Module keras.api.keras.datasets.fashion_mnist
Public API for tf.keras.datasets.fashion_mnist namespace.
Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.datasets.fashion_mnist namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.datasets.fashion_mnist import load_data
del _print_function
from tensorflow.python.util import module_wrapper as _module_wrapper
if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
_sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
_sys.modules[__name__], "keras.datasets.fashion_mnist", public_apis=None, deprecation=True,
has_lite=False)
Functions
def load_data()
-
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
ofNumPy 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:
(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.
Expand source code
@keras_export('keras.datasets.fashion_mnist.load_data') def load_data(): """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). """ dirname = os.path.join('datasets', 'fashion-mnist') base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/' files = [ 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz' ] paths = [] for fname in files: paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname)) with gzip.open(paths[0], 'rb') as lbpath: y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8) with gzip.open(paths[1], 'rb') as imgpath: x_train = np.frombuffer( imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28) with gzip.open(paths[2], 'rb') as lbpath: y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8) with gzip.open(paths[3], 'rb') as imgpath: x_test = np.frombuffer( imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28) return (x_train, y_train), (x_test, y_test)