(self)
return (X_train, y_train), (X_test, y_test), 10
Fetch the CIFAR-10 dataset and load it into memory.

        Arguments:
            path (str, optional): Local directory in which to cache the raw
                                  dataset.  Defaults to current directory.
            normalize (bool, optional): Whether to scale values between 0 and 1.
                                        Defaults to True.

        Returns:
            tuple: Both training and test sets are returned.
Fetch the CIFAR-10 dataset and load it into memory.
Fetch the CIFAR - 10 dataset and load it into memory .
def load_data(self):
        """
        Fetch the CIFAR-10 dataset and load it into memory.

        Arguments:
            path (str, optional): Local directory in which to cache the raw
                                  dataset.  Defaults to current directory.
            normalize (bool, optional): Whether to scale values between 0 and 1.
                                        Defaults to True.

        Returns:
            tuple: Both training and test sets are returned.
        """
        workdir, filepath = self._valid_path_append(self.path, '', self.filename)
        batchdir = os.path.join(workdir, 'cifar-10-batches-py')
        if not os.path.exists(os.path.join(batchdir, 'data_batch_1')):
            if not os.path.exists(filepath):
                self.fetch_dataset(self.url, self.filename, filepath, self.size)
            with tarfile.open(filepath, 'r:gz') as f:
                f.extractall(workdir)

        train_batches = [os.path.join(batchdir, 'data_batch_' + str(i)) for i in range(1, 6)]
        Xlist, ylist = [], []
        for batch in train_batches:
            with open(batch, 'rb') as f:
                d = pickle_load(f)
                Xlist.append(d['data'])
                ylist.append(d['labels'])

        X_train = np.vstack(Xlist)
        y_train = np.vstack(ylist)

        with open(os.path.join(batchdir, 'test_batch'), 'rb') as f:
            d = pickle_load(f)
            X_test, y_test = d['data'], d['labels']

        y_train = y_train.reshape(-1, 1)
        y_test = np.array(y_test).reshape(-1, 1)

        if self.contrast_normalize:
            norm_scale = 55.0  # Goodfellow
            X_train = self.global_contrast_normalize(X_train, scale=norm_scale)
            X_test = self.global_contrast_normalize(X_test, scale=norm_scale)

        if self.normalize:
            X_train = X_train / 255.
            X_test = X_test / 255.

        if self.whiten:
            zca_cache = os.path.join(workdir, 'cifar-10-zca-cache.pkl')
            X_train, X_test = self.zca_whiten(X_train, X_test, cache=zca_cache)

        return (X_train, y_train), (X_test, y_test), 10
def load_data ( self ) : workdir , filepath = self . _valid_path_append ( self . path , '' , self . filename ) batchdir = os . path . join ( workdir , 'cifar-10-batches-py' ) if not os . path . exists ( os . path . join ( batchdir , 'data_batch_1' ) ) : if not os . path . exists ( filepath ) : self . fetch_dataset ( self . url , self . filename , filepath , self . size ) with tarfile . open ( filepath , 'r:gz' ) as f : f . extractall ( workdir ) train_batches = [ os . path . join ( batchdir , 'data_batch_' + str ( i ) ) for i in range ( 1 , 6 ) ] Xlist , ylist = [ ] , [ ] for batch in train_batches : with open ( batch , 'rb' ) as f : d = pickle_load ( f ) Xlist . append ( d [ 'data' ] ) ylist . append ( d [ 'labels' ] ) X_train = np . vstack ( Xlist ) y_train = np . vstack ( ylist ) with open ( os . path . join ( batchdir , 'test_batch' ) , 'rb' ) as f : d = pickle_load ( f ) X_test , y_test = d [ 'data' ] , d [ 'labels' ] y_train = y_train . reshape ( - 1 , 1 ) y_test = np . array ( y_test ) . reshape ( - 1 , 1 ) if self . contrast_normalize : norm_scale = 55.0 # Goodfellow X_train = self . global_contrast_normalize ( X_train , scale = norm_scale ) X_test = self . global_contrast_normalize ( X_test , scale = norm_scale ) if self . normalize : X_train = X_train / 255. X_test = X_test / 255. if self . whiten : zca_cache = os . path . join ( workdir , 'cifar-10-zca-cache.pkl' ) X_train , X_test = self . zca_whiten ( X_train , X_test , cache = zca_cache ) return ( X_train , y_train ) , ( X_test , y_test ) , 10