fashion-mnist example (#180)

* fashion mnist example

* fix from review

mnist/mnist.py

@@ -8,7 +8,9 @@ from urllib import request
 import numpy as np
 
 
-def mnist(save_dir="/tmp"):
+def mnist(
+    save_dir="/tmp", base_url="http://yann.lecun.com/exdb/mnist/", filename="mnist.pkl"
+):
     """
     Load the MNIST dataset in 4 tensors: train images, train labels,
     test images, and test labels.
@@ -20,7 +22,6 @@ def mnist(save_dir="/tmp"):
     """
 
     def download_and_save(save_file):
-        base_url = "http://yann.lecun.com/exdb/mnist/"
         filename = [
             ["training_images", "train-images-idx3-ubyte.gz"],
             ["test_images", "t10k-images-idx3-ubyte.gz"],
@@ -45,13 +46,15 @@ def mnist(save_dir="/tmp"):
         with open(save_file, "wb") as f:
             pickle.dump(mnist, f)
 
-    save_file = os.path.join(save_dir, "mnist.pkl")
+    save_file = os.path.join(save_dir, filename)
     if not os.path.exists(save_file):
         download_and_save(save_file)
     with open(save_file, "rb") as f:
         mnist = pickle.load(f)
 
-    preproc = lambda x: x.astype(np.float32) / 255.0
+    def preproc(x):
+        return x.astype(np.float32) / 255.0
+
     mnist["training_images"] = preproc(mnist["training_images"])
     mnist["test_images"] = preproc(mnist["test_images"])
     return (
@@ -62,6 +65,14 @@ def mnist(save_dir="/tmp"):
     )
 
 
+def fashion_mnist(save_dir="/tmp"):
+    return mnist(
+        save_dir,
+        base_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/",
+        filename="fashion_mnist.pkl",
+    )
+
+
 if __name__ == "__main__":
     train_x, train_y, test_x, test_y = mnist()
     assert train_x.shape == (60000, 28 * 28), "Wrong training set size"