Fashion mnist dataset (keras-team#7809)

* fixed typo

* added fashion-mnist dataset

* added docs

* pep8

* grammer

* use offset instead of struct

* reshape as in docs

docs/templates/datasets.md

@@ -146,6 +146,39 @@ from keras.datasets import mnist
     - __path__: if you do not have the index file locally (at `'~/.keras/datasets/' + path`), it will be downloaded to this location.
 
 
+---
+
+## Fashion-MNIST database of fashion articles
+
+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 class labels 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 |
+
+### Usage:
+
+```python
+from keras.datasets import fashion_mnist
+
+(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+```
+
+- __Returns:__
+    - 2 tuples:
+        - __x_train, x_test__: uint8 array of grayscale image data with shape (num_samples, 28, 28).
+        - __y_train, y_test__: uint8 array of labels (integers in range 0-9) with shape (num_samples,).
+
+
 ---
 
 ## Boston housing price regression dataset

keras/datasets/__init__.py

@@ -6,3 +6,4 @@
 from . import cifar10
 from . import cifar100
 from . import boston_housing
+from . import fashion_mnist

keras/datasets/fashion_mnist.py

@@ -0,0 +1,37 @@
+import gzip
+import os
+
+from ..utils.data_utils import get_file
+import numpy as np
+
+
+def load_data():
+    """Loads the Fashion-MNIST dataset.
+
+    # Returns
+        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
+    """
+    dirname = os.path.join('datasets', 'fashion-mnist')
+    base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
+    files = ['train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
+             't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz']
+
+    paths = []
+    for file in files:
+        paths.append(get_file(file, origin=base + file, 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)

tests/keras/datasets/test_datasets.py

@@ -8,6 +8,7 @@
 from keras.datasets import imdb
 from keras.datasets import mnist
 from keras.datasets import boston_housing
+from keras.datasets import fashion_mnist
 
 
 def test_cifar():
@@ -75,5 +76,15 @@ def test_boston_housing():
         assert len(x_test) == len(y_test)
 
 
+def test_fashion_mnist():
+    # only run data download tests 20% of the time
+    # to speed up frequent testing
+    random.seed(time.time())
+    if random.random() > 0.8:
+        (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+        assert len(x_train) == len(y_train) == 60000
+        assert len(x_test) == len(y_test) == 10000
+
+
 if __name__ == '__main__':
     pytest.main([__file__])