a Sic+@sHdZddlZddlZddlZddlmZddlmZedddZ dS)zFashion-MNIST dataset.N)get_file) keras_exportz&keras.datasets.fashion_mnist.load_datac Cstjdd}d}gd}g}|D]}|t||||dq"t|dd&}tj| tj dd }Wd n1s|0Yt|d d4}tj| tj d d  t |d d }Wd n1s0Yt|dd&}tj| tj dd } Wd n1s0Yt|dd4}tj| tj d d  t | d d } Wd n1sn0Y||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). datasetsz fashion-mnistzs