View source on GitHub
|
Loads the MNIST dataset.
tf.keras.datasets.mnist.load_data(
path='mnist.npz'
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
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.
Args | |
|---|---|
path
|
path where to cache the dataset locally
(relative to ~/.keras/datasets).
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Returns | |
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Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test).
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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:
(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.