@eureka7mt Thanks for the contribution, could you add a unit test for this case?

@mxnet-label-bot add[Gluon, pr-awaiting-review]

@eureka7mt Could you fix the Trailing whitespace issue?

Don't know why it failed in test_multinomial_generator() which is in the file '/work/mxnet/tests/python/gpu/../unittest/test_random.py' with unix-gpu.

@eureka7mt could you re-trigger the CI?

@eureka7mt Could you please look into the CI failures?

@mxnet-label-bot update [pr-awaiting-merge]

Adding the if-else statement make an error.Though the default value of pos_weight is set to be 1,the pos_weight is usually an (1,N) NDArray.And it seems that an error happen in if-else statement when input is a symbol

@eureka7mt
I see. Since pos_weight is a tensor, it is better to default pos_weight=None as you wrote before. I will update it. Thanks!

Edit: I think the pos_weight is a scalar, since it is a binary classification loss.

@wkcn It could be a scalar for classifying a single class.But for multi-class and multi-label classifying,it should be a tensor.Because in this situation,the number of positive examples and negative examples isn't same for each class.
And in pytorch,it's also defined as a tensor.See the pytorch docs.

I change the order of SigmoidBinaryCrossEntropyLoss inputs from (self, F, pred, label, pos_weight=None, sample_weight=None) to (self, F, pred, label, sample_weight=None, pos_weight=None), since we need the compatibilty for other projects

Sorry that I trigger the sanity problem.
Could someone please help me solve it? Thanks!

Maybe the broadcast_mul isn't necessary.I think that a NDArray * a NDArray will do broadcast_mul automatically.

@eureka7mt I think we may pass Symbol into SigmoidBinaryCrossEntropyLoss. Symbol will not broadcast_mul automatically in my test.

import mxnet as mx
from mxnet.gluon import nn

class TestBlock(nn.HybridBlock):
    def __init__(self):
        super(TestBlock, self).__init__()
    def hybrid_forward(self, F, x, y):
        return x * y

block = TestBlock()
block.hybridize()
a = mx.nd.zeros((10, 1))
b = mx.nd.ones((1, 5))
c = block(a, b)
print (c.asnumpy())

The PR has been merged.
Thanks for your contribution: )