§ ~Wêh+ãób—dZddlZddlZddlZddlmZddlmZed¦«d„¦«Z dS)zFashion-MNIST dataset.éN)Úget_file)Ú keras_exportz&keras.datasets.fashion_mnist.load_datacót—tj dd¦«}d}gd¢}g}|D]*}| t |||z|¬¦«¦«Œ+t j|dd¦«5}tj|  ¦«tj d¬ ¦«}d d d ¦«n #1swxYwYt j|d d¦«5}tj|  ¦«tj d ¬ ¦«  t|¦«d d ¦«}d d d ¦«n #1swxYwYt j|dd¦«5}tj|  ¦«tj d¬ ¦«} d d d ¦«n #1swxYwYt j|dd¦«5}tj|  ¦«tj d ¬ ¦«  t| ¦«d d ¦«} d d d ¦«n #1swxYwY||f| | ffS)a^Loads 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-mnistzr0s‡ððÐà € € € Ø € € € àÐÐÐà+Ð+Ð+Ð+Ð+Ð+ð:Ð9Ð9Ð9Ð9Ð9ð€Ð6Ñ7Ô7ðR0ðR0ñ8Ô7ðR0ðR0ðR0r/