The number of trainable parameters is more larger than FC densenet in paper

[Shaun2016]

I use the code to see the number of parameters for the model: def parameter_num(model_structure): with open(model_structure) as model_file: model = models.model_from_json(model_file.read()) print(model.summary()) but the results are more larger than models in Simon Jegou et al 's paper: 4 Total params: 33,927,266 Trainable params: 33,902,114 Non-trainable params: 25,152

5 Total params: 59,332,562 Trainable params: 59,295,842 Non-trainable params: 36,720

6 Total params: 95,044,274 Trainable params: 94,993,778 Non-trainable params: 50,496

[ahuizxc]

this issue caused by missing concat layer in denseblock.