mygrad.nnet.losses.multiclass_hinge#
- mygrad.nnet.losses.multiclass_hinge(x: ArrayLike, y_true: ArrayLike, hinge: float = 1.0, *, constant: bool | None = None) Tensor[source]#
Computes the average multiclass hinge loss.
- Parameters:
- xArrayLike, shape=(N, K)
The K class scores for each of the N pieces of data.
- y_trueArrayLike, shape=(N,)
The correct class-indices, in [0, K), for each datum.
- hingefloat
The size of the “hinge” outside of which a nonzero loss is incurred.
- constantbool, optional(default=False)
If
True, the returned tensor is a constant (it does not back-propagate a gradient)
- Returns:
- Tensor, shape-() (scalar)
The average multiclass hinge loss
- Raises:
- TypeError
y_true must be an integer-type array-like object
- ValueError
x must be a 2-dimensional array-like object y_true must be a shape-(N,) array-like object