Difference between revisions of "PyTorch"
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== MNIST == | == MNIST == | ||
* https://github.com/pytorch/examples/tree/main/mnist | * https://github.com/pytorch/examples/tree/main/mnist | ||
| − | <source lang='bash'> | + | Tried on 2.3 GHz 8 Core Intel Core i9 with no GPU, 16 GB RAM with python 3.10 |
| + | <source lang='bash' highlight='1'> | ||
date;python main.py --save-model;date | date;python main.py --save-model;date | ||
Sa 31 Dez 2022 18:30:38 MST | Sa 31 Dez 2022 18:30:38 MST | ||
| Line 17: | Line 18: | ||
Train Epoch: 1 [5120/60000 (9%)] Loss: 0.584077 | Train Epoch: 1 [5120/60000 (9%)] Loss: 0.584077 | ||
Train Epoch: 1 [5760/60000 (10%)] Loss: 0.223495 | Train Epoch: 1 [5760/60000 (10%)] Loss: 0.223495 | ||
| + | ... | ||
| + | Train Epoch: 14 [57600/60000 (96%)] Loss: 0.036891 | ||
| + | Train Epoch: 14 [58240/60000 (97%)] Loss: 0.000181 | ||
| + | Train Epoch: 14 [58880/60000 (98%)] Loss: 0.007485 | ||
| + | Train Epoch: 14 [59520/60000 (99%)] Loss: 0.000201 | ||
| + | |||
| + | Test set: Average loss: 0.0258, Accuracy: 9919/10000 (99%) | ||
| + | |||
| + | Sa 31 Dez 2022 18:45:17 MST | ||
</source> | </source> | ||
= Links = | = Links = | ||
* https://stackoverflow.com/questions/tagged/pytorch | * https://stackoverflow.com/questions/tagged/pytorch | ||
Revision as of 03:50, 1 January 2023
Tutorial
Examples
MNIST
Tried on 2.3 GHz 8 Core Intel Core i9 with no GPU, 16 GB RAM with python 3.10
date;python main.py --save-model;date
Sa 31 Dez 2022 18:30:38 MST
Train Epoch: 1 [0/60000 (0%)] Loss: 2.305400
Train Epoch: 1 [640/60000 (1%)] Loss: 1.359776
Train Epoch: 1 [1280/60000 (2%)] Loss: 0.842926
Train Epoch: 1 [1920/60000 (3%)] Loss: 0.593794
Train Epoch: 1 [2560/60000 (4%)] Loss: 0.366338
Train Epoch: 1 [3200/60000 (5%)] Loss: 0.469323
Train Epoch: 1 [3840/60000 (6%)] Loss: 0.265342
Train Epoch: 1 [4480/60000 (7%)] Loss: 0.287802
Train Epoch: 1 [5120/60000 (9%)] Loss: 0.584077
Train Epoch: 1 [5760/60000 (10%)] Loss: 0.223495
...
Train Epoch: 14 [57600/60000 (96%)] Loss: 0.036891
Train Epoch: 14 [58240/60000 (97%)] Loss: 0.000181
Train Epoch: 14 [58880/60000 (98%)] Loss: 0.007485
Train Epoch: 14 [59520/60000 (99%)] Loss: 0.000201
Test set: Average loss: 0.0258, Accuracy: 9919/10000 (99%)
Sa 31 Dez 2022 18:45:17 MST