keras-team/keras · error · ValueError

Must specify exactly two of top_padding, bottom_padding, tar

Error message

Must specify exactly two of top_padding, bottom_padding, target_height. Received: top_padding={top_padding}, bottom_padding={bottom_padding}, target_height={target_height}

What it means

pad_images (and its layer) requires exactly two of top_padding, bottom_padding, target_height to be given (the third is derived). This validator raises when zero, one, or all three are None — the vertical output size is then over- or under-determined.

Source

Thrown at keras/src/ops/image.py:1548

        fill_value,
    )


def _validate_non_negative(value, name):
    if value is not None and value < 0:
        raise ValueError(f"{name} must be >= 0. Received: {name}={value}")


def _validate_pad_images_args(
    top_padding,
    left_padding,
    bottom_padding,
    right_padding,
    target_height,
    target_width,
):
    if [top_padding, bottom_padding, target_height].count(None) != 1:
        raise ValueError(
            "Must specify exactly two of "
            "top_padding, bottom_padding, target_height. "
            f"Received: top_padding={top_padding}, "
            f"bottom_padding={bottom_padding}, "
            f"target_height={target_height}"
        )
    if [left_padding, right_padding, target_width].count(None) != 1:
        raise ValueError(
            "Must specify exactly two of "
            "left_padding, right_padding, target_width. "
            f"Received: left_padding={left_padding}, "
            f"right_padding={right_padding}, "
            f"target_width={target_width}"
        )

    _validate_non_negative(top_padding, "top_padding")
    _validate_non_negative(bottom_padding, "bottom_padding")
    _validate_non_negative(target_height, "target_height")

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Specify exactly two, e.g. pad_images(img, target_height=256, bottom_padding=8)
2. For symmetric pad of known amount: top_padding=b, bottom_padding=b and leave target None
3. For pad-to-size: target_height plus one side

Example fix

# before
out = pad_images(img)

# after
out = pad_images(img, top_padding=8, bottom_padding=8)

assert [top_padding, bottom_padding, target_height].count(None) == 1

def height_triple_ok(t, b, th) -> bool:
    return [t, b, th].count(None) == 1

When it happens

Trigger: keras.ops.image.pad_images(images) with none of the three set (all None defaults), or with all three set, or only one set.

Common situations: Calling pad_images with no arguments expecting a no-op; copying a partial config where one of the three keys is missing or extra; version migration where target_height was previously optional.

Related errors

• {name} must be >= 0. Received: {name}={value}
• Must specify exactly two of left_padding, right_padding, tar
• The `weights` argument should be either `None` (random initi
• `padding` should have two elements. Received: padding={paddi
• Invalid padding: {padding}

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/2a2a4e0f141152f2. Report an issue: GitHub.