加载Fashion MNIST数据集时遭遇EOFError问题，重新下载文件无法解决

Hey there, sorry to hear you're stuck with this EOFError even after deleting and re-downloading the dataset. Let's walk through some other troubleshooting steps that should help resolve this issue:

• Manually download and replace dataset files
  First, locate TensorFlow's default storage path for Fashion MNIST:

  • Windows: C:\Users\<YourUsername>\.keras\datasets\fashion-mnist
  • macOS/Linux: ~/.keras/datasets/fashion-mnist

  Download the four required dataset files (ensure they're fully downloaded and not corrupted—check file sizes: ~26MB for train-images-idx3-ubyte.gz, ~29KB for train-labels-idx1-ubyte.gz, ~4.3MB for t10k-images-idx3-ubyte.gz, ~5KB for t10k-labels-idx1-ubyte.gz). Place these files into the default path, overwriting any existing corrupted files, then re-run your original code.

• Load dataset from a custom path
  If the default directory has read/write issues, download the files to a custom folder and use direct file reading instead of TensorFlow's load_data():

  import tensorflow as tf
  from tensorflow import keras
  import numpy as np
  import gzip
  
  # Replace with your custom folder path
  custom_data_path = "path/to/your/fashion-mnist-files/"
  
  # Load training data
  with gzip.open(custom_data_path + 'train-labels-idx1-ubyte.gz', 'rb') as lbpath:
      train_labels = np.frombuffer(lbpath.read(), np.uint8, offset=8)
  with gzip.open(custom_data_path + 'train-images-idx3-ubyte.gz', 'rb') as imgpath:
      train_images = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(train_labels), 28, 28)
  
  # Load test data
  with gzip.open(custom_data_path + 't10k-labels-idx1-ubyte.gz', 'rb') as lbpath:
      test_labels = np.frombuffer(lbpath.read(), np.uint8, offset=8)
  with gzip.open(custom_data_path + 't10k-images-idx3-ubyte.gz', 'rb') as imgpath:
      test_images = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(test_labels), 28, 28)
  

• Check network and proxy settings
  Incomplete downloads often happen due to unstable networks or proxy/firewall interference:

  • Temporarily disable any proxy servers if you're using them
  • Switch to a stable wired network, delete the existing corrupted dataset files, and re-run the download code

• Update TensorFlow and dependencies
  Older TensorFlow versions might have bugs in the dataset download pipeline. Update to the latest stable version:

  pip install --upgrade tensorflow numpy gzip
  

  After updating, delete the old dataset files and try loading again.

Give these steps a shot—one of them should get your dataset loading correctly.

内容的提问来源于stack exchange，提问作者Slimshady