Feature/cifar10 - Cifar10 dataset included (#194)

* cifar10 dataset included via cifar10.py

* Refactor variable names

* cifar10 dataset included

* mnist.py for merge with master deleted

deep_bottleneck/datasets/cifar10.py

@@ -0,0 +1,33 @@
+from collections import namedtuple
+
+import numpy as np
+
+from tensorflow import keras
+from tensorflow.python.keras import utils as keras_utils
+from deep_bottleneck.datasets.base_dataset import Dataset
+
+
+def load():
+    """Load the CIFAR 10 dataset
+    Returns:
+        CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes
+        Returns two namedtuples, the first one containing training
+        and the second one containing test data respectively. Both come with fields X, y and Y:
+        - X is the data
+        - y is class, with numbers from 0 to 9
+        - Y is class, but coded as a 10-dim vector with one entry set to 1 at the column index corresponding to the class
+    """
+    n_classes = 10
+    (X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()
+    X_train = np.reshape(X_train, [X_train.shape[0], -1]).astype('float32') / 255.0
+    X_test = np.reshape(X_test, [X_test.shape[0], -1]).astype('float32') / 255.0
+
+    y_train = np.squeeze(y_train)
+    y_test = np.squeeze(y_test)
+
+    X_train = X_train * 2.0 - 1.0
+    X_test = X_test * 2.0 - 1.0
+
+    dataset = Dataset.from_labelled_subset(X_train, y_train, X_test, y_test, n_classes)
+
+    return dataset