Fri Jan 3 12:42:01 2025 movie_review(): python version: 3.10.12 numpy version: 1.26.4 tensorflow version: 2.16.1 Neural network to classify movie reviews. First data item and its label: [1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 2, 19, 178, 32] 1 Check that the maximum word index is 4,999: max word index = 4999 Print a decoded review: ? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly ? was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little ? that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big ? for the whole film but these children are amazing and should be ? for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was ? with us all First item of training data, after vectorization. [0. 1. 1. ... 0. 0. 0.] Epoch 1/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 23s 607ms/step - accuracy: 0.4629 - loss: 0.7022 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.6077 - loss: 0.6527  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.6729 - loss: 0.6077 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7093 - loss: 0.5748 40/40 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.7135 - loss: 0.5704 - val_accuracy: 0.8670 - val_loss: 0.3624 Epoch 2/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - accuracy: 0.8926 - loss: 0.3249 14/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.8867 - loss: 0.3278  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.8861 - loss: 0.3240 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.8863 - loss: 0.3192 - val_accuracy: 0.8788 - val_loss: 0.3074 Epoch 3/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8867 - loss: 0.2591 14/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9016 - loss: 0.2583  27/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9060 - loss: 0.2542 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9076 - loss: 0.2517 - val_accuracy: 0.8864 - val_loss: 0.2906 Epoch 4/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 37ms/step - accuracy: 0.9160 - loss: 0.2081 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9201 - loss: 0.2128  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9196 - loss: 0.2147 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9195 - loss: 0.2155 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9194 - loss: 0.2156 - val_accuracy: 0.8782 - val_loss: 0.3107 Epoch 5/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - accuracy: 0.9336 - loss: 0.1962 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9333 - loss: 0.1884  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9324 - loss: 0.1889 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9308 - loss: 0.1917 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9307 - loss: 0.1918 - val_accuracy: 0.8864 - val_loss: 0.2839 Epoch 6/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9609 - loss: 0.1437 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9445 - loss: 0.1687  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9412 - loss: 0.1722 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9393 - loss: 0.1743 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9389 - loss: 0.1746 - val_accuracy: 0.8824 - val_loss: 0.3066 Epoch 7/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - accuracy: 0.9473 - loss: 0.1560 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9426 - loss: 0.1607  24/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9423 - loss: 0.1623 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9415 - loss: 0.1637 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9414 - loss: 0.1639 - val_accuracy: 0.8810 - val_loss: 0.3039 Epoch 8/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.9453 - loss: 0.1568 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9501 - loss: 0.1414  23/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9489 - loss: 0.1442 36/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9475 - loss: 0.1466 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9472 - loss: 0.1473 - val_accuracy: 0.8826 - val_loss: 0.3097 Epoch 9/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 31ms/step - accuracy: 0.9531 - loss: 0.1339 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9525 - loss: 0.1413  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9507 - loss: 0.1425 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9501 - loss: 0.1430 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9500 - loss: 0.1432 - val_accuracy: 0.8824 - val_loss: 0.3201 Epoch 10/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - accuracy: 0.9609 - loss: 0.1143 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9605 - loss: 0.1217  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9583 - loss: 0.1244 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9565 - loss: 0.1264 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9564 - loss: 0.1266 - val_accuracy: 0.8608 - val_loss: 0.4091 Epoch 11/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - accuracy: 0.9121 - loss: 0.1747 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9458 - loss: 0.1366  24/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9515 - loss: 0.1284 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9532 - loss: 0.1262 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9533 - loss: 0.1257 - val_accuracy: 0.8780 - val_loss: 0.3425 Epoch 12/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9668 - loss: 0.1046 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9674 - loss: 0.1008  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9646 - loss: 0.1053 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9636 - loss: 0.1073 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9634 - loss: 0.1076 - val_accuracy: 0.8760 - val_loss: 0.3528 Epoch 13/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9785 - loss: 0.0746 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9773 - loss: 0.0846  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9753 - loss: 0.0870 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9728 - loss: 0.0907 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9723 - loss: 0.0913 - val_accuracy: 0.8726 - val_loss: 0.3720 Epoch 14/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9785 - loss: 0.0739 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9776 - loss: 0.0768  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9744 - loss: 0.0833 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9734 - loss: 0.0852 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9733 - loss: 0.0854 - val_accuracy: 0.8658 - val_loss: 0.4444 Epoch 15/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - accuracy: 0.9375 - loss: 0.1330 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9667 - loss: 0.0933  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9713 - loss: 0.0861 39/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9725 - loss: 0.0841 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9725 - loss: 0.0841 - val_accuracy: 0.8678 - val_loss: 0.4256 Epoch 16/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - accuracy: 0.9766 - loss: 0.0659 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9824 - loss: 0.0665  25/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9816 - loss: 0.0680 38/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9809 - loss: 0.0691 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9806 - loss: 0.0695 - val_accuracy: 0.8718 - val_loss: 0.4141 Epoch 17/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - accuracy: 0.9883 - loss: 0.0466 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9881 - loss: 0.0504  24/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9870 - loss: 0.0536 37/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9854 - loss: 0.0570 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9851 - loss: 0.0576 - val_accuracy: 0.8714 - val_loss: 0.4296 Epoch 18/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 35ms/step - accuracy: 0.9902 - loss: 0.0359 12/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9895 - loss: 0.0434  23/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9862 - loss: 0.0507 35/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9852 - loss: 0.0531 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9848 - loss: 0.0539 - val_accuracy: 0.8720 - val_loss: 0.4481 Epoch 19/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - accuracy: 0.9941 - loss: 0.0390 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9910 - loss: 0.0404  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9889 - loss: 0.0442 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9882 - loss: 0.0457 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9882 - loss: 0.0457 - val_accuracy: 0.8680 - val_loss: 0.4676 Epoch 20/20  1/40 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9824 - loss: 0.0614 13/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9897 - loss: 0.0477  26/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9901 - loss: 0.0463 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9895 - loss: 0.0466 40/40 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9894 - loss: 0.0466 - val_accuracy: 0.8712 - val_loss: 0.4789 dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss']) Model loss and accuracy on training data: Final training loss 0.04629147797822952 Final training accuracy 0.9886000156402588 Model loss and accuracy on validation data: Final validation loss 0.47891634702682495 Final validation accuracy 0.8712000250816345 Graphics saved as "movie_review_loss.png" Graphics saved as "movie_review_accuracy.png" Epoch 1/4  1/49 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - accuracy: 0.9707 - loss: 0.1449 13/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9728 - loss: 0.1168  25/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9705 - loss: 0.1188 38/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9696 - loss: 0.1195 49/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9690 - loss: 0.1202 Epoch 2/4  1/49 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - accuracy: 0.9824 - loss: 0.0671 12/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9778 - loss: 0.0844  23/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9776 - loss: 0.0859 34/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9765 - loss: 0.0879 46/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9760 - loss: 0.0887 49/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9758 - loss: 0.0888 Epoch 3/4  1/49 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - accuracy: 0.9863 - loss: 0.0765 12/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9852 - loss: 0.0649  24/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9834 - loss: 0.0666 36/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9824 - loss: 0.0683 49/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9816 - loss: 0.0695 49/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9816 - loss: 0.0695 Epoch 4/4  1/49 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.9941 - loss: 0.0588 12/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9907 - loss: 0.0520  23/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9888 - loss: 0.0529 35/49 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9877 - loss: 0.0535 48/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9868 - loss: 0.0549 49/49 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.9867 - loss: 0.0552 Apply the model on the test data:  1/782 ━━━━━━━━━━━━━━━━━━━━ 23s 30ms/step - accuracy: 0.8125 - loss: 0.6170  73/782 ━━━━━━━━━━━━━━━━━━━━ 0s 699us/step - accuracy: 0.8368 - loss: 0.5207 150/782 ━━━━━━━━━━━━━━━━━━━━ 0s 677us/step - accuracy: 0.8409 - loss: 0.5194 225/782 ━━━━━━━━━━━━━━━━━━━━ 0s 676us/step - accuracy: 0.8437 - loss: 0.5151 298/782 ━━━━━━━━━━━━━━━━━━━━ 0s 679us/step - accuracy: 0.8457 - loss: 0.5118 367/782 ━━━━━━━━━━━━━━━━━━━━ 0s 689us/step - accuracy: 0.8467 - loss: 0.5122 439/782 ━━━━━━━━━━━━━━━━━━━━ 0s 691us/step - accuracy: 0.8477 - loss: 0.5121 513/782 ━━━━━━━━━━━━━━━━━━━━ 0s 689us/step - accuracy: 0.8486 - loss: 0.5111 590/782 ━━━━━━━━━━━━━━━━━━━━ 0s 685us/step - accuracy: 0.8494 - loss: 0.5096 668/782 ━━━━━━━━━━━━━━━━━━━━ 0s 680us/step - accuracy: 0.8502 - loss: 0.5076 744/782 ━━━━━━━━━━━━━━━━━━━━ 0s 679us/step - accuracy: 0.8510 - loss: 0.5058 782/782 ━━━━━━━━━━━━━━━━━━━━ 1s 681us/step - accuracy: 0.8514 - loss: 0.5050 Model loss and accuracy on test data: loss 0.49045830965042114 compile_metrics 0.8583199977874756 Model predictions on test data:  1/782 ━━━━━━━━━━━━━━━━━━━━ 27s 35ms/step  65/782 ━━━━━━━━━━━━━━━━━━━━ 0s 787us/step 143/782 ━━━━━━━━━━━━━━━━━━━━ 0s 707us/step 224/782 ━━━━━━━━━━━━━━━━━━━━ 0s 675us/step 306/782 ━━━━━━━━━━━━━━━━━━━━ 0s 659us/step 385/782 ━━━━━━━━━━━━━━━━━━━━ 0s 655us/step 462/782 ━━━━━━━━━━━━━━━━━━━━ 0s 654us/step 542/782 ━━━━━━━━━━━━━━━━━━━━ 0s 650us/step 622/782 ━━━━━━━━━━━━━━━━━━━━ 0s 647us/step 701/782 ━━━━━━━━━━━━━━━━━━━━ 0s 646us/step 774/782 ━━━━━━━━━━━━━━━━━━━━ 0s 650us/step 782/782 ━━━━━━━━━━━━━━━━━━━━ 0s 723us/step 782/782 ━━━━━━━━━━━━━━━━━━━━ 1s 723us/step [[0.03116204] [0.9999945 ] [0.05032537] ... [0.121198 ] [0.00335931] [0.9518929 ]] movie_review(): Normal end of execution. Fri Jan 3 12:42:28 2025