سورس تشخیص سرطان در پایتون (شبکه عصبی مصنوعی)

2.13.0
Data Shape:  (569, 30)
data.target:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0
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 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0
 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1
 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1
 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
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 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 0 0 0 0 0 0 1]
data target_names:  ['malignant' 'benign']
data target shape:  (569,)
data feature_names:  ['mean radius' 'mean texture' 'mean perimeter' 'mean area'
 'mean smoothness' 'mean compactness' 'mean concavity'
 'mean concave points' 'mean symmetry' 'mean fractal dimension'
 'radius error' 'texture error' 'perimeter error' 'area error'
 'smoothness error' 'compactness error' 'concavity error'
 'concave points error' 'symmetry error' 'fractal dimension error'
 'worst radius' 'worst texture' 'worst perimeter' 'worst area'
 'worst smoothness' 'worst compactness' 'worst concavity'
 'worst concave points' 'worst symmetry' 'worst fractal dimension']
Epoch 1/100
12/12 [==============================] - 0s 11ms/step - loss: 1.3559 - accuracy: 0.2861 - val_loss: 1.2722 - val_accuracy: 0.3245
Epoch 2/100
12/12 [==============================] - 0s 2ms/step - loss: 1.2403 - accuracy: 0.3228 - val_loss: 1.1599 - val_accuracy: 0.3670
Epoch 3/100
12/12 [==============================] - 0s 2ms/step - loss: 1.1340 - accuracy: 0.3543 - val_loss: 1.0553 - val_accuracy: 0.3989
Epoch 4/100
12/12 [==============================] - 0s 2ms/step - loss: 1.0349 - accuracy: 0.3885 - val_loss: 0.9602 - val_accuracy: 0.4309
Epoch 5/100
12/12 [==============================] - 0s 2ms/step - loss: 0.9477 - accuracy: 0.4514 - val_loss: 0.8735 - val_accuracy: 0.4734
Epoch 6/100
12/12 [==============================] - 0s 2ms/step - loss: 0.8657 - accuracy: 0.5039 - val_loss: 0.7989 - val_accuracy: 0.5213
Epoch 7/100
12/12 [==============================] - 0s 2ms/step - loss: 0.7954 - accuracy: 0.5617 - val_loss: 0.7330 - val_accuracy: 0.6011
Epoch 8/100
12/12 [==============================] - 0s 2ms/step - loss: 0.7344 - accuracy: 0.6142 - val_loss: 0.6739 - val_accuracy: 0.6064
Epoch 9/100
12/12 [==============================] - 0s 2ms/step - loss: 0.6771 - accuracy: 0.6535 - val_loss: 0.6241 - val_accuracy: 0.6383
Epoch 10/100
12/12 [==============================] - 0s 2ms/step - loss: 0.6282 - accuracy: 0.6824 - val_loss: 0.5808 - val_accuracy: 0.7128
Epoch 11/100
12/12 [==============================] - 0s 2ms/step - loss: 0.5846 - accuracy: 0.7087 - val_loss: 0.5430 - val_accuracy: 0.7394
Epoch 12/100
12/12 [==============================] - 0s 2ms/step - loss: 0.5477 - accuracy: 0.7297 - val_loss: 0.5085 - val_accuracy: 0.7979
Epoch 13/100
12/12 [==============================] - 0s 2ms/step - loss: 0.5130 - accuracy: 0.7454 - val_loss: 0.4789 - val_accuracy: 0.8245
Epoch 14/100
12/12 [==============================] - 0s 2ms/step - loss: 0.4834 - accuracy: 0.7690 - val_loss: 0.4522 - val_accuracy: 0.8298
Epoch 15/100
12/12 [==============================] - 0s 2ms/step - loss: 0.4564 - accuracy: 0.8005 - val_loss: 0.4286 - val_accuracy: 0.8351
Epoch 16/100
12/12 [==============================] - 0s 2ms/step - loss: 0.4329 - accuracy: 0.8241 - val_loss: 0.4072 - val_accuracy: 0.8457
Epoch 17/100
12/12 [==============================] - 0s 2ms/step - loss: 0.4113 - accuracy: 0.8346 - val_loss: 0.3885 - val_accuracy: 0.8564
Epoch 18/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3918 - accuracy: 0.8478 - val_loss: 0.3715 - val_accuracy: 0.8723
Epoch 19/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3749 - accuracy: 0.8556 - val_loss: 0.3555 - val_accuracy: 0.8723
Epoch 20/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3588 - accuracy: 0.8635 - val_loss: 0.3415 - val_accuracy: 0.8723
Epoch 21/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3448 - accuracy: 0.8714 - val_loss: 0.3284 - val_accuracy: 0.8723
Epoch 22/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3315 - accuracy: 0.8845 - val_loss: 0.3163 - val_accuracy: 0.8830
Epoch 23/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3198 - accuracy: 0.8871 - val_loss: 0.3050 - val_accuracy: 0.8989
Epoch 24/100
12/12 [==============================] - 0s 2ms/step - loss: 0.3086 - accuracy: 0.8950 - val_loss: 0.2947 - val_accuracy: 0.8989
Epoch 25/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2985 - accuracy: 0.9003 - val_loss: 0.2853 - val_accuracy: 0.8989
Epoch 26/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2891 - accuracy: 0.9029 - val_loss: 0.2763 - val_accuracy: 0.9043
Epoch 27/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2803 - accuracy: 0.9029 - val_loss: 0.2684 - val_accuracy: 0.9202
Epoch 28/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2725 - accuracy: 0.9081 - val_loss: 0.2603 - val_accuracy: 0.9255
Epoch 29/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2648 - accuracy: 0.9108 - val_loss: 0.2530 - val_accuracy: 0.9255
Epoch 30/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2579 - accuracy: 0.9108 - val_loss: 0.2461 - val_accuracy: 0.9255
Epoch 31/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2511 - accuracy: 0.9134 - val_loss: 0.2398 - val_accuracy: 0.9309
Epoch 32/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2450 - accuracy: 0.9186 - val_loss: 0.2336 - val_accuracy: 0.9309
Epoch 33/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2390 - accuracy: 0.9186 - val_loss: 0.2281 - val_accuracy: 0.9309
Epoch 34/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2336 - accuracy: 0.9186 - val_loss: 0.2227 - val_accuracy: 0.9309
Epoch 35/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2285 - accuracy: 0.9213 - val_loss: 0.2175 - val_accuracy: 0.9362
Epoch 36/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.9239 - val_loss: 0.2129 - val_accuracy: 0.9362
Epoch 37/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2189 - accuracy: 0.9239 - val_loss: 0.2081 - val_accuracy: 0.9468
Epoch 38/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2145 - accuracy: 0.9239 - val_loss: 0.2039 - val_accuracy: 0.9521
Epoch 39/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2103 - accuracy: 0.9265 - val_loss: 0.1997 - val_accuracy: 0.9521
Epoch 40/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2065 - accuracy: 0.9265 - val_loss: 0.1956 - val_accuracy: 0.9521
Epoch 41/100
12/12 [==============================] - 0s 2ms/step - loss: 0.2026 - accuracy: 0.9318 - val_loss: 0.1916 - val_accuracy: 0.9521
Epoch 42/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1988 - accuracy: 0.9344 - val_loss: 0.1880 - val_accuracy: 0.9521
Epoch 43/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1955 - accuracy: 0.9370 - val_loss: 0.1845 - val_accuracy: 0.9521
Epoch 44/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1922 - accuracy: 0.9370 - val_loss: 0.1813 - val_accuracy: 0.9521
Epoch 45/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1890 - accuracy: 0.9370 - val_loss: 0.1781 - val_accuracy: 0.9521
Epoch 46/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1861 - accuracy: 0.9370 - val_loss: 0.1749 - val_accuracy: 0.9521
Epoch 47/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1831 - accuracy: 0.9370 - val_loss: 0.1722 - val_accuracy: 0.9521
Epoch 48/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1803 - accuracy: 0.9370 - val_loss: 0.1694 - val_accuracy: 0.9521
Epoch 49/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1779 - accuracy: 0.9449 - val_loss: 0.1665 - val_accuracy: 0.9521
Epoch 50/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1751 - accuracy: 0.9475 - val_loss: 0.1641 - val_accuracy: 0.9468
Epoch 51/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1728 - accuracy: 0.9475 - val_loss: 0.1616 - val_accuracy: 0.9521
Epoch 52/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1704 - accuracy: 0.9475 - val_loss: 0.1592 - val_accuracy: 0.9521
Epoch 53/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1681 - accuracy: 0.9475 - val_loss: 0.1569 - val_accuracy: 0.9468
Epoch 54/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1659 - accuracy: 0.9501 - val_loss: 0.1547 - val_accuracy: 0.9468
Epoch 55/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1638 - accuracy: 0.9501 - val_loss: 0.1526 - val_accuracy: 0.9521
Epoch 56/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1618 - accuracy: 0.9501 - val_loss: 0.1506 - val_accuracy: 0.9521
Epoch 57/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1599 - accuracy: 0.9528 - val_loss: 0.1485 - val_accuracy: 0.9521
Epoch 58/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1580 - accuracy: 0.9528 - val_loss: 0.1468 - val_accuracy: 0.9574
Epoch 59/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1562 - accuracy: 0.9528 - val_loss: 0.1449 - val_accuracy: 0.9628
Epoch 60/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1544 - accuracy: 0.9554 - val_loss: 0.1431 - val_accuracy: 0.9628
Epoch 61/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1527 - accuracy: 0.9554 - val_loss: 0.1413 - val_accuracy: 0.9628
Epoch 62/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1510 - accuracy: 0.9554 - val_loss: 0.1397 - val_accuracy: 0.9628
Epoch 63/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1494 - accuracy: 0.9554 - val_loss: 0.1382 - val_accuracy: 0.9628
Epoch 64/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1479 - accuracy: 0.9554 - val_loss: 0.1366 - val_accuracy: 0.9628
Epoch 65/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1464 - accuracy: 0.9554 - val_loss: 0.1351 - val_accuracy: 0.9628
Epoch 66/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1450 - accuracy: 0.9580 - val_loss: 0.1337 - val_accuracy: 0.9628
Epoch 67/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1436 - accuracy: 0.9580 - val_loss: 0.1322 - val_accuracy: 0.9628
Epoch 68/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1422 - accuracy: 0.9580 - val_loss: 0.1309 - val_accuracy: 0.9628
Epoch 69/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1410 - accuracy: 0.9580 - val_loss: 0.1295 - val_accuracy: 0.9628
Epoch 70/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1396 - accuracy: 0.9580 - val_loss: 0.1283 - val_accuracy: 0.9628
Epoch 71/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1384 - accuracy: 0.9580 - val_loss: 0.1271 - val_accuracy: 0.9628
Epoch 72/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1372 - accuracy: 0.9580 - val_loss: 0.1259 - val_accuracy: 0.9628
Epoch 73/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1360 - accuracy: 0.9580 - val_loss: 0.1247 - val_accuracy: 0.9681
Epoch 74/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1349 - accuracy: 0.9580 - val_loss: 0.1236 - val_accuracy: 0.9681
Epoch 75/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1338 - accuracy: 0.9580 - val_loss: 0.1224 - val_accuracy: 0.9681
Epoch 76/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1327 - accuracy: 0.9606 - val_loss: 0.1214 - val_accuracy: 0.9681
Epoch 77/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1317 - accuracy: 0.9606 - val_loss: 0.1203 - val_accuracy: 0.9681
Epoch 78/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1306 - accuracy: 0.9606 - val_loss: 0.1193 - val_accuracy: 0.9681
Epoch 79/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1296 - accuracy: 0.9633 - val_loss: 0.1184 - val_accuracy: 0.9681
Epoch 80/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1287 - accuracy: 0.9633 - val_loss: 0.1174 - val_accuracy: 0.9681
Epoch 81/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1277 - accuracy: 0.9633 - val_loss: 0.1164 - val_accuracy: 0.9681
Epoch 82/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1268 - accuracy: 0.9659 - val_loss: 0.1155 - val_accuracy: 0.9734
Epoch 83/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1259 - accuracy: 0.9659 - val_loss: 0.1146 - val_accuracy: 0.9734
Epoch 84/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1251 - accuracy: 0.9659 - val_loss: 0.1138 - val_accuracy: 0.9734
Epoch 85/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1242 - accuracy: 0.9659 - val_loss: 0.1129 - val_accuracy: 0.9734
Epoch 86/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1234 - accuracy: 0.9659 - val_loss: 0.1121 - val_accuracy: 0.9734
Epoch 87/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1226 - accuracy: 0.9659 - val_loss: 0.1113 - val_accuracy: 0.9734
Epoch 88/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1218 - accuracy: 0.9659 - val_loss: 0.1105 - val_accuracy: 0.9734
Epoch 89/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1211 - accuracy: 0.9659 - val_loss: 0.1097 - val_accuracy: 0.9734
Epoch 90/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1202 - accuracy: 0.9659 - val_loss: 0.1089 - val_accuracy: 0.9734
Epoch 91/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1195 - accuracy: 0.9659 - val_loss: 0.1082 - val_accuracy: 0.9734
Epoch 92/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1188 - accuracy: 0.9685 - val_loss: 0.1074 - val_accuracy: 0.9734
Epoch 93/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1181 - accuracy: 0.9685 - val_loss: 0.1067 - val_accuracy: 0.9734
Epoch 94/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1174 - accuracy: 0.9685 - val_loss: 0.1060 - val_accuracy: 0.9734
Epoch 95/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1167 - accuracy: 0.9685 - val_loss: 0.1053 - val_accuracy: 0.9734
Epoch 96/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1161 - accuracy: 0.9685 - val_loss: 0.1047 - val_accuracy: 0.9734
Epoch 97/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1154 - accuracy: 0.9711 - val_loss: 0.1040 - val_accuracy: 0.9734
Epoch 98/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1148 - accuracy: 0.9711 - val_loss: 0.1034 - val_accuracy: 0.9734
Epoch 99/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1142 - accuracy: 0.9738 - val_loss: 0.1027 - val_accuracy: 0.9734
Epoch 100/100
12/12 [==============================] - 0s 2ms/step - loss: 0.1136 - accuracy: 0.9738 - val_loss: 0.1021 - val_accuracy: 0.9734
12/12 [==============================] - 0s 455us/step - loss: 0.1132 - accuracy: 0.9738
Train score: [0.11321526765823364, 0.9737532734870911]
6/6 [==============================] - 0s 600us/step - loss: 0.1021 - accuracy: 0.9734
Test score: [0.10211259871721268, 0.9734042286872864]
6/6 [==============================] - 0s 600us/step - loss: 0.1021 - accuracy: 0.9734
[0.10211259871721268, 0.9734042286872864]