Hi @VanDavv, Given the original context of transfer learning, with Gluon you can just provide the parameters for the last layer to the `Trainer` object. Usually you'd give all of the parameters with `net.collect_params()`, but instead only provide the parameters you're interested in learning/tuning.
See https://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html for an example, and use `dense_layer` if you only want to train the last layer and not fine-tune the whole network: ``` trainer = gluon.Trainer(dense_layer.collect_params(), 'sgd', {'learning_rate': LEARNING_RATE, 'wd':WDECAY, 'momentum':MOMENTUM}) ``` [ Full content available at: https://github.com/apache/incubator-mxnet/issues/12392 ] This message was relayed via gitbox.apache.org for [email protected]
