k折交叉验证法的优势和劣势



Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0020, Accuracy: 55673/60000 (92.7883%)
Test set: Average loss: 0.0020, Accuracy: 9284/10000 (92.8400%)

now k_split_value is: 10
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0015, Accuracy: 59648/63000 (94.6794%)
Test set: Average loss: 0.0018, Accuracy: 6550/7000 (93.5714%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0015, Accuracy: 59698/63000 (94.7587%)
Test set: Average loss: 0.0015, Accuracy: 6622/7000 (94.6000%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0014, Accuracy: 59751/63000 (94.8429%)
Test set: Average loss: 0.0014, Accuracy: 6639/7000 (94.8429%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.096612
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0014, Accuracy: 59757/63000 (94.8524%)
Test set: Average loss: 0.0013, Accuracy: 6669/7000 (95.2714%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0014, Accuracy: 59879/63000 (95.0460%)
Test set: Average loss: 0.0013, Accuracy: 6682/7000 (95.4571%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0013, Accuracy: 59914/63000 (95.1016%)
Test set: Average loss: 0.0013, Accuracy: 6652/7000 (95.0286%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0013, Accuracy: 59986/63000 (95.2159%)
Test set: Average loss: 0.0013, Accuracy: 6674/7000 (95.3429%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0013, Accuracy: 60056/63000 (95.3270%)
Test set: Average loss: 0.0013, Accuracy: 6685/7000 (95.5000%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0012, Accuracy: 60162/63000 (95.4952%)
Test set: Average loss: 0.0013, Accuracy: 6683/7000 (95.4714%)
tensor([0.])
Train Epoch: 0 [0/60000 (0%)] Loss: 0.
Train Epoch: 0 [12800/60000 (21%)] Loss: 0.
Train Epoch: 0 [25600/60000 (43%)] Loss: 0.
Train Epoch: 0 [38400/60000 (64%)] Loss: 0.
Train Epoch: 0 [51200/60000 (85%)] Loss: 0.
Train set: Average loss: 0.0012, Accuracy: 60174/63000 (95.5143%)
Test set: Average loss: 0.0012, Accuracy: 6716/7000 (95.9429%)

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