Tensorflow EOFError:压缩文件在到达流结束标记之前结束

Tensorflow EOFError:压缩文件在到达流结束标记之前结束,tensorflow,keras,conda,Tensorflow,Keras,Conda,我正在下载“keras.datasets.fashion_mnist” 然后互联网连接就断了。 当我再次启动程序时,我有这个错误 遇到同样的问题,只需删除此处不完整的下载集:C:\Users\Shady\.keras\datasets\fashion mnist C:\Users\Shady\.conda\envs\TFnEW\pythonw.exe C:/Users/Shady/PycharmProjects/CNN_TensorF/CNNTF.py Traceback (most rec

我正在下载“keras.datasets.fashion_mnist” 然后互联网连接就断了。 当我再次启动程序时,我有这个错误





遇到同样的问题,只需删除此处不完整的下载集:C:\Users\Shady\.keras\datasets\fashion mnist

C:\Users\Shady\.conda\envs\TFnEW\pythonw.exe C:/Users/Shady/PycharmProjects/CNN_TensorF/CNNTF.py
Traceback (most recent call last):
  File "C:/Users/Shady/PycharmProjects/CNN_TensorF/CNNTF.py", line 8, in <module>
    (train_images, train_labels), (test_images, test_labels) = data.load_data()
  File "C:\Users\Shady\.conda\envs\TFnEW\lib\site-packages\tensorflow\python\keras\datasets\fashion_mnist.py", line 59, in load_data
    imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)
  File "C:\Users\Shady\.conda\envs\TFnEW\lib\gzip.py", line 274, in read
    return self._buffer.read(size)
  File "C:\Users\Shady\.conda\envs\TFnEW\lib\gzip.py", line 480, in read
    raise EOFError("Compressed file ended before the "
EOFError: Compressed file ended before the end-of-stream marker was reached
Process finished with exit code 1***
_____________________________________________________________________________
    import tensorflow as tf
    from tensorflow import keras
    import numpy as np
    import matplotlib.pyplot as plt
    data = keras.datasets.fashion_mnist
    (train_images, train_labels), (test_images, test_labels) = data.load_data()
    class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                   'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
    train_images = train_images/255.0
    test_images = test_images/255.0

    model = keras.Sequential([
        keras.layers.Flatten(input_shape=(28,28)),
        keras.layers.Dense(128, activation="relu"),
        keras.layers.Dense(10, activation="softmax")
        ])
    model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
    model.fit(train_images, train_labels, epochs=5)
    test_loss, test_acc = model.evaluate(test_images, test_labels)
    print('\nTest accuracy:', test_acc)***