def setup_data():
    """Load and shape data for training with Keras + Pescador.

    Returns
    -------
    input_shape : tuple, len=3
        Shape of each sample; adapts to channel configuration of Keras.

    X_train, y_train : np.ndarrays
        Images and labels for training.

    X_test, y_test : np.ndarrays
        Images and labels for test.
    """
    # The data, shuffled and split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    return input_shape, (x_train, y_train), (x_test, y_test)
def setup_data ( ) : ( x_train , y_train ) , ( x_test , y_test ) = mnist . load_data ( ) if K . image_data_format ( ) == 'channels_first' : x_train = x_train . reshape ( x_train . shape [ 0 ] , 1 , img_rows , img_cols ) x_test = x_test . reshape ( x_test . shape [ 0 ] , 1 , img_rows , img_cols ) input_shape = ( 1 , img_rows , img_cols ) else : x_train = x_train . reshape ( x_train . shape [ 0 ] , img_rows , img_cols , 1 ) x_test = x_test . reshape ( x_test . shape [ 0 ] , img_rows , img_cols , 1 ) input_shape = ( img_rows , img_cols , 1 ) x_train = x_train . astype ( 'float32' ) x_test = x_test . astype ( 'float32' ) x_train /= 255 x_test /= 255 print ( 'x_train shape:' , x_train . shape ) print ( x_train . shape [ 0 ] , 'train samples' ) print ( x_test . shape [ 0 ] , 'test samples' ) y_train = keras . utils . to_categorical ( y_train , num_classes ) y_test = keras . utils . to_categorical ( y_test , num_classes ) return input_shape , ( x_train , y_train ) , ( x_test , y_test )
Load and shape data for training with Keras + Pescador.

    Returns
    -------
    input_shape : tuple, len=3
        Shape of each sample; adapts to channel configuration of Keras.

    X_train, y_train : np.ndarrays
        Images and labels for training.

    X_test, y_test : np.ndarrays
        Images and labels for test.