FINISHED

last upload

read_cifar.py

@@ -11,16 +11,9 @@ import pickle
 
 
 
-# batch.meta 
-#{b'num_cases_per_batch': 10000, b'label_names': [b'airplane', b'automobile', b'bird', b'cat', b'deer', b'dog', b'frog', b'horse', b'ship', b'truck'], b'num_vis': 3072}
-
 def read_cifar_batch(file):
     with open(file, 'rb') as fo:
         dict = pickle.load(fo, encoding='bytes')
-    # keys = [b'batch_label',
-    #     b'labels',
-    #     b'data',
-    #     b'filenames']
     return (np.array(dict[b'data']).astype('float32'), np.array(dict[b'labels']).astype('int64'))
 
 def read_cifar(path):
@@ -47,10 +40,6 @@ def read_cifar(path):
 
 
 def split_dataset(data, labels, split):
-    """
-    Cette fonction divise l'ensemble de notre data en training data set et testing data set.
-
-    """
     n = data.shape[0]
     indices = np.random.permutation(n)
     train_idx, test_idx = indices[:int(split*n)], indices[int(split*n):]
@@ -62,6 +51,7 @@ def split_dataset(data, labels, split):
 
 
 
+
 if __name__ == "__main__":
    path = r'data\cifar-10-batches-py\data_batch_1'
    main_path = r'data\cifar-10-batches-py'