_classes,
alpha=alpha,
num_estimators=num_estimators,
last=True,
)
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 = rearrange(
out, "e (m c) h w -> (m e) c h w", m=self.num_estimators
)
out = torch.flatten(out, 1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
return self.fc3(out) # Again, no softmax in the model
# Instantiate the model, the images are in grayscale so the number of channels is 1
packed_model = PackedLeNet(
in_channels=1, num_classes=10, alpha=2, num_estimators=4
)
# 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
packed_routine = ClassificationRoutine(
is_ensemble=True,
num_classes=10,
model=packed_model,
loss=nn.CrossEntropyLoss(),
format_batch_fn=RepeatTarget(4),
optim_recipe=optim_recipe(packed_model, 4.0),
eval_ood=True,
)
# In practice, avoid performing the validation on the test set
trainer.fit(packed_routine, train_dataloaders=train_dl, val_dataloaders=test_dl)
packed_perf = trainer.test(packed_routine, dataloaders=[test_dl, ood_dl])
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Epoch 0: 6%|▌ | 110/1875 [00:02<00:33, 52.25it/s, v_num=8, train_loss=2.190]
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Epoch 0: 96%|█████████▌| 1801/1875 [00:34<00:01, 52.00it/s, v_num=8, train_loss=0.244]
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Epoch 1: 1%|▏ | 26/1875 [00:00<00:35, 52.68it/s, v_num=8, train_loss=0.513, Acc%=96.80]
Epoch 1: 1%|▏ | 26/1875 [00:00<00:35, 52.64it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 1%|▏ | 27/1875 [00:00<00:35, 52.72it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 1%|▏ | 27/1875 [00:00<00:35, 52.69it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 1%|▏ | 28/1875 [00:00<00:35, 52.76it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 1%|▏ | 28/1875 [00:00<00:35, 52.73it/s, v_num=8, train_loss=0.268, Acc%=96.80]
Epoch 1: 2%|▏ | 29/1875 [00:00<00:34, 52.80it/s, v_num=8, train_loss=0.268, Acc%=96.80]
Epoch 1: 2%|▏ | 29/1875 [00:00<00:34, 52.77it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 2%|▏ | 30/1875 [00:00<00:34, 52.85it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 2%|▏ | 30/1875 [00:00<00:34, 52.83it/s, v_num=8, train_loss=0.494, Acc%=96.80]
Epoch 1: 2%|▏ | 31/1875 [00:00<00:34, 52.89it/s, v_num=8, train_loss=0.494, Acc%=96.80]
Epoch 1: 2%|▏ | 31/1875 [00:00<00:34, 52.86it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:34, 52.93it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 2%|▏ | 32/1875 [00:00<00:34, 52.91it/s, v_num=8, train_loss=0.333, Acc%=96.80]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:34, 52.97it/s, v_num=8, train_loss=0.333, Acc%=96.80]
Epoch 1: 2%|▏ | 33/1875 [00:00<00:34, 52.94it/s, v_num=8, train_loss=0.477, Acc%=96.80]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:34, 52.99it/s, v_num=8, train_loss=0.477, Acc%=96.80]
Epoch 1: 2%|▏ | 34/1875 [00:00<00:34, 52.96it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:34, 52.99it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 2%|▏ | 35/1875 [00:00<00:34, 52.96it/s, v_num=8, train_loss=0.367, Acc%=96.80]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:34, 53.01it/s, v_num=8, train_loss=0.367, Acc%=96.80]
Epoch 1: 2%|▏ | 36/1875 [00:00<00:34, 52.99it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:34, 53.04it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 2%|▏ | 37/1875 [00:00<00:34, 53.02it/s, v_num=8, train_loss=0.214, Acc%=96.80]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:34, 53.07it/s, v_num=8, train_loss=0.214, Acc%=96.80]
Epoch 1: 2%|▏ | 38/1875 [00:00<00:34, 53.05it/s, v_num=8, train_loss=0.345, Acc%=96.80]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:34, 53.10it/s, v_num=8, train_loss=0.345, Acc%=96.80]
Epoch 1: 2%|▏ | 39/1875 [00:00<00:34, 53.08it/s, v_num=8, train_loss=0.311, Acc%=96.80]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:34, 53.13it/s, v_num=8, train_loss=0.311, Acc%=96.80]
Epoch 1: 2%|▏ | 40/1875 [00:00<00:34, 53.11it/s, v_num=8, train_loss=0.212, Acc%=96.80]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:34, 53.16it/s, v_num=8, train_loss=0.212, Acc%=96.80]
Epoch 1: 2%|▏ | 41/1875 [00:00<00:34, 53.14it/s, v_num=8, train_loss=0.419, Acc%=96.80]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:34, 53.18it/s, v_num=8, train_loss=0.419, Acc%=96.80]
Epoch 1: 2%|▏ | 42/1875 [00:00<00:34, 53.16it/s, v_num=8, train_loss=0.188, Acc%=96.80]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:34, 53.21it/s, v_num=8, train_loss=0.188, Acc%=96.80]
Epoch 1: 2%|▏ | 43/1875 [00:00<00:34, 53.19it/s, v_num=8, train_loss=0.474, Acc%=96.80]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:34, 53.23it/s, v_num=8, train_loss=0.474, Acc%=96.80]
Epoch 1: 2%|▏ | 44/1875 [00:00<00:34, 53.21it/s, v_num=8, train_loss=0.323, Acc%=96.80]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:34, 53.25it/s, v_num=8, train_loss=0.323, Acc%=96.80]
Epoch 1: 2%|▏ | 45/1875 [00:00<00:34, 53.24it/s, v_num=8, train_loss=0.353, Acc%=96.80]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:34, 53.28it/s, v_num=8, train_loss=0.353, Acc%=96.80]
Epoch 1: 2%|▏ | 46/1875 [00:00<00:34, 53.26it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:34, 53.29it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 3%|▎ | 47/1875 [00:00<00:34, 53.28it/s, v_num=8, train_loss=0.256, Acc%=96.80]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:34, 53.31it/s, v_num=8, train_loss=0.256, Acc%=96.80]
Epoch 1: 3%|▎ | 48/1875 [00:00<00:34, 53.29it/s, v_num=8, train_loss=0.182, Acc%=96.80]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:34, 53.32it/s, v_num=8, train_loss=0.182, Acc%=96.80]
Epoch 1: 3%|▎ | 49/1875 [00:00<00:34, 53.30it/s, v_num=8, train_loss=0.268, Acc%=96.80]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:34, 53.33it/s, v_num=8, train_loss=0.268, Acc%=96.80]
Epoch 1: 3%|▎ | 50/1875 [00:00<00:34, 53.31it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:34, 53.33it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 3%|▎ | 51/1875 [00:00<00:34, 53.31it/s, v_num=8, train_loss=0.189, Acc%=96.80]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:34, 53.34it/s, v_num=8, train_loss=0.189, Acc%=96.80]
Epoch 1: 3%|▎ | 52/1875 [00:00<00:34, 53.32it/s, v_num=8, train_loss=0.189, Acc%=96.80]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:34, 53.35it/s, v_num=8, train_loss=0.189, Acc%=96.80]
Epoch 1: 3%|▎ | 53/1875 [00:00<00:34, 53.34it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 3%|▎ | 54/1875 [00:01<00:34, 53.36it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 3%|▎ | 54/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 3%|▎ | 55/1875 [00:01<00:34, 53.36it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 3%|▎ | 55/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.286, Acc%=96.80]
Epoch 1: 3%|▎ | 56/1875 [00:01<00:34, 53.37it/s, v_num=8, train_loss=0.286, Acc%=96.80]
Epoch 1: 3%|▎ | 56/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.247, Acc%=96.80]
Epoch 1: 3%|▎ | 57/1875 [00:01<00:34, 53.37it/s, v_num=8, train_loss=0.247, Acc%=96.80]
Epoch 1: 3%|▎ | 57/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.780, Acc%=96.80]
Epoch 1: 3%|▎ | 58/1875 [00:01<00:34, 53.36it/s, v_num=8, train_loss=0.780, Acc%=96.80]
Epoch 1: 3%|▎ | 58/1875 [00:01<00:34, 53.34it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 3%|▎ | 59/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 3%|▎ | 59/1875 [00:01<00:34, 53.34it/s, v_num=8, train_loss=0.119, Acc%=96.80]
Epoch 1: 3%|▎ | 60/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.119, Acc%=96.80]
Epoch 1: 3%|▎ | 60/1875 [00:01<00:34, 53.33it/s, v_num=8, train_loss=0.504, Acc%=96.80]
Epoch 1: 3%|▎ | 61/1875 [00:01<00:34, 53.35it/s, v_num=8, train_loss=0.504, Acc%=96.80]
Epoch 1: 3%|▎ | 61/1875 [00:01<00:34, 53.33it/s, v_num=8, train_loss=0.412, Acc%=96.80]
Epoch 1: 3%|▎ | 62/1875 [00:01<00:33, 53.36it/s, v_num=8, train_loss=0.412, Acc%=96.80]
Epoch 1: 3%|▎ | 62/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.251, Acc%=96.80]
Epoch 1: 3%|▎ | 63/1875 [00:01<00:33, 53.36it/s, v_num=8, train_loss=0.251, Acc%=96.80]
Epoch 1: 3%|▎ | 63/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 3%|▎ | 64/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 3%|▎ | 64/1875 [00:01<00:33, 53.32it/s, v_num=8, train_loss=0.565, Acc%=96.80]
Epoch 1: 3%|▎ | 65/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.565, Acc%=96.80]
Epoch 1: 3%|▎ | 65/1875 [00:01<00:33, 53.32it/s, v_num=8, train_loss=0.383, Acc%=96.80]
Epoch 1: 4%|▎ | 66/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.383, Acc%=96.80]
Epoch 1: 4%|▎ | 66/1875 [00:01<00:33, 53.31it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 4%|▎ | 67/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 4%|▎ | 67/1875 [00:01<00:33, 53.32it/s, v_num=8, train_loss=0.386, Acc%=96.80]
Epoch 1: 4%|▎ | 68/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.386, Acc%=96.80]
Epoch 1: 4%|▎ | 68/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.555, Acc%=96.80]
Epoch 1: 4%|▎ | 69/1875 [00:01<00:33, 53.35it/s, v_num=8, train_loss=0.555, Acc%=96.80]
Epoch 1: 4%|▎ | 69/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 4%|▎ | 70/1875 [00:01<00:33, 53.35it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 4%|▎ | 70/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 4%|▍ | 71/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 4%|▍ | 71/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.069, Acc%=96.80]
Epoch 1: 4%|▍ | 72/1875 [00:01<00:33, 53.35it/s, v_num=8, train_loss=0.069, Acc%=96.80]
Epoch 1: 4%|▍ | 72/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 4%|▍ | 73/1875 [00:01<00:33, 53.34it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 4%|▍ | 73/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.482, Acc%=96.80]
Epoch 1: 4%|▍ | 74/1875 [00:01<00:33, 53.33it/s, v_num=8, train_loss=0.482, Acc%=96.80]
Epoch 1: 4%|▍ | 74/1875 [00:01<00:33, 53.32it/s, v_num=8, train_loss=0.294, Acc%=96.80]
Epoch 1: 4%|▍ | 75/1875 [00:01<00:33, 53.31it/s, v_num=8, train_loss=0.294, Acc%=96.80]
Epoch 1: 4%|▍ | 75/1875 [00:01<00:33, 53.29it/s, v_num=8, train_loss=0.256, Acc%=96.80]
Epoch 1: 4%|▍ | 76/1875 [00:01<00:33, 53.25it/s, v_num=8, train_loss=0.256, Acc%=96.80]
Epoch 1: 4%|▍ | 76/1875 [00:01<00:33, 53.23it/s, v_num=8, train_loss=0.391, Acc%=96.80]
Epoch 1: 4%|▍ | 77/1875 [00:01<00:33, 53.23it/s, v_num=8, train_loss=0.391, Acc%=96.80]
Epoch 1: 4%|▍ | 77/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.188, Acc%=96.80]
Epoch 1: 4%|▍ | 78/1875 [00:01<00:33, 53.22it/s, v_num=8, train_loss=0.188, Acc%=96.80]
Epoch 1: 4%|▍ | 78/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.264, Acc%=96.80]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:33, 53.22it/s, v_num=8, train_loss=0.264, Acc%=96.80]
Epoch 1: 4%|▍ | 79/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:33, 53.22it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 4%|▍ | 80/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.224, Acc%=96.80]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.224, Acc%=96.80]
Epoch 1: 4%|▍ | 81/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 4%|▍ | 82/1875 [00:01<00:33, 53.19it/s, v_num=8, train_loss=0.320, Acc%=96.80]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.320, Acc%=96.80]
Epoch 1: 4%|▍ | 83/1875 [00:01<00:33, 53.19it/s, v_num=8, train_loss=0.289, Acc%=96.80]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.289, Acc%=96.80]
Epoch 1: 4%|▍ | 84/1875 [00:01<00:33, 53.19it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 5%|▍ | 85/1875 [00:01<00:33, 53.19it/s, v_num=8, train_loss=0.175, Acc%=96.80]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.175, Acc%=96.80]
Epoch 1: 5%|▍ | 86/1875 [00:01<00:33, 53.19it/s, v_num=8, train_loss=0.194, Acc%=96.80]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.194, Acc%=96.80]
Epoch 1: 5%|▍ | 87/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 5%|▍ | 88/1875 [00:01<00:33, 53.20it/s, v_num=8, train_loss=0.178, Acc%=96.80]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:33, 53.22it/s, v_num=8, train_loss=0.178, Acc%=96.80]
Epoch 1: 5%|▍ | 89/1875 [00:01<00:33, 53.21it/s, v_num=8, train_loss=0.136, Acc%=96.80]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:33, 53.23it/s, v_num=8, train_loss=0.136, Acc%=96.80]
Epoch 1: 5%|▍ | 90/1875 [00:01<00:33, 53.22it/s, v_num=8, train_loss=0.221, Acc%=96.80]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:33, 53.24it/s, v_num=8, train_loss=0.221, Acc%=96.80]
Epoch 1: 5%|▍ | 91/1875 [00:01<00:33, 53.23it/s, v_num=8, train_loss=0.296, Acc%=96.80]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:33, 53.25it/s, v_num=8, train_loss=0.296, Acc%=96.80]
Epoch 1: 5%|▍ | 92/1875 [00:01<00:33, 53.24it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 5%|▍ | 93/1875 [00:01<00:33, 53.25it/s, v_num=8, train_loss=0.404, Acc%=96.80]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.404, Acc%=96.80]
Epoch 1: 5%|▌ | 94/1875 [00:01<00:33, 53.25it/s, v_num=8, train_loss=0.135, Acc%=96.80]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.135, Acc%=96.80]
Epoch 1: 5%|▌ | 95/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.345, Acc%=96.80]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.345, Acc%=96.80]
Epoch 1: 5%|▌ | 96/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 5%|▌ | 97/1875 [00:01<00:33, 53.25it/s, v_num=8, train_loss=0.296, Acc%=96.80]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.296, Acc%=96.80]
Epoch 1: 5%|▌ | 98/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.577, Acc%=96.80]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.577, Acc%=96.80]
Epoch 1: 5%|▌ | 99/1875 [00:01<00:33, 53.26it/s, v_num=8, train_loss=0.474, Acc%=96.80]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:33, 53.28it/s, v_num=8, train_loss=0.474, Acc%=96.80]
Epoch 1: 5%|▌ | 100/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.307, Acc%=96.80]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:33, 53.28it/s, v_num=8, train_loss=0.307, Acc%=96.80]
Epoch 1: 5%|▌ | 101/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.352, Acc%=96.80]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:33, 53.28it/s, v_num=8, train_loss=0.352, Acc%=96.80]
Epoch 1: 5%|▌ | 102/1875 [00:01<00:33, 53.27it/s, v_num=8, train_loss=0.148, Acc%=96.80]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:33, 53.29it/s, v_num=8, train_loss=0.148, Acc%=96.80]
Epoch 1: 5%|▌ | 103/1875 [00:01<00:33, 53.28it/s, v_num=8, train_loss=0.449, Acc%=96.80]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:33, 53.29it/s, v_num=8, train_loss=0.449, Acc%=96.80]
Epoch 1: 6%|▌ | 104/1875 [00:01<00:33, 53.29it/s, v_num=8, train_loss=0.300, Acc%=96.80]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:33, 53.30it/s, v_num=8, train_loss=0.300, Acc%=96.80]
Epoch 1: 6%|▌ | 105/1875 [00:01<00:33, 53.29it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:33, 53.30it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 6%|▌ | 106/1875 [00:01<00:33, 53.30it/s, v_num=8, train_loss=0.120, Acc%=96.80]
Epoch 1: 6%|▌ | 107/1875 [00:02<00:33, 53.31it/s, v_num=8, train_loss=0.120, Acc%=96.80]
Epoch 1: 6%|▌ | 107/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.528, Acc%=96.80]
Epoch 1: 6%|▌ | 108/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.528, Acc%=96.80]
Epoch 1: 6%|▌ | 108/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.384, Acc%=96.80]
Epoch 1: 6%|▌ | 109/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.384, Acc%=96.80]
Epoch 1: 6%|▌ | 109/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.498, Acc%=96.80]
Epoch 1: 6%|▌ | 110/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.498, Acc%=96.80]
Epoch 1: 6%|▌ | 110/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 6%|▌ | 111/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 6%|▌ | 111/1875 [00:02<00:33, 53.28it/s, v_num=8, train_loss=0.246, Acc%=96.80]
Epoch 1: 6%|▌ | 112/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.246, Acc%=96.80]
Epoch 1: 6%|▌ | 112/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.229, Acc%=96.80]
Epoch 1: 6%|▌ | 113/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.229, Acc%=96.80]
Epoch 1: 6%|▌ | 113/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 6%|▌ | 114/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 6%|▌ | 114/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.157, Acc%=96.80]
Epoch 1: 6%|▌ | 115/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.157, Acc%=96.80]
Epoch 1: 6%|▌ | 115/1875 [00:02<00:33, 53.29it/s, v_num=8, train_loss=0.479, Acc%=96.80]
Epoch 1: 6%|▌ | 116/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.479, Acc%=96.80]
Epoch 1: 6%|▌ | 116/1875 [00:02<00:33, 53.30it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 6%|▌ | 117/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 6%|▌ | 117/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 6%|▋ | 118/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 6%|▋ | 118/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.176, Acc%=96.80]
Epoch 1: 6%|▋ | 119/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.176, Acc%=96.80]
Epoch 1: 6%|▋ | 119/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.414, Acc%=96.80]
Epoch 1: 6%|▋ | 120/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.414, Acc%=96.80]
Epoch 1: 6%|▋ | 120/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 6%|▋ | 121/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 6%|▋ | 121/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 7%|▋ | 122/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 7%|▋ | 122/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.214, Acc%=96.80]
Epoch 1: 7%|▋ | 123/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.214, Acc%=96.80]
Epoch 1: 7%|▋ | 123/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.159, Acc%=96.80]
Epoch 1: 7%|▋ | 124/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.159, Acc%=96.80]
Epoch 1: 7%|▋ | 124/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 7%|▋ | 125/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 7%|▋ | 125/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.159, Acc%=96.80]
Epoch 1: 7%|▋ | 126/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.159, Acc%=96.80]
Epoch 1: 7%|▋ | 126/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.253, Acc%=96.80]
Epoch 1: 7%|▋ | 127/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.253, Acc%=96.80]
Epoch 1: 7%|▋ | 127/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 7%|▋ | 128/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 7%|▋ | 128/1875 [00:02<00:32, 53.27it/s, v_num=8, train_loss=0.533, Acc%=96.80]
Epoch 1: 7%|▋ | 129/1875 [00:02<00:32, 53.14it/s, v_num=8, train_loss=0.533, Acc%=96.80]
Epoch 1: 7%|▋ | 129/1875 [00:02<00:32, 53.14it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 7%|▋ | 130/1875 [00:02<00:32, 53.15it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 7%|▋ | 130/1875 [00:02<00:32, 53.15it/s, v_num=8, train_loss=0.0969, Acc%=96.80]
Epoch 1: 7%|▋ | 131/1875 [00:02<00:32, 53.17it/s, v_num=8, train_loss=0.0969, Acc%=96.80]
Epoch 1: 7%|▋ | 131/1875 [00:02<00:32, 53.16it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 7%|▋ | 132/1875 [00:02<00:32, 53.17it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 7%|▋ | 132/1875 [00:02<00:32, 53.17it/s, v_num=8, train_loss=0.302, Acc%=96.80]
Epoch 1: 7%|▋ | 133/1875 [00:02<00:32, 53.18it/s, v_num=8, train_loss=0.302, Acc%=96.80]
Epoch 1: 7%|▋ | 133/1875 [00:02<00:32, 53.17it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 7%|▋ | 134/1875 [00:02<00:32, 53.18it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 7%|▋ | 134/1875 [00:02<00:32, 53.17it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 7%|▋ | 135/1875 [00:02<00:32, 53.19it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 7%|▋ | 135/1875 [00:02<00:32, 53.18it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 7%|▋ | 136/1875 [00:02<00:32, 53.20it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 7%|▋ | 136/1875 [00:02<00:32, 53.19it/s, v_num=8, train_loss=0.285, Acc%=96.80]
Epoch 1: 7%|▋ | 137/1875 [00:02<00:32, 53.21it/s, v_num=8, train_loss=0.285, Acc%=96.80]
Epoch 1: 7%|▋ | 137/1875 [00:02<00:32, 53.20it/s, v_num=8, train_loss=0.252, Acc%=96.80]
Epoch 1: 7%|▋ | 138/1875 [00:02<00:32, 53.22it/s, v_num=8, train_loss=0.252, Acc%=96.80]
Epoch 1: 7%|▋ | 138/1875 [00:02<00:32, 53.21it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 7%|▋ | 139/1875 [00:02<00:32, 53.23it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 7%|▋ | 139/1875 [00:02<00:32, 53.22it/s, v_num=8, train_loss=0.173, Acc%=96.80]
Epoch 1: 7%|▋ | 140/1875 [00:02<00:32, 53.23it/s, v_num=8, train_loss=0.173, Acc%=96.80]
Epoch 1: 7%|▋ | 140/1875 [00:02<00:32, 53.22it/s, v_num=8, train_loss=0.206, Acc%=96.80]
Epoch 1: 8%|▊ | 141/1875 [00:02<00:32, 53.24it/s, v_num=8, train_loss=0.206, Acc%=96.80]
Epoch 1: 8%|▊ | 141/1875 [00:02<00:32, 53.23it/s, v_num=8, train_loss=0.262, Acc%=96.80]
Epoch 1: 8%|▊ | 142/1875 [00:02<00:32, 53.24it/s, v_num=8, train_loss=0.262, Acc%=96.80]
Epoch 1: 8%|▊ | 142/1875 [00:02<00:32, 53.24it/s, v_num=8, train_loss=0.320, Acc%=96.80]
Epoch 1: 8%|▊ | 143/1875 [00:02<00:32, 53.25it/s, v_num=8, train_loss=0.320, Acc%=96.80]
Epoch 1: 8%|▊ | 143/1875 [00:02<00:32, 53.25it/s, v_num=8, train_loss=0.225, Acc%=96.80]
Epoch 1: 8%|▊ | 144/1875 [00:02<00:32, 53.26it/s, v_num=8, train_loss=0.225, Acc%=96.80]
Epoch 1: 8%|▊ | 144/1875 [00:02<00:32, 53.26it/s, v_num=8, train_loss=0.302, Acc%=96.80]
Epoch 1: 8%|▊ | 145/1875 [00:02<00:32, 53.27it/s, v_num=8, train_loss=0.302, Acc%=96.80]
Epoch 1: 8%|▊ | 145/1875 [00:02<00:32, 53.27it/s, v_num=8, train_loss=0.115, Acc%=96.80]
Epoch 1: 8%|▊ | 146/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.115, Acc%=96.80]
Epoch 1: 8%|▊ | 146/1875 [00:02<00:32, 53.27it/s, v_num=8, train_loss=0.105, Acc%=96.80]
Epoch 1: 8%|▊ | 147/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.105, Acc%=96.80]
Epoch 1: 8%|▊ | 147/1875 [00:02<00:32, 53.28it/s, v_num=8, train_loss=0.280, Acc%=96.80]
Epoch 1: 8%|▊ | 148/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.280, Acc%=96.80]
Epoch 1: 8%|▊ | 148/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.225, Acc%=96.80]
Epoch 1: 8%|▊ | 149/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.225, Acc%=96.80]
Epoch 1: 8%|▊ | 149/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 8%|▊ | 150/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 8%|▊ | 150/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.227, Acc%=96.80]
Epoch 1: 8%|▊ | 151/1875 [00:02<00:32, 53.32it/s, v_num=8, train_loss=0.227, Acc%=96.80]
Epoch 1: 8%|▊ | 151/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 8%|▊ | 152/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 8%|▊ | 152/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.120, Acc%=96.80]
Epoch 1: 8%|▊ | 153/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.120, Acc%=96.80]
Epoch 1: 8%|▊ | 153/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.619, Acc%=96.80]
Epoch 1: 8%|▊ | 154/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.619, Acc%=96.80]
Epoch 1: 8%|▊ | 154/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.451, Acc%=96.80]
Epoch 1: 8%|▊ | 155/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.451, Acc%=96.80]
Epoch 1: 8%|▊ | 155/1875 [00:02<00:32, 53.29it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 8%|▊ | 156/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 8%|▊ | 156/1875 [00:02<00:32, 53.30it/s, v_num=8, train_loss=0.431, Acc%=96.80]
Epoch 1: 8%|▊ | 157/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.431, Acc%=96.80]
Epoch 1: 8%|▊ | 157/1875 [00:02<00:32, 53.31it/s, v_num=8, train_loss=0.310, Acc%=96.80]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:32, 53.32it/s, v_num=8, train_loss=0.310, Acc%=96.80]
Epoch 1: 8%|▊ | 158/1875 [00:02<00:32, 53.32it/s, v_num=8, train_loss=0.198, Acc%=96.80]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:32, 53.33it/s, v_num=8, train_loss=0.198, Acc%=96.80]
Epoch 1: 8%|▊ | 159/1875 [00:02<00:32, 53.33it/s, v_num=8, train_loss=0.111, Acc%=96.80]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:32, 53.34it/s, v_num=8, train_loss=0.111, Acc%=96.80]
Epoch 1: 9%|▊ | 160/1875 [00:02<00:32, 53.34it/s, v_num=8, train_loss=0.385, Acc%=96.80]
Epoch 1: 9%|▊ | 161/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.385, Acc%=96.80]
Epoch 1: 9%|▊ | 161/1875 [00:03<00:32, 53.34it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 9%|▊ | 162/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 9%|▊ | 162/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.0886, Acc%=96.80]
Epoch 1: 9%|▊ | 163/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.0886, Acc%=96.80]
Epoch 1: 9%|▊ | 163/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.288, Acc%=96.80]
Epoch 1: 9%|▊ | 164/1875 [00:03<00:32, 53.36it/s, v_num=8, train_loss=0.288, Acc%=96.80]
Epoch 1: 9%|▊ | 164/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 9%|▉ | 165/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 9%|▉ | 165/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.228, Acc%=96.80]
Epoch 1: 9%|▉ | 166/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.228, Acc%=96.80]
Epoch 1: 9%|▉ | 166/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.392, Acc%=96.80]
Epoch 1: 9%|▉ | 167/1875 [00:03<00:32, 53.36it/s, v_num=8, train_loss=0.392, Acc%=96.80]
Epoch 1: 9%|▉ | 167/1875 [00:03<00:32, 53.35it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 9%|▉ | 168/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 9%|▉ | 168/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 9%|▉ | 169/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 9%|▉ | 169/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.250, Acc%=96.80]
Epoch 1: 9%|▉ | 170/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.250, Acc%=96.80]
Epoch 1: 9%|▉ | 170/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 9%|▉ | 171/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 9%|▉ | 171/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 9%|▉ | 172/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 9%|▉ | 172/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.288, Acc%=96.80]
Epoch 1: 9%|▉ | 173/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.288, Acc%=96.80]
Epoch 1: 9%|▉ | 173/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.378, Acc%=96.80]
Epoch 1: 9%|▉ | 174/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.378, Acc%=96.80]
Epoch 1: 9%|▉ | 174/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.358, Acc%=96.80]
Epoch 1: 9%|▉ | 175/1875 [00:03<00:31, 53.39it/s, v_num=8, train_loss=0.358, Acc%=96.80]
Epoch 1: 9%|▉ | 175/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.190, Acc%=96.80]
Epoch 1: 9%|▉ | 176/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.190, Acc%=96.80]
Epoch 1: 9%|▉ | 176/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 9%|▉ | 177/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.410, Acc%=96.80]
Epoch 1: 9%|▉ | 177/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.218, Acc%=96.80]
Epoch 1: 9%|▉ | 178/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.218, Acc%=96.80]
Epoch 1: 9%|▉ | 178/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 10%|▉ | 179/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 10%|▉ | 179/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 10%|▉ | 180/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 10%|▉ | 180/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.275, Acc%=96.80]
Epoch 1: 10%|▉ | 181/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.275, Acc%=96.80]
Epoch 1: 10%|▉ | 181/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 10%|▉ | 182/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 10%|▉ | 182/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.342, Acc%=96.80]
Epoch 1: 10%|▉ | 183/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.342, Acc%=96.80]
Epoch 1: 10%|▉ | 183/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 10%|▉ | 184/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 10%|▉ | 184/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.248, Acc%=96.80]
Epoch 1: 10%|▉ | 185/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.248, Acc%=96.80]
Epoch 1: 10%|▉ | 185/1875 [00:03<00:31, 53.33it/s, v_num=8, train_loss=0.102, Acc%=96.80]
Epoch 1: 10%|▉ | 186/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.102, Acc%=96.80]
Epoch 1: 10%|▉ | 186/1875 [00:03<00:31, 53.33it/s, v_num=8, train_loss=0.278, Acc%=96.80]
Epoch 1: 10%|▉ | 187/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.278, Acc%=96.80]
Epoch 1: 10%|▉ | 187/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.103, Acc%=96.80]
Epoch 1: 10%|█ | 188/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.103, Acc%=96.80]
Epoch 1: 10%|█ | 188/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.208, Acc%=96.80]
Epoch 1: 10%|█ | 189/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.208, Acc%=96.80]
Epoch 1: 10%|█ | 189/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.0801, Acc%=96.80]
Epoch 1: 10%|█ | 190/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.0801, Acc%=96.80]
Epoch 1: 10%|█ | 190/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.175, Acc%=96.80]
Epoch 1: 10%|█ | 191/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.175, Acc%=96.80]
Epoch 1: 10%|█ | 191/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.151, Acc%=96.80]
Epoch 1: 10%|█ | 192/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.151, Acc%=96.80]
Epoch 1: 10%|█ | 192/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.201, Acc%=96.80]
Epoch 1: 10%|█ | 193/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.201, Acc%=96.80]
Epoch 1: 10%|█ | 193/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.221, Acc%=96.80]
Epoch 1: 10%|█ | 194/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.221, Acc%=96.80]
Epoch 1: 10%|█ | 194/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.323, Acc%=96.80]
Epoch 1: 10%|█ | 195/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.323, Acc%=96.80]
Epoch 1: 10%|█ | 195/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.396, Acc%=96.80]
Epoch 1: 10%|█ | 196/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.396, Acc%=96.80]
Epoch 1: 10%|█ | 196/1875 [00:03<00:31, 53.34it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 11%|█ | 197/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 11%|█ | 197/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.404, Acc%=96.80]
Epoch 1: 11%|█ | 198/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.404, Acc%=96.80]
Epoch 1: 11%|█ | 198/1875 [00:03<00:31, 53.35it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 11%|█ | 199/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 11%|█ | 199/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 11%|█ | 200/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 11%|█ | 200/1875 [00:03<00:31, 53.36it/s, v_num=8, train_loss=0.191, Acc%=96.80]
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Epoch 1: 11%|█ | 205/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.340, Acc%=96.80]
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Epoch 1: 11%|█ | 206/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.103, Acc%=96.80]
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Epoch 1: 11%|█ | 207/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.279, Acc%=96.80]
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Epoch 1: 11%|█ | 209/1875 [00:03<00:31, 53.37it/s, v_num=8, train_loss=0.614, Acc%=96.80]
Epoch 1: 11%|█ | 210/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.614, Acc%=96.80]
Epoch 1: 11%|█ | 210/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.165, Acc%=96.80]
Epoch 1: 11%|█▏ | 211/1875 [00:03<00:31, 53.39it/s, v_num=8, train_loss=0.165, Acc%=96.80]
Epoch 1: 11%|█▏ | 211/1875 [00:03<00:31, 53.38it/s, v_num=8, train_loss=0.405, Acc%=96.80]
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Epoch 1: 11%|█▏ | 214/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.529, Acc%=96.80]
Epoch 1: 11%|█▏ | 215/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.529, Acc%=96.80]
Epoch 1: 11%|█▏ | 215/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.266, Acc%=96.80]
Epoch 1: 12%|█▏ | 216/1875 [00:04<00:31, 53.40it/s, v_num=8, train_loss=0.266, Acc%=96.80]
Epoch 1: 12%|█▏ | 216/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.625, Acc%=96.80]
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Epoch 1: 12%|█▏ | 217/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.198, Acc%=96.80]
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Epoch 1: 12%|█▏ | 218/1875 [00:04<00:31, 53.39it/s, v_num=8, train_loss=0.160, Acc%=96.80]
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Epoch 1: 12%|█▏ | 219/1875 [00:04<00:31, 53.40it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 12%|█▏ | 220/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 12%|█▏ | 220/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.140, Acc%=96.80]
Epoch 1: 12%|█▏ | 221/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.140, Acc%=96.80]
Epoch 1: 12%|█▏ | 221/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 12%|█▏ | 222/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 12%|█▏ | 222/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 12%|█▏ | 223/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 12%|█▏ | 223/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.202, Acc%=96.80]
Epoch 1: 12%|█▏ | 224/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.202, Acc%=96.80]
Epoch 1: 12%|█▏ | 224/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.142, Acc%=96.80]
Epoch 1: 12%|█▏ | 225/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.142, Acc%=96.80]
Epoch 1: 12%|█▏ | 225/1875 [00:04<00:30, 53.37it/s, v_num=8, train_loss=0.281, Acc%=96.80]
Epoch 1: 12%|█▏ | 226/1875 [00:04<00:30, 53.36it/s, v_num=8, train_loss=0.281, Acc%=96.80]
Epoch 1: 12%|█▏ | 226/1875 [00:04<00:30, 53.36it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 12%|█▏ | 227/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 12%|█▏ | 227/1875 [00:04<00:30, 53.34it/s, v_num=8, train_loss=0.435, Acc%=96.80]
Epoch 1: 12%|█▏ | 228/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.435, Acc%=96.80]
Epoch 1: 12%|█▏ | 228/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.173, Acc%=96.80]
Epoch 1: 12%|█▏ | 229/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.173, Acc%=96.80]
Epoch 1: 12%|█▏ | 229/1875 [00:04<00:30, 53.34it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 12%|█▏ | 230/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 12%|█▏ | 230/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.403, Acc%=96.80]
Epoch 1: 12%|█▏ | 231/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.403, Acc%=96.80]
Epoch 1: 12%|█▏ | 231/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.167, Acc%=96.80]
Epoch 1: 12%|█▏ | 232/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.167, Acc%=96.80]
Epoch 1: 12%|█▏ | 232/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.261, Acc%=96.80]
Epoch 1: 12%|█▏ | 233/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.261, Acc%=96.80]
Epoch 1: 12%|█▏ | 233/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.300, Acc%=96.80]
Epoch 1: 12%|█▏ | 234/1875 [00:04<00:30, 53.36it/s, v_num=8, train_loss=0.300, Acc%=96.80]
Epoch 1: 12%|█▏ | 234/1875 [00:04<00:30, 53.35it/s, v_num=8, train_loss=0.191, Acc%=96.80]
Epoch 1: 13%|█▎ | 235/1875 [00:04<00:30, 53.37it/s, v_num=8, train_loss=0.191, Acc%=96.80]
Epoch 1: 13%|█▎ | 235/1875 [00:04<00:30, 53.36it/s, v_num=8, train_loss=0.273, Acc%=96.80]
Epoch 1: 13%|█▎ | 236/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.273, Acc%=96.80]
Epoch 1: 13%|█▎ | 236/1875 [00:04<00:30, 53.37it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 13%|█▎ | 237/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.282, Acc%=96.80]
Epoch 1: 13%|█▎ | 237/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.234, Acc%=96.80]
Epoch 1: 13%|█▎ | 238/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.234, Acc%=96.80]
Epoch 1: 13%|█▎ | 238/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.409, Acc%=96.80]
Epoch 1: 13%|█▎ | 239/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.409, Acc%=96.80]
Epoch 1: 13%|█▎ | 239/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.469, Acc%=96.80]
Epoch 1: 13%|█▎ | 240/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.469, Acc%=96.80]
Epoch 1: 13%|█▎ | 240/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.492, Acc%=96.80]
Epoch 1: 13%|█▎ | 241/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.492, Acc%=96.80]
Epoch 1: 13%|█▎ | 241/1875 [00:04<00:30, 53.38it/s, v_num=8, train_loss=0.274, Acc%=96.80]
Epoch 1: 13%|█▎ | 242/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.274, Acc%=96.80]
Epoch 1: 13%|█▎ | 242/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.569, Acc%=96.80]
Epoch 1: 13%|█▎ | 243/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.569, Acc%=96.80]
Epoch 1: 13%|█▎ | 243/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 13%|█▎ | 244/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 13%|█▎ | 244/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.480, Acc%=96.80]
Epoch 1: 13%|█▎ | 245/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.480, Acc%=96.80]
Epoch 1: 13%|█▎ | 245/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.299, Acc%=96.80]
Epoch 1: 13%|█▎ | 246/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.299, Acc%=96.80]
Epoch 1: 13%|█▎ | 246/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.331, Acc%=96.80]
Epoch 1: 13%|█▎ | 247/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.331, Acc%=96.80]
Epoch 1: 13%|█▎ | 247/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.252, Acc%=96.80]
Epoch 1: 13%|█▎ | 248/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.252, Acc%=96.80]
Epoch 1: 13%|█▎ | 248/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 13%|█▎ | 249/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 13%|█▎ | 249/1875 [00:04<00:30, 53.39it/s, v_num=8, train_loss=0.426, Acc%=96.80]
Epoch 1: 13%|█▎ | 250/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.426, Acc%=96.80]
Epoch 1: 13%|█▎ | 250/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 13%|█▎ | 251/1875 [00:04<00:30, 53.41it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 13%|█▎ | 251/1875 [00:04<00:30, 53.40it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 13%|█▎ | 252/1875 [00:04<00:30, 53.41it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 13%|█▎ | 252/1875 [00:04<00:30, 53.41it/s, v_num=8, train_loss=0.212, Acc%=96.80]
Epoch 1: 13%|█▎ | 253/1875 [00:04<00:30, 53.42it/s, v_num=8, train_loss=0.212, Acc%=96.80]
Epoch 1: 13%|█▎ | 253/1875 [00:04<00:30, 53.41it/s, v_num=8, train_loss=0.202, Acc%=96.80]
Epoch 1: 14%|█▎ | 254/1875 [00:04<00:30, 53.42it/s, v_num=8, train_loss=0.202, Acc%=96.80]
Epoch 1: 14%|█▎ | 254/1875 [00:04<00:30, 53.42it/s, v_num=8, train_loss=0.220, Acc%=96.80]
Epoch 1: 14%|█▎ | 255/1875 [00:04<00:30, 53.43it/s, v_num=8, train_loss=0.220, Acc%=96.80]
Epoch 1: 14%|█▎ | 255/1875 [00:04<00:30, 53.42it/s, v_num=8, train_loss=0.567, Acc%=96.80]
Epoch 1: 14%|█▎ | 256/1875 [00:04<00:30, 53.43it/s, v_num=8, train_loss=0.567, Acc%=96.80]
Epoch 1: 14%|█▎ | 256/1875 [00:04<00:30, 53.43it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 14%|█▎ | 257/1875 [00:04<00:30, 53.44it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 14%|█▎ | 257/1875 [00:04<00:30, 53.43it/s, v_num=8, train_loss=0.226, Acc%=96.80]
Epoch 1: 14%|█▍ | 258/1875 [00:04<00:30, 53.44it/s, v_num=8, train_loss=0.226, Acc%=96.80]
Epoch 1: 14%|█▍ | 258/1875 [00:04<00:30, 53.44it/s, v_num=8, train_loss=0.292, Acc%=96.80]
Epoch 1: 14%|█▍ | 259/1875 [00:04<00:30, 53.45it/s, v_num=8, train_loss=0.292, Acc%=96.80]
Epoch 1: 14%|█▍ | 259/1875 [00:04<00:30, 53.44it/s, v_num=8, train_loss=0.553, Acc%=96.80]
Epoch 1: 14%|█▍ | 260/1875 [00:04<00:30, 53.45it/s, v_num=8, train_loss=0.553, Acc%=96.80]
Epoch 1: 14%|█▍ | 260/1875 [00:04<00:30, 53.45it/s, v_num=8, train_loss=0.499, Acc%=96.80]
Epoch 1: 14%|█▍ | 261/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.499, Acc%=96.80]
Epoch 1: 14%|█▍ | 261/1875 [00:04<00:30, 53.45it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 14%|█▍ | 262/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.343, Acc%=96.80]
Epoch 1: 14%|█▍ | 262/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.356, Acc%=96.80]
Epoch 1: 14%|█▍ | 263/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.356, Acc%=96.80]
Epoch 1: 14%|█▍ | 263/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.267, Acc%=96.80]
Epoch 1: 14%|█▍ | 264/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.267, Acc%=96.80]
Epoch 1: 14%|█▍ | 264/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 14%|█▍ | 265/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 14%|█▍ | 265/1875 [00:04<00:30, 53.46it/s, v_num=8, train_loss=0.250, Acc%=96.80]
Epoch 1: 14%|█▍ | 266/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.250, Acc%=96.80]
Epoch 1: 14%|█▍ | 266/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.233, Acc%=96.80]
Epoch 1: 14%|█▍ | 267/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.233, Acc%=96.80]
Epoch 1: 14%|█▍ | 267/1875 [00:04<00:30, 53.47it/s, v_num=8, train_loss=0.194, Acc%=96.80]
Epoch 1: 14%|█▍ | 268/1875 [00:05<00:30, 53.47it/s, v_num=8, train_loss=0.194, Acc%=96.80]
Epoch 1: 14%|█▍ | 268/1875 [00:05<00:30, 53.47it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 14%|█▍ | 269/1875 [00:05<00:30, 53.48it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 14%|█▍ | 269/1875 [00:05<00:30, 53.47it/s, v_num=8, train_loss=0.454, Acc%=96.80]
Epoch 1: 14%|█▍ | 270/1875 [00:05<00:30, 53.48it/s, v_num=8, train_loss=0.454, Acc%=96.80]
Epoch 1: 14%|█▍ | 270/1875 [00:05<00:30, 53.47it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 14%|█▍ | 271/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.211, Acc%=96.80]
Epoch 1: 14%|█▍ | 271/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.403, Acc%=96.80]
Epoch 1: 15%|█▍ | 272/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.403, Acc%=96.80]
Epoch 1: 15%|█▍ | 272/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 15%|█▍ | 273/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 15%|█▍ | 273/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.0982, Acc%=96.80]
Epoch 1: 15%|█▍ | 274/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.0982, Acc%=96.80]
Epoch 1: 15%|█▍ | 274/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 15%|█▍ | 275/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.295, Acc%=96.80]
Epoch 1: 15%|█▍ | 275/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.131, Acc%=96.80]
Epoch 1: 15%|█▍ | 276/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.131, Acc%=96.80]
Epoch 1: 15%|█▍ | 276/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 15%|█▍ | 277/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 15%|█▍ | 277/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 15%|█▍ | 278/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 15%|█▍ | 278/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.437, Acc%=96.80]
Epoch 1: 15%|█▍ | 279/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.437, Acc%=96.80]
Epoch 1: 15%|█▍ | 279/1875 [00:05<00:29, 53.48it/s, v_num=8, train_loss=0.259, Acc%=96.80]
Epoch 1: 15%|█▍ | 280/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.259, Acc%=96.80]
Epoch 1: 15%|█▍ | 280/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 15%|█▍ | 281/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.177, Acc%=96.80]
Epoch 1: 15%|█▍ | 281/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.124, Acc%=96.80]
Epoch 1: 15%|█▌ | 282/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.124, Acc%=96.80]
Epoch 1: 15%|█▌ | 282/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.717, Acc%=96.80]
Epoch 1: 15%|█▌ | 283/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.717, Acc%=96.80]
Epoch 1: 15%|█▌ | 283/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.145, Acc%=96.80]
Epoch 1: 15%|█▌ | 284/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.145, Acc%=96.80]
Epoch 1: 15%|█▌ | 284/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 15%|█▌ | 285/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.231, Acc%=96.80]
Epoch 1: 15%|█▌ | 285/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.191, Acc%=96.80]
Epoch 1: 15%|█▌ | 286/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.191, Acc%=96.80]
Epoch 1: 15%|█▌ | 286/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.378, Acc%=96.80]
Epoch 1: 15%|█▌ | 287/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.378, Acc%=96.80]
Epoch 1: 15%|█▌ | 287/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 15%|█▌ | 288/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 15%|█▌ | 288/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.261, Acc%=96.80]
Epoch 1: 15%|█▌ | 289/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.261, Acc%=96.80]
Epoch 1: 15%|█▌ | 289/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.126, Acc%=96.80]
Epoch 1: 15%|█▌ | 290/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.126, Acc%=96.80]
Epoch 1: 15%|█▌ | 290/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.314, Acc%=96.80]
Epoch 1: 16%|█▌ | 291/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.314, Acc%=96.80]
Epoch 1: 16%|█▌ | 291/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.264, Acc%=96.80]
Epoch 1: 16%|█▌ | 292/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.264, Acc%=96.80]
Epoch 1: 16%|█▌ | 292/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 16%|█▌ | 293/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 16%|█▌ | 293/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.347, Acc%=96.80]
Epoch 1: 16%|█▌ | 294/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.347, Acc%=96.80]
Epoch 1: 16%|█▌ | 294/1875 [00:05<00:29, 53.49it/s, v_num=8, train_loss=0.220, Acc%=96.80]
Epoch 1: 16%|█▌ | 295/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.220, Acc%=96.80]
Epoch 1: 16%|█▌ | 295/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.376, Acc%=96.80]
Epoch 1: 16%|█▌ | 296/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.376, Acc%=96.80]
Epoch 1: 16%|█▌ | 296/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.381, Acc%=96.80]
Epoch 1: 16%|█▌ | 297/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.381, Acc%=96.80]
Epoch 1: 16%|█▌ | 297/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.297, Acc%=96.80]
Epoch 1: 16%|█▌ | 298/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.297, Acc%=96.80]
Epoch 1: 16%|█▌ | 298/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.161, Acc%=96.80]
Epoch 1: 16%|█▌ | 299/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.161, Acc%=96.80]
Epoch 1: 16%|█▌ | 299/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.333, Acc%=96.80]
Epoch 1: 16%|█▌ | 300/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.333, Acc%=96.80]
Epoch 1: 16%|█▌ | 300/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.306, Acc%=96.80]
Epoch 1: 16%|█▌ | 301/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.306, Acc%=96.80]
Epoch 1: 16%|█▌ | 301/1875 [00:05<00:29, 53.50it/s, v_num=8, train_loss=0.0889, Acc%=96.80]
Epoch 1: 16%|█▌ | 302/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.0889, Acc%=96.80]
Epoch 1: 16%|█▌ | 302/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.178, Acc%=96.80]
Epoch 1: 16%|█▌ | 303/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.178, Acc%=96.80]
Epoch 1: 16%|█▌ | 303/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.152, Acc%=96.80]
Epoch 1: 16%|█▌ | 304/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.152, Acc%=96.80]
Epoch 1: 16%|█▌ | 304/1875 [00:05<00:29, 53.51it/s, v_num=8, train_loss=0.213, Acc%=96.80]
Epoch 1: 16%|█▋ | 305/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.213, Acc%=96.80]
Epoch 1: 16%|█▋ | 305/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.183, Acc%=96.80]
Epoch 1: 16%|█▋ | 306/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.183, Acc%=96.80]
Epoch 1: 16%|█▋ | 306/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 16%|█▋ | 307/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.197, Acc%=96.80]
Epoch 1: 16%|█▋ | 307/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.171, Acc%=96.80]
Epoch 1: 16%|█▋ | 308/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.171, Acc%=96.80]
Epoch 1: 16%|█▋ | 308/1875 [00:05<00:29, 53.52it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 16%|█▋ | 309/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.337, Acc%=96.80]
Epoch 1: 16%|█▋ | 309/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 17%|█▋ | 310/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 17%|█▋ | 310/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.180, Acc%=96.80]
Epoch 1: 17%|█▋ | 311/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.180, Acc%=96.80]
Epoch 1: 17%|█▋ | 311/1875 [00:05<00:29, 53.53it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 17%|█▋ | 312/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.272, Acc%=96.80]
Epoch 1: 17%|█▋ | 312/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.422, Acc%=96.80]
Epoch 1: 17%|█▋ | 313/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.422, Acc%=96.80]
Epoch 1: 17%|█▋ | 313/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.487, Acc%=96.80]
Epoch 1: 17%|█▋ | 314/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.487, Acc%=96.80]
Epoch 1: 17%|█▋ | 314/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.242, Acc%=96.80]
Epoch 1: 17%|█▋ | 315/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.242, Acc%=96.80]
Epoch 1: 17%|█▋ | 315/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.125, Acc%=96.80]
Epoch 1: 17%|█▋ | 316/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.125, Acc%=96.80]
Epoch 1: 17%|█▋ | 316/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 17%|█▋ | 317/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 17%|█▋ | 317/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 17%|█▋ | 318/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.133, Acc%=96.80]
Epoch 1: 17%|█▋ | 318/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.328, Acc%=96.80]
Epoch 1: 17%|█▋ | 319/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.328, Acc%=96.80]
Epoch 1: 17%|█▋ | 319/1875 [00:05<00:29, 53.54it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 17%|█▋ | 320/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.374, Acc%=96.80]
Epoch 1: 17%|█▋ | 320/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 17%|█▋ | 321/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.327, Acc%=96.80]
Epoch 1: 17%|█▋ | 321/1875 [00:05<00:29, 53.55it/s, v_num=8, train_loss=0.470, Acc%=96.80]
Epoch 1: 17%|█▋ | 322/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.470, Acc%=96.80]
Epoch 1: 17%|█▋ | 322/1875 [00:06<00:29, 53.55it/s, v_num=8, train_loss=0.722, Acc%=96.80]
Epoch 1: 17%|█▋ | 323/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.722, Acc%=96.80]
Epoch 1: 17%|█▋ | 323/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.228, Acc%=96.80]
Epoch 1: 17%|█▋ | 324/1875 [00:06<00:28, 53.56it/s, v_num=8, train_loss=0.228, Acc%=96.80]
Epoch 1: 17%|█▋ | 324/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.140, Acc%=96.80]
Epoch 1: 17%|█▋ | 325/1875 [00:06<00:28, 53.56it/s, v_num=8, train_loss=0.140, Acc%=96.80]
Epoch 1: 17%|█▋ | 325/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.344, Acc%=96.80]
Epoch 1: 17%|█▋ | 326/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.344, Acc%=96.80]
Epoch 1: 17%|█▋ | 326/1875 [00:06<00:28, 53.54it/s, v_num=8, train_loss=0.426, Acc%=96.80]
Epoch 1: 17%|█▋ | 327/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.426, Acc%=96.80]
Epoch 1: 17%|█▋ | 327/1875 [00:06<00:28, 53.54it/s, v_num=8, train_loss=0.390, Acc%=96.80]
Epoch 1: 17%|█▋ | 328/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.390, Acc%=96.80]
Epoch 1: 17%|█▋ | 328/1875 [00:06<00:28, 53.54it/s, v_num=8, train_loss=0.354, Acc%=96.80]
Epoch 1: 18%|█▊ | 329/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.354, Acc%=96.80]
Epoch 1: 18%|█▊ | 329/1875 [00:06<00:28, 53.54it/s, v_num=8, train_loss=0.280, Acc%=96.80]
Epoch 1: 18%|█▊ | 330/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.280, Acc%=96.80]
Epoch 1: 18%|█▊ | 330/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.115, Acc%=96.80]
Epoch 1: 18%|█▊ | 331/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.115, Acc%=96.80]
Epoch 1: 18%|█▊ | 331/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.341, Acc%=96.80]
Epoch 1: 18%|█▊ | 332/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.341, Acc%=96.80]
Epoch 1: 18%|█▊ | 332/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.391, Acc%=96.80]
Epoch 1: 18%|█▊ | 333/1875 [00:06<00:28, 53.55it/s, v_num=8, train_loss=0.391, Acc%=96.80]
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Epoch 1: 57%|█████▋ | 1076/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.483, Acc%=96.80]
Epoch 1: 57%|█████▋ | 1077/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.483, Acc%=96.80]
Epoch 1: 57%|█████▋ | 1077/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.406, Acc%=96.80]
Epoch 1: 57%|█████▋ | 1078/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.406, Acc%=96.80]
Epoch 1: 57%|█████▋ | 1078/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.265, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1079/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.265, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1079/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.328, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1080/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.328, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1080/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.123, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1081/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.123, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1081/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.201, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1082/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.253, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1083/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.181, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1084/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.181, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1084/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.093, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1085/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0504, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1086/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0504, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1086/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.148, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1087/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.174, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1088/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.102, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1089/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.161, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1090/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.313, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1091/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.313, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1091/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.365, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1092/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.365, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1092/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0814, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1093/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0814, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1093/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.194, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1094/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.297, Acc%=96.80]
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Epoch 1: 58%|█████▊ | 1095/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1096/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.109, Acc%=96.80]
Epoch 1: 58%|█████▊ | 1096/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.151, Acc%=96.80]
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Epoch 1: 59%|█████▊ | 1097/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.158, Acc%=96.80]
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Epoch 1: 59%|█████▊ | 1098/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.116, Acc%=96.80]
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Epoch 1: 59%|█████▊ | 1100/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.270, Acc%=96.80]
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Epoch 1: 59%|█████▊ | 1101/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.121, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1102/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.108, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1103/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.331, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1105/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.284, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1106/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.204, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1109/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.132, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1109/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.164, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1110/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.145, Acc%=96.80]
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Epoch 1: 59%|█████▉ | 1111/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1112/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.185, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1112/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0694, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1113/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.0694, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1113/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.112, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1114/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.112, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1114/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.259, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1115/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.259, Acc%=96.80]
Epoch 1: 59%|█████▉ | 1115/1875 [00:20<00:14, 53.70it/s, v_num=8, train_loss=0.117, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1116/1875 [00:20<00:14, 53.69it/s, v_num=8, train_loss=0.117, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1116/1875 [00:20<00:14, 53.69it/s, v_num=8, train_loss=0.329, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1117/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.329, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1117/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.248, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1118/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.248, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1118/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.331, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1119/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.331, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1119/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.224, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1120/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.224, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1120/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1121/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1121/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.159, Acc%=96.80]
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Epoch 1: 60%|█████▉ | 1122/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.396, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1123/1875 [00:20<00:14, 53.69it/s, v_num=8, train_loss=0.396, Acc%=96.80]
Epoch 1: 60%|█████▉ | 1123/1875 [00:20<00:14, 53.68it/s, v_num=8, train_loss=0.148, Acc%=96.80]
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Epoch 1: 60%|█████▉ | 1124/1875 [00:20<00:13, 53.69it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 60%|██████ | 1125/1875 [00:20<00:13, 53.69it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 60%|██████ | 1125/1875 [00:20<00:13, 53.69it/s, v_num=8, train_loss=0.109, Acc%=96.80]
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Epoch 1: 60%|██████ | 1127/1875 [00:20<00:13, 53.68it/s, v_num=8, train_loss=0.237, Acc%=96.80]
Epoch 1: 60%|██████ | 1127/1875 [00:20<00:13, 53.68it/s, v_num=8, train_loss=0.289, Acc%=96.80]
Epoch 1: 60%|██████ | 1128/1875 [00:21<00:13, 53.68it/s, v_num=8, train_loss=0.289, Acc%=96.80]
Epoch 1: 60%|██████ | 1128/1875 [00:21<00:13, 53.68it/s, v_num=8, train_loss=0.233, Acc%=96.80]
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Epoch 1: 60%|██████ | 1132/1875 [00:21<00:13, 53.69it/s, v_num=8, train_loss=0.355, Acc%=96.80]
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Epoch 1: 60%|██████ | 1133/1875 [00:21<00:13, 53.68it/s, v_num=8, train_loss=0.469, Acc%=96.80]
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Epoch 1: 62%|██████▏ | 1163/1875 [00:21<00:13, 53.65it/s, v_num=8, train_loss=0.264, Acc%=96.80]
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Epoch 1: 63%|██████▎ | 1184/1875 [00:22<00:12, 53.64it/s, v_num=8, train_loss=0.360, Acc%=96.80]
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Epoch 1: 63%|██████▎ | 1185/1875 [00:22<00:12, 53.64it/s, v_num=8, train_loss=0.219, Acc%=96.80]
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Epoch 1: 63%|██████▎ | 1186/1875 [00:22<00:12, 53.64it/s, v_num=8, train_loss=0.389, Acc%=96.80]
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Epoch 1: 63%|██████▎ | 1187/1875 [00:22<00:12, 53.64it/s, v_num=8, train_loss=0.209, Acc%=96.80]
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Epoch 1: 64%|██████▎ | 1193/1875 [00:22<00:12, 53.63it/s, v_num=8, train_loss=0.169, Acc%=96.80]
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Epoch 1: 64%|██████▎ | 1195/1875 [00:22<00:12, 53.64it/s, v_num=8, train_loss=0.204, Acc%=96.80]
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Epoch 1: 69%|██████▉ | 1295/1875 [00:24<00:10, 53.60it/s, v_num=8, train_loss=0.113, Acc%=96.80]
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Epoch 1: 71%|███████ | 1329/1875 [00:24<00:10, 53.63it/s, v_num=8, train_loss=0.116, Acc%=96.80]
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Epoch 1: 71%|███████ | 1331/1875 [00:24<00:10, 53.63it/s, v_num=8, train_loss=0.0714, Acc%=96.80]
Epoch 1: 71%|███████ | 1331/1875 [00:24<00:10, 53.63it/s, v_num=8, train_loss=0.117, Acc%=96.80]
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Epoch 1: 71%|███████ | 1333/1875 [00:24<00:10, 53.63it/s, v_num=8, train_loss=0.225, Acc%=96.80]
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Epoch 1: 71%|███████▏ | 1339/1875 [00:24<00:09, 53.62it/s, v_num=8, train_loss=0.240, Acc%=96.80]
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Epoch 1: 71%|███████▏ | 1340/1875 [00:24<00:09, 53.62it/s, v_num=8, train_loss=0.313, Acc%=96.80]
Epoch 1: 72%|███████▏ | 1341/1875 [00:25<00:09, 53.62it/s, v_num=8, train_loss=0.313, Acc%=96.80]
Epoch 1: 72%|███████▏ | 1341/1875 [00:25<00:09, 53.62it/s, v_num=8, train_loss=0.209, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1365/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.368, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1366/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.307, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1367/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.304, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1368/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.245, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1369/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.112, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1370/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.332, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1371/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.202, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1372/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.329, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1373/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.128, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1374/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.272, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1375/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.198, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1376/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.310, Acc%=96.80]
Epoch 1: 73%|███████▎ | 1377/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.310, Acc%=96.80]
Epoch 1: 73%|███████▎ | 1377/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.193, Acc%=96.80]
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Epoch 1: 73%|███████▎ | 1378/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.0634, Acc%=96.80]
Epoch 1: 74%|███████▎ | 1379/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.0634, Acc%=96.80]
Epoch 1: 74%|███████▎ | 1379/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.266, Acc%=96.80]
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Epoch 1: 74%|███████▎ | 1380/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 74%|███████▎ | 1381/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.216, Acc%=96.80]
Epoch 1: 74%|███████▎ | 1381/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.442, Acc%=96.80]
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Epoch 1: 74%|███████▎ | 1382/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.231, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1385/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.180, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1386/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.220, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1387/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.208, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1388/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.151, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1389/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.181, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1390/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.342, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1391/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.284, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1392/1875 [00:25<00:09, 53.63it/s, v_num=8, train_loss=0.433, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1393/1875 [00:25<00:08, 53.63it/s, v_num=8, train_loss=0.350, Acc%=96.80]
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Epoch 1: 74%|███████▍ | 1395/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.218, Acc%=96.80]
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Epoch 1: 75%|███████▍ | 1398/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.376, Acc%=96.80]
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Epoch 1: 75%|███████▍ | 1399/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 75%|███████▍ | 1400/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.235, Acc%=96.80]
Epoch 1: 75%|███████▍ | 1400/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.195, Acc%=96.80]
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Epoch 1: 75%|███████▍ | 1401/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.251, Acc%=96.80]
Epoch 1: 75%|███████▍ | 1402/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.251, Acc%=96.80]
Epoch 1: 75%|███████▍ | 1402/1875 [00:26<00:08, 53.63it/s, v_num=8, train_loss=0.408, Acc%=96.80]
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Epoch 1: 76%|███████▋ | 1434/1875 [00:26<00:08, 53.64it/s, v_num=8, train_loss=0.279, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1435/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.0907, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1436/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.189, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1437/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.190, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1438/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.197, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1439/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.231, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1440/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.203, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1441/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.117, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1442/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.123, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1443/1875 [00:26<00:08, 53.65it/s, v_num=8, train_loss=0.462, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1444/1875 [00:26<00:08, 53.64it/s, v_num=8, train_loss=0.204, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1445/1875 [00:26<00:08, 53.64it/s, v_num=8, train_loss=0.245, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1446/1875 [00:26<00:07, 53.64it/s, v_num=8, train_loss=0.136, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1447/1875 [00:26<00:07, 53.64it/s, v_num=8, train_loss=0.276, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1448/1875 [00:26<00:07, 53.64it/s, v_num=8, train_loss=0.276, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1448/1875 [00:26<00:07, 53.64it/s, v_num=8, train_loss=0.183, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1449/1875 [00:27<00:07, 53.64it/s, v_num=8, train_loss=0.335, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1450/1875 [00:27<00:07, 53.64it/s, v_num=8, train_loss=0.335, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1450/1875 [00:27<00:07, 53.64it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1451/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 77%|███████▋ | 1451/1875 [00:27<00:07, 53.64it/s, v_num=8, train_loss=0.311, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1452/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0657, Acc%=96.80]
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Epoch 1: 77%|███████▋ | 1453/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.390, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1454/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.390, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1454/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.253, Acc%=96.80]
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Epoch 1: 78%|███████▊ | 1455/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.138, Acc%=96.80]
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Epoch 1: 78%|███████▊ | 1456/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.356, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1457/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.356, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1457/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.247, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1458/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.247, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1458/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.101, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1459/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.101, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1459/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1460/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.118, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1460/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.183, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1461/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.183, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1461/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.261, Acc%=96.80]
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Epoch 1: 78%|███████▊ | 1462/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.304, Acc%=96.80]
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Epoch 1: 78%|███████▊ | 1463/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.329, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1464/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.329, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1464/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.178, Acc%=96.80]
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Epoch 1: 78%|███████▊ | 1465/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1466/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.382, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1466/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1467/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.318, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1467/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.359, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1468/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.359, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1468/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0952, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1469/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0952, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1469/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0533, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1470/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0533, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1470/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.412, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1471/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.412, Acc%=96.80]
Epoch 1: 78%|███████▊ | 1471/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.200, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1472/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.200, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1472/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1473/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.243, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1473/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1474/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.401, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1474/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.155, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1475/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.155, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1475/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.270, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1476/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.270, Acc%=96.80]
Epoch 1: 79%|███████▊ | 1476/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0844, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1477/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.0844, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1477/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.254, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1478/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.254, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1478/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1479/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 79%|███████▉ | 1479/1875 [00:27<00:07, 53.65it/s, v_num=8, train_loss=0.129, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1480/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.193, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1481/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.219, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1482/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.274, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1483/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.180, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1484/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.130, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1485/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.183, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1486/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.377, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1488/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.341, Acc%=96.80]
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Epoch 1: 79%|███████▉ | 1489/1875 [00:27<00:07, 53.66it/s, v_num=8, train_loss=0.319, Acc%=96.80]
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Epoch 1: 80%|████████ | 1500/1875 [00:27<00:06, 53.67it/s, v_num=8, train_loss=0.409, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1576/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.0801, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1577/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.215, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1578/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.241, Acc%=96.80]
Epoch 1: 84%|████████▍ | 1579/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.241, Acc%=96.80]
Epoch 1: 84%|████████▍ | 1579/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.160, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1580/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.340, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1581/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.182, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1582/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.464, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1583/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.154, Acc%=96.80]
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Epoch 1: 84%|████████▍ | 1584/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.383, Acc%=96.80]
Epoch 1: 85%|████████▍ | 1585/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.383, Acc%=96.80]
Epoch 1: 85%|████████▍ | 1585/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.303, Acc%=96.80]
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Epoch 1: 85%|████████▍ | 1586/1875 [00:29<00:05, 53.71it/s, v_num=8, train_loss=0.133, Acc%=96.80]
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Epoch 1: 85%|████████▍ | 1587/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.181, Acc%=96.80]
Epoch 1: 85%|████████▍ | 1588/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.181, Acc%=96.80]
Epoch 1: 85%|████████▍ | 1588/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.159, Acc%=96.80]
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Epoch 1: 85%|████████▍ | 1590/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.315, Acc%=96.80]
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Epoch 1: 85%|████████▍ | 1591/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.336, Acc%=96.80]
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Epoch 1: 85%|████████▍ | 1592/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.211, Acc%=96.80]
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Epoch 1: 85%|████████▌ | 1597/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.276, Acc%=96.80]
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Epoch 1: 85%|████████▌ | 1598/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.148, Acc%=96.80]
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Epoch 1: 85%|████████▌ | 1600/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.0953, Acc%=96.80]
Epoch 1: 85%|████████▌ | 1600/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.177, Acc%=96.80]
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Epoch 1: 85%|████████▌ | 1601/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.237, Acc%=96.80]
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Epoch 1: 85%|████████▌ | 1602/1875 [00:29<00:05, 53.72it/s, v_num=8, train_loss=0.0808, Acc%=96.80]
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Epoch 1: 86%|████████▌ | 1609/1875 [00:29<00:04, 53.72it/s, v_num=8, train_loss=0.249, Acc%=96.80]
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Epoch 1: 86%|████████▌ | 1611/1875 [00:29<00:04, 53.72it/s, v_num=8, train_loss=0.321, Acc%=96.80]
Epoch 1: 86%|████████▌ | 1612/1875 [00:30<00:04, 53.72it/s, v_num=8, train_loss=0.321, Acc%=96.80]
Epoch 1: 86%|████████▌ | 1612/1875 [00:30<00:04, 53.72it/s, v_num=8, train_loss=0.446, Acc%=96.80]
Epoch 1: 86%|████████▌ | 1613/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.446, Acc%=96.80]
Epoch 1: 86%|████████▌ | 1613/1875 [00:30<00:04, 53.72it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 86%|████████▌ | 1614/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.215, Acc%=96.80]
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Epoch 1: 86%|████████▌ | 1615/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.202, Acc%=96.80]
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Epoch 1: 86%|████████▌ | 1616/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.150, Acc%=96.80]
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Epoch 1: 86%|████████▋ | 1618/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.296, Acc%=96.80]
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Epoch 1: 86%|████████▋ | 1620/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.375, Acc%=96.80]
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Epoch 1: 86%|████████▋ | 1621/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.331, Acc%=96.80]
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Epoch 1: 87%|████████▋ | 1622/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.229, Acc%=96.80]
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Epoch 1: 87%|████████▋ | 1623/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.253, Acc%=96.80]
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Epoch 1: 87%|████████▋ | 1627/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.308, Acc%=96.80]
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Epoch 1: 87%|████████▋ | 1628/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.134, Acc%=96.80]
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Epoch 1: 87%|████████▋ | 1629/1875 [00:30<00:04, 53.73it/s, v_num=8, train_loss=0.168, Acc%=96.80]
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Epoch 1: 88%|████████▊ | 1644/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.415, Acc%=96.80]
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Epoch 1: 88%|████████▊ | 1645/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.294, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1646/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.294, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1646/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.165, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1647/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.165, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1647/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1648/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.134, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1648/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.080, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1649/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.080, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1649/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.101, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1650/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.101, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1650/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.0576, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1651/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.0576, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1651/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.176, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1652/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.176, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1652/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.142, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1653/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.142, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1653/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.156, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1654/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.156, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1654/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.265, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1655/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.265, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1655/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.137, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1656/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.137, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1656/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.514, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1657/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.514, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1657/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.391, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1658/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.391, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1658/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.317, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1659/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.317, Acc%=96.80]
Epoch 1: 88%|████████▊ | 1659/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.420, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1660/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.420, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1660/1875 [00:30<00:04, 53.74it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1661/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1661/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.195, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1662/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.195, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1662/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1663/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.222, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1663/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.0665, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1664/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.0665, Acc%=96.80]
Epoch 1: 89%|████████▊ | 1664/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1665/1875 [00:30<00:03, 53.74it/s, v_num=8, train_loss=0.143, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1665/1875 [00:30<00:03, 53.73it/s, v_num=8, train_loss=0.364, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1666/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.364, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1666/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.427, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1667/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.427, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1667/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.163, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1668/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.163, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1668/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.242, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1669/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.242, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1669/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1670/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1670/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1671/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.372, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1671/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.213, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1672/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.213, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1672/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.110, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1673/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.110, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1673/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1674/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1674/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.249, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1675/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.249, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1675/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1676/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1676/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.375, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1677/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.375, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1677/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0814, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1678/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0814, Acc%=96.80]
Epoch 1: 89%|████████▉ | 1678/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.156, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1679/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.156, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1679/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1680/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.215, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1680/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.276, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1681/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.276, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1681/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.555, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1682/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.555, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1682/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1683/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.104, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1683/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.161, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1684/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.161, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1684/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1685/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.244, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1685/1875 [00:31<00:03, 53.72it/s, v_num=8, train_loss=0.354, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1686/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.354, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1686/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1687/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 90%|████████▉ | 1687/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.308, Acc%=96.80]
Epoch 1: 90%|█████████ | 1688/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.308, Acc%=96.80]
Epoch 1: 90%|█████████ | 1688/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0721, Acc%=96.80]
Epoch 1: 90%|█████████ | 1689/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0721, Acc%=96.80]
Epoch 1: 90%|█████████ | 1689/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.233, Acc%=96.80]
Epoch 1: 90%|█████████ | 1690/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.233, Acc%=96.80]
Epoch 1: 90%|█████████ | 1690/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.137, Acc%=96.80]
Epoch 1: 90%|█████████ | 1691/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.137, Acc%=96.80]
Epoch 1: 90%|█████████ | 1691/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.116, Acc%=96.80]
Epoch 1: 90%|█████████ | 1692/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.116, Acc%=96.80]
Epoch 1: 90%|█████████ | 1692/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0499, Acc%=96.80]
Epoch 1: 90%|█████████ | 1693/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0499, Acc%=96.80]
Epoch 1: 90%|█████████ | 1693/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.254, Acc%=96.80]
Epoch 1: 90%|█████████ | 1694/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.254, Acc%=96.80]
Epoch 1: 90%|█████████ | 1694/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.152, Acc%=96.80]
Epoch 1: 90%|█████████ | 1695/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.152, Acc%=96.80]
Epoch 1: 90%|█████████ | 1695/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.239, Acc%=96.80]
Epoch 1: 90%|█████████ | 1696/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.239, Acc%=96.80]
Epoch 1: 90%|█████████ | 1696/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.417, Acc%=96.80]
Epoch 1: 91%|█████████ | 1697/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.417, Acc%=96.80]
Epoch 1: 91%|█████████ | 1697/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.316, Acc%=96.80]
Epoch 1: 91%|█████████ | 1698/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.316, Acc%=96.80]
Epoch 1: 91%|█████████ | 1698/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.166, Acc%=96.80]
Epoch 1: 91%|█████████ | 1699/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.166, Acc%=96.80]
Epoch 1: 91%|█████████ | 1699/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.227, Acc%=96.80]
Epoch 1: 91%|█████████ | 1700/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.227, Acc%=96.80]
Epoch 1: 91%|█████████ | 1700/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0534, Acc%=96.80]
Epoch 1: 91%|█████████ | 1701/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.0534, Acc%=96.80]
Epoch 1: 91%|█████████ | 1701/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.274, Acc%=96.80]
Epoch 1: 91%|█████████ | 1702/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.274, Acc%=96.80]
Epoch 1: 91%|█████████ | 1702/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.267, Acc%=96.80]
Epoch 1: 91%|█████████ | 1703/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.267, Acc%=96.80]
Epoch 1: 91%|█████████ | 1703/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 91%|█████████ | 1704/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.172, Acc%=96.80]
Epoch 1: 91%|█████████ | 1704/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.170, Acc%=96.80]
Epoch 1: 91%|█████████ | 1705/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.170, Acc%=96.80]
Epoch 1: 91%|█████████ | 1705/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.219, Acc%=96.80]
Epoch 1: 91%|█████████ | 1706/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.219, Acc%=96.80]
Epoch 1: 91%|█████████ | 1706/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.204, Acc%=96.80]
Epoch 1: 91%|█████████ | 1707/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.204, Acc%=96.80]
Epoch 1: 91%|█████████ | 1707/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.154, Acc%=96.80]
Epoch 1: 91%|█████████ | 1708/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.154, Acc%=96.80]
Epoch 1: 91%|█████████ | 1708/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.346, Acc%=96.80]
Epoch 1: 91%|█████████ | 1709/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.346, Acc%=96.80]
Epoch 1: 91%|█████████ | 1709/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.110, Acc%=96.80]
Epoch 1: 91%|█████████ | 1710/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.110, Acc%=96.80]
Epoch 1: 91%|█████████ | 1710/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1711/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.291, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1711/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1712/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.205, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1712/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.125, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1713/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.125, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1713/1875 [00:31<00:03, 53.73it/s, v_num=8, train_loss=0.223, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1714/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.223, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1714/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.131, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1715/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.131, Acc%=96.80]
Epoch 1: 91%|█████████▏| 1715/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.285, Acc%=96.80]
Epoch 1: 92%|█████████▏| 1716/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.285, Acc%=96.80]
Epoch 1: 92%|█████████▏| 1716/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.176, Acc%=96.80]
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Epoch 1: 92%|█████████▏| 1717/1875 [00:31<00:02, 53.73it/s, v_num=8, train_loss=0.0872, Acc%=96.80]
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Epoch 1: 93%|█████████▎| 1752/1875 [00:32<00:02, 53.74it/s, v_num=8, train_loss=0.172, Acc%=96.80]
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Epoch 1: 95%|█████████▌| 1786/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.0838, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1787/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.0838, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1787/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.462, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1788/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.462, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1788/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.273, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1789/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.273, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1789/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.480, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1790/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.480, Acc%=96.80]
Epoch 1: 95%|█████████▌| 1790/1875 [00:33<00:01, 53.76it/s, v_num=8, train_loss=0.650, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1791/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.650, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1791/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.304, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1792/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.304, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1792/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1793/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.238, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1793/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.315, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1794/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.315, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1794/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.121, Acc%=96.80]
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Epoch 1: 96%|█████████▌| 1795/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.456, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1796/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.456, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1796/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.111, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1797/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.111, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1797/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.357, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1798/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.357, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1798/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1799/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.210, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1799/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.0953, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1800/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.0953, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1800/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.136, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1801/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.136, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1801/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.465, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1802/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.465, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1802/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.141, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1803/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.141, Acc%=96.80]
Epoch 1: 96%|█████████▌| 1803/1875 [00:33<00:01, 53.75it/s, v_num=8, train_loss=0.335, Acc%=96.80]
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Epoch 1: 96%|█████████▋| 1806/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.407, Acc%=96.80]
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Epoch 1: 96%|█████████▋| 1807/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.142, Acc%=96.80]
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Epoch 1: 96%|█████████▋| 1808/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.112, Acc%=96.80]
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Epoch 1: 96%|█████████▋| 1809/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.123, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1810/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.102, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1811/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.111, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1812/1875 [00:33<00:01, 53.74it/s, v_num=8, train_loss=0.155, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1821/1875 [00:33<00:01, 53.73it/s, v_num=8, train_loss=0.193, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1822/1875 [00:33<00:00, 53.73it/s, v_num=8, train_loss=0.226, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1823/1875 [00:33<00:00, 53.73it/s, v_num=8, train_loss=0.0803, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1824/1875 [00:33<00:00, 53.73it/s, v_num=8, train_loss=0.109, Acc%=96.80]
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Epoch 1: 97%|█████████▋| 1826/1875 [00:33<00:00, 53.73it/s, v_num=8, train_loss=0.179, Acc%=96.80]
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Epoch 1: 98%|█████████▊| 1830/1875 [00:34<00:00, 53.72it/s, v_num=8, train_loss=0.460, Acc%=96.80]
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┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Acc │ 97.83% │
│ Brier │ 0.03869 │
│ Entropy │ 0.17207 │
│ NLL │ 0.08735 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Calibration ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ECE │ 0.02990 │
│ aECE │ 0.02991 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ OOD Detection ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUPR │ 95.38% │
│ AUROC │ 96.47% │
│ Entropy │ 1.28370 │
│ FPR95 │ 13.43% │
│ ens_Disagre… │ 0.60702 │
│ ens_Entropy │ 0.82825 │
│ ens_MI │ 0.45545 │
└──────────────┴───────────────────────────┘
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Test metric ┃ Selective Classification ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ AUGRC │ 0.09% │
│ AURC │ 0.10% │
│ Cov@5Risk │ 100.00% │
│ Risk@80Cov │ 0.05% │
└──────────────┴───────────────────────────┘
The training time should be approximately similar to the one of the single model that you trained before. However, please note that we are working with very small models, hence completely underusing your GPU. As such, the training time is not representative of what you would observe with larger models.
You can read more on Packed-Ensembles in the paper or the Medium post.
To Go Further & More Concepts of Uncertainty in ML¶
Question 1: Have a look at the models in the “lightning_logs”. If you are on your own machine, try to visualize the learning curves with tensorboard –logdir lightning_logs.
Question 2: Add a cell below and try to find the errors made by packed-ensembles on the test set. Visualize the errors and their labels and look at the predictions of the different sub-models. Are they similar? Can you think of uncertainty scores that could help you identify these errors?
Selective Classification¶
Selective classification or “prediction with rejection” is a paradigm in uncertainty-aware machine learning where the model can decide not to make a prediction if the confidence score given by the model is below some pre-computed threshold. This can be useful in real-world applications where the cost of making a wrong prediction is high.
In constrast to calibration, the values of the confidence scores are not important, only the order of the scores. Ideally, the best model will order all the correct predictions first, and all the incorrect predictions last. In this case, there will be a threshold so that all the predictions above the threshold are correct, and all the predictions below the threshold are incorrect.
In TorchUncertainty, we look at 3 different metrics for selective classification: - AURC: The area under the Risk (% of errors) vs. Coverage (% of classified samples) curve. This curve expresses how the risk of the model evolves as we increase the coverage (the proportion of predictions that are above the selection threshold). This metric will be minimized by a model able to perfectly separate the correct and incorrect predictions.
The following metrics are computed at a fixed risk and coverage level and that have practical interests. The idea of these metrics is that you can set the selection threshold to achieve a certain level of risk and coverage, as required by the technical constraints of your application: - Coverage at 5% Risk: The proportion of predictions that are above the selection threshold when it is set for the risk to egal 5%. Set the risk threshold to your application constraints. The higher the better. - Risk at 80% Coverage: The proportion of errors when the coverage is set to 80%. Set the coverage threshold to your application constraints. The lower the better.
Grouping Loss¶
The grouping loss is a measure of uncertainty orthogonal to calibration. Have a look at this paper to learn about it. Check out their small library GLest. TorchUncertainty includes a wrapper of the library to compute the grouping loss with eval_grouping_loss parameter.
Total running time of the script: (3 minutes 36.488 seconds)