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qatBinMid.log
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Using GPU!
Moving model to cuda...
Loading data...
Done!
Training...
[1, 100] loss: 3.320
[1, 300] loss: 0.130
[1, 500] loss: 0.087
[1, 700] loss: 0.217
[1, 900] loss: 0.195
[1, 1100] loss: 9.505
[1, 1300] loss: 9.548
[1, 1500] loss: 6.445
[1, 1700] loss: 0.173
[1, 1900] loss: 0.087
[1, 2100] loss: 12.673
[1, 2300] loss: 9.592
[1, 2500] loss: 6.402
Epoch 1 Training Accuracy: 76.70% Test Accuracy: 75.49%
[2, 100] loss: 6.358
[2, 300] loss: 68.858
[2, 500] loss: 75.152
[2, 700] loss: 84.440
[2, 900] loss: 68.967
[2, 1100] loss: 75.130
[2, 1300] loss: 75.130
[2, 1500] loss: 68.945
[2, 1700] loss: 6.380
[2, 1900] loss: 0.087
[2, 2100] loss: 9.505
[2, 2300] loss: 6.423
[2, 2500] loss: 6.445
Epoch 2 Training Accuracy: 41.67% Test Accuracy: 75.35%
[3, 100] loss: 3.428
[3, 300] loss: 3.277
[3, 500] loss: 6.293
[3, 700] loss: 9.462
[3, 900] loss: 3.255
[3, 1100] loss: 15.712
[3, 1300] loss: 9.592
[3, 1500] loss: 9.548
[3, 1700] loss: 6.293
[3, 1900] loss: 9.548
[3, 2100] loss: 6.380
[3, 2300] loss: 3.320
[3, 2500] loss: 9.505
Epoch 3 Training Accuracy: 75.99% Test Accuracy: 75.08%
[4, 100] loss: 3.298
[4, 300] loss: 9.462
[4, 500] loss: 9.505
[4, 700] loss: 9.527
[4, 900] loss: 3.320
[4, 1100] loss: 9.527
[4, 1300] loss: 9.505
[4, 1500] loss: 6.402
[4, 1700] loss: 6.380
[4, 1900] loss: 12.717
[4, 2100] loss: 6.337
[4, 2300] loss: 6.337
[4, 2500] loss: 9.483
Epoch 4 Training Accuracy: 75.81% Test Accuracy: 75.31%
[5, 100] loss: 0.043
[5, 300] loss: 3.233
[5, 500] loss: 9.570
[5, 700] loss: 9.483
[5, 900] loss: 15.777
[5, 1100] loss: 6.402
[5, 1300] loss: 12.608
[5, 1500] loss: 6.380
[5, 1700] loss: 0.130
[5, 1900] loss: 9.592
[5, 2100] loss: 15.712
[5, 2300] loss: 6.445
[5, 2500] loss: 9.483
Epoch 5 Training Accuracy: 75.96% Test Accuracy: 75.37%
[6, 100] loss: 3.255
[6, 300] loss: 12.652
[6, 500] loss: 12.630
[6, 700] loss: 9.527
[6, 900] loss: 6.467
[6, 1100] loss: 6.445
[6, 1300] loss: 9.570
[6, 1500] loss: 0.065
[6, 1700] loss: 0.130
[6, 1900] loss: 6.402
[6, 2100] loss: 0.087
[6, 2300] loss: 12.695
[6, 2500] loss: 3.320
Epoch 6 Training Accuracy: 75.87% Test Accuracy: 75.15%
[7, 100] loss: 9.505
[7, 300] loss: 6.423
[7, 500] loss: 12.565
[7, 700] loss: 0.108
[7, 900] loss: 0.065
[7, 1100] loss: 9.548
[7, 1300] loss: 9.505
[7, 1500] loss: 3.233
[7, 1700] loss: 3.320
[7, 1900] loss: 12.630
[7, 2100] loss: 12.652
[7, 2300] loss: 6.488
[7, 2500] loss: 6.358
Epoch 7 Training Accuracy: 74.93% Test Accuracy: 74.63%
[8, 100] loss: 6.467
[8, 300] loss: 9.462
[8, 500] loss: 0.130
[8, 700] loss: 6.337
[8, 900] loss: 3.320
[8, 1100] loss: 3.212
[8, 1300] loss: 3.233
[8, 1500] loss: 3.255
[8, 1700] loss: 0.108
[8, 1900] loss: 6.445
[8, 2100] loss: 6.445
[8, 2300] loss: 9.592
[8, 2500] loss: 3.277
Epoch 8 Training Accuracy: 74.84% Test Accuracy: 75.03%
[9, 100] loss: 12.673
[9, 300] loss: 9.462
[9, 500] loss: 9.505
[9, 700] loss: 6.402
[9, 900] loss: 12.695
[9, 1100] loss: 12.608
[9, 1300] loss: 3.233
[9, 1500] loss: 3.320
[9, 1700] loss: 12.652
[9, 1900] loss: 0.087
[9, 2100] loss: 6.445
[9, 2300] loss: 6.380
[9, 2500] loss: 9.548
Epoch 9 Training Accuracy: 75.25% Test Accuracy: 75.48%
[10, 100] loss: 0.173
[10, 300] loss: 3.298
[10, 500] loss: 3.233
[10, 700] loss: 6.467
[10, 900] loss: 3.298
[10, 1100] loss: 3.233
[10, 1300] loss: 9.548
[10, 1500] loss: 3.277
[10, 1700] loss: 9.548
[10, 1900] loss: 3.277
[10, 2100] loss: 6.358
[10, 2300] loss: 3.342
[10, 2500] loss: 12.673
Epoch 10 Training Accuracy: 75.72% Test Accuracy: 76.02%
[11, 100] loss: 3.342
[11, 300] loss: 9.483
[11, 500] loss: 12.630
[11, 700] loss: 9.527
[11, 900] loss: 6.488
[11, 1100] loss: 0.238
[11, 1300] loss: 9.505
[11, 1500] loss: 3.298
[11, 1700] loss: 9.527
[11, 1900] loss: 3.277
[11, 2100] loss: 0.152
[11, 2300] loss: 6.445
[11, 2500] loss: 6.445
Epoch 11 Training Accuracy: 76.38% Test Accuracy: 76.44%
[12, 100] loss: 6.358
[12, 300] loss: 9.462
[12, 500] loss: 3.277
[12, 700] loss: 3.255
[12, 900] loss: 6.380
[12, 1100] loss: 6.337
[12, 1300] loss: 3.342
[12, 1500] loss: 3.255
[12, 1700] loss: 3.277
[12, 1900] loss: 9.462
[12, 2100] loss: 6.380
[12, 2300] loss: 3.233
[12, 2500] loss: 6.315
Epoch 12 Training Accuracy: 76.93% Test Accuracy: 76.93%
[13, 100] loss: 15.777
[13, 300] loss: 3.342
[13, 500] loss: 6.423
[13, 700] loss: 3.255
[13, 900] loss: 12.652
[13, 1100] loss: 3.233
[13, 1300] loss: 12.695
[13, 1500] loss: 6.467
[13, 1700] loss: 6.402
[13, 1900] loss: 3.320
[13, 2100] loss: 9.548
[13, 2300] loss: 0.173
[13, 2500] loss: 12.652
Epoch 13 Training Accuracy: 77.18% Test Accuracy: 77.11%
[14, 100] loss: 6.402
[14, 300] loss: 0.043
[14, 500] loss: 9.527
[14, 700] loss: 9.462
[14, 900] loss: 15.712
[14, 1100] loss: 12.630
[14, 1300] loss: 9.570
[14, 1500] loss: 12.587
[14, 1700] loss: 6.488
[14, 1900] loss: 3.320
[14, 2100] loss: 3.168
[14, 2300] loss: 3.255
[14, 2500] loss: 6.402
Epoch 14 Training Accuracy: 77.31% Test Accuracy: 77.26%
[15, 100] loss: 6.380
[15, 300] loss: 0.130
[15, 500] loss: 0.108
[15, 700] loss: 6.337
[15, 900] loss: 0.087
[15, 1100] loss: 6.423
[15, 1300] loss: 3.342
[15, 1500] loss: 6.380
[15, 1700] loss: 6.402
[15, 1900] loss: 6.358
[15, 2100] loss: 0.195
[15, 2300] loss: 6.402
[15, 2500] loss: 6.358
Epoch 15 Training Accuracy: 77.45% Test Accuracy: 77.60%
[16, 100] loss: 6.293
[16, 300] loss: 6.402
[16, 500] loss: 12.608
[16, 700] loss: 12.608
[16, 900] loss: 6.445
[16, 1100] loss: 6.380
[16, 1300] loss: 6.445
[16, 1500] loss: 3.168
[16, 1700] loss: 15.777
[16, 1900] loss: 12.608
[16, 2100] loss: 6.380
[16, 2300] loss: 3.233
[16, 2500] loss: 0.152
Epoch 16 Training Accuracy: 77.47% Test Accuracy: 77.52%
[17, 100] loss: 3.298
[17, 300] loss: 3.277
[17, 500] loss: 3.233
[17, 700] loss: 6.467
[17, 900] loss: 9.548
[17, 1100] loss: 9.440
[17, 1300] loss: 9.483
[17, 1500] loss: 3.212
[17, 1700] loss: 0.130
[17, 1900] loss: 3.255
[17, 2100] loss: 0.173
[17, 2300] loss: 12.565
[17, 2500] loss: 0.173
Epoch 17 Training Accuracy: 77.60% Test Accuracy: 77.51%
[18, 100] loss: 3.277
[18, 300] loss: 3.255
[18, 500] loss: 3.233
[18, 700] loss: 12.673
[18, 900] loss: 18.880
[18, 1100] loss: 3.190
[18, 1300] loss: 6.402
[18, 1500] loss: 9.548
[18, 1700] loss: 6.402
[18, 1900] loss: 6.445
[18, 2100] loss: 6.423
[18, 2300] loss: 3.233
[18, 2500] loss: 9.527
Epoch 18 Training Accuracy: 77.69% Test Accuracy: 77.66%
[19, 100] loss: 3.277
[19, 300] loss: 9.548
[19, 500] loss: 6.293
[19, 700] loss: 3.298
[19, 900] loss: 6.337
[19, 1100] loss: 6.380
[19, 1300] loss: 9.483
[19, 1500] loss: 0.087
[19, 1700] loss: 3.363
[19, 1900] loss: 6.445
[19, 2100] loss: 0.065
[19, 2300] loss: 12.673
[19, 2500] loss: 0.130
Epoch 19 Training Accuracy: 77.74% Test Accuracy: 78.03%
[20, 100] loss: 12.673
[20, 300] loss: 9.570
[20, 500] loss: 15.712
[20, 700] loss: 15.733
[20, 900] loss: 6.380
[20, 1100] loss: 6.402
[20, 1300] loss: 9.418
[20, 1500] loss: 6.315
[20, 1700] loss: 6.402
[20, 1900] loss: 3.298
[20, 2100] loss: 12.587
[20, 2300] loss: 0.195
[20, 2500] loss: 3.298
Epoch 20 Training Accuracy: 78.95% Test Accuracy: 80.08%
Test accuracy 0.8007905566385098
===================================
INT8 quantized model performance:
===================================
Moving model to cpu for testing