Update read_cifar.py

read_cifar.py

@@ -2,26 +2,14 @@
 import numpy as np
 from six.moves import cPickle as pickle
 import os
-import platform
-#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))
-
+import random
+def unpickle(file):
+  '''loads the data dictionnary.'''
+  with open(file, 'rb') as fo:
+    dict = pickle.load(fo, encoding='bytes')
+  return dict
 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']
@@ -32,16 +20,22 @@ def read_cifar_batch (batch_path):
 
 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 )
+    files=['/data_batch_1','/data_batch_2','/data_batch_3','/data_batch_4','/data_batch_5','/test_batch']
+    A=10000
+    N=60000
+    P=3072
+    data=np.empty((N,P),dtype=np.float)
+    labels=np.empty(A,dtype=np.int64)
+    for i in range(len(files)):
+        fichier=directory_path+files[i]
+        data_dict=unpickle(fichier)
+        M=data_dict[b'data']
+        L=data_dict[b'labels']
+        L=np.array(L)
+        data=np.vstack((X,M))
+        labels=np.hstack((Y,L))
+    data=X[N:2*N,]
+    labels=Y[A:,]
     return data,labels
 
 def split_dataset(data,labels,split):