Multiple losses

[simomaur]

one remark:

Gluon: access layer weights

also referring to Multiple losses it turned out that given the following loss function (I still don’t get why in MXNet the losses return from hybrid_forward have size (batch_size,) instead of a scalar loss) class SomeLoss(mx.gluon.loss.Loss): def __init__(self, weight=1., batch_axis=0, **kwargs): super(SomeLoss, self).__init__(weight=weight, batch_axis=batch_axis, **kwargs) def hybrid_forward(self, F, x, sample_weight=None): y = F.sign(data=x) b_n = 0.5 * (y + 1) mu_m = F.mean…