(self)
return splits
Create splits for MiniImageNet and store them in the default path.

    If no split file is provided, and the default location for MiniImageNet
    splits does not contain a split file, splits are created in this function
    according to the Ravi & Larochelle specification and then stored in that
    default location.

    Returns:
      The splits for this dataset, represented as a dictionary mapping each of
      'train', 'valid', and 'test' to a list of class names.
Create splits for MiniImageNet and store them in the default path.
Create splits for MiniImageNet and store them in the default path .
def create_splits(self):
    """Create splits for MiniImageNet and store them in the default path.

    If no split file is provided, and the default location for MiniImageNet
    splits does not contain a split file, splits are created in this function
    according to the Ravi & Larochelle specification and then stored in that
    default location.

    Returns:
      The splits for this dataset, represented as a dictionary mapping each of
      'train', 'valid', and 'test' to a list of class names.
    """
    start_stop = np.cumsum([
        0, self.NUM_TRAIN_CLASSES, self.NUM_VALID_CLASSES, self.NUM_TEST_CLASSES
    ])
    train_inds = list(range(start_stop[0], start_stop[1]))
    valid_inds = list(range(start_stop[1], start_stop[2]))
    test_inds = list(range(start_stop[2], start_stop[3]))
    splits = {'train': train_inds, 'valid': valid_inds, 'test': test_inds}
    return splits
def create_splits ( self ) : start_stop = np . cumsum ( [ 0 , self . NUM_TRAIN_CLASSES , self . NUM_VALID_CLASSES , self . NUM_TEST_CLASSES ] ) train_inds = list ( range ( start_stop [ 0 ] , start_stop [ 1 ] ) ) valid_inds = list ( range ( start_stop [ 1 ] , start_stop [ 2 ] ) ) test_inds = list ( range ( start_stop [ 2 ] , start_stop [ 3 ] ) ) splits = { 'train' : train_inds , 'valid' : valid_inds , 'test' : test_inds } return splits