FashionMNIST in PyTorch

explains can use Fashion-MNIST dataset as shown below:

*Memos:

• The 1st argument is root(Required-Type:str or pathlib.Path). *An absolute or relative path is possible.


• The 2nd argument is train(Optional-Default:True-Type:bool). *If it's True, train data(60,000 images) is used while if it's False, test data(10,000 images) is used.


• The 3rd argument is transform(Optional-Default:None-Type:callable).


• The 4th argument is target_transform(Optional-Default:None-Type:callable).


• The 5th argument is download(Optional-Default:False-Type:bool):
  *Memos:
  
  
  
  
  • If it's True, the dataset is downloaded from the internet and extracted(unzipped) to root.
  
  
  • If it's True and the dataset is already downloaded, it's extracted.
  
  
  • If it's True and the dataset is already downloaded and extracted, nothing happens.
  
  
  • It should be False if the dataset is already downloaded and extracted because it's faster.
  
  
  • You can manually download and extract the dataset(t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz and train-labels-idx1-ubyte.gz) from
    Vollständiger Original-Bericht

    Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf dev.to.

    ↗ Original-Artikel auf dev.to lesen