Server global evaluation total number

[shuangyichen]

Below is a eval record of server. I wonder why test_total and val_total are decimals? From my understanding, this is global model evaluation. If the total number of my test set is 872, the number of test samples in each evaluation is 872 or 872/num of clients (in my case, it is 3)?

{'Role': 'Server #', 'Round': 1, 'Results_weighted_avg': {'test_loss': 205.44600348516343, 'test_total': 290.6666666666667, 'test_avg_loss': 0.7068027881307339, 'test_acc': 0.5091743119266054, 'val_loss': 154.90539731894472, 'val_total': 224.66666666666666, 'val_avg_loss': 0.6894755830396885, 'val_acc': 0.5534124629080118}, 'Results_avg': {'test_loss': 205.44401041666666, 'test_total': 290.6666666666667, 'test_avg_loss': 0.7067977845605226, 'test_acc': 0.5091875024686969, 'val_loss': 154.90218098958334, 'val_total': 224.66666666666666, 'val_avg_loss': 0.6894642857142856, 'val_acc': 0.5534457671957672}, 'Results_fairness': {'test_total': 290.6666666666667, 'val_total': 224.66666666666666, 'test_loss_std': 2.7915573098698245, 'test_loss_bottom_decile': 203.2431640625, 'test_loss_top_decile': 209.3828125, 'test_loss_min': 203.2431640625, 'test_loss_max': 209.3828125, 'test_loss_bottom10%': nan, 'test_loss_top10%': 209.3828125, 'test_loss_cos1': 0.9999076969612101, 'test_loss_entropy': 1.0985202582012823, 'test_avg_loss_std': 0.009149269452723627, 'test_avg_loss_bottom_decile': 0.6984301170532646, 'test_avg_loss_top_decile': 0.7195285652920962, 'test_avg_loss_min': 0.6984301170532646, 'test_avg_loss_max': 0.7195285652920962, 'test_avg_loss_bottom10%': nan, 'test_avg_loss_top10%': 0.7195285652920962, 'test_avg_loss_cos1': 0.999916228188608, 'test_avg_loss_entropy': 1.0985287222378004, 'test_acc_std': 0.02519823611017372, 'test_acc_bottom_decile': 0.4742268041237113, 'test_acc_top_decile': 0.5326460481099656, 'test_acc_min': 0.4742268041237113, 'test_acc_max': 0.5326460481099656, 'test_acc_bottom10%': nan, 'test_acc_top10%': 0.5326460481099656, 'test_acc_cos1': 0.998777755685617, 'test_acc_entropy': 1.097375117975531, 'val_loss_std': 2.4467666521872635, 'val_loss_bottom_decile': 152.734375, 'val_loss_top_decile': 158.32177734375, 'val_loss_min': 152.734375, 'val_loss_max': 158.32177734375, 'val_loss_bottom10%': nan, 'val_loss_top10%': 158.32177734375, 'val_loss_cos1': 0.9998752734853387, 'val_loss_entropy': 1.0984879472148361, 'val_avg_loss_std': 0.010041464909396417, 'val_avg_loss_bottom_decile': 0.6818498883928571, 'val_avg_loss_top_decile': 0.70365234375, 'val_avg_loss_min': 0.6818498883928571, 'val_avg_loss_max': 0.70365234375, 'val_avg_loss_bottom10%': nan, 'val_avg_loss_top10%': 0.70365234375, 'val_avg_loss_cos1': 0.9998939595599015, 'val_avg_loss_entropy': 1.0985065869345862, 'val_acc_std': 0.026944572693224266, 'val_acc_bottom_decile': 0.5155555555555555, 'val_acc_top_decile': 0.5758928571428571, 'val_acc_min': 0.5155555555555555, 'val_acc_max': 0.5758928571428571, 'val_acc_bottom10%': nan, 'val_acc_top10%': 0.5758928571428571, 'val_acc_cos1': 0.9988169822369142, 'val_acc_entropy': 1.0974135282771185}}

[rayrayraykk]

Yes, you are right.