Update read_cifar.py

read_cifar.py

+#Importing the useful libraries we will be using later
+import numpy as np
+from six.moves import cPickle as pickle
+import os
+import platform
+from sklearn.utils import shuffle
+#Defining the classes contained in the CIFAR-10 dataset
+classes = ('plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+img_rows, img_cols = 32, 32    #The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes
+input_shape = (img_rows, img_cols, 3)
+
+def load_pickle(f):
+    #This function takes a file name as an input and loads it in order to work on it later.
+    version = platform.python_version_tuple()
+    #The loading of the file depends on the version of python we are using.
+    if version[0] == '2':
+        return  pickle.load(f)
+    elif version[0] == '3':
+        return  pickle.load(f, encoding='latin1')
+    raise ValueError("invalid python version: {}".format(version))
+
+def read_cifar_batch (batch_path):
+    #This function takes as parameter the path of a single batch as a string, and returns a matrix data of size (batch_size x data_size) and a a vector labels of size batch_size.
+    with open(batch_path, 'rb') as bp:
+        data_dict = load_pickle(bp)
+        data = data_dict['data']
+        labels = data_dict['labels']
+        data = data.reshape(10000,3072)
+        data = data.astype('f')  #data must be np.float32 array.
+        labels = np.array(labels, dtype='i')  #labels must be np.int64 array.
+        return data, labels
+
+def read_cifar(directory_path):
+    #This function takes as parameter the path of the directory containing the six batches and returns a matrix data a vector lables of size batch_size
+    data=[]
+    labels=[]
+    for b in range(1,6):
+        file = os.path.join(directory_path, 'data_batch_%d'% (b, ))
+        Xd, Yd = read_cifar_batch(file)
+        data.append( Xd )
+        labels.append( Yd )
+    Xt, Yt = read_cifar_batch(os.path.join(directory_path, 'test_batch'))
+    data.append( Xt )
+    labels.append( Yt )
+    return data,labels
+
+def split_dataset(data, labels, split):
+    #This function splits the dataset into a training set and a test set
+    #It takes as parameter data and labels, two arrays that have the same size in the first dimension. And a split, a float between 0 and 1 which determines the split factor of the training set with respect to the test set.
+    data_train, labels_train = shuffle(data.sample(frac=split, random_state=25),)
+    data_test = shuffle(data.drop(data_train.index))
+
+    return data_train, data_test, labels_train, labels_test