Hi,
I was trying to import NCF by MXNet relay. Check this: [MXNet NCF](https://github.com/apache/incubator-mxnet/tree/master/example/neural_collaborative_filtering) There is an unsupported operator: LogisticRegressionOutput According to the SigmoidBinaryCrossEntropyLoss code: https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/loss.py#L229 I tried to implement it by myself. ``` def _mx_logistic_regression_output(inputs, attrs): label = inputs[1] pred = inputs[0] # We use the stable formula: # max(pred, 0) - pred * label + log(1 + exp(-abs(pred))) one = _expr.const(1, dtype="float32") exp_neg_abs_x = _op.exp(_op.negative(_op.abs(pred))) # exp(-abs(pred)) soft_relu = _op.add(_op.log(_op.add(one, exp_neg_abs_x)), _op.nn.relu(pred)) loss = _op.nn.relu(pred) - pred * label + soft_relu return loss ``` and test the function by the following code: ``` def test_forward_logistic_regression(): m = mx.nd.ones((3, 4)) n = mx.nd.zeros((3, 4)) vm = mx.sym.Variable('m') vn = mx.sym.Variable('n') out = mx.sym.LogisticRegressionOutput(data=vm, label=vn) exec_ = out.bind(mx.cpu(), {'m': m, 'n': n}) exec_.forward() exec_.outputs[0].asnumpy() shape_dict = {"m": m.shape, "n": n.shape} mod, params = relay.frontend.from_mxnet(out, shape_dict) intrp = relay.create_executor("graph", mod=mod, target='llvm') op_res = intrp.evaluate()(m.asnumpy(), n.asnumpy()) print(op_res) print(exec_.outputs[0].asnumpy()) ``` TVM result is different from MXNet result. I also tried other formulas. Still not work. I'm very confusing how to implement it. Does anyone know how to fix this? Thanks!!! --- [Visit Topic](https://discuss.tvm.ai/t/unsupported-op-logisticregressionoutput-when-import-ncf-model-from-mxnet/6863/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/656eba5617af4702804aa297235e1deebc1f4b18fd988534832999d34110e3be).
