-6dB的效果也不错
2024-05-15 11:07:47
G:\python\python.exe "F:/my_code/spikingjelly-master-20201221/spikingjelly/clock_driven/ann2snn/examples/if_cnn_mnist_work - 副本.py"
All the temp files are saved to ./log-cnn_mnist1622381673.6093786
ann2snn config:{'simulation': {'reset_to_zero': False, 'encoder': {'possion': False}, 'avg_pool': {'has_neuron': True}, 'max_pool': {'if_spatial_avg': False, 'if_wta': False, 'momentum': None}}, 'parser': {'robust_norm': True}}
Device is cuda
Epoch 0 [1/782] ANN Training Loss:1.606 Accuracy:0.188
Epoch 0 [101/782] ANN Training Loss:0.706 Accuracy:0.739
Epoch 0 [201/782] ANN Training Loss:0.545 Accuracy:0.799
Epoch 0 [301/782] ANN Training Loss:0.480 Accuracy:0.798
Epoch 0 [401/782] ANN Training Loss:0.445 Accuracy:0.798
Epoch 0 [501/782] ANN Training Loss:0.423 Accuracy:0.798
Epoch 0 [601/782] ANN Training Loss:0.405 Accuracy:0.810
Epoch 0 [701/782] ANN Training Loss:0.395 Accuracy:0.796
Epoch 0 [157/157] ANN Validating Loss:0.328 Accuracy:0.800
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 1 [1/782] ANN Training Loss:0.305 Accuracy:0.812
Epoch 1 [101/782] ANN Training Loss:0.328 Accuracy:0.800
Epoch 1 [201/782] ANN Training Loss:0.325 Accuracy:0.802
Epoch 1 [301/782] ANN Training Loss:0.327 Accuracy:0.798
Epoch 1 [401/782] ANN Training Loss:0.329 Accuracy:0.794
Epoch 1 [501/782] ANN Training Loss:0.327 Accuracy:0.803
Epoch 1 [601/782] ANN Training Loss:0.326 Accuracy:0.803
Epoch 1 [701/782] ANN Training Loss:0.325 Accuracy:0.803
Epoch 1 [157/157] ANN Validating Loss:0.324 Accuracy:0.800
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 2 [1/782] ANN Training Loss:0.355 Accuracy:0.781
Epoch 2 [101/782] ANN Training Loss:0.314 Accuracy:0.807
Epoch 2 [201/782] ANN Training Loss:0.320 Accuracy:0.800
Epoch 2 [301/782] ANN Training Loss:0.318 Accuracy:0.806
Epoch 2 [401/782] ANN Training Loss:0.321 Accuracy:0.797
Epoch 2 [501/782] ANN Training Loss:0.313 Accuracy:0.857
Epoch 2 [601/782] ANN Training Loss:0.263 Accuracy:1.000
Epoch 2 [701/782] ANN Training Loss:0.226 Accuracy:1.000
Epoch 2 [157/157] ANN Validating Loss:0.004 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 3 [1/782] ANN Training Loss:0.001 Accuracy:1.000
Epoch 3 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [301/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 3 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 3 [157/157] ANN Validating Loss:0.005 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 4 [1/782] ANN Training Loss:0.001 Accuracy:1.000
Epoch 4 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 4 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 4 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 4 [401/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 4 [501/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 4 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 4 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 4 [157/157] ANN Validating Loss:0.003 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 5 [1/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 5 [157/157] ANN Validating Loss:0.011 Accuracy:0.998
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 6 [1/782] ANN Training Loss:0.004 Accuracy:1.000
Epoch 6 [101/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 6 [201/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 6 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 6 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 6 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 6 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 6 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 6 [157/157] ANN Validating Loss:0.002 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 7 [1/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [601/782] ANN Training Loss:0.003 Accuracy:1.000
Epoch 7 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 7 [157/157] ANN Validating Loss:0.002 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 8 [1/782] ANN Training Loss:0.001 Accuracy:1.000
Epoch 8 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 8 [157/157] ANN Validating Loss:0.002 Accuracy:1.000
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Epoch 9 [1/782] ANN Training Loss:0.001 Accuracy:1.000
Epoch 9 [101/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [201/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [301/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [401/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [501/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [601/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [701/782] ANN Training Loss:0.002 Accuracy:1.000
Epoch 9 [157/157] ANN Validating Loss:0.018 Accuracy:0.998
Save model to: ./log-cnn_mnist1622381673.6093786\cnn_mnist.pkl
Using 100 pictures as norm set
torchvision.datasets.folder.ImageFolder
torch.Size([100, 1, 28, 28])
Load best model for Model:cnn_mnist...
ANN Validating Accuracy:0.998
Save model to: ./log-cnn_mnist1622381673.6093786\parsed_cnn_mnist.pkl
Using robust normalization...
normalize with bias...
Print Parsed ANN model Structure:
Pytorch_Parser((network): Sequential((0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))(1): ReLU()(2): AvgPool2d(kernel_size=2, stride=2, padding=0)(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))(4): ReLU()(5): AvgPool2d(kernel_size=2, stride=2, padding=0)(6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))(7): ReLU()(8): AvgPool2d(kernel_size=2, stride=2, padding=0)(9): Flatten(start_dim=1, end_dim=-1)(10): Linear(in_features=32, out_features=5, bias=True)(11): ReLU())
)
Save model to: ./log-cnn_mnist1622381673.6093786\normalized_cnn_mnist.pkl
Print Simulated SNN model Structure:
PyTorch_Converter((network): Sequential((0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))(1): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(2): AvgPool2d(kernel_size=2, stride=2, padding=0)(3): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(4): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))(5): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(6): AvgPool2d(kernel_size=2, stride=2, padding=0)(7): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(8): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))(9): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(10): AvgPool2d(kernel_size=2, stride=2, padding=0)(11): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True))(12): Flatten(start_dim=1, end_dim=-1)(13): Linear(in_features=32, out_features=5, bias=True)(14): IFNode(v_threshold=1.0, v_reset=None, detach_reset=False(surrogate_function): Sigmoid(alpha=1.0, spiking=True)))
)
100%|██████████| 100/100 [00:00<00:00, 514.42it/s]
[SNN Simulating... 0.64%] Acc:1.00054%|█████▍ | 54/100 [00:00<00:00, 533.31it/s][SNN Simulating... 1.28%] Acc:1.000
100%|██████████| 100/100 [00:00<00:00, 536.25it/s]56%|█████▌ | 56/100 [00:00<00:00, 558.51it/s][SNN Simulating... 1.92%] Acc:1.000
100%|██████████| 100/100 [00:00<00:00, 560.30it/s]57%|█████▋ | 57/100 [00:00<00:00, 568.64it/s][SNN Simulating... 2.56%] Acc:1.000
100%|██████████| 100/100 [00:00<00:00, 557.26it/s]55%|█████▌ | 55/100 [00:00<00:00, 543.26it/s][SNN Simulating... 3.20%] Acc:1.000
100%|██████████| 100/100 [00:00<00:00, 548.07it/s]
100%|██████████| 100/100 [00:00<00:00, 553.99it/s]
[SNN Simulating... 3.84%] Acc:0.99754%|█████▍ | 54/100 [00:00<00:00, 538.58it/s][SNN Simulating... 4.48%] Acc:0.998
100%|██████████| 100/100 [00:00<00:00, 536.22it/s]
100%|██████████| 100/100 [00:00<00:00, 536.19it/s]
[SNN Simulating... 5.12%] Acc:0.99854%|█████▍ | 54/100 [00:00<00:00, 538.42it/s][SNN Simulating... 5.76%] Acc:0.998
100%|██████████| 100/100 [00:00<00:00, 536.13it/s]
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]
[SNN Simulating... 6.40%] Acc:0.99854%|█████▍ | 54/100 [00:00<00:00, 533.21it/s][SNN Simulating... 7.04%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]
100%|██████████| 100/100 [00:00<00:00, 539.10it/s]
[SNN Simulating... 7.68%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 8.32%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.28it/s]
100%|██████████| 100/100 [00:00<00:00, 533.26it/s]
[SNN Simulating... 8.96%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 548.69it/s][SNN Simulating... 9.60%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 551.10it/s]55%|█████▌ | 55/100 [00:00<00:00, 548.40it/s][SNN Simulating... 10.24%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 544.92it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 10.88%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 11.52%] Acc:0.99953%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 12.16%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 511.46it/s]
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]
[SNN Simulating... 12.80%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.57it/s][SNN Simulating... 13.44%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 547.99it/s]
100%|██████████| 100/100 [00:00<00:00, 545.00it/s]
[SNN Simulating... 14.08%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 544.99it/s]
[SNN Simulating... 14.72%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 15.36%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
[SNN Simulating... 16.00%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 544.99it/s]
[SNN Simulating... 16.64%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 552.98it/s][SNN Simulating... 17.28%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.38it/s][SNN Simulating... 17.92%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.42it/s]54%|█████▍ | 54/100 [00:00<00:00, 538.71it/s][SNN Simulating... 18.56%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 545.07it/s]
100%|██████████| 100/100 [00:00<00:00, 547.99it/s]
[SNN Simulating... 19.20%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 554.16it/s]
[SNN Simulating... 19.84%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 551.02it/s]
[SNN Simulating... 20.48%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.56it/s][SNN Simulating... 21.12%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.21it/s]55%|█████▌ | 55/100 [00:00<00:00, 548.54it/s][SNN Simulating... 21.76%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]54%|█████▍ | 54/100 [00:00<00:00, 538.56it/s][SNN Simulating... 22.40%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.26it/s]
100%|██████████| 100/100 [00:00<00:00, 551.02it/s]
[SNN Simulating... 23.04%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.17it/s]
[SNN Simulating... 23.68%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]
[SNN Simulating... 24.32%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 551.01it/s]
[SNN Simulating... 24.96%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.26it/s]
[SNN Simulating... 25.60%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.17it/s]
[SNN Simulating... 26.24%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 551.02it/s]
[SNN Simulating... 26.88%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 551.19it/s][SNN Simulating... 27.52%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 544.02it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 28.16%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]53%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 28.80%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]53%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 29.44%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 554.16it/s]
[SNN Simulating... 30.08%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.26it/s]
[SNN Simulating... 30.72%] Acc:0.99957%|█████▋ | 57/100 [00:00<00:00, 568.49it/s][SNN Simulating... 31.36%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 563.47it/s]
100%|██████████| 100/100 [00:00<00:00, 563.56it/s]
[SNN Simulating... 32.00%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 560.30it/s]
[SNN Simulating... 32.64%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 558.66it/s][SNN Simulating... 33.28%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 560.39it/s]
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]
[SNN Simulating... 33.92%] Acc:0.99957%|█████▋ | 57/100 [00:00<00:00, 563.01it/s][SNN Simulating... 34.56%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 563.56it/s]
100%|██████████| 100/100 [00:00<00:00, 563.56it/s]
[SNN Simulating... 35.20%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 558.66it/s][SNN Simulating... 35.84%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 560.39it/s]53%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 36.48%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 37.12%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.56it/s][SNN Simulating... 37.76%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]
100%|██████████| 100/100 [00:00<00:00, 545.04it/s]
[SNN Simulating... 38.40%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 39.04%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 39.68%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 40.32%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 40.96%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
100%|██████████| 100/100 [00:00<00:00, 551.10it/s]
[SNN Simulating... 41.60%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
[SNN Simulating... 42.24%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
[SNN Simulating... 42.88%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.24it/s][SNN Simulating... 43.52%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 44.16%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]
[SNN Simulating... 44.80%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 560.31it/s]
[SNN Simulating... 45.44%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 46.08%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 548.54it/s][SNN Simulating... 46.72%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.17it/s]
100%|██████████| 100/100 [00:00<00:00, 545.00it/s]
[SNN Simulating... 47.36%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 48.00%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]55%|█████▌ | 55/100 [00:00<00:00, 543.11it/s][SNN Simulating... 48.64%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 539.10it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 49.28%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]55%|█████▌ | 55/100 [00:00<00:00, 543.10it/s][SNN Simulating... 49.92%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]54%|█████▍ | 54/100 [00:00<00:00, 538.57it/s][SNN Simulating... 50.56%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]53%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 51.20%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 51.84%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 52.48%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 53.12%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 53.76%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
[SNN Simulating... 54.40%] Acc:0.99953%|█████▎ | 53/100 [00:00<00:00, 528.59it/s][SNN Simulating... 55.04%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 55.68%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 56.32%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 56.96%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 539.19it/s]
[SNN Simulating... 57.60%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 548.54it/s][SNN Simulating... 58.24%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]55%|█████▌ | 55/100 [00:00<00:00, 543.25it/s][SNN Simulating... 58.88%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.11it/s]57%|█████▋ | 57/100 [00:00<00:00, 564.82it/s][SNN Simulating... 59.52%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 555.16it/s]
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
[SNN Simulating... 60.16%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 553.13it/s][SNN Simulating... 60.80%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 545.08it/s]
100%|██████████| 100/100 [00:00<00:00, 539.02it/s]
[SNN Simulating... 61.44%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.28it/s]
[SNN Simulating... 62.08%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 550.93it/s]
[SNN Simulating... 62.72%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.41it/s]
[SNN Simulating... 63.36%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.36it/s]
[SNN Simulating... 64.00%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.07it/s][SNN Simulating... 64.64%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 539.10it/s]
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]
[SNN Simulating... 65.28%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 548.56it/s][SNN Simulating... 65.92%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 66.56%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.32it/s]
[SNN Simulating... 67.20%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 547.97it/s]
[SNN Simulating... 67.84%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.20it/s][SNN Simulating... 68.48%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]
100%|██████████| 100/100 [00:00<00:00, 533.41it/s]
[SNN Simulating... 69.12%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.38it/s][SNN Simulating... 69.76%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.19it/s]55%|█████▌ | 55/100 [00:00<00:00, 543.10it/s][SNN Simulating... 70.40%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 545.08it/s]54%|█████▍ | 54/100 [00:00<00:00, 538.40it/s][SNN Simulating... 71.04%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.11it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 71.68%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.33it/s]
100%|██████████| 100/100 [00:00<00:00, 557.14it/s]
[SNN Simulating... 72.32%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
[SNN Simulating... 72.96%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.35it/s]
[SNN Simulating... 73.60%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.40it/s][SNN Simulating... 74.24%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.35it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 74.88%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.02it/s]
[SNN Simulating... 75.52%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.24it/s][SNN Simulating... 76.16%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 542.11it/s]
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]
[SNN Simulating... 76.80%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.07it/s]
[SNN Simulating... 77.44%] Acc:0.99956%|█████▌ | 56/100 [00:00<00:00, 558.37it/s][SNN Simulating... 78.08%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 553.99it/s]
100%|██████████| 100/100 [00:00<00:00, 551.01it/s]
[SNN Simulating... 78.72%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 548.69it/s][SNN Simulating... 79.36%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 547.95it/s]56%|█████▌ | 56/100 [00:00<00:00, 558.66it/s][SNN Simulating... 80.00%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 557.17it/s]
100%|██████████| 100/100 [00:00<00:00, 554.00it/s]
[SNN Simulating... 80.64%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 544.92it/s]
[SNN Simulating... 81.28%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 551.03it/s]
[SNN Simulating... 81.92%] Acc:0.99953%|█████▎ | 53/100 [00:00<00:00, 528.73it/s][SNN Simulating... 82.56%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 83.20%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 539.09it/s]
[SNN Simulating... 83.84%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.36it/s][SNN Simulating... 84.48%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 545.07it/s]
100%|██████████| 100/100 [00:00<00:00, 539.02it/s]
[SNN Simulating... 85.12%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.34it/s][SNN Simulating... 85.76%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.12it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 86.40%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 542.96it/s][SNN Simulating... 87.04%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 539.10it/s]54%|█████▍ | 54/100 [00:00<00:00, 538.55it/s][SNN Simulating... 87.68%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.28it/s]
100%|██████████| 100/100 [00:00<00:00, 536.21it/s]
[SNN Simulating... 88.32%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.39it/s][SNN Simulating... 88.96%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.09it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 89.60%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 90.24%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 90.88%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
100%|██████████| 100/100 [00:00<00:00, 542.03it/s]
[SNN Simulating... 91.52%] Acc:0.99955%|█████▌ | 55/100 [00:00<00:00, 543.12it/s][SNN Simulating... 92.16%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 539.11it/s]
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
[SNN Simulating... 92.80%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.21it/s]
[SNN Simulating... 93.44%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 538.56it/s][SNN Simulating... 94.08%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 545.00it/s]56%|█████▌ | 56/100 [00:00<00:00, 558.69it/s][SNN Simulating... 94.72%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 560.40it/s]54%|█████▍ | 54/100 [00:00<00:00, 533.24it/s][SNN Simulating... 95.36%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 536.20it/s]
100%|██████████| 100/100 [00:00<00:00, 542.04it/s]
[SNN Simulating... 96.00%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 554.08it/s]
[SNN Simulating... 96.64%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 97.28%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
[SNN Simulating... 97.92%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 530.50it/s]
[SNN Simulating... 98.56%] Acc:0.99954%|█████▍ | 54/100 [00:00<00:00, 533.23it/s][SNN Simulating... 99.20%] Acc:0.999
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
100%|██████████| 100/100 [00:00<00:00, 533.34it/s]
[SNN Simulating... 99.84%] Acc:0.99982%|████████▏ | 82/100 [00:00<00:00, 809.94it/s][SNN Simulating... 100.00%] Acc:0.999
SNN Simulating Accuracy:0.999
Summary: ANN Accuracy:99.7500% SNN Accuracy:99.9000% [Increased 0.1500%]
100%|██████████| 100/100 [00:00<00:00, 804.44it/s]Process finished with exit code 0-6dB的效果也不错相关推荐
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