TensorFlow 1 version
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View source on GitHub
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Loads the Fashion-MNIST dataset.
tf.keras.datasets.fashion_mnist.load_data()
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).
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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:
(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.