fashion-mnist example (#180)

* fashion mnist example

* fix from review

mnist/README.md

@@ -14,10 +14,16 @@ Run the example with:
 python main.py
 ```
 
-By default the example runs on the CPU. To run on the GPU, use: 
+By default, the example runs on the CPU. To run on the GPU, use: 
 
 ```
 python main.py --gpu
 ```
 
+For a full list of options run:
+
+```
+python main.py --help
+```
+
 To run the PyTorch or Jax examples install the respective framework.

mnist/jax_main.py

@@ -54,10 +54,10 @@ if __name__ == "__main__":
     batch_size = 256
     num_epochs = 10
     learning_rate = 1e-1
+    dataset = "mnist"
 
     # Load the data
-    train_images, train_labels, test_images, test_labels = mnist.mnist()
-
+    train_images, train_labels, test_images, test_labels = getattr(mnist, dataset)()
     # Load the model
     key, subkey = jax.random.split(jax.random.PRNGKey(seed))
     params = init_model(

mnist/main.py

@@ -45,7 +45,7 @@ def batch_iterate(batch_size, X, y):
         yield X[ids], y[ids]
 
 
-def main():
+def main(args):
     seed = 0
     num_layers = 2
     hidden_dim = 32
@@ -57,7 +57,9 @@ def main():
     np.random.seed(seed)
 
     # Load the data
-    train_images, train_labels, test_images, test_labels = map(mx.array, mnist.mnist())
+    train_images, train_labels, test_images, test_labels = map(
+        mx.array, getattr(mnist, args.dataset)()
+    )
 
     # Load the model
     model = MLP(num_layers, train_images.shape[-1], hidden_dim, num_classes)
@@ -83,7 +85,14 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("Train a simple MLP on MNIST with MLX.")
     parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mnist",
+        choices=["mnist", "fashion_mnist"],
+        help="The dataset to use.",
+    )
     args = parser.parse_args()
     if not args.gpu:
         mx.set_default_device(mx.cpu)
-    main()
+    main(args)

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"

mnist/torch_main.py

@@ -49,6 +49,13 @@ def batch_iterate(batch_size, X, y, device):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("Train a simple MLP on MNIST with PyTorch.")
     parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mnist",
+        choices=["mnist", "fashion_mnist"],
+        help="The dataset to use.",
+    )
     args = parser.parse_args()
 
     if not args.gpu:
@@ -71,7 +78,9 @@ if __name__ == "__main__":
         else:
             return torch.from_numpy(x.astype(int)).to(device)
 
-    train_images, train_labels, test_images, test_labels = map(to_tensor, mnist.mnist())
+    train_images, train_labels, test_images, test_labels = map(
+        to_tensor, getattr(mnist, args.dataset)()
+    )
 
     # Load the model
     model = MLP(num_layers, train_images.shape[-1], hidden_dim, num_classes).to(device)