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Train the MNIST dataset using LSTM model implemented by PyTorch.

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PyTorch LSTM MNIST

Train the MNIST dataset using LSTM model implemented by PyTorch.

Install

pip install -r requirements.txt

Usage

git clone https://github.com/XavierJiezou/pytorch-lstm-mnist.git
cd pytorch-lstm-mnist
python ./code/train_on_mnist.py

Note: you must unzip the file mnist.7z before training.

Config

You can also modify the configuration file.

data:
  data_root: ./data/mnist # Path to data
  train_ratio: 0.8 # Ratio of training set
  val_ratio: 0.1 # Ratio of validation set
  batch_size: 64 # How many samples per batch to load
  visualize_data_save: ./image/training_data_mnist.png

model:
  input_size: 28 # Number of expected features in the input
  hidden_size: 64 # Number of features in the hidden state
  num_layers: 1 # Number of recurrent layers
  output_size: 10 # Number of expected features in the output

train:
  num_epochs: 100 # How many epochs to use for data training
  sequence_length: 28 # Length of the input sequence
  learning_rate: 0.001 # Learning_rate
  device: cuda:0 # On which a `torch.Tensor` is or will be allocated
  save_path: ./checkpoint/mnist.pth # Path to save the trained model

log:
  sink: ./log/mnist.log # Path to save the logging file
  level: INFO # Logging level: DEBUG | INFO | WARNING | ERROR | SUCCESS | CRITICAL
  format: '{message}' # logging output format. Example: '{time:YYYY-MM-DD at HH:mm:ss} {level} {message}'
  visualize_log_save: ./image/training_log_mnist.png # Path to save the visualization result

Dataset

MNIST: https://yann.lecun.com/exdb/mnist/

mnist

Result

Dataset Sequence Length Input Size Accuracy
MNIST 28 28 0.9892

result

View more 
EPOCH: 001/100 LR: 0.0010 TRAIN-LOSS: 0.6444 TRAIN-ACC: 0.7916  VAL-LOSS: 0.2733 VAL-ACC: 0.9208  EPOCH-TIME: 0m10s
EPOCH: 002/100 LR: 0.0010 TRAIN-LOSS: 0.1911 TRAIN-ACC: 0.9446  VAL-LOSS: 0.1564 VAL-ACC: 0.9533  EPOCH-TIME: 0m10s
EPOCH: 003/100 LR: 0.0010 TRAIN-LOSS: 0.1317 TRAIN-ACC: 0.9617  VAL-LOSS: 0.1323 VAL-ACC: 0.9621  EPOCH-TIME: 0m9s
EPOCH: 004/100 LR: 0.0010 TRAIN-LOSS: 0.1077 TRAIN-ACC: 0.9686  VAL-LOSS: 0.1204 VAL-ACC: 0.9643  EPOCH-TIME: 0m9s
EPOCH: 005/100 LR: 0.0010 TRAIN-LOSS: 0.0894 TRAIN-ACC: 0.9735  VAL-LOSS: 0.1078 VAL-ACC: 0.9690  EPOCH-TIME: 0m9s
EPOCH: 006/100 LR: 0.0010 TRAIN-LOSS: 0.0776 TRAIN-ACC: 0.9775  VAL-LOSS: 0.0802 VAL-ACC: 0.9756  EPOCH-TIME: 0m9s
EPOCH: 007/100 LR: 0.0010 TRAIN-LOSS: 0.0695 TRAIN-ACC: 0.9794  VAL-LOSS: 0.0675 VAL-ACC: 0.9791  EPOCH-TIME: 0m9s
EPOCH: 008/100 LR: 0.0010 TRAIN-LOSS: 0.0589 TRAIN-ACC: 0.9827  VAL-LOSS: 0.0769 VAL-ACC: 0.9773  EPOCH-TIME: 0m9s
EPOCH: 009/100 LR: 0.0010 TRAIN-LOSS: 0.0536 TRAIN-ACC: 0.9838  VAL-LOSS: 0.0701 VAL-ACC: 0.9790  EPOCH-TIME: 0m9s
EPOCH: 010/100 LR: 0.0010 TRAIN-LOSS: 0.0488 TRAIN-ACC: 0.9850  VAL-LOSS: 0.0669 VAL-ACC: 0.9808  EPOCH-TIME: 0m10s
EPOCH: 011/100 LR: 0.0010 TRAIN-LOSS: 0.0441 TRAIN-ACC: 0.9862  VAL-LOSS: 0.0646 VAL-ACC: 0.9808  EPOCH-TIME: 0m10s
EPOCH: 012/100 LR: 0.0010 TRAIN-LOSS: 0.0403 TRAIN-ACC: 0.9881  VAL-LOSS: 0.0564 VAL-ACC: 0.9837  EPOCH-TIME: 0m10s
EPOCH: 013/100 LR: 0.0010 TRAIN-LOSS: 0.0371 TRAIN-ACC: 0.9889  VAL-LOSS: 0.0485 VAL-ACC: 0.9853  EPOCH-TIME: 0m9s
EPOCH: 014/100 LR: 0.0010 TRAIN-LOSS: 0.0334 TRAIN-ACC: 0.9900  VAL-LOSS: 0.0562 VAL-ACC: 0.9834  EPOCH-TIME: 0m10s
EPOCH: 015/100 LR: 0.0010 TRAIN-LOSS: 0.0320 TRAIN-ACC: 0.9905  VAL-LOSS: 0.0575 VAL-ACC: 0.9834  EPOCH-TIME: 0m9s
EPOCH: 016/100 LR: 0.0010 TRAIN-LOSS: 0.0318 TRAIN-ACC: 0.9906  VAL-LOSS: 0.0617 VAL-ACC: 0.9821  EPOCH-TIME: 0m9s
EPOCH: 017/100 LR: 0.0010 TRAIN-LOSS: 0.0278 TRAIN-ACC: 0.9913  VAL-LOSS: 0.0573 VAL-ACC: 0.9837  EPOCH-TIME: 0m10s
EPOCH: 018/100 LR: 0.0010 TRAIN-LOSS: 0.0258 TRAIN-ACC: 0.9918  VAL-LOSS: 0.0554 VAL-ACC: 0.9841  EPOCH-TIME: 0m9s
EPOCH: 019/100 LR: 0.0010 TRAIN-LOSS: 0.0217 TRAIN-ACC: 0.9933  VAL-LOSS: 0.0490 VAL-ACC: 0.9853  EPOCH-TIME: 0m10s
EPOCH: 020/100 LR: 0.0010 TRAIN-LOSS: 0.0215 TRAIN-ACC: 0.9933  VAL-LOSS: 0.0460 VAL-ACC: 0.9864  EPOCH-TIME: 0m10s
EPOCH: 021/100 LR: 0.0010 TRAIN-LOSS: 0.0206 TRAIN-ACC: 0.9933  VAL-LOSS: 0.0586 VAL-ACC: 0.9847  EPOCH-TIME: 0m10s
EPOCH: 022/100 LR: 0.0010 TRAIN-LOSS: 0.0191 TRAIN-ACC: 0.9939  VAL-LOSS: 0.0533 VAL-ACC: 0.9863  EPOCH-TIME: 0m10s
EPOCH: 023/100 LR: 0.0010 TRAIN-LOSS: 0.0179 TRAIN-ACC: 0.9945  VAL-LOSS: 0.0470 VAL-ACC: 0.9856  EPOCH-TIME: 0m10s
EPOCH: 024/100 LR: 0.0010 TRAIN-LOSS: 0.0182 TRAIN-ACC: 0.9940  VAL-LOSS: 0.0434 VAL-ACC: 0.9884  EPOCH-TIME: 0m10s
EPOCH: 025/100 LR: 0.0010 TRAIN-LOSS: 0.0140 TRAIN-ACC: 0.9957  VAL-LOSS: 0.0458 VAL-ACC: 0.9877  EPOCH-TIME: 0m10s
EPOCH: 026/100 LR: 0.0010 TRAIN-LOSS: 0.0149 TRAIN-ACC: 0.9954  VAL-LOSS: 0.0599 VAL-ACC: 0.9838  EPOCH-TIME: 0m10s
EPOCH: 027/100 LR: 0.0010 TRAIN-LOSS: 0.0154 TRAIN-ACC: 0.9951  VAL-LOSS: 0.0508 VAL-ACC: 0.9858  EPOCH-TIME: 0m10s
EPOCH: 028/100 LR: 0.0010 TRAIN-LOSS: 0.0160 TRAIN-ACC: 0.9949  VAL-LOSS: 0.0471 VAL-ACC: 0.9878  EPOCH-TIME: 0m10s
EPOCH: 029/100 LR: 0.0010 TRAIN-LOSS: 0.0128 TRAIN-ACC: 0.9957  VAL-LOSS: 0.0477 VAL-ACC: 0.9860  EPOCH-TIME: 0m10s
EPOCH: 030/100 LR: 0.0010 TRAIN-LOSS: 0.0107 TRAIN-ACC: 0.9966  VAL-LOSS: 0.0552 VAL-ACC: 0.9856  EPOCH-TIME: 0m10s
EPOCH: 031/100 LR: 0.0010 TRAIN-LOSS: 0.0107 TRAIN-ACC: 0.9967  VAL-LOSS: 0.0514 VAL-ACC: 0.9867  EPOCH-TIME: 0m9s
EPOCH: 032/100 LR: 0.0010 TRAIN-LOSS: 0.0122 TRAIN-ACC: 0.9958  VAL-LOSS: 0.0491 VAL-ACC: 0.9883  EPOCH-TIME: 0m10s
EPOCH: 033/100 LR: 0.0010 TRAIN-LOSS: 0.0106 TRAIN-ACC: 0.9967  VAL-LOSS: 0.0491 VAL-ACC: 0.9878  EPOCH-TIME: 0m9s
EPOCH: 034/100 LR: 0.0010 TRAIN-LOSS: 0.0081 TRAIN-ACC: 0.9973  VAL-LOSS: 0.0516 VAL-ACC: 0.9873  EPOCH-TIME: 0m10s
EPOCH: 035/100 LR: 0.0010 TRAIN-LOSS: 0.0136 TRAIN-ACC: 0.9958  VAL-LOSS: 0.0547 VAL-ACC: 0.9851  EPOCH-TIME: 0m9s
EPOCH: 036/100 LR: 0.0010 TRAIN-LOSS: 0.0082 TRAIN-ACC: 0.9975  VAL-LOSS: 0.0484 VAL-ACC: 0.9878  EPOCH-TIME: 0m10s
EPOCH: 037/100 LR: 0.0010 TRAIN-LOSS: 0.0104 TRAIN-ACC: 0.9965  VAL-LOSS: 0.0533 VAL-ACC: 0.9870  EPOCH-TIME: 0m9s
EPOCH: 038/100 LR: 0.0010 TRAIN-LOSS: 0.0084 TRAIN-ACC: 0.9975  VAL-LOSS: 0.0653 VAL-ACC: 0.9840  EPOCH-TIME: 0m10s
EPOCH: 039/100 LR: 0.0010 TRAIN-LOSS: 0.0084 TRAIN-ACC: 0.9975  VAL-LOSS: 0.0516 VAL-ACC: 0.9880  EPOCH-TIME: 0m9s
EPOCH: 040/100 LR: 0.0010 TRAIN-LOSS: 0.0081 TRAIN-ACC: 0.9974  VAL-LOSS: 0.0474 VAL-ACC: 0.9886  EPOCH-TIME: 0m9s
EPOCH: 041/100 LR: 0.0010 TRAIN-LOSS: 0.0079 TRAIN-ACC: 0.9976  VAL-LOSS: 0.0574 VAL-ACC: 0.9858  EPOCH-TIME: 0m9s
EPOCH: 042/100 LR: 0.0010 TRAIN-LOSS: 0.0072 TRAIN-ACC: 0.9979  VAL-LOSS: 0.0564 VAL-ACC: 0.9866  EPOCH-TIME: 0m9s
EPOCH: 043/100 LR: 0.0010 TRAIN-LOSS: 0.0095 TRAIN-ACC: 0.9971  VAL-LOSS: 0.0557 VAL-ACC: 0.9868  EPOCH-TIME: 0m10s
EPOCH: 044/100 LR: 0.0010 TRAIN-LOSS: 0.0061 TRAIN-ACC: 0.9980  VAL-LOSS: 0.0469 VAL-ACC: 0.9896  EPOCH-TIME: 0m10s
EPOCH: 045/100 LR: 0.0010 TRAIN-LOSS: 0.0089 TRAIN-ACC: 0.9971  VAL-LOSS: 0.0569 VAL-ACC: 0.9868  EPOCH-TIME: 0m10s
EPOCH: 046/100 LR: 0.0010 TRAIN-LOSS: 0.0068 TRAIN-ACC: 0.9979  VAL-LOSS: 0.0540 VAL-ACC: 0.9867  EPOCH-TIME: 0m9s
EPOCH: 047/100 LR: 0.0010 TRAIN-LOSS: 0.0049 TRAIN-ACC: 0.9985  VAL-LOSS: 0.0569 VAL-ACC: 0.9881  EPOCH-TIME: 0m10s
EPOCH: 048/100 LR: 0.0010 TRAIN-LOSS: 0.0079 TRAIN-ACC: 0.9975  VAL-LOSS: 0.0524 VAL-ACC: 0.9880  EPOCH-TIME: 0m9s
EPOCH: 049/100 LR: 0.0010 TRAIN-LOSS: 0.0037 TRAIN-ACC: 0.9991  VAL-LOSS: 0.0541 VAL-ACC: 0.9876  EPOCH-TIME: 0m9s
EPOCH: 050/100 LR: 0.0010 TRAIN-LOSS: 0.0075 TRAIN-ACC: 0.9976  VAL-LOSS: 0.0564 VAL-ACC: 0.9866  EPOCH-TIME: 0m9s
EPOCH: 051/100 LR: 0.0001 TRAIN-LOSS: 0.0031 TRAIN-ACC: 0.9992  VAL-LOSS: 0.0504 VAL-ACC: 0.9890  EPOCH-TIME: 0m9s
EPOCH: 052/100 LR: 0.0001 TRAIN-LOSS: 0.0013 TRAIN-ACC: 0.9998  VAL-LOSS: 0.0497 VAL-ACC: 0.9893  EPOCH-TIME: 0m10s
EPOCH: 053/100 LR: 0.0001 TRAIN-LOSS: 0.0010 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0491 VAL-ACC: 0.9901  EPOCH-TIME: 0m10s
EPOCH: 054/100 LR: 0.0001 TRAIN-LOSS: 0.0008 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0496 VAL-ACC: 0.9897  EPOCH-TIME: 0m9s
EPOCH: 055/100 LR: 0.0001 TRAIN-LOSS: 0.0007 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0504 VAL-ACC: 0.9897  EPOCH-TIME: 0m9s
EPOCH: 056/100 LR: 0.0001 TRAIN-LOSS: 0.0006 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0503 VAL-ACC: 0.9896  EPOCH-TIME: 0m10s
EPOCH: 057/100 LR: 0.0001 TRAIN-LOSS: 0.0005 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0503 VAL-ACC: 0.9900  EPOCH-TIME: 0m10s
EPOCH: 058/100 LR: 0.0001 TRAIN-LOSS: 0.0005 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0525 VAL-ACC: 0.9896  EPOCH-TIME: 0m10s
EPOCH: 059/100 LR: 0.0001 TRAIN-LOSS: 0.0004 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0532 VAL-ACC: 0.9896  EPOCH-TIME: 0m10s
EPOCH: 060/100 LR: 0.0001 TRAIN-LOSS: 0.0003 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0534 VAL-ACC: 0.9898  EPOCH-TIME: 0m10s
EPOCH: 061/100 LR: 0.0001 TRAIN-LOSS: 0.0003 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0546 VAL-ACC: 0.9891  EPOCH-TIME: 0m10s
EPOCH: 062/100 LR: 0.0001 TRAIN-LOSS: 0.0003 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0555 VAL-ACC: 0.9894  EPOCH-TIME: 0m10s
EPOCH: 063/100 LR: 0.0001 TRAIN-LOSS: 0.0002 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0556 VAL-ACC: 0.9894  EPOCH-TIME: 0m10s
EPOCH: 064/100 LR: 0.0001 TRAIN-LOSS: 0.0002 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0586 VAL-ACC: 0.9898  EPOCH-TIME: 0m9s
EPOCH: 065/100 LR: 0.0001 TRAIN-LOSS: 0.0002 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0584 VAL-ACC: 0.9894  EPOCH-TIME: 0m10s
EPOCH: 066/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0579 VAL-ACC: 0.9893  EPOCH-TIME: 0m9s
EPOCH: 067/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0599 VAL-ACC: 0.9887  EPOCH-TIME: 0m9s
EPOCH: 068/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0611 VAL-ACC: 0.9891  EPOCH-TIME: 0m10s
EPOCH: 069/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0634 VAL-ACC: 0.9890  EPOCH-TIME: 0m9s
EPOCH: 070/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0621 VAL-ACC: 0.9888  EPOCH-TIME: 0m9s
EPOCH: 071/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0620 VAL-ACC: 0.9890  EPOCH-TIME: 0m10s
EPOCH: 072/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0657 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 073/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0667 VAL-ACC: 0.9887  EPOCH-TIME: 0m10s
EPOCH: 074/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0665 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 075/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0675 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 076/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0686 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 077/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0692 VAL-ACC: 0.9888  EPOCH-TIME: 0m9s
EPOCH: 078/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0707 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 079/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0725 VAL-ACC: 0.9884  EPOCH-TIME: 0m10s
EPOCH: 080/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0749 VAL-ACC: 0.9887  EPOCH-TIME: 0m10s
EPOCH: 081/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0750 VAL-ACC: 0.9886  EPOCH-TIME: 0m10s
EPOCH: 082/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0759 VAL-ACC: 0.9887  EPOCH-TIME: 0m9s
EPOCH: 083/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0784 VAL-ACC: 0.9884  EPOCH-TIME: 0m10s
EPOCH: 084/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0810 VAL-ACC: 0.9884  EPOCH-TIME: 0m10s
EPOCH: 085/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0788 VAL-ACC: 0.9887  EPOCH-TIME: 0m10s
EPOCH: 086/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0815 VAL-ACC: 0.9888  EPOCH-TIME: 0m10s
EPOCH: 087/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0817 VAL-ACC: 0.9886  EPOCH-TIME: 0m10s
EPOCH: 088/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0824 VAL-ACC: 0.9884  EPOCH-TIME: 0m10s
EPOCH: 089/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0850 VAL-ACC: 0.9887  EPOCH-TIME: 0m9s
EPOCH: 090/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0869 VAL-ACC: 0.9886  EPOCH-TIME: 0m10s
EPOCH: 091/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0892 VAL-ACC: 0.9883  EPOCH-TIME: 0m9s
EPOCH: 092/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0903 VAL-ACC: 0.9883  EPOCH-TIME: 0m10s
EPOCH: 093/100 LR: 0.0001 TRAIN-LOSS: 0.0001 TRAIN-ACC: 1.0000  VAL-LOSS: 0.1042 VAL-ACC: 0.9868  EPOCH-TIME: 0m9s
EPOCH: 094/100 LR: 0.0001 TRAIN-LOSS: 0.0003 TRAIN-ACC: 0.9999  VAL-LOSS: 0.0972 VAL-ACC: 0.9874  EPOCH-TIME: 0m10s
EPOCH: 095/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0969 VAL-ACC: 0.9874  EPOCH-TIME: 0m9s
EPOCH: 096/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0966 VAL-ACC: 0.9877  EPOCH-TIME: 0m9s
EPOCH: 097/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0962 VAL-ACC: 0.9877  EPOCH-TIME: 0m9s
EPOCH: 098/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0959 VAL-ACC: 0.9877  EPOCH-TIME: 0m9s
EPOCH: 099/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0956 VAL-ACC: 0.9877  EPOCH-TIME: 0m9s
EPOCH: 100/100 LR: 0.0001 TRAIN-LOSS: 0.0000 TRAIN-ACC: 1.0000  VAL-LOSS: 0.0953 VAL-ACC: 0.9877  EPOCH-TIME: 0m10s

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Train the MNIST dataset using LSTM model implemented by PyTorch.

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pytorch lstm mnist

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