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Is it right for me to do this? The code works, but I don't know if this is the right process...
# Set up the regressor.
device = chainer.get_device(args.device)
model_path = os.path.join(args.in_dir, args.model_filename)
metrics_fun = {'mae': F.mean_absolute_error, 'rmse': rmse}
regressor = Regressor.load_pickle('result/pretrain_qm9.pkl', device=device)
mlp = MLP(out_dim=class_num, hidden_dim=args.unit_num)
predictor = regressor.predictor
new_predictor = GraphConvPredictor(predictor, mlp=mlp)
new_regressor = Regressor(new_predictor,lossfun=F.mean_squared_error,
metrics_fun=metrics_fun, device=device)
I want to use megnet to pre train a qm9 model, and then use the weight of this model to continue to train my own data, because my data set is very small, but I don't seem to know if transfer learning is used in this way?
Is it right for me to do this? The code works, but I don't know if this is the right process...
# Set up the regressor.
device = chainer.get_device(args.device)
model_path = os.path.join(args.in_dir, args.model_filename)
metrics_fun = {'mae': F.mean_absolute_error, 'rmse': rmse}
regressor = Regressor.load_pickle('result/pretrain_qm9.pkl', device=device)
mlp = MLP(out_dim=class_num, hidden_dim=args.unit_num)
predictor = regressor.predictor
new_predictor = GraphConvPredictor(predictor, mlp=mlp)
new_regressor = Regressor(new_predictor,lossfun=F.mean_squared_error,
metrics_fun=metrics_fun, device=device)
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