Keras problem

Keras results

69/69 [==============================] - 7s - loss: 0.8184 - binary_accuracy: 0.4949 - val_loss: 0.7716 - val_binary_accuracy: 0.5008
Epoch 2/100
69/69 [==============================] - 4s - loss: 0.7679 - binary_accuracy: 0.5000 - val_loss: 0.7476 - val_binary_accuracy: 0.5008
Epoch 3/100
69/69 [==============================] - 4s - loss: 0.7416 - binary_accuracy: 0.5022 - val_loss: 0.7283 - val_binary_accuracy: 0.5020
Epoch 4/100
69/69 [==============================] - 4s - loss: 0.7242 - binary_accuracy: 0.4965 - val_loss: 0.7157 - val_binary_accuracy: 0.5008
Epoch 5/100
69/69 [==============================] - 4s - loss: 0.7121 - binary_accuracy: 0.4966 - val_loss: 0.7092 - val_binary_accuracy: 0.5020
Epoch 6/100
69/69 [==============================] - 4s - loss: 0.7034 - binary_accuracy: 0.5221 - val_loss: 0.7124 - val_binary_accuracy: 0.4328
Epoch 7/100
69/69 [==============================] - 4s - loss: 0.6614 - binary_accuracy: 0.6026 - val_loss: 0.6538 - val_binary_accuracy: 0.5902
Epoch 8/100
69/69 [==============================] - 4s - loss: 0.4170 - binary_accuracy: 0.8450 - val_loss: 0.3994 - val_binary_accuracy: 0.8422
Epoch 9/100
69/69 [==============================] - 4s - loss: 0.2554 - binary_accuracy: 0.9204 - val_loss: 0.3083 - val_binary_accuracy: 0.8809
Epoch 10/100
69/69 [==============================] - 4s - loss: 0.2027 - binary_accuracy: 0.9383 - val_loss: 0.2558 - val_binary_accuracy: 0.9062
Epoch 11/100
69/69 [==============================] - 4s - loss: 0.1677 - binary_accuracy: 0.9495 - val_loss: 0.2713 - val_binary_accuracy: 0.9016
Epoch 12/100
69/69 [==============================] - 4s - loss: 0.1476 - binary_accuracy: 0.9581 - val_loss: 0.2681 - val_binary_accuracy: 0.9062
Epoch 13/100
69/69 [==============================] - 4s - loss: 0.1262 - binary_accuracy: 0.9678 - val_loss: 0.1772 - val_binary_accuracy: 0.9367
Epoch 14/100
69/69 [==============================] - 4s - loss: 0.1111 - binary_accuracy: 0.9735 - val_loss: 0.1364 - val_binary_accuracy: 0.9703
Epoch 15/100
69/69 [==============================] - 4s - loss: 0.0964 - binary_accuracy: 0.9777 - val_loss: 0.1755 - val_binary_accuracy: 0.9777
Epoch 16/100
69/69 [==============================] - 4s - loss: 0.1109 - binary_accuracy: 0.9748 - val_loss: 0.1655 - val_binary_accuracy: 0.9887
Epoch 17/100
69/69 [==============================] - 4s - loss: 0.0974 - binary_accuracy: 0.9777 - val_loss: 0.1140 - val_binary_accuracy: 0.9855
Epoch 18/100
69/69 [==============================] - 4s - loss: 0.0840 - binary_accuracy: 0.9805 - val_loss: 0.1304 - val_binary_accuracy: 0.9906
Epoch 19/100
69/69 [==============================] - 4s - loss: 0.0737 - binary_accuracy: 0.9852 - val_loss: 0.1201 - val_binary_accuracy: 0.9914
Epoch 20/100
69/69 [==============================] - 4s - loss: 0.0695 - binary_accuracy: 0.9873 - val_loss: 0.1116 - val_binary_accuracy: 0.9918
Epoch 21/100
69/69 [==============================] - 4s - loss: 0.0694 - binary_accuracy: 0.9853 - val_loss: 0.0917 - val_binary_accuracy: 0.9910
Epoch 22/100
69/69 [==============================] - 4s - loss: 0.0604 - binary_accuracy: 0.9895 - val_loss: 0.1713 - val_binary_accuracy: 0.9727
Epoch 23/100
69/69 [==============================] - 4s - loss: 0.0610 - binary_accuracy: 0.9895 - val_loss: 0.1370 - val_binary_accuracy: 0.9914
Epoch 24/100
69/69 [==============================] - 5s - loss: 0.0614 - binary_accuracy: 0.9888 - val_loss: 0.0788 - val_binary_accuracy: 0.9926
Epoch 25/100
69/69 [==============================] - 4s - loss: 0.0504 - binary_accuracy: 0.9920 - val_loss: 0.1357 - val_binary_accuracy: 0.9848
Epoch 26/100
69/69 [==============================] - 4s - loss: 0.0515 - binary_accuracy: 0.9917 - val_loss: 0.0611 - val_binary_accuracy: 0.9879
Epoch 27/100
69/69 [==============================] - 4s - loss: 0.0658 - binary_accuracy: 0.9873 - val_loss: 0.0832 - val_binary_accuracy: 0.9812
Epoch 28/100
69/69 [==============================] - 4s - loss: 0.0440 - binary_accuracy: 0.9947 - val_loss: 0.0685 - val_binary_accuracy: 0.9852
Epoch 29/100
69/69 [==============================] - 4s - loss: 0.0444 - binary_accuracy: 0.9946 - val_loss: 0.0610 - val_binary_accuracy: 0.9875
Epoch 30/100
69/69 [==============================] - 4s - loss: 0.0509 - binary_accuracy: 0.9922 - val_loss: 0.0647 - val_binary_accuracy: 0.9875
Epoch 31/100
69/69 [==============================] - 4s - loss: 0.0575 - binary_accuracy: 0.9897 - val_loss: 0.0629 - val_binary_accuracy: 0.9859
Epoch 32/100
69/69 [==============================] - 4s - loss: 0.0481 - binary_accuracy: 0.9933 - val_loss: 0.0623 - val_binary_accuracy: 0.9867
Epoch 33/100
69/69 [==============================] - 4s - loss: 0.0718 - binary_accuracy: 0.9879 - val_loss: 0.0745 - val_binary_accuracy: 0.9836
Epoch 34/100
69/69 [==============================] - 4s - loss: 0.0411 - binary_accuracy: 0.9955 - val_loss: 0.0656 - val_binary_accuracy: 0.9844
Epoch 35/100
69/69 [==============================] - 4s - loss: 0.0387 - binary_accuracy: 0.9962 - val_loss: 0.0566 - val_binary_accuracy: 0.9891
Epoch 36/100
69/69 [==============================] - 4s - loss: 0.0433 - binary_accuracy: 0.9946 - val_loss: 0.0611 - val_binary_accuracy: 0.9875
Epoch 37/100
69/69 [==============================] - 4s - loss: 0.0409 - binary_accuracy: 0.9946 - val_loss: 0.0581 - val_binary_accuracy: 0.9879
Epoch 38/100
69/69 [==============================] - 4s - loss: 0.0385 - binary_accuracy: 0.9962 - val_loss: 0.0653 - val_binary_accuracy: 0.9859
Epoch 39/100
69/69 [==============================] - 4s - loss: 0.0345 - binary_accuracy: 0.9971 - val_loss: 0.0539 - val_binary_accuracy: 0.9902
Epoch 40/100
69/69 [==============================] - 4s - loss: 0.0413 - binary_accuracy: 0.9950 - val_loss: 0.0556 - val_binary_accuracy: 0.9891
Epoch 41/100
69/69 [==============================] - 4s - loss: 0.0554 - binary_accuracy: 0.9891 - val_loss: 0.0680 - val_binary_accuracy: 0.9852
Epoch 42/100
69/69 [==============================] - 4s - loss: 0.0308 - binary_accuracy: 0.9985 - val_loss: 0.0609 - val_binary_accuracy: 0.9871
Epoch 43/100
69/69 [==============================] - 5s - loss: 0.0374 - binary_accuracy: 0.9956 - val_loss: 0.0702 - val_binary_accuracy: 0.9863
Epoch 44/100
69/69 [==============================] - 4s - loss: 0.0339 - binary_accuracy: 0.9968 - val_loss: 0.0615 - val_binary_accuracy: 0.9883
Epoch 45/100
69/69 [==============================] - 4s - loss: 0.0386 - binary_accuracy: 0.9941 - val_loss: 0.0773 - val_binary_accuracy: 0.9820
Epoch 46/100
69/69 [==============================] - 4s - loss: 0.0341 - binary_accuracy: 0.9982 - val_loss: 0.0532 - val_binary_accuracy: 0.9906
Epoch 47/100
69/69 [==============================] - 4s - loss: 0.0374 - binary_accuracy: 0.9948 - val_loss: 0.0602 - val_binary_accuracy: 0.9887
Epoch 48/100
69/69 [==============================] - 4s - loss: 0.0354 - binary_accuracy: 0.9957 - val_loss: 0.0571 - val_binary_accuracy: 0.9883
Epoch 49/100
69/69 [==============================] - 4s - loss: 0.0321 - binary_accuracy: 0.9974 - val_loss: 0.0517 - val_binary_accuracy: 0.9906
Epoch 50/100
69/69 [==============================] - 4s - loss: 0.0348 - binary_accuracy: 0.9962 - val_loss: 0.0584 - val_binary_accuracy: 0.9875
Epoch 51/100
69/69 [==============================] - 4s - loss: 0.0307 - binary_accuracy: 0.9973 - val_loss: 0.0511 - val_binary_accuracy: 0.9898
Epoch 52/100
69/69 [==============================] - 4s - loss: 0.0342 - binary_accuracy: 0.9964 - val_loss: 0.0593 - val_binary_accuracy: 0.9875
Epoch 53/100
69/69 [==============================] - 4s - loss: 0.0378 - binary_accuracy: 0.9928 - val_loss: 0.0572 - val_binary_accuracy: 0.9895
Epoch 54/100
69/69 [==============================] - 4s - loss: 0.0262 - binary_accuracy: 0.9988 - val_loss: 0.0483 - val_binary_accuracy: 0.9891
Epoch 55/100
69/69 [==============================] - 4s - loss: 0.0318 - binary_accuracy: 0.9975 - val_loss: 0.0459 - val_binary_accuracy: 0.9906
Epoch 56/100
69/69 [==============================] - 4s - loss: 0.0448 - binary_accuracy: 0.9907 - val_loss: 0.0628 - val_binary_accuracy: 0.9840
Epoch 57/100
69/69 [==============================] - 4s - loss: 0.0280 - binary_accuracy: 0.9981 - val_loss: 0.0550 - val_binary_accuracy: 0.9871
Epoch 58/100
69/69 [==============================] - 4s - loss: 0.0315 - binary_accuracy: 0.9964 - val_loss: 0.0526 - val_binary_accuracy: 0.9891
Epoch 59/100
69/69 [==============================] - 4s - loss: 0.0311 - binary_accuracy: 0.9955 - val_loss: 0.0588 - val_binary_accuracy: 0.9871
Epoch 60/100
69/69 [==============================] - 4s - loss: 0.0278 - binary_accuracy: 0.9981 - val_loss: 0.0598 - val_binary_accuracy: 0.9852
Epoch 61/100
69/69 [==============================] - 4s - loss: 0.0309 - binary_accuracy: 0.9962 - val_loss: 0.0722 - val_binary_accuracy: 0.9836
Epoch 62/100
69/69 [==============================] - 4s - loss: 0.0352 - binary_accuracy: 0.9935 - val_loss: 0.0661 - val_binary_accuracy: 0.9828
Epoch 63/100
69/69 [==============================] - 4s - loss: 0.0282 - binary_accuracy: 0.9968 - val_loss: 0.0514 - val_binary_accuracy: 0.9902
Epoch 64/100
69/69 [==============================] - 4s - loss: 0.0242 - binary_accuracy: 0.9984 - val_loss: 0.0590 - val_binary_accuracy: 0.9863
Epoch 65/100
69/69 [==============================] - 4s - loss: 0.0262 - binary_accuracy: 0.9982 - val_loss: 0.0596 - val_binary_accuracy: 0.9863
Epoch 66/100
69/69 [==============================] - 4s - loss: 0.0288 - binary_accuracy: 0.9982 - val_loss: 0.0503 - val_binary_accuracy: 0.9875
Epoch 67/100
69/69 [==============================] - 4s - loss: 0.0293 - binary_accuracy: 0.9959 - val_loss: 0.0484 - val_binary_accuracy: 0.9887
Epoch 68/100
69/69 [==============================] - 4s - loss: 0.0243 - binary_accuracy: 0.9988 - val_loss: 0.0522 - val_binary_accuracy: 0.9859
Epoch 69/100
69/69 [==============================] - 4s - loss: 0.0286 - binary_accuracy: 0.9958 - val_loss: 0.0643 - val_binary_accuracy: 0.9840
Epoch 70/100
69/69 [==============================] - 4s - loss: 0.0288 - binary_accuracy: 0.9969 - val_loss: 0.0558 - val_binary_accuracy: 0.9867
Epoch 71/100
69/69 [==============================] - 4s - loss: 0.0255 - binary_accuracy: 0.9975 - val_loss: 0.0658 - val_binary_accuracy: 0.9840
Epoch 72/100
69/69 [==============================] - 4s - loss: 0.0459 - binary_accuracy: 0.9928 - val_loss: 0.0631 - val_binary_accuracy: 0.9832
Epoch 73/100
69/69 [==============================] - 4s - loss: 0.0241 - binary_accuracy: 0.9975 - val_loss: 0.0736 - val_binary_accuracy: 0.9793
Epoch 74/100
69/69 [==============================] - 4s - loss: 0.0511 - binary_accuracy: 0.9924 - val_loss: 0.0608 - val_binary_accuracy: 0.9840
Epoch 75/100
69/69 [==============================] - 4s - loss: 0.0220 - binary_accuracy: 0.9988 - val_loss: 0.0637 - val_binary_accuracy: 0.9844
Epoch 76/100
69/69 [==============================] - 4s - loss: 0.0350 - binary_accuracy: 0.9933 - val_loss: 0.0733 - val_binary_accuracy: 0.9816
Epoch 77/100
69/69 [==============================] - 4s - loss: 0.0246 - binary_accuracy: 0.9981 - val_loss: 0.0563 - val_binary_accuracy: 0.9840
Epoch 78/100
69/69 [==============================] - 4s - loss: 0.0242 - binary_accuracy: 0.9982 - val_loss: 0.0599 - val_binary_accuracy: 0.9859
Epoch 79/100
69/69 [==============================] - 4s - loss: 0.0259 - binary_accuracy: 0.9984 - val_loss: 0.0672 - val_binary_accuracy: 0.9840
Epoch 80/100
69/69 [==============================] - 4s - loss: 0.0256 - binary_accuracy: 0.9975 - val_loss: 0.0688 - val_binary_accuracy: 0.9824
Epoch 81/100
69/69 [==============================] - 4s - loss: 0.0221 - binary_accuracy: 0.9989 - val_loss: 0.0423 - val_binary_accuracy: 0.9906
Epoch 82/100
69/69 [==============================] - 4s - loss: 0.0226 - binary_accuracy: 0.9983 - val_loss: 0.0458 - val_binary_accuracy: 0.9875
Epoch 83/100
69/69 [==============================] - 4s - loss: 0.0258 - binary_accuracy: 0.9968 - val_loss: 0.0427 - val_binary_accuracy: 0.9906
Epoch 84/100
69/69 [==============================] - 4s - loss: 0.0237 - binary_accuracy: 0.9981 - val_loss: 0.0435 - val_binary_accuracy: 0.9887
Epoch 85/100
69/69 [==============================] - 4s - loss: 0.0236 - binary_accuracy: 0.9981 - val_loss: 0.0440 - val_binary_accuracy: 0.9902
Epoch 86/100
69/69 [==============================] - 4s - loss: 0.0230 - binary_accuracy: 0.9980 - val_loss: 0.0557 - val_binary_accuracy: 0.9859
Epoch 87/100
69/69 [==============================] - 4s - loss: 0.0212 - binary_accuracy: 0.9988 - val_loss: 0.0613 - val_binary_accuracy: 0.9836
Epoch 88/100
69/69 [==============================] - 4s - loss: 0.0233 - binary_accuracy: 0.9980 - val_loss: 0.0385 - val_binary_accuracy: 0.9922
Epoch 89/100
69/69 [==============================] - 4s - loss: 0.0326 - binary_accuracy: 0.9943 - val_loss: 0.0639 - val_binary_accuracy: 0.9812
Epoch 90/100
69/69 [==============================] - 4s - loss: 0.0220 - binary_accuracy: 0.9982 - val_loss: 0.0495 - val_binary_accuracy: 0.9879
Epoch 91/100
69/69 [==============================] - 4s - loss: 0.0280 - binary_accuracy: 0.9946 - val_loss: 0.0599 - val_binary_accuracy: 0.9848
Epoch 92/100
69/69 [==============================] - 4s - loss: 0.0248 - binary_accuracy: 0.9959 - val_loss: 0.0642 - val_binary_accuracy: 0.9824
Epoch 93/100
69/69 [==============================] - 4s - loss: 0.0270 - binary_accuracy: 0.9940 - val_loss: 0.0560 - val_binary_accuracy: 0.9848
Epoch 94/100
69/69 [==============================] - 4s - loss: 0.0266 - binary_accuracy: 0.9952 - val_loss: 0.0592 - val_binary_accuracy: 0.9867
Epoch 95/100
69/69 [==============================] - 4s - loss: 0.0200 - binary_accuracy: 0.9991 - val_loss: 0.0420 - val_binary_accuracy: 0.9906
Epoch 96/100
69/69 [==============================] - 4s - loss: 0.0208 - binary_accuracy: 0.9988 - val_loss: 0.0425 - val_binary_accuracy: 0.9902
Epoch 97/100
69/69 [==============================] - 4s - loss: 0.0271 - binary_accuracy: 0.9956 - val_loss: 0.0447 - val_binary_accuracy: 0.9902
Epoch 98/100
69/69 [==============================] - 4s - loss: 0.0194 - binary_accuracy: 0.9992 - val_loss: 0.0388 - val_binary_accuracy: 0.9914
Epoch 99/100
69/69 [==============================] - 4s - loss: 0.0208 - binary_accuracy: 0.9984 - val_loss: 0.0358 - val_binary_accuracy: 0.9934
Epoch 100/100
69/69 [==============================] - 4s - loss: 0.0202 - binary_accuracy: 0.9988 - val_loss: 0.0597 - val_binary_accuracy: 0.9832
Testing the model using Training dataset
Confusion Matrix:
[[4381    0]
 [4383   0]]
Accuracy:
0.5000000011

Testing the model using Testing dataset
Confusion Matrix:
[[3073    0]
 [3071    0]]
Accuracy:
0.500162760417
Keras accuracy metrics for Testing dataset:
Results: ['loss', 'binary_accuracy'] - [0.057065334171056747, 0.98502604166666663]
Done.