ó 6êbi3ãóT•SrSSKrSSKrSSKrSSKJr SSKJr \"S5S5r g)zFashion-MNIST dataset.éN)Úget_file)Ú keras_exportz&keras.datasets.fashion_mnist.load_datac óF•[RRSS5nSn/SQn/nUHnUR[ XAU-US95 M! [ R "USS5n[R"UR5[RSS 9nS S S 5 [ R "US S5n[R"UR5[RS S 9R[W5S S 5nS S S 5 [ R "USS5n[R"UR5[RSS 9n S S S 5 [ R "USS5n[R"UR5[RS S 9R[W 5S S 5n S S S 5 WW4W W 44$!,(df  GNJ=f!,(df  Nì=f!,(df  N¨=f!,(df  NJ=f)aÂLoads the Fashion-MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The classes are: | Label | Description | |:-----:|-------------| | 0 | T-shirt/top | | 1 | Trouser | | 2 | Pullover | | 3 | Dress | | 4 | Coat | | 5 | Sandal | | 6 | Shirt | | 7 | Sneaker | | 8 | Bag | | 9 | Ankle boot | Returns: Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`. **x_train**: uint8 NumPy array of grayscale image data with shapes `(60000, 28, 28)`, containing the training data. **y_train**: uint8 NumPy array of labels (integers in range 0-9) with shape `(60000,)` for the training data. **x_test**: uint8 NumPy array of grayscale image data with shapes (10000, 28, 28), containing the test data. **y_test**: uint8 NumPy array of labels (integers in range 0-9) with shape `(10000,)` for the test data. Example: ```python (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() assert x_train.shape == (60000, 28, 28) assert x_test.shape == (10000, 28, 28) assert y_train.shape == (60000,) assert y_test.shape == (10000,) ``` License: The copyright for Fashion-MNIST is held by Zalando SE. Fashion-MNIST is licensed under the [MIT license]( https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE). Údatasetsz fashion-mnistzÈ HÈ H ) Ú__doc__rrÚnumpyrÚtf_keras.src.utils.data_utilsrÚ tensorflow.python.util.tf_exportrr)©ór(Úr0s7ðñã Û ãå2õ:ñÐ6Ó7ñR0ó8ñR0r/