liuzh91 opened a new issue #16942: Validation model can be different from fit model in estimator class URL: https://github.com/apache/incubator-mxnet/issues/16942 ## Description In current estimator class implementation, validation model is the same network as the training model `self.net`. Code of `evaluate_batch()` is shown below: ``` data, label = self._get_data_and_label(val_batch, self.context, batch_axis) pred = [self.net(x) for x in data] loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)] ``` This assumption does not hold true in the general case. It is common to have a different validation model than the training model on many tasks, e.g., the language model. ``` model_eval = nlp.model.AWDRNN(args.model, len(vocab), args.emsize, args.nhid, args.nlayers, args.tied, args.dropout, args.weight_dropout, args.dropout_h, args.dropout_i, args.dropout_e) model = nlp.model.train.AWDRNN(args.model, len(vocab), args.emsize, args.nhid, args.nlayers, args.tied, args.dropout, args.weight_dropout, args.dropout_h, args.dropout_i, args.dropout_e) ``` Please refer to (https://github.com/dmlc/gluon-nlp/blob/master/scripts/language_model/word_language_model.py#L154) for a detailed reference.
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