Ë 2ÆVhY ãóF—dZddlZddlmZddlmZed«dd„«Zy)z!MNIST handwritten digits dataset.éN)Ú keras_export)Úget_filezkeras.datasets.mnist.load_datacóº—d}t||dzd¬«}tj|d¬«5}|d|d}}|d |d }}||f||ffcd d d «S#1swYy xYw) aYLoads the MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. More info can be found at the [MNIST homepage](http://yann.lecun.com/exdb/mnist/). Args: path: path where to cache the dataset locally (relative 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. Pixel values range from 0 to 255. **`y_train`**: `uint8` NumPy array of digit 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. Pixel values range from 0 to 255. **`y_test`**: `uint8` NumPy array of digit labels (integers in range 0-9) with shape `(10000,)` for the test data. Example: ```python (x_train, y_train), (x_test, y_test) = keras.datasets.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: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the [Creative Commons Attribution-Share Alike 3.0 license.]( https://creativecommons.org/licenses/by-sa/3.0/) zrs+ðÙ'ãå-Ý/ñÐ.Ó/ò=4ó0ñ=4r