cc8d7ba5562cb2335d975b67e88a81a6bc9b0f46,thinc/loss.py,,categorical_crossentropy,#Any#Any#,8

Before Change

def categorical_crossentropy(scores: Array2d, labels: Array2d) -> Tuple[Array2d, float]:
    xp = get_array_module(scores)
    target = xp.zeros(scores.shape, dtype="float32")
    loss = 0.0
    for i in range(len(labels)):
        target[i, int(labels[i])] = 1.0
        loss += (1.0 - scores[i, int(labels[i])]) ** 2
    return scores - target, loss


@registry.losses("categorical_crossentropy.v0")

After Change

    if labels.ndim != scores.ndim:
        target = to_categorical(labels, n_classes=scores.shape[-1])
    else:
        target = cast(Array2d, labels)
    if scores.shape != target.shape:
        raise ValueError(
            f"Cannot calculate loss: mismatched shapes. {scores.shape} vs {target.shape}"
        )

Instances