Hello
How can I assign class weights in gluon? I see the “weight” parameter in the loss function SoftmaxCrossEntropy, but the documentation is very poor.
Best regards
Hello
How can I assign class weights in gluon? I see the “weight” parameter in the loss function SoftmaxCrossEntropy, but the documentation is very poor.
Best regards
weight
is just a global scalar value. You need to use sample_weight
that allows you to weight each sample in the batch.
If by “class weights” you mean class parameters like weights and bias, then here is an example:
class NormalizationHybridLayer(gluon.HybridBlock):
def __init__(self, hidden_units, scales):
super(NormalizationHybridLayer, self).__init__()
with self.name_scope():
# here you are creating your weights
self.weights = self.params.get('weights', # you can set name whatever you want
shape=(hidden_units, 0),
allow_deferred_init=True)
# here you are creating another parameter
self.scales = self.params.get('scales',
shape=scales.shape,
init=mx.init.Constant(scales.asnumpy().tolist()), # Convert to regular list to make this object serializable
differentiable=False)
def hybrid_forward(self, F, x, weights, scales):
normalized_data = F.broadcast_div(F.broadcast_sub(x, F.min(x)), (F.broadcast_sub(F.max(x), F.min(x))))
weighted_data = F.FullyConnected(normalized_data, weights, num_hidden=self.weights.shape[0], no_bias=True)
scaled_data = F.broadcast_mul(scales, weighted_data)
return scaled_data
More info available here
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The weight argument that you saw within SoftmayCrossEntropyLoss actually is just a value that would be multiplied by the final loss computed, for instance if your SoftmaxCrossEntropyLoss is L, then it will return weight * L.
Hope this helps.
Hi, could you provide some sample codes? or some links more specific for applying class weights