0hVdZddlZddlZddlZddlmZddlmZedddZ y)zFashion-MNIST dataset.N)get_file) keras_exportz&keras.datasets.fashion_mnist.load_datac tjjdd}d}gd}|r6tjj|}tj|dg}|D]#}|j t |||z||%tj|dd 5}tj|jtjd }d d d tj|d d 5}tj|jtjd jtdd} d d d tj|dd 5}tj|jtjd } d d d tj|dd 5}tj|jtjd jt dd} d d d  f  ffS#1swYHxYw#1swYxYw#1swYxYw#1swY6xYw)aLoads 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 | Args: cache_dir: directory where to cache the dataset locally. When None, defaults to `~/.keras/datasets`. 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-mnistzAIH'*H36H?I )N) __doc__rrnumpyrtf_keras.src.utils.data_utilsr tensorflow.python.util.tf_exportrr-r,r4s7 2:67^08^0r3