Cifar-10 dataset (#78)

* Added cifar10

* custom mlp model with scaled array params

* copied mnist classifier and edited to use cifar as a test

* removed trial files

---------

Co-authored-by: samho <>

experiments/mnist/datasets.py

@@ -18,7 +18,9 @@
 import array
 import gzip
 import os
+import pickle
 import struct
+import tarfile
 import urllib.request
 from os import path
 
@@ -47,6 +49,19 @@ def _one_hot(x, k, dtype=np.float32):
     return np.array(x[:, None] == np.arange(k), dtype)
 
 
+def _unzip(file):
+    file = tarfile.open(file)
+    file.extractall(_DATA)
+    file.close()
+    return
+
+
+def _unpickle(file):
+    with open(file, "rb") as fo:
+        dict = pickle.load(fo, encoding="bytes")
+    return dict
+
+
 def mnist_raw():
     """Download and parse the raw MNIST dataset."""
     # CVDF mirror of http://yann.lecun.com/exdb/mnist/
@@ -93,3 +108,46 @@ def mnist(permute_train=False):
         train_labels = train_labels[perm]
 
     return train_images, train_labels, test_images, test_labels
+
+
+def cifar_raw():
+    """Download, unzip and parse the raw cifar dataset."""
+
+    filename = "cifar-10-python.tar.gz"
+    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+    _download(url, filename)
+    _unzip(path.join(_DATA, filename))
+
+    data_batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
+    data = []
+    labels = []
+    for batch in data_batches:
+        tmp_dict = _unpickle(path.join(_DATA, "cifar-10-batches-py", batch))
+        data.append(tmp_dict[b"data"])
+        labels.append(tmp_dict[b"labels"])
+    train_images = np.concatenate(data)
+    train_labels = np.concatenate(labels)
+
+    test_dict = _unpickle(path.join(_DATA, "cifar-10-batches-py", "test_batch"))
+    test_images = test_dict[b"data"]
+    test_labels = np.array(test_dict[b"labels"])
+
+    return train_images, train_labels, test_images, test_labels
+
+
+def cifar(permute_train=False):
+    """Download, parse and process cifar data to unit scale and one-hot labels."""
+
+    train_images, train_labels, test_images, test_labels = cifar_raw()
+
+    train_images = train_images / np.float32(255.0)
+    test_images = test_images / np.float32(255.0)
+    train_labels = _one_hot(train_labels, 10)
+    test_labels = _one_hot(test_labels, 10)
+
+    if permute_train:
+        perm = np.random.RandomState(0).permutation(train_images.shape[0])
+        train_images = train_images[perm]
+        train_labels = train_labels[perm]
+
+    return train_images, train_labels, test_images, test_labels