(self)
return (X_train, y_train), (X_test, y_test), 10
Fetch the MNIST 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 MNIST dataset and load it into memory.
Fetch the MNIST dataset and load it into memory .
def load_data(self):
        """
        Fetch the MNIST 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.
        """
        filepath = self._valid_path_append(self.path, self.filename)
        if not os.path.exists(filepath):
            self.fetch_dataset(self.url, self.filename, filepath, self.size)

        with gzip.open(filepath, 'rb') as mnist:
            (X_train, y_train), (X_test, y_test) = pickle_load(mnist)

            if self.subset_pct < 100:
                X_train = X_train[:int(X_train.shape[0] * self.subset_pct / 100.)]
                y_train = y_train[:int(y_train.shape[0] * self.subset_pct / 100.)]
                X_test = X_test[:int(X_test.shape[0] * self.subset_pct / 100.)]
                y_test = y_test[:int(y_test.shape[0] * self.subset_pct / 100.)]
                logger.debug("subset %d%% of data", self.subset_pct*100)

            if self.size > 28:
                n_train, n_test = X_train.shape[0], X_test.shape[0]
                X_train_ = np.zeros(shape=(n_train, self.size, self.size))
                X_test_ = np.zeros(shape=(n_test, self.size, self.size))
                X_train_[:, :28, :28] = X_train
                X_test_[:, :28, :28] = X_test
            else:
                X_train_ = X_train[:, :self.size, :self.size]
                X_test_ = X_test[:, :self.size, :self.size]
            X_train = X_train_.reshape(-1, self.size*self.size)
            X_test = X_test_.reshape(-1, self.size*self.size)

            if self.normalize:
                X_train = X_train / 255.
                X_test = X_test / 255.
                if self.sym_range:
                    X_train = X_train * 2. - 1.
                    X_test = X_test * 2. - 1.

            if self.shuffle:
                np.random.seed(0)
                np.random.shuffle(X_train)

        return (X_train, y_train), (X_test, y_test), 10
def load_data ( self ) : filepath = self . _valid_path_append ( self . path , self . filename ) if not os . path . exists ( filepath ) : self . fetch_dataset ( self . url , self . filename , filepath , self . size ) with gzip . open ( filepath , 'rb' ) as mnist : ( X_train , y_train ) , ( X_test , y_test ) = pickle_load ( mnist ) if self . subset_pct < 100 : X_train = X_train [ : int ( X_train . shape [ 0 ] * self . subset_pct / 100. ) ] y_train = y_train [ : int ( y_train . shape [ 0 ] * self . subset_pct / 100. ) ] X_test = X_test [ : int ( X_test . shape [ 0 ] * self . subset_pct / 100. ) ] y_test = y_test [ : int ( y_test . shape [ 0 ] * self . subset_pct / 100. ) ] logger . debug ( "subset %d%% of data" , self . subset_pct * 100 ) if self . size > 28 : n_train , n_test = X_train . shape [ 0 ] , X_test . shape [ 0 ] X_train_ = np . zeros ( shape = ( n_train , self . size , self . size ) ) X_test_ = np . zeros ( shape = ( n_test , self . size , self . size ) ) X_train_ [ : , : 28 , : 28 ] = X_train X_test_ [ : , : 28 , : 28 ] = X_test else : X_train_ = X_train [ : , : self . size , : self . size ] X_test_ = X_test [ : , : self . size , : self . size ] X_train = X_train_ . reshape ( - 1 , self . size * self . size ) X_test = X_test_ . reshape ( - 1 , self . size * self . size ) if self . normalize : X_train = X_train / 255. X_test = X_test / 255. if self . sym_range : X_train = X_train * 2. - 1. X_test = X_test * 2. - 1. if self . shuffle : np . random . seed ( 0 ) np . random . shuffle ( X_train ) return ( X_train , y_train ) , ( X_test , y_test ) , 10