Canceled hyperparameter at 3x2400/2x1200, added future plans to presentation
Tuan-Dat Tran
parent
33941658b9
commit
d3807c1fc6
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"cell_type": "code",
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"execution_count": null,
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"id": "ff6a75e9",
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"execution_count": 12,
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"id": "6f2dc487",
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"metadata": {},
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"outputs": [
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{
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@ -939,7 +939,427 @@
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 2400\n"
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.00\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.61\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.68\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.00\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.21\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.99\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.20\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 77.88\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 77.83\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 77.78\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 77.93\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 77.98\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.02\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.04\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 77.59\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 77.97\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 77.55\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 77.38\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 77.40\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 77.10\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 600\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 76.91\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.61\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.61\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.44\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.40\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.46\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.59\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.17\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.02\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.15\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 77.58\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1200\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 77.02\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.60\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.39\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.58\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 78.49\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.37\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.45\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.02\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 2\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 77.69\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 3\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 77.95\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
|
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" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 1800\n",
|
||||
" Dense Count 2: 3\n",
|
||||
" Dense Neurons 2: 1200\n",
|
||||
"Accuracy: 77.59\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 1800\n",
|
||||
" Dense Count 2: 3\n",
|
||||
" Dense Neurons 2: 1800\n",
|
||||
"Accuracy: 77.53\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 1800\n",
|
||||
" Dense Count 2: 3\n",
|
||||
" Dense Neurons 2: 2400\n",
|
||||
"Accuracy: 77.26\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 1\n",
|
||||
" Dense Neurons 2: 600\n",
|
||||
"Accuracy: 78.33\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 1\n",
|
||||
" Dense Neurons 2: 1200\n",
|
||||
"Accuracy: 78.39\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 1\n",
|
||||
" Dense Neurons 2: 1800\n",
|
||||
"Accuracy: 78.46\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 1\n",
|
||||
" Dense Neurons 2: 2400\n",
|
||||
"Accuracy: 78.38\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 2\n",
|
||||
" Dense Neurons 2: 600\n",
|
||||
"Accuracy: 78.62\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 2\n",
|
||||
" Dense Neurons 2: 1200\n",
|
||||
"Accuracy: 78.02\n",
|
||||
"Testing with: Threshold: 70\n",
|
||||
" Leeway: 0\n",
|
||||
" Epoch: 20\n",
|
||||
" Dense Count 1: 3\n",
|
||||
" Dense Neurons 1: 2400\n",
|
||||
" Dense Count 2: 2\n",
|
||||
" Dense Neurons 2: 1800\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
|
||||
"\u001b[0;32m<ipython-input-9-47e2893956f1>\u001b[0m in \u001b[0;36mget_avg_acc\u001b[0;34m(X_train, y_train, X_test, y_test, epoch, dcount, dnons, dcount2, dnons2)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAVG_FROM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdcount2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m model.fit(X_train, y_train, \n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1130\u001b[0m steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1131\u001b[0;31m val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m 1132\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)\u001b[0m\n\u001b[1;32m 1387\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1388\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1390\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 826\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 829\u001b[0m \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 863\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2940\u001b[0m (graph_function,\n\u001b[1;32m 2941\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2942\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 2943\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1916\u001b[0m and executing_eagerly):\n\u001b[1;32m 1917\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1919\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1920\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 556\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -974,13 +1394,14 @@
|
|||
" 'DENSE_COUNT2': dc2,\n",
|
||||
" 'DENSE_NEURON2': dn2,\n",
|
||||
" 'Accuracy': acc}, ignore_index=True)\n",
|
||||
" print(f\"Accuracy: {acc*100:.2f}\\n\\n\")"
|
||||
" print(f\"Accuracy: {acc*100:.2f}\\n\\n\")\n",
|
||||
" result.to_csv('results.csv', header=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "89a47c03",
|
||||
"execution_count": 13,
|
||||
"id": "88b3193a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -989,8 +1410,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7520408c",
|
||||
"execution_count": 14,
|
||||
"id": "5219e081",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -1 +1,139 @@
|
|||
,Threshold,Leeway,Epoch,DENSE_COUNT1,DENSE_NEURON1,DENSE_COUNT2,DENSE_NEURON2,Accuracy
|
||||
0,70.0,0.0,20.0,1.0,600.0,1.0,600.0,0.7682723144690196
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||||
1,70.0,0.0,20.0,1.0,600.0,1.0,1200.0,0.7767373164494832
|
||||
2,70.0,0.0,20.0,1.0,600.0,1.0,1800.0,0.7784842669963836
|
||||
3,70.0,0.0,20.0,1.0,600.0,1.0,2400.0,0.778047525882721
|
||||
4,70.0,0.0,20.0,1.0,600.0,2.0,600.0,0.7818882505098979
|
||||
5,70.0,0.0,20.0,1.0,600.0,2.0,1200.0,0.7789466917514801
|
||||
6,70.0,0.0,20.0,1.0,600.0,2.0,1800.0,0.7772382815678914
|
||||
7,70.0,0.0,20.0,1.0,600.0,2.0,2400.0,0.7803982019424438
|
||||
8,70.0,0.0,20.0,1.0,600.0,3.0,600.0,0.7823635200659435
|
||||
9,70.0,0.0,20.0,1.0,600.0,3.0,1200.0,0.784881184498469
|
||||
10,70.0,0.0,20.0,1.0,600.0,3.0,1800.0,0.7835324327150981
|
||||
11,70.0,0.0,20.0,1.0,600.0,3.0,2400.0,0.7761978129545848
|
||||
12,70.0,0.0,20.0,1.0,1200.0,1.0,600.0,0.7726268450419108
|
||||
13,70.0,0.0,20.0,1.0,1200.0,1.0,1200.0,0.7799614687760671
|
||||
14,70.0,0.0,20.0,1.0,1200.0,1.0,1800.0,0.7784328818321228
|
||||
15,70.0,0.0,20.0,1.0,1200.0,1.0,2400.0,0.7781631370385488
|
||||
16,70.0,0.0,20.0,1.0,1200.0,2.0,600.0,0.7773410419623057
|
||||
17,70.0,0.0,20.0,1.0,1200.0,2.0,1200.0,0.7804495811462402
|
||||
18,70.0,0.0,20.0,1.0,1200.0,2.0,1800.0,0.7802440643310546
|
||||
19,70.0,0.0,20.0,1.0,1200.0,2.0,2400.0,0.7829672475655873
|
||||
20,70.0,0.0,20.0,1.0,1200.0,3.0,600.0,0.7805908799171448
|
||||
21,70.0,0.0,20.0,1.0,1200.0,3.0,1200.0,0.783121387163798
|
||||
22,70.0,0.0,20.0,1.0,1200.0,3.0,1800.0,0.7843673706054688
|
||||
23,70.0,0.0,20.0,1.0,1200.0,3.0,2400.0,0.7823763648668925
|
||||
24,70.0,0.0,20.0,1.0,1800.0,1.0,600.0,0.7720873514811198
|
||||
25,70.0,0.0,20.0,1.0,1800.0,1.0,1200.0,0.7787925561269124
|
||||
26,70.0,0.0,20.0,1.0,1800.0,1.0,1800.0,0.7745921631654104
|
||||
27,70.0,0.0,20.0,1.0,1800.0,1.0,2400.0,0.779884394009908
|
||||
28,70.0,0.0,20.0,1.0,1800.0,2.0,600.0,0.7813487490018208
|
||||
29,70.0,0.0,20.0,1.0,1800.0,2.0,1200.0,0.7857803463935852
|
||||
30,70.0,0.0,20.0,1.0,1800.0,2.0,1800.0,0.7819010992844899
|
||||
31,70.0,0.0,20.0,1.0,1800.0,2.0,2400.0,0.7834810515244802
|
||||
32,70.0,0.0,20.0,1.0,1800.0,3.0,600.0,0.7837379515171051
|
||||
33,70.0,0.0,20.0,1.0,1800.0,3.0,1200.0,0.7852922280629476
|
||||
34,70.0,0.0,20.0,1.0,1800.0,3.0,1800.0,0.7848426441351573
|
||||
35,70.0,0.0,20.0,1.0,1800.0,3.0,2400.0,0.7807193279266358
|
||||
36,70.0,0.0,20.0,1.0,2400.0,1.0,600.0,0.7676300545533498
|
||||
37,70.0,0.0,20.0,1.0,2400.0,1.0,1200.0,0.7773153483867645
|
||||
38,70.0,0.0,20.0,1.0,2400.0,1.0,1800.0,0.7793063541253408
|
||||
39,70.0,0.0,20.0,1.0,2400.0,1.0,2400.0,0.7774823367595672
|
||||
40,70.0,0.0,20.0,1.0,2400.0,2.0,600.0,0.7816827217737834
|
||||
41,70.0,0.0,20.0,1.0,2400.0,2.0,1200.0,0.7836480398972829
|
||||
42,70.0,0.0,20.0,1.0,2400.0,2.0,1800.0,0.7850867052872975
|
||||
43,70.0,0.0,20.0,1.0,2400.0,2.0,2400.0,0.7797687868277232
|
||||
44,70.0,0.0,20.0,1.0,2400.0,3.0,600.0,0.7827360272407532
|
||||
45,70.0,0.0,20.0,1.0,2400.0,3.0,1200.0,0.7847912609577179
|
||||
46,70.0,0.0,20.0,1.0,2400.0,3.0,1800.0,0.7861785511175792
|
||||
47,70.0,0.0,20.0,1.0,2400.0,3.0,2400.0,0.7813230554262797
|
||||
48,70.0,0.0,20.0,2.0,600.0,1.0,600.0,0.7796917120615642
|
||||
49,70.0,0.0,20.0,2.0,600.0,1.0,1200.0,0.778869624932607
|
||||
50,70.0,0.0,20.0,2.0,600.0,1.0,1800.0,0.7777263998985291
|
||||
51,70.0,0.0,20.0,2.0,600.0,1.0,2400.0,0.7782016774018605
|
||||
52,70.0,0.0,20.0,2.0,600.0,2.0,600.0,0.7792292873064677
|
||||
53,70.0,0.0,20.0,2.0,600.0,2.0,1200.0,0.7812845230102539
|
||||
54,70.0,0.0,20.0,2.0,600.0,2.0,1800.0,0.7793705920378368
|
||||
55,70.0,0.0,20.0,2.0,600.0,2.0,2400.0,0.780757870276769
|
||||
56,70.0,0.0,20.0,2.0,600.0,3.0,600.0,0.7792164385318756
|
||||
57,70.0,0.0,20.0,2.0,600.0,3.0,1200.0,0.7799743115901947
|
||||
58,70.0,0.0,20.0,2.0,600.0,3.0,1800.0,0.7761335849761963
|
||||
59,70.0,0.0,20.0,2.0,600.0,3.0,2400.0,0.7748233755429585
|
||||
60,70.0,0.0,20.0,2.0,1200.0,1.0,600.0,0.7816955665747325
|
||||
61,70.0,0.0,20.0,2.0,1200.0,1.0,1200.0,0.7834938963254293
|
||||
62,70.0,0.0,20.0,2.0,1200.0,1.0,1800.0,0.7819010933240255
|
||||
63,70.0,0.0,20.0,2.0,1200.0,1.0,2400.0,0.7837508042653402
|
||||
64,70.0,0.0,20.0,2.0,1200.0,2.0,600.0,0.7782915830612183
|
||||
65,70.0,0.0,20.0,2.0,1200.0,2.0,1200.0,0.7820809185504913
|
||||
66,70.0,0.0,20.0,2.0,1200.0,2.0,1800.0,0.7830956975618998
|
||||
67,70.0,0.0,20.0,2.0,1200.0,2.0,2400.0,0.7822607517242431
|
||||
68,70.0,0.0,20.0,2.0,1200.0,3.0,600.0,0.7854463696479798
|
||||
69,70.0,0.0,20.0,2.0,1200.0,3.0,1200.0,0.7852536916732789
|
||||
70,70.0,0.0,20.0,2.0,1200.0,3.0,1800.0,0.781425819794337
|
||||
71,70.0,0.0,20.0,2.0,1200.0,3.0,2400.0,0.7780732174714406
|
||||
72,70.0,0.0,20.0,2.0,1800.0,1.0,600.0,0.7881181756655375
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||||
73,70.0,0.0,20.0,2.0,1800.0,1.0,1200.0,0.7832626859347026
|
||||
74,70.0,0.0,20.0,2.0,1800.0,1.0,1800.0,0.784264612197876
|
||||
75,70.0,0.0,20.0,2.0,1800.0,1.0,2400.0,0.7857675015926361
|
||||
76,70.0,0.0,20.0,2.0,1800.0,2.0,600.0,0.7841618498166402
|
||||
77,70.0,0.0,20.0,2.0,1800.0,2.0,1200.0,0.7858317295710245
|
||||
78,70.0,0.0,20.0,2.0,1800.0,2.0,1800.0,0.7843930661678314
|
||||
79,70.0,0.0,20.0,2.0,1800.0,2.0,2400.0,0.781836861371994
|
||||
80,70.0,0.0,20.0,2.0,1800.0,3.0,600.0,0.7841618518034618
|
||||
81,70.0,0.0,20.0,2.0,1800.0,3.0,1200.0,0.7839563290278116
|
||||
82,70.0,0.0,20.0,2.0,1800.0,3.0,1800.0,0.7809248546759288
|
||||
83,70.0,0.0,20.0,2.0,1800.0,3.0,2400.0,0.7755812446276347
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||||
84,70.0,0.0,20.0,2.0,2400.0,1.0,600.0,0.7810276170571645
|
||||
85,70.0,0.0,20.0,2.0,2400.0,1.0,1200.0,0.7848683357238769
|
||||
86,70.0,0.0,20.0,2.0,2400.0,1.0,1800.0,0.7824020544687907
|
||||
87,70.0,0.0,20.0,2.0,2400.0,1.0,2400.0,0.7800000031789144
|
||||
88,70.0,0.0,20.0,2.0,2400.0,2.0,600.0,0.786101472377777
|
||||
89,70.0,0.0,20.0,2.0,2400.0,2.0,1200.0,0.786769425868988
|
||||
90,70.0,0.0,20.0,2.0,2400.0,2.0,1800.0,0.7799743076165517
|
||||
91,70.0,0.0,20.0,2.0,2400.0,2.0,2400.0,0.7820809264977773
|
||||
92,70.0,0.0,20.0,2.0,2400.0,3.0,600.0,0.7899293502171835
|
||||
93,70.0,0.0,20.0,2.0,2400.0,3.0,1200.0,0.7819910069306691
|
||||
94,70.0,0.0,20.0,2.0,2400.0,3.0,1800.0,0.7788439293702444
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||||
95,70.0,0.0,20.0,2.0,2400.0,3.0,2400.0,0.7783172746499379
|
||||
96,70.0,0.0,20.0,3.0,600.0,1.0,600.0,0.7778034687042237
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||||
97,70.0,0.0,20.0,3.0,600.0,1.0,1200.0,0.7792549788951874
|
||||
98,70.0,0.0,20.0,3.0,600.0,1.0,1800.0,0.7797687828540802
|
||||
99,70.0,0.0,20.0,3.0,600.0,1.0,2400.0,0.7802440543969472
|
||||
100,70.0,0.0,20.0,3.0,600.0,2.0,600.0,0.7803982039292653
|
||||
101,70.0,0.0,20.0,3.0,600.0,2.0,1200.0,0.7759280701478323
|
||||
102,70.0,0.0,20.0,3.0,600.0,2.0,1800.0,0.7796917140483857
|
||||
103,70.0,0.0,20.0,3.0,600.0,2.0,2400.0,0.7755041797955831
|
||||
104,70.0,0.0,20.0,3.0,600.0,3.0,600.0,0.7737572213013967
|
||||
105,70.0,0.0,20.0,3.0,600.0,3.0,1200.0,0.7740398168563842
|
||||
106,70.0,0.0,20.0,3.0,600.0,3.0,1800.0,0.7710468788941701
|
||||
107,70.0,0.0,20.0,3.0,600.0,3.0,2400.0,0.7690944095452626
|
||||
108,70.0,0.0,20.0,3.0,1200.0,1.0,600.0,0.7860501031080882
|
||||
109,70.0,0.0,20.0,3.0,1200.0,1.0,1200.0,0.7860500951608022
|
||||
110,70.0,0.0,20.0,3.0,1200.0,1.0,1800.0,0.7844444433848063
|
||||
111,70.0,0.0,20.0,3.0,1200.0,1.0,2400.0,0.7839820186297098
|
||||
112,70.0,0.0,20.0,3.0,1200.0,2.0,600.0,0.7845600485801697
|
||||
113,70.0,0.0,20.0,3.0,1200.0,2.0,1200.0,0.7859344879786173
|
||||
114,70.0,0.0,20.0,3.0,1200.0,2.0,1800.0,0.7817469457785289
|
||||
115,70.0,0.0,20.0,3.0,1200.0,2.0,2400.0,0.7801669915517171
|
||||
116,70.0,0.0,20.0,3.0,1200.0,3.0,600.0,0.7815414230028789
|
||||
117,70.0,0.0,20.0,3.0,1200.0,3.0,1200.0,0.7769813776016236
|
||||
118,70.0,0.0,20.0,3.0,1200.0,3.0,1800.0,0.7757867693901062
|
||||
119,70.0,0.0,20.0,3.0,1200.0,3.0,2400.0,0.7701862533887227
|
||||
120,70.0,0.0,20.0,3.0,1800.0,1.0,600.0,0.7860115627447765
|
||||
121,70.0,0.0,20.0,3.0,1800.0,1.0,1200.0,0.7839049438635508
|
||||
122,70.0,0.0,20.0,3.0,1800.0,1.0,1800.0,0.7858060359954834
|
||||
123,70.0,0.0,20.0,3.0,1800.0,1.0,2400.0,0.7849325656890869
|
||||
124,70.0,0.0,20.0,3.0,1800.0,2.0,600.0,0.7836608866850535
|
||||
125,70.0,0.0,20.0,3.0,1800.0,2.0,1200.0,0.7844958245754242
|
||||
126,70.0,0.0,20.0,3.0,1800.0,2.0,1800.0,0.7802055180072784
|
||||
127,70.0,0.0,20.0,3.0,1800.0,2.0,2400.0,0.7769428412119548
|
||||
128,70.0,0.0,20.0,3.0,1800.0,3.0,600.0,0.7795247296492259
|
||||
129,70.0,0.0,20.0,3.0,1800.0,3.0,1200.0,0.7758638401826222
|
||||
130,70.0,0.0,20.0,3.0,1800.0,3.0,1800.0,0.7753114978472392
|
||||
131,70.0,0.0,20.0,3.0,1800.0,3.0,2400.0,0.7726396898428599
|
||||
132,70.0,0.0,20.0,3.0,2400.0,1.0,600.0,0.783314069112142
|
||||
133,70.0,0.0,20.0,3.0,2400.0,1.0,1200.0,0.7838664094607035
|
||||
134,70.0,0.0,20.0,3.0,2400.0,1.0,1800.0,0.7846371273199717
|
||||
135,70.0,0.0,20.0,3.0,2400.0,1.0,2400.0,0.7837508062521616
|
||||
136,70.0,0.0,20.0,3.0,2400.0,2.0,600.0,0.7862427830696106
|
||||
137,70.0,0.0,20.0,3.0,2400.0,2.0,1200.0,0.7801669875780741
|
||||
|
|
|
|||
|
Loading…
Reference in New Issue