112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Fashion-MNIST dataset."""
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import gzip
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import os
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import numpy as np
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from keras.utils.data_utils import get_file
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.datasets.fashion_mnist.load_data")
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def load_data():
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"""Loads the Fashion-MNIST dataset.
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This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories,
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along with a test set of 10,000 images. This dataset can be used as
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a drop-in replacement for MNIST.
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The classes are:
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| Label | Description |
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|:-----:|-------------|
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| 0 | T-shirt/top |
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| 1 | Trouser |
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| 2 | Pullover |
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| 3 | Dress |
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| 4 | Coat |
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| 5 | Sandal |
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| 6 | Shirt |
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| 7 | Sneaker |
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| 8 | Bag |
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| 9 | Ankle boot |
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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
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`(60000, 28, 28)`, containing the training data.
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**y_train**: uint8 NumPy array of labels (integers in range 0-9)
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with shape `(60000,)` for the training data.
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**x_test**: uint8 NumPy array of grayscale image data with shapes
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(10000, 28, 28), containing the test data.
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**y_test**: uint8 NumPy array of labels (integers in range 0-9)
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with shape `(10000,)` for the test data.
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Example:
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```python
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(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
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assert x_train.shape == (60000, 28, 28)
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assert x_test.shape == (10000, 28, 28)
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assert y_train.shape == (60000,)
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assert y_test.shape == (10000,)
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```
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License:
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The copyright for Fashion-MNIST is held by Zalando SE.
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Fashion-MNIST is licensed under the [MIT license](
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https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE).
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"""
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dirname = os.path.join("datasets", "fashion-mnist")
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base = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
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files = [
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"train-labels-idx1-ubyte.gz",
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"train-images-idx3-ubyte.gz",
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"t10k-labels-idx1-ubyte.gz",
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"t10k-images-idx3-ubyte.gz",
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]
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paths = []
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for fname in files:
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paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))
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with gzip.open(paths[0], "rb") as lbpath:
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y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)
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with gzip.open(paths[1], "rb") as imgpath:
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x_train = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(
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len(y_train), 28, 28
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)
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with gzip.open(paths[2], "rb") as lbpath:
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y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)
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with gzip.open(paths[3], "rb") as imgpath:
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x_test = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(
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len(y_test), 28, 28
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)
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return (x_train, y_train), (x_test, y_test)
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