[hint]Solution1a_WeightIncreasing.ipynb

{"cells":[{"cell_type":"markdown","source":["## Load Data"],"metadata":{"id":"HItsinp0SFqA"},"id":"HItsinp0SFqA"},{"cell_type":"code","execution_count":12,"id":"5eaa4ce2","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":756,"status":"ok","timestamp":1669887565799,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"},"user_tz":-420},"id":"5eaa4ce2","outputId":"d08db5f5-eb62-43da-b5bb-5f3c0358d915"},"outputs":[{"output_type":"stream","name":"stdout","text":["(60000, 28, 28)\n","(60000,)\n","(10000, 28, 28)\n","(10000,)\n"]}],"source":["# fashion_mnist\n","import tensorflow as tf\n","import tensorflow.keras as keras\n","import numpy as np\n","tf.keras.utils.set_random_seed(1)\n","tf.config.experimental.enable_op_determinism()\n","\n","(X_train, y_train), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n","print(X_train.shape)\n","print(y_train.shape)\n","print(X_test.shape)\n","print(y_test.shape)"]},{"cell_type":"markdown","source":["## Reshape Data"],"metadata":{"id":"75eeaa46"},"id":"75eeaa46"},{"cell_type":"code","execution_count":13,"id":"589bb0df","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1669887565800,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"},"user_tz":-420},"id":"589bb0df","outputId":"b2f663d3-dffa-49b7-e33e-b2303aecee29"},"outputs":[{"output_type":"stream","name":"stdout","text":["(60000, 784)\n","(60000,)\n","(10000, 784)\n","(10000,)\n"]}],"source":["X_train = X_train.reshape(60000, -1)\n","X_test  = X_test.reshape(10000, -1)\n","\n","print(X_train.shape)\n","print(y_train.shape)\n","print(X_test.shape)\n","print(y_test.shape)"]},{"cell_type":"markdown","id":"d63c2c97","metadata":{"id":"d63c2c97"},"source":["## Weight Increasing (std=1)"]},{"cell_type":"code","execution_count":14,"id":"9bcd06ca","metadata":{"id":"9bcd06ca","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669888249611,"user_tz":-420,"elapsed":683814,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}},"outputId":"b6eefe73-dbf8-4313-a690-97eddb8beadf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/300\n","118/118 - 3s - loss: 2.3269 - accuracy: 0.1245 - val_loss: 2.2766 - val_accuracy: 0.1343 - 3s/epoch - 26ms/step\n","Epoch 2/300\n","118/118 - 2s - loss: 2.2551 - accuracy: 0.1644 - val_loss: 2.2349 - val_accuracy: 0.1764 - 2s/epoch - 18ms/step\n","Epoch 3/300\n","118/118 - 2s - loss: 2.2106 - accuracy: 0.2090 - val_loss: 2.1884 - val_accuracy: 0.2196 - 2s/epoch - 18ms/step\n","Epoch 4/300\n","118/118 - 2s - loss: 2.1604 - accuracy: 0.2458 - val_loss: 2.1350 - val_accuracy: 0.2561 - 2s/epoch - 18ms/step\n","Epoch 5/300\n","118/118 - 2s - loss: 2.1013 - accuracy: 0.2796 - val_loss: 2.0721 - val_accuracy: 0.2893 - 2s/epoch - 18ms/step\n","Epoch 6/300\n","118/118 - 2s - loss: 2.0382 - accuracy: 0.3098 - val_loss: 2.0088 - val_accuracy: 0.3208 - 2s/epoch - 18ms/step\n","Epoch 7/300\n","118/118 - 2s - loss: 1.9727 - accuracy: 0.3352 - val_loss: 1.9443 - val_accuracy: 0.3408 - 2s/epoch - 18ms/step\n","Epoch 8/300\n","118/118 - 2s - loss: 1.9069 - accuracy: 0.3595 - val_loss: 1.8797 - val_accuracy: 0.3633 - 2s/epoch - 18ms/step\n","Epoch 9/300\n","118/118 - 2s - loss: 1.8431 - accuracy: 0.3788 - val_loss: 1.8207 - val_accuracy: 0.3837 - 2s/epoch - 18ms/step\n","Epoch 10/300\n","118/118 - 2s - loss: 1.7832 - accuracy: 0.3986 - val_loss: 1.7660 - val_accuracy: 0.3973 - 2s/epoch - 19ms/step\n","Epoch 11/300\n","118/118 - 2s - loss: 1.7286 - accuracy: 0.4134 - val_loss: 1.7118 - val_accuracy: 0.4121 - 2s/epoch - 19ms/step\n","Epoch 12/300\n","118/118 - 2s - loss: 1.6764 - accuracy: 0.4265 - val_loss: 1.6655 - val_accuracy: 0.4232 - 2s/epoch - 19ms/step\n","Epoch 13/300\n","118/118 - 2s - loss: 1.6297 - accuracy: 0.4381 - val_loss: 1.6213 - val_accuracy: 0.4395 - 2s/epoch - 19ms/step\n","Epoch 14/300\n","118/118 - 2s - loss: 1.5867 - accuracy: 0.4499 - val_loss: 1.5816 - val_accuracy: 0.4502 - 2s/epoch - 19ms/step\n","Epoch 15/300\n","118/118 - 2s - loss: 1.5480 - accuracy: 0.4605 - val_loss: 1.5442 - val_accuracy: 0.4558 - 2s/epoch - 19ms/step\n","Epoch 16/300\n","118/118 - 2s - loss: 1.5118 - accuracy: 0.4700 - val_loss: 1.5118 - val_accuracy: 0.4588 - 2s/epoch - 19ms/step\n","Epoch 17/300\n","118/118 - 2s - loss: 1.4794 - accuracy: 0.4789 - val_loss: 1.4779 - val_accuracy: 0.4764 - 2s/epoch - 19ms/step\n","Epoch 18/300\n","118/118 - 2s - loss: 1.4489 - accuracy: 0.4898 - val_loss: 1.4523 - val_accuracy: 0.4824 - 2s/epoch - 18ms/step\n","Epoch 19/300\n","118/118 - 2s - loss: 1.4214 - accuracy: 0.4990 - val_loss: 1.4220 - val_accuracy: 0.4912 - 2s/epoch - 18ms/step\n","Epoch 20/300\n","118/118 - 2s - loss: 1.3969 - accuracy: 0.5057 - val_loss: 1.3989 - val_accuracy: 0.4974 - 2s/epoch - 18ms/step\n","Epoch 21/300\n","118/118 - 2s - loss: 1.3738 - accuracy: 0.5123 - val_loss: 1.3786 - val_accuracy: 0.5044 - 2s/epoch - 19ms/step\n","Epoch 22/300\n","118/118 - 2s - loss: 1.3525 - accuracy: 0.5195 - val_loss: 1.3568 - val_accuracy: 0.5124 - 2s/epoch - 19ms/step\n","Epoch 23/300\n","118/118 - 2s - loss: 1.3320 - accuracy: 0.5246 - val_loss: 1.3401 - val_accuracy: 0.5183 - 2s/epoch - 19ms/step\n","Epoch 24/300\n","118/118 - 2s - loss: 1.3128 - accuracy: 0.5314 - val_loss: 1.3235 - val_accuracy: 0.5253 - 2s/epoch - 18ms/step\n","Epoch 25/300\n","118/118 - 2s - loss: 1.2950 - accuracy: 0.5373 - val_loss: 1.3060 - val_accuracy: 0.5306 - 2s/epoch - 18ms/step\n","Epoch 26/300\n","118/118 - 2s - loss: 1.2767 - accuracy: 0.5426 - val_loss: 1.2895 - val_accuracy: 0.5321 - 2s/epoch - 18ms/step\n","Epoch 27/300\n","118/118 - 2s - loss: 1.2603 - accuracy: 0.5490 - val_loss: 1.2737 - val_accuracy: 0.5412 - 2s/epoch - 18ms/step\n","Epoch 28/300\n","118/118 - 2s - loss: 1.2459 - accuracy: 0.5513 - val_loss: 1.2584 - val_accuracy: 0.5449 - 2s/epoch - 18ms/step\n","Epoch 29/300\n","118/118 - 2s - loss: 1.2308 - accuracy: 0.5592 - val_loss: 1.2446 - val_accuracy: 0.5511 - 2s/epoch - 18ms/step\n","Epoch 30/300\n","118/118 - 2s - loss: 1.2165 - accuracy: 0.5636 - val_loss: 1.2277 - val_accuracy: 0.5553 - 2s/epoch - 18ms/step\n","Epoch 31/300\n","118/118 - 2s - loss: 1.2019 - accuracy: 0.5692 - val_loss: 1.2149 - val_accuracy: 0.5574 - 2s/epoch - 19ms/step\n","Epoch 32/300\n","118/118 - 2s - loss: 1.1895 - accuracy: 0.5723 - val_loss: 1.2051 - val_accuracy: 0.5600 - 2s/epoch - 19ms/step\n","Epoch 33/300\n","118/118 - 2s - loss: 1.1766 - accuracy: 0.5786 - val_loss: 1.1922 - val_accuracy: 0.5637 - 2s/epoch - 18ms/step\n","Epoch 34/300\n","118/118 - 2s - loss: 1.1640 - accuracy: 0.5817 - val_loss: 1.1790 - val_accuracy: 0.5699 - 2s/epoch - 18ms/step\n","Epoch 35/300\n","118/118 - 2s - loss: 1.1507 - accuracy: 0.5865 - val_loss: 1.1683 - val_accuracy: 0.5733 - 2s/epoch - 18ms/step\n","Epoch 36/300\n","118/118 - 2s - loss: 1.1402 - accuracy: 0.5898 - val_loss: 1.1572 - val_accuracy: 0.5720 - 2s/epoch - 20ms/step\n","Epoch 37/300\n","118/118 - 2s - loss: 1.1304 - accuracy: 0.5926 - val_loss: 1.1456 - val_accuracy: 0.5826 - 2s/epoch - 18ms/step\n","Epoch 38/300\n","118/118 - 2s - loss: 1.1194 - accuracy: 0.5961 - val_loss: 1.1322 - val_accuracy: 0.5879 - 2s/epoch - 18ms/step\n","Epoch 39/300\n","118/118 - 2s - loss: 1.1078 - accuracy: 0.6013 - val_loss: 1.1204 - val_accuracy: 0.5936 - 2s/epoch - 19ms/step\n","Epoch 40/300\n","118/118 - 2s - loss: 1.0984 - accuracy: 0.6048 - val_loss: 1.1130 - val_accuracy: 0.5907 - 2s/epoch - 19ms/step\n","Epoch 41/300\n","118/118 - 2s - loss: 1.0880 - accuracy: 0.6094 - val_loss: 1.1025 - val_accuracy: 0.5960 - 2s/epoch - 19ms/step\n","Epoch 42/300\n","118/118 - 2s - loss: 1.0798 - accuracy: 0.6098 - val_loss: 1.0978 - val_accuracy: 0.5975 - 2s/epoch - 19ms/step\n","Epoch 43/300\n","118/118 - 2s - loss: 1.0700 - accuracy: 0.6147 - val_loss: 1.0866 - val_accuracy: 0.6001 - 2s/epoch - 19ms/step\n","Epoch 44/300\n","118/118 - 2s - loss: 1.0626 - accuracy: 0.6164 - val_loss: 1.0795 - val_accuracy: 0.6029 - 2s/epoch - 19ms/step\n","Epoch 45/300\n","118/118 - 2s - loss: 1.0528 - accuracy: 0.6196 - val_loss: 1.0715 - val_accuracy: 0.6065 - 2s/epoch - 19ms/step\n","Epoch 46/300\n","118/118 - 2s - loss: 1.0438 - accuracy: 0.6225 - val_loss: 1.0632 - val_accuracy: 0.6115 - 2s/epoch - 19ms/step\n","Epoch 47/300\n","118/118 - 2s - loss: 1.0361 - accuracy: 0.6258 - val_loss: 1.0596 - val_accuracy: 0.6146 - 2s/epoch - 19ms/step\n","Epoch 48/300\n","118/118 - 2s - loss: 1.0292 - accuracy: 0.6280 - val_loss: 1.0484 - val_accuracy: 0.6152 - 2s/epoch - 18ms/step\n","Epoch 49/300\n","118/118 - 2s - loss: 1.0202 - accuracy: 0.6308 - val_loss: 1.0425 - val_accuracy: 0.6156 - 2s/epoch - 19ms/step\n","Epoch 50/300\n","118/118 - 2s - loss: 1.0141 - accuracy: 0.6353 - val_loss: 1.0369 - val_accuracy: 0.6218 - 2s/epoch - 18ms/step\n","Epoch 51/300\n","118/118 - 2s - loss: 1.0078 - accuracy: 0.6358 - val_loss: 1.0300 - val_accuracy: 0.6276 - 2s/epoch - 19ms/step\n","Epoch 52/300\n","118/118 - 3s - loss: 1.0002 - accuracy: 0.6384 - val_loss: 1.0232 - val_accuracy: 0.6278 - 3s/epoch - 25ms/step\n","Epoch 53/300\n","118/118 - 2s - loss: 0.9952 - accuracy: 0.6387 - val_loss: 1.0162 - val_accuracy: 0.6311 - 2s/epoch - 19ms/step\n","Epoch 54/300\n","118/118 - 2s - loss: 0.9885 - accuracy: 0.6414 - val_loss: 1.0105 - val_accuracy: 0.6309 - 2s/epoch - 18ms/step\n","Epoch 55/300\n","118/118 - 2s - loss: 0.9816 - accuracy: 0.6438 - val_loss: 1.0059 - val_accuracy: 0.6358 - 2s/epoch - 19ms/step\n","Epoch 56/300\n","118/118 - 2s - loss: 0.9769 - accuracy: 0.6466 - val_loss: 0.9994 - val_accuracy: 0.6397 - 2s/epoch - 19ms/step\n","Epoch 57/300\n","118/118 - 2s - loss: 0.9711 - accuracy: 0.6483 - val_loss: 0.9920 - val_accuracy: 0.6403 - 2s/epoch - 19ms/step\n","Epoch 58/300\n","118/118 - 2s - loss: 0.9659 - accuracy: 0.6505 - val_loss: 0.9879 - val_accuracy: 0.6409 - 2s/epoch - 19ms/step\n","Epoch 59/300\n","118/118 - 2s - loss: 0.9606 - accuracy: 0.6531 - val_loss: 0.9839 - val_accuracy: 0.6417 - 2s/epoch - 19ms/step\n","Epoch 60/300\n","118/118 - 2s - loss: 0.9551 - accuracy: 0.6553 - val_loss: 0.9789 - val_accuracy: 0.6434 - 2s/epoch - 19ms/step\n","Epoch 61/300\n","118/118 - 2s - loss: 0.9509 - accuracy: 0.6547 - val_loss: 0.9726 - val_accuracy: 0.6471 - 2s/epoch - 19ms/step\n","Epoch 62/300\n","118/118 - 2s - loss: 0.9441 - accuracy: 0.6581 - val_loss: 0.9692 - val_accuracy: 0.6496 - 2s/epoch - 19ms/step\n","Epoch 63/300\n","118/118 - 2s - loss: 0.9392 - accuracy: 0.6608 - val_loss: 0.9669 - val_accuracy: 0.6469 - 2s/epoch - 18ms/step\n","Epoch 64/300\n","118/118 - 2s - loss: 0.9321 - accuracy: 0.6635 - val_loss: 0.9593 - val_accuracy: 0.6521 - 2s/epoch - 19ms/step\n","Epoch 65/300\n","118/118 - 2s - loss: 0.9272 - accuracy: 0.6656 - val_loss: 0.9532 - val_accuracy: 0.6570 - 2s/epoch - 19ms/step\n","Epoch 66/300\n","118/118 - 2s - loss: 0.9230 - accuracy: 0.6663 - val_loss: 0.9485 - val_accuracy: 0.6572 - 2s/epoch - 19ms/step\n","Epoch 67/300\n","118/118 - 2s - loss: 0.9194 - accuracy: 0.6691 - val_loss: 0.9465 - val_accuracy: 0.6557 - 2s/epoch - 19ms/step\n","Epoch 68/300\n","118/118 - 2s - loss: 0.9148 - accuracy: 0.6707 - val_loss: 0.9386 - val_accuracy: 0.6599 - 2s/epoch - 19ms/step\n","Epoch 69/300\n","118/118 - 2s - loss: 0.9115 - accuracy: 0.6719 - val_loss: 0.9386 - val_accuracy: 0.6572 - 2s/epoch - 19ms/step\n","Epoch 70/300\n","118/118 - 2s - loss: 0.9073 - accuracy: 0.6735 - val_loss: 0.9310 - val_accuracy: 0.6643 - 2s/epoch - 19ms/step\n","Epoch 71/300\n","118/118 - 2s - loss: 0.9034 - accuracy: 0.6760 - val_loss: 0.9268 - val_accuracy: 0.6655 - 2s/epoch - 19ms/step\n","Epoch 72/300\n","118/118 - 2s - loss: 0.8992 - accuracy: 0.6775 - val_loss: 0.9263 - val_accuracy: 0.6633 - 2s/epoch - 19ms/step\n","Epoch 73/300\n","118/118 - 2s - loss: 0.8967 - accuracy: 0.6774 - val_loss: 0.9242 - val_accuracy: 0.6629 - 2s/epoch - 19ms/step\n","Epoch 74/300\n","118/118 - 2s - loss: 0.8916 - accuracy: 0.6807 - val_loss: 0.9197 - val_accuracy: 0.6663 - 2s/epoch - 19ms/step\n","Epoch 75/300\n","118/118 - 2s - loss: 0.8869 - accuracy: 0.6813 - val_loss: 0.9175 - val_accuracy: 0.6650 - 2s/epoch - 19ms/step\n","Epoch 76/300\n","118/118 - 2s - loss: 0.8843 - accuracy: 0.6825 - val_loss: 0.9149 - val_accuracy: 0.6655 - 2s/epoch - 18ms/step\n","Epoch 77/300\n","118/118 - 2s - loss: 0.8800 - accuracy: 0.6819 - val_loss: 0.9101 - val_accuracy: 0.6706 - 2s/epoch - 19ms/step\n","Epoch 78/300\n","118/118 - 2s - loss: 0.8757 - accuracy: 0.6862 - val_loss: 0.9076 - val_accuracy: 0.6704 - 2s/epoch - 18ms/step\n","Epoch 79/300\n","118/118 - 2s - loss: 0.8747 - accuracy: 0.6841 - val_loss: 0.9034 - val_accuracy: 0.6730 - 2s/epoch - 19ms/step\n","Epoch 80/300\n","118/118 - 2s - loss: 0.8710 - accuracy: 0.6855 - val_loss: 0.9038 - val_accuracy: 0.6724 - 2s/epoch - 19ms/step\n","Epoch 81/300\n","118/118 - 2s - loss: 0.8671 - accuracy: 0.6859 - val_loss: 0.9009 - val_accuracy: 0.6763 - 2s/epoch - 18ms/step\n","Epoch 82/300\n","118/118 - 2s - loss: 0.8628 - accuracy: 0.6871 - val_loss: 0.8940 - val_accuracy: 0.6765 - 2s/epoch - 18ms/step\n","Epoch 83/300\n","118/118 - 2s - loss: 0.8583 - accuracy: 0.6906 - val_loss: 0.8897 - val_accuracy: 0.6787 - 2s/epoch - 18ms/step\n","Epoch 84/300\n","118/118 - 2s - loss: 0.8551 - accuracy: 0.6916 - val_loss: 0.8894 - val_accuracy: 0.6796 - 2s/epoch - 18ms/step\n","Epoch 85/300\n","118/118 - 2s - loss: 0.8503 - accuracy: 0.6930 - val_loss: 0.8879 - val_accuracy: 0.6744 - 2s/epoch - 19ms/step\n","Epoch 86/300\n","118/118 - 2s - loss: 0.8479 - accuracy: 0.6937 - val_loss: 0.8858 - val_accuracy: 0.6791 - 2s/epoch - 18ms/step\n","Epoch 87/300\n","118/118 - 2s - loss: 0.8463 - accuracy: 0.6946 - val_loss: 0.8813 - val_accuracy: 0.6805 - 2s/epoch - 18ms/step\n","Epoch 88/300\n","118/118 - 2s - loss: 0.8422 - accuracy: 0.6966 - val_loss: 0.8774 - val_accuracy: 0.6816 - 2s/epoch - 18ms/step\n","Epoch 89/300\n","118/118 - 2s - loss: 0.8395 - accuracy: 0.6969 - val_loss: 0.8777 - val_accuracy: 0.6783 - 2s/epoch - 18ms/step\n","Epoch 90/300\n","118/118 - 2s - loss: 0.8374 - accuracy: 0.6985 - val_loss: 0.8744 - val_accuracy: 0.6792 - 2s/epoch - 19ms/step\n","Epoch 91/300\n","118/118 - 2s - loss: 0.8349 - accuracy: 0.7006 - val_loss: 0.8690 - val_accuracy: 0.6829 - 2s/epoch - 19ms/step\n","Epoch 92/300\n","118/118 - 2s - loss: 0.8291 - accuracy: 0.7011 - val_loss: 0.8694 - val_accuracy: 0.6814 - 2s/epoch - 19ms/step\n","Epoch 93/300\n","118/118 - 2s - loss: 0.8262 - accuracy: 0.7009 - val_loss: 0.8670 - val_accuracy: 0.6850 - 2s/epoch - 18ms/step\n","Epoch 94/300\n","118/118 - 2s - loss: 0.8235 - accuracy: 0.7027 - val_loss: 0.8686 - val_accuracy: 0.6855 - 2s/epoch - 19ms/step\n","Epoch 95/300\n","118/118 - 2s - loss: 0.8220 - accuracy: 0.7023 - val_loss: 0.8623 - val_accuracy: 0.6860 - 2s/epoch - 19ms/step\n","Epoch 96/300\n","118/118 - 2s - loss: 0.8186 - accuracy: 0.7042 - val_loss: 0.8578 - val_accuracy: 0.6889 - 2s/epoch - 18ms/step\n","Epoch 97/300\n","118/118 - 2s - loss: 0.8163 - accuracy: 0.7045 - val_loss: 0.8570 - val_accuracy: 0.6890 - 2s/epoch - 19ms/step\n","Epoch 98/300\n","118/118 - 2s - loss: 0.8158 - accuracy: 0.7039 - val_loss: 0.8570 - val_accuracy: 0.6902 - 2s/epoch - 19ms/step\n","Epoch 99/300\n","118/118 - 2s - loss: 0.8146 - accuracy: 0.7043 - val_loss: 0.8552 - val_accuracy: 0.6877 - 2s/epoch - 19ms/step\n","Epoch 100/300\n","118/118 - 2s - loss: 0.8112 - accuracy: 0.7055 - val_loss: 0.8525 - val_accuracy: 0.6897 - 2s/epoch - 19ms/step\n","Epoch 101/300\n","118/118 - 2s - loss: 0.8070 - accuracy: 0.7084 - val_loss: 0.8491 - val_accuracy: 0.6913 - 2s/epoch - 19ms/step\n","Epoch 102/300\n","118/118 - 2s - loss: 0.8043 - accuracy: 0.7089 - val_loss: 0.8476 - val_accuracy: 0.6931 - 2s/epoch - 19ms/step\n","Epoch 103/300\n","118/118 - 2s - loss: 0.8001 - accuracy: 0.7099 - val_loss: 0.8454 - val_accuracy: 0.6962 - 2s/epoch - 19ms/step\n","Epoch 104/300\n","118/118 - 2s - loss: 0.7988 - accuracy: 0.7109 - val_loss: 0.8422 - val_accuracy: 0.6944 - 2s/epoch - 18ms/step\n","Epoch 105/300\n","118/118 - 2s - loss: 0.7965 - accuracy: 0.7110 - val_loss: 0.8404 - val_accuracy: 0.6932 - 2s/epoch - 19ms/step\n","Epoch 106/300\n","118/118 - 2s - loss: 0.7953 - accuracy: 0.7120 - val_loss: 0.8390 - val_accuracy: 0.6960 - 2s/epoch - 19ms/step\n","Epoch 107/300\n","118/118 - 2s - loss: 0.7932 - accuracy: 0.7125 - val_loss: 0.8362 - val_accuracy: 0.6999 - 2s/epoch - 19ms/step\n","Epoch 108/300\n","118/118 - 2s - loss: 0.7907 - accuracy: 0.7136 - val_loss: 0.8363 - val_accuracy: 0.7002 - 2s/epoch - 19ms/step\n","Epoch 109/300\n","118/118 - 2s - loss: 0.7888 - accuracy: 0.7146 - val_loss: 0.8306 - val_accuracy: 0.7000 - 2s/epoch - 19ms/step\n","Epoch 110/300\n","118/118 - 2s - loss: 0.7868 - accuracy: 0.7139 - val_loss: 0.8299 - val_accuracy: 0.6995 - 2s/epoch - 19ms/step\n","Epoch 111/300\n","118/118 - 2s - loss: 0.7850 - accuracy: 0.7161 - val_loss: 0.8295 - val_accuracy: 0.6989 - 2s/epoch - 18ms/step\n","Epoch 112/300\n","118/118 - 2s - loss: 0.7836 - accuracy: 0.7163 - val_loss: 0.8262 - val_accuracy: 0.7025 - 2s/epoch - 19ms/step\n","Epoch 113/300\n","118/118 - 2s - loss: 0.7826 - accuracy: 0.7168 - val_loss: 0.8247 - val_accuracy: 0.7051 - 2s/epoch - 18ms/step\n","Epoch 114/300\n","118/118 - 2s - loss: 0.7805 - accuracy: 0.7176 - val_loss: 0.8237 - val_accuracy: 0.7011 - 2s/epoch - 19ms/step\n","Epoch 115/300\n","118/118 - 2s - loss: 0.7802 - accuracy: 0.7182 - val_loss: 0.8217 - val_accuracy: 0.7039 - 2s/epoch - 19ms/step\n","Epoch 116/300\n","118/118 - 2s - loss: 0.7781 - accuracy: 0.7193 - val_loss: 0.8194 - val_accuracy: 0.7046 - 2s/epoch - 18ms/step\n","Epoch 117/300\n","118/118 - 2s - loss: 0.7758 - accuracy: 0.7195 - val_loss: 0.8176 - val_accuracy: 0.7045 - 2s/epoch - 18ms/step\n","Epoch 118/300\n","118/118 - 2s - loss: 0.7755 - accuracy: 0.7197 - val_loss: 0.8184 - val_accuracy: 0.7025 - 2s/epoch - 18ms/step\n","Epoch 119/300\n","118/118 - 2s - loss: 0.7734 - accuracy: 0.7195 - val_loss: 0.8179 - val_accuracy: 0.7053 - 2s/epoch - 18ms/step\n","Epoch 120/300\n","118/118 - 2s - loss: 0.7713 - accuracy: 0.7213 - val_loss: 0.8177 - val_accuracy: 0.7039 - 2s/epoch - 19ms/step\n","Epoch 121/300\n","118/118 - 2s - loss: 0.7682 - accuracy: 0.7214 - val_loss: 0.8138 - val_accuracy: 0.7051 - 2s/epoch - 19ms/step\n","Epoch 122/300\n","118/118 - 2s - loss: 0.7686 - accuracy: 0.7205 - val_loss: 0.8128 - val_accuracy: 0.7073 - 2s/epoch - 19ms/step\n","Epoch 123/300\n","118/118 - 2s - loss: 0.7677 - accuracy: 0.7208 - val_loss: 0.8100 - val_accuracy: 0.7075 - 2s/epoch - 19ms/step\n","Epoch 124/300\n","118/118 - 2s - loss: 0.7647 - accuracy: 0.7226 - val_loss: 0.8082 - val_accuracy: 0.7067 - 2s/epoch - 19ms/step\n","Epoch 125/300\n","118/118 - 2s - loss: 0.7632 - accuracy: 0.7236 - val_loss: 0.8052 - val_accuracy: 0.7104 - 2s/epoch - 19ms/step\n","Epoch 126/300\n","118/118 - 2s - loss: 0.7604 - accuracy: 0.7240 - val_loss: 0.8030 - val_accuracy: 0.7100 - 2s/epoch - 18ms/step\n","Epoch 127/300\n","118/118 - 2s - loss: 0.7587 - accuracy: 0.7249 - val_loss: 0.8028 - val_accuracy: 0.7111 - 2s/epoch - 19ms/step\n","Epoch 128/300\n","118/118 - 2s - loss: 0.7576 - accuracy: 0.7243 - val_loss: 0.8023 - val_accuracy: 0.7140 - 2s/epoch - 19ms/step\n","Epoch 129/300\n","118/118 - 2s - loss: 0.7554 - accuracy: 0.7244 - val_loss: 0.8003 - val_accuracy: 0.7125 - 2s/epoch - 19ms/step\n","Epoch 130/300\n","118/118 - 2s - loss: 0.7527 - accuracy: 0.7253 - val_loss: 0.7979 - val_accuracy: 0.7142 - 2s/epoch - 19ms/step\n","Epoch 131/300\n","118/118 - 2s - loss: 0.7523 - accuracy: 0.7274 - val_loss: 0.7981 - val_accuracy: 0.7160 - 2s/epoch - 18ms/step\n","Epoch 132/300\n","118/118 - 2s - loss: 0.7506 - accuracy: 0.7273 - val_loss: 0.7967 - val_accuracy: 0.7168 - 2s/epoch - 18ms/step\n","Epoch 133/300\n","118/118 - 2s - loss: 0.7496 - accuracy: 0.7276 - val_loss: 0.7965 - val_accuracy: 0.7157 - 2s/epoch - 19ms/step\n","Epoch 134/300\n","118/118 - 2s - loss: 0.7486 - accuracy: 0.7272 - val_loss: 0.7913 - val_accuracy: 0.7146 - 2s/epoch - 19ms/step\n","Epoch 135/300\n","118/118 - 2s - loss: 0.7474 - accuracy: 0.7276 - val_loss: 0.7924 - val_accuracy: 0.7142 - 2s/epoch - 19ms/step\n","Epoch 136/300\n","118/118 - 2s - loss: 0.7454 - accuracy: 0.7289 - val_loss: 0.7886 - val_accuracy: 0.7163 - 2s/epoch - 18ms/step\n","Epoch 137/300\n","118/118 - 2s - loss: 0.7442 - accuracy: 0.7292 - val_loss: 0.7864 - val_accuracy: 0.7185 - 2s/epoch - 18ms/step\n","Epoch 138/300\n","118/118 - 2s - loss: 0.7413 - accuracy: 0.7303 - val_loss: 0.7852 - val_accuracy: 0.7166 - 2s/epoch - 19ms/step\n","Epoch 139/300\n","118/118 - 2s - loss: 0.7406 - accuracy: 0.7304 - val_loss: 0.7849 - val_accuracy: 0.7177 - 2s/epoch - 18ms/step\n","Epoch 140/300\n","118/118 - 2s - loss: 0.7393 - accuracy: 0.7309 - val_loss: 0.7862 - val_accuracy: 0.7185 - 2s/epoch - 19ms/step\n","Epoch 141/300\n","118/118 - 2s - loss: 0.7376 - accuracy: 0.7316 - val_loss: 0.7859 - val_accuracy: 0.7178 - 2s/epoch - 19ms/step\n","Epoch 142/300\n","118/118 - 2s - loss: 0.7365 - accuracy: 0.7324 - val_loss: 0.7835 - val_accuracy: 0.7181 - 2s/epoch - 19ms/step\n","Epoch 143/300\n","118/118 - 2s - loss: 0.7356 - accuracy: 0.7326 - val_loss: 0.7814 - val_accuracy: 0.7171 - 2s/epoch - 19ms/step\n","Epoch 144/300\n","118/118 - 2s - loss: 0.7330 - accuracy: 0.7342 - val_loss: 0.7826 - val_accuracy: 0.7167 - 2s/epoch - 18ms/step\n","Epoch 145/300\n","118/118 - 2s - loss: 0.7339 - accuracy: 0.7330 - val_loss: 0.7851 - val_accuracy: 0.7164 - 2s/epoch - 19ms/step\n","Epoch 146/300\n","118/118 - 2s - loss: 0.7317 - accuracy: 0.7340 - val_loss: 0.7834 - val_accuracy: 0.7153 - 2s/epoch - 18ms/step\n","Epoch 147/300\n","118/118 - 2s - loss: 0.7305 - accuracy: 0.7349 - val_loss: 0.7840 - val_accuracy: 0.7175 - 2s/epoch - 18ms/step\n","Epoch 148/300\n","118/118 - 2s - loss: 0.7302 - accuracy: 0.7335 - val_loss: 0.7783 - val_accuracy: 0.7205 - 2s/epoch - 18ms/step\n","Epoch 149/300\n","118/118 - 2s - loss: 0.7283 - accuracy: 0.7345 - val_loss: 0.7823 - val_accuracy: 0.7178 - 2s/epoch - 19ms/step\n","Epoch 150/300\n","118/118 - 2s - loss: 0.7254 - accuracy: 0.7360 - val_loss: 0.7750 - val_accuracy: 0.7187 - 2s/epoch - 19ms/step\n","Epoch 151/300\n","118/118 - 2s - loss: 0.7251 - accuracy: 0.7366 - val_loss: 0.7751 - val_accuracy: 0.7174 - 2s/epoch - 18ms/step\n","Epoch 152/300\n","118/118 - 2s - loss: 0.7250 - accuracy: 0.7357 - val_loss: 0.7743 - val_accuracy: 0.7181 - 2s/epoch - 18ms/step\n","Epoch 153/300\n","118/118 - 2s - loss: 0.7235 - accuracy: 0.7367 - val_loss: 0.7698 - val_accuracy: 0.7191 - 2s/epoch - 19ms/step\n","Epoch 154/300\n","118/118 - 2s - loss: 0.7207 - accuracy: 0.7380 - val_loss: 0.7689 - val_accuracy: 0.7202 - 2s/epoch - 19ms/step\n","Epoch 155/300\n","118/118 - 2s - loss: 0.7185 - accuracy: 0.7383 - val_loss: 0.7678 - val_accuracy: 0.7214 - 2s/epoch - 19ms/step\n","Epoch 156/300\n","118/118 - 2s - loss: 0.7153 - accuracy: 0.7400 - val_loss: 0.7635 - val_accuracy: 0.7236 - 2s/epoch - 20ms/step\n","Epoch 157/300\n","118/118 - 2s - loss: 0.7146 - accuracy: 0.7403 - val_loss: 0.7645 - val_accuracy: 0.7239 - 2s/epoch - 19ms/step\n","Epoch 158/300\n","118/118 - 2s - loss: 0.7157 - accuracy: 0.7397 - val_loss: 0.7645 - val_accuracy: 0.7241 - 2s/epoch - 19ms/step\n","Epoch 159/300\n","118/118 - 3s - loss: 0.7141 - accuracy: 0.7402 - val_loss: 0.7678 - val_accuracy: 0.7204 - 3s/epoch - 26ms/step\n","Epoch 160/300\n","118/118 - 2s - loss: 0.7126 - accuracy: 0.7412 - val_loss: 0.7631 - val_accuracy: 0.7254 - 2s/epoch - 20ms/step\n","Epoch 161/300\n","118/118 - 2s - loss: 0.7124 - accuracy: 0.7420 - val_loss: 0.7629 - val_accuracy: 0.7258 - 2s/epoch - 20ms/step\n","Epoch 162/300\n","118/118 - 2s - loss: 0.7117 - accuracy: 0.7423 - val_loss: 0.7585 - val_accuracy: 0.7256 - 2s/epoch - 21ms/step\n","Epoch 163/300\n","118/118 - 2s - loss: 0.7099 - accuracy: 0.7416 - val_loss: 0.7598 - val_accuracy: 0.7242 - 2s/epoch - 20ms/step\n","Epoch 164/300\n","118/118 - 2s - loss: 0.7102 - accuracy: 0.7419 - val_loss: 0.7580 - val_accuracy: 0.7255 - 2s/epoch - 20ms/step\n","Epoch 165/300\n","118/118 - 2s - loss: 0.7070 - accuracy: 0.7427 - val_loss: 0.7587 - val_accuracy: 0.7253 - 2s/epoch - 20ms/step\n","Epoch 166/300\n","118/118 - 2s - loss: 0.7051 - accuracy: 0.7438 - val_loss: 0.7573 - val_accuracy: 0.7247 - 2s/epoch - 20ms/step\n","Epoch 167/300\n","118/118 - 2s - loss: 0.7042 - accuracy: 0.7445 - val_loss: 0.7574 - val_accuracy: 0.7264 - 2s/epoch - 20ms/step\n","Epoch 168/300\n","118/118 - 2s - loss: 0.7048 - accuracy: 0.7438 - val_loss: 0.7583 - val_accuracy: 0.7256 - 2s/epoch - 19ms/step\n","Epoch 169/300\n","118/118 - 2s - loss: 0.7018 - accuracy: 0.7451 - val_loss: 0.7587 - val_accuracy: 0.7247 - 2s/epoch - 19ms/step\n","Epoch 170/300\n","118/118 - 2s - loss: 0.7005 - accuracy: 0.7461 - val_loss: 0.7565 - val_accuracy: 0.7256 - 2s/epoch - 20ms/step\n","Epoch 171/300\n","118/118 - 2s - loss: 0.6991 - accuracy: 0.7464 - val_loss: 0.7515 - val_accuracy: 0.7278 - 2s/epoch - 19ms/step\n","Epoch 172/300\n","118/118 - 2s - loss: 0.6985 - accuracy: 0.7465 - val_loss: 0.7498 - val_accuracy: 0.7264 - 2s/epoch - 19ms/step\n","Epoch 173/300\n","118/118 - 2s - loss: 0.6978 - accuracy: 0.7462 - val_loss: 0.7476 - val_accuracy: 0.7297 - 2s/epoch - 19ms/step\n","Epoch 174/300\n","118/118 - 2s - loss: 0.6985 - accuracy: 0.7456 - val_loss: 0.7519 - val_accuracy: 0.7279 - 2s/epoch - 20ms/step\n","Epoch 175/300\n","118/118 - 2s - loss: 0.6967 - accuracy: 0.7452 - val_loss: 0.7516 - val_accuracy: 0.7273 - 2s/epoch - 20ms/step\n","Epoch 176/300\n","118/118 - 2s - loss: 0.6965 - accuracy: 0.7456 - val_loss: 0.7508 - val_accuracy: 0.7280 - 2s/epoch - 20ms/step\n","Epoch 177/300\n","118/118 - 2s - loss: 0.6964 - accuracy: 0.7469 - val_loss: 0.7486 - val_accuracy: 0.7294 - 2s/epoch - 20ms/step\n","Epoch 178/300\n","118/118 - 2s - loss: 0.6938 - accuracy: 0.7469 - val_loss: 0.7499 - val_accuracy: 0.7286 - 2s/epoch - 19ms/step\n","Epoch 179/300\n","118/118 - 2s - loss: 0.6918 - accuracy: 0.7483 - val_loss: 0.7463 - val_accuracy: 0.7302 - 2s/epoch - 20ms/step\n","Epoch 180/300\n","118/118 - 2s - loss: 0.6913 - accuracy: 0.7482 - val_loss: 0.7464 - val_accuracy: 0.7295 - 2s/epoch - 20ms/step\n","Epoch 181/300\n","118/118 - 2s - loss: 0.6909 - accuracy: 0.7490 - val_loss: 0.7477 - val_accuracy: 0.7291 - 2s/epoch - 20ms/step\n","Epoch 182/300\n","118/118 - 2s - loss: 0.6913 - accuracy: 0.7492 - val_loss: 0.7507 - val_accuracy: 0.7275 - 2s/epoch - 20ms/step\n","Epoch 183/300\n","118/118 - 2s - loss: 0.6910 - accuracy: 0.7500 - val_loss: 0.7465 - val_accuracy: 0.7299 - 2s/epoch - 20ms/step\n","Epoch 184/300\n","118/118 - 2s - loss: 0.6896 - accuracy: 0.7490 - val_loss: 0.7459 - val_accuracy: 0.7304 - 2s/epoch - 20ms/step\n","Epoch 185/300\n","118/118 - 2s - loss: 0.6885 - accuracy: 0.7493 - val_loss: 0.7442 - val_accuracy: 0.7325 - 2s/epoch - 19ms/step\n","Epoch 186/300\n","118/118 - 2s - loss: 0.6869 - accuracy: 0.7503 - val_loss: 0.7442 - val_accuracy: 0.7311 - 2s/epoch - 19ms/step\n","Epoch 187/300\n","118/118 - 2s - loss: 0.6867 - accuracy: 0.7500 - val_loss: 0.7458 - val_accuracy: 0.7300 - 2s/epoch - 19ms/step\n","Epoch 188/300\n","118/118 - 2s - loss: 0.6847 - accuracy: 0.7519 - val_loss: 0.7402 - val_accuracy: 0.7311 - 2s/epoch - 19ms/step\n","Epoch 189/300\n","118/118 - 2s - loss: 0.6838 - accuracy: 0.7512 - val_loss: 0.7382 - val_accuracy: 0.7296 - 2s/epoch - 19ms/step\n","Epoch 190/300\n","118/118 - 2s - loss: 0.6827 - accuracy: 0.7529 - val_loss: 0.7399 - val_accuracy: 0.7310 - 2s/epoch - 19ms/step\n","Epoch 191/300\n","118/118 - 2s - loss: 0.6836 - accuracy: 0.7526 - val_loss: 0.7394 - val_accuracy: 0.7302 - 2s/epoch - 19ms/step\n","Epoch 192/300\n","118/118 - 2s - loss: 0.6833 - accuracy: 0.7526 - val_loss: 0.7397 - val_accuracy: 0.7326 - 2s/epoch - 20ms/step\n","Epoch 193/300\n","118/118 - 2s - loss: 0.6809 - accuracy: 0.7540 - val_loss: 0.7364 - val_accuracy: 0.7350 - 2s/epoch - 21ms/step\n","Epoch 194/300\n","118/118 - 2s - loss: 0.6796 - accuracy: 0.7539 - val_loss: 0.7387 - val_accuracy: 0.7318 - 2s/epoch - 19ms/step\n","Epoch 195/300\n","118/118 - 2s - loss: 0.6810 - accuracy: 0.7536 - val_loss: 0.7375 - val_accuracy: 0.7312 - 2s/epoch - 19ms/step\n","Epoch 196/300\n","118/118 - 2s - loss: 0.6800 - accuracy: 0.7529 - val_loss: 0.7384 - val_accuracy: 0.7321 - 2s/epoch - 20ms/step\n","Epoch 197/300\n","118/118 - 2s - loss: 0.6770 - accuracy: 0.7551 - val_loss: 0.7372 - val_accuracy: 0.7322 - 2s/epoch - 20ms/step\n","Epoch 198/300\n","118/118 - 2s - loss: 0.6762 - accuracy: 0.7553 - val_loss: 0.7335 - val_accuracy: 0.7345 - 2s/epoch - 19ms/step\n","Epoch 199/300\n","118/118 - 2s - loss: 0.6761 - accuracy: 0.7549 - val_loss: 0.7325 - val_accuracy: 0.7332 - 2s/epoch - 19ms/step\n","Epoch 200/300\n","118/118 - 2s - loss: 0.6754 - accuracy: 0.7552 - val_loss: 0.7345 - val_accuracy: 0.7322 - 2s/epoch - 19ms/step\n","Epoch 201/300\n","118/118 - 2s - loss: 0.6766 - accuracy: 0.7549 - val_loss: 0.7282 - val_accuracy: 0.7358 - 2s/epoch - 20ms/step\n","Epoch 202/300\n","118/118 - 2s - loss: 0.6759 - accuracy: 0.7547 - val_loss: 0.7310 - val_accuracy: 0.7340 - 2s/epoch - 20ms/step\n","Epoch 203/300\n","118/118 - 2s - loss: 0.6736 - accuracy: 0.7555 - val_loss: 0.7303 - val_accuracy: 0.7352 - 2s/epoch - 20ms/step\n","Epoch 204/300\n","118/118 - 2s - loss: 0.6725 - accuracy: 0.7565 - val_loss: 0.7311 - val_accuracy: 0.7346 - 2s/epoch - 20ms/step\n","Epoch 205/300\n","118/118 - 2s - loss: 0.6714 - accuracy: 0.7567 - val_loss: 0.7284 - val_accuracy: 0.7372 - 2s/epoch - 20ms/step\n","Epoch 206/300\n","118/118 - 2s - loss: 0.6712 - accuracy: 0.7565 - val_loss: 0.7275 - val_accuracy: 0.7373 - 2s/epoch - 20ms/step\n","Epoch 207/300\n","118/118 - 2s - loss: 0.6717 - accuracy: 0.7572 - val_loss: 0.7290 - val_accuracy: 0.7372 - 2s/epoch - 20ms/step\n","Epoch 208/300\n","118/118 - 2s - loss: 0.6712 - accuracy: 0.7568 - val_loss: 0.7264 - val_accuracy: 0.7354 - 2s/epoch - 20ms/step\n","Epoch 209/300\n","118/118 - 2s - loss: 0.6695 - accuracy: 0.7571 - val_loss: 0.7262 - val_accuracy: 0.7337 - 2s/epoch - 20ms/step\n","Epoch 210/300\n","118/118 - 2s - loss: 0.6691 - accuracy: 0.7577 - val_loss: 0.7294 - val_accuracy: 0.7369 - 2s/epoch - 19ms/step\n","Epoch 211/300\n","118/118 - 2s - loss: 0.6672 - accuracy: 0.7584 - val_loss: 0.7261 - val_accuracy: 0.7354 - 2s/epoch - 20ms/step\n","Epoch 212/300\n","118/118 - 2s - loss: 0.6658 - accuracy: 0.7590 - val_loss: 0.7246 - val_accuracy: 0.7375 - 2s/epoch - 20ms/step\n","Epoch 213/300\n","118/118 - 2s - loss: 0.6675 - accuracy: 0.7575 - val_loss: 0.7256 - val_accuracy: 0.7349 - 2s/epoch - 19ms/step\n","Epoch 214/300\n","118/118 - 2s - loss: 0.6653 - accuracy: 0.7587 - val_loss: 0.7203 - val_accuracy: 0.7389 - 2s/epoch - 19ms/step\n","Epoch 215/300\n","118/118 - 2s - loss: 0.6648 - accuracy: 0.7592 - val_loss: 0.7221 - val_accuracy: 0.7396 - 2s/epoch - 19ms/step\n","Epoch 216/300\n","118/118 - 2s - loss: 0.6652 - accuracy: 0.7589 - val_loss: 0.7204 - val_accuracy: 0.7392 - 2s/epoch - 19ms/step\n","Epoch 217/300\n","118/118 - 2s - loss: 0.6644 - accuracy: 0.7591 - val_loss: 0.7212 - val_accuracy: 0.7389 - 2s/epoch - 19ms/step\n","Epoch 218/300\n","118/118 - 2s - loss: 0.6634 - accuracy: 0.7598 - val_loss: 0.7208 - val_accuracy: 0.7379 - 2s/epoch - 19ms/step\n","Epoch 219/300\n","118/118 - 2s - loss: 0.6631 - accuracy: 0.7598 - val_loss: 0.7219 - val_accuracy: 0.7390 - 2s/epoch - 20ms/step\n","Epoch 220/300\n","118/118 - 2s - loss: 0.6630 - accuracy: 0.7603 - val_loss: 0.7203 - val_accuracy: 0.7395 - 2s/epoch - 19ms/step\n","Epoch 221/300\n","118/118 - 2s - loss: 0.6624 - accuracy: 0.7599 - val_loss: 0.7199 - val_accuracy: 0.7390 - 2s/epoch - 20ms/step\n","Epoch 222/300\n","118/118 - 2s - loss: 0.6602 - accuracy: 0.7602 - val_loss: 0.7220 - val_accuracy: 0.7386 - 2s/epoch - 20ms/step\n","Epoch 223/300\n","118/118 - 2s - loss: 0.6602 - accuracy: 0.7615 - val_loss: 0.7238 - val_accuracy: 0.7365 - 2s/epoch - 20ms/step\n","Epoch 224/300\n","118/118 - 2s - loss: 0.6589 - accuracy: 0.7619 - val_loss: 0.7212 - val_accuracy: 0.7360 - 2s/epoch - 19ms/step\n","Epoch 225/300\n","118/118 - 2s - loss: 0.6584 - accuracy: 0.7621 - val_loss: 0.7185 - val_accuracy: 0.7402 - 2s/epoch - 19ms/step\n","Epoch 226/300\n","118/118 - 2s - loss: 0.6578 - accuracy: 0.7621 - val_loss: 0.7194 - val_accuracy: 0.7416 - 2s/epoch - 19ms/step\n","Epoch 227/300\n","118/118 - 2s - loss: 0.6566 - accuracy: 0.7627 - val_loss: 0.7205 - val_accuracy: 0.7396 - 2s/epoch - 19ms/step\n","Epoch 228/300\n","118/118 - 2s - loss: 0.6562 - accuracy: 0.7626 - val_loss: 0.7210 - val_accuracy: 0.7406 - 2s/epoch - 20ms/step\n","Epoch 229/300\n","118/118 - 2s - loss: 0.6554 - accuracy: 0.7627 - val_loss: 0.7191 - val_accuracy: 0.7409 - 2s/epoch - 19ms/step\n","Epoch 230/300\n","118/118 - 2s - loss: 0.6544 - accuracy: 0.7639 - val_loss: 0.7190 - val_accuracy: 0.7431 - 2s/epoch - 20ms/step\n","Epoch 231/300\n","118/118 - 2s - loss: 0.6543 - accuracy: 0.7630 - val_loss: 0.7168 - val_accuracy: 0.7420 - 2s/epoch - 21ms/step\n","Epoch 232/300\n","118/118 - 2s - loss: 0.6539 - accuracy: 0.7635 - val_loss: 0.7157 - val_accuracy: 0.7437 - 2s/epoch - 20ms/step\n","Epoch 233/300\n","118/118 - 2s - loss: 0.6534 - accuracy: 0.7641 - val_loss: 0.7147 - val_accuracy: 0.7443 - 2s/epoch - 20ms/step\n","Epoch 234/300\n","118/118 - 2s - loss: 0.6520 - accuracy: 0.7636 - val_loss: 0.7132 - val_accuracy: 0.7423 - 2s/epoch - 20ms/step\n","Epoch 235/300\n","118/118 - 2s - loss: 0.6504 - accuracy: 0.7649 - val_loss: 0.7124 - val_accuracy: 0.7417 - 2s/epoch - 20ms/step\n","Epoch 236/300\n","118/118 - 2s - loss: 0.6492 - accuracy: 0.7649 - val_loss: 0.7139 - val_accuracy: 0.7435 - 2s/epoch - 20ms/step\n","Epoch 237/300\n","118/118 - 2s - loss: 0.6489 - accuracy: 0.7654 - val_loss: 0.7138 - val_accuracy: 0.7430 - 2s/epoch - 20ms/step\n","Epoch 238/300\n","118/118 - 2s - loss: 0.6483 - accuracy: 0.7658 - val_loss: 0.7135 - val_accuracy: 0.7429 - 2s/epoch - 19ms/step\n","Epoch 239/300\n","118/118 - 2s - loss: 0.6463 - accuracy: 0.7668 - val_loss: 0.7147 - val_accuracy: 0.7417 - 2s/epoch - 20ms/step\n","Epoch 240/300\n","118/118 - 2s - loss: 0.6478 - accuracy: 0.7655 - val_loss: 0.7123 - val_accuracy: 0.7431 - 2s/epoch - 20ms/step\n","Epoch 241/300\n","118/118 - 2s - loss: 0.6472 - accuracy: 0.7658 - val_loss: 0.7140 - val_accuracy: 0.7409 - 2s/epoch - 19ms/step\n","Epoch 242/300\n","118/118 - 2s - loss: 0.6460 - accuracy: 0.7664 - val_loss: 0.7128 - val_accuracy: 0.7406 - 2s/epoch - 20ms/step\n","Epoch 243/300\n","118/118 - 2s - loss: 0.6458 - accuracy: 0.7666 - val_loss: 0.7128 - val_accuracy: 0.7406 - 2s/epoch - 20ms/step\n","Epoch 244/300\n","118/118 - 2s - loss: 0.6436 - accuracy: 0.7685 - val_loss: 0.7132 - val_accuracy: 0.7403 - 2s/epoch - 20ms/step\n","Epoch 245/300\n","118/118 - 2s - loss: 0.6429 - accuracy: 0.7686 - val_loss: 0.7143 - val_accuracy: 0.7405 - 2s/epoch - 20ms/step\n","Epoch 246/300\n","118/118 - 2s - loss: 0.6432 - accuracy: 0.7682 - val_loss: 0.7115 - val_accuracy: 0.7407 - 2s/epoch - 19ms/step\n","Epoch 247/300\n","118/118 - 2s - loss: 0.6424 - accuracy: 0.7681 - val_loss: 0.7086 - val_accuracy: 0.7428 - 2s/epoch - 19ms/step\n","Epoch 248/300\n","118/118 - 2s - loss: 0.6414 - accuracy: 0.7684 - val_loss: 0.7081 - val_accuracy: 0.7425 - 2s/epoch - 20ms/step\n","Epoch 249/300\n","118/118 - 2s - loss: 0.6426 - accuracy: 0.7684 - val_loss: 0.7067 - val_accuracy: 0.7424 - 2s/epoch - 20ms/step\n","Epoch 250/300\n","118/118 - 2s - loss: 0.6438 - accuracy: 0.7677 - val_loss: 0.7056 - val_accuracy: 0.7433 - 2s/epoch - 20ms/step\n","Epoch 251/300\n","118/118 - 2s - loss: 0.6415 - accuracy: 0.7685 - val_loss: 0.7041 - val_accuracy: 0.7432 - 2s/epoch - 20ms/step\n","Epoch 252/300\n","118/118 - 2s - loss: 0.6417 - accuracy: 0.7678 - val_loss: 0.7062 - val_accuracy: 0.7419 - 2s/epoch - 20ms/step\n","Epoch 253/300\n","118/118 - 2s - loss: 0.6420 - accuracy: 0.7684 - val_loss: 0.7087 - val_accuracy: 0.7407 - 2s/epoch - 20ms/step\n","Epoch 254/300\n","118/118 - 2s - loss: 0.6404 - accuracy: 0.7694 - val_loss: 0.7054 - val_accuracy: 0.7425 - 2s/epoch - 20ms/step\n","Epoch 255/300\n","118/118 - 2s - loss: 0.6387 - accuracy: 0.7695 - val_loss: 0.7068 - val_accuracy: 0.7424 - 2s/epoch - 21ms/step\n","Epoch 256/300\n","118/118 - 2s - loss: 0.6394 - accuracy: 0.7688 - val_loss: 0.7067 - val_accuracy: 0.7442 - 2s/epoch - 20ms/step\n","Epoch 257/300\n","118/118 - 2s - loss: 0.6376 - accuracy: 0.7698 - val_loss: 0.7067 - val_accuracy: 0.7421 - 2s/epoch - 20ms/step\n","Epoch 258/300\n","118/118 - 2s - loss: 0.6359 - accuracy: 0.7698 - val_loss: 0.7038 - val_accuracy: 0.7416 - 2s/epoch - 20ms/step\n","Epoch 259/300\n","118/118 - 3s - loss: 0.6359 - accuracy: 0.7706 - val_loss: 0.7041 - val_accuracy: 0.7455 - 3s/epoch - 25ms/step\n","Epoch 260/300\n","118/118 - 3s - loss: 0.6364 - accuracy: 0.7702 - val_loss: 0.7026 - val_accuracy: 0.7464 - 3s/epoch - 22ms/step\n","Epoch 261/300\n","118/118 - 2s - loss: 0.6353 - accuracy: 0.7717 - val_loss: 0.7000 - val_accuracy: 0.7450 - 2s/epoch - 20ms/step\n","Epoch 262/300\n","118/118 - 2s - loss: 0.6339 - accuracy: 0.7707 - val_loss: 0.7000 - val_accuracy: 0.7442 - 2s/epoch - 20ms/step\n","Epoch 263/300\n","118/118 - 2s - loss: 0.6328 - accuracy: 0.7716 - val_loss: 0.6997 - val_accuracy: 0.7442 - 2s/epoch - 20ms/step\n","Epoch 264/300\n","118/118 - 2s - loss: 0.6325 - accuracy: 0.7717 - val_loss: 0.7008 - val_accuracy: 0.7433 - 2s/epoch - 20ms/step\n","Epoch 265/300\n","118/118 - 2s - loss: 0.6322 - accuracy: 0.7710 - val_loss: 0.6978 - val_accuracy: 0.7455 - 2s/epoch - 20ms/step\n","Epoch 266/300\n","118/118 - 2s - loss: 0.6318 - accuracy: 0.7717 - val_loss: 0.6978 - val_accuracy: 0.7415 - 2s/epoch - 19ms/step\n","Epoch 267/300\n","118/118 - 2s - loss: 0.6307 - accuracy: 0.7724 - val_loss: 0.6953 - val_accuracy: 0.7463 - 2s/epoch - 19ms/step\n","Epoch 268/300\n","118/118 - 2s - loss: 0.6310 - accuracy: 0.7715 - val_loss: 0.6943 - val_accuracy: 0.7445 - 2s/epoch - 20ms/step\n","Epoch 269/300\n","118/118 - 2s - loss: 0.6301 - accuracy: 0.7723 - val_loss: 0.6962 - val_accuracy: 0.7446 - 2s/epoch - 20ms/step\n","Epoch 270/300\n","118/118 - 2s - loss: 0.6291 - accuracy: 0.7719 - val_loss: 0.6953 - val_accuracy: 0.7471 - 2s/epoch - 20ms/step\n","Epoch 271/300\n","118/118 - 2s - loss: 0.6273 - accuracy: 0.7724 - val_loss: 0.6938 - val_accuracy: 0.7482 - 2s/epoch - 20ms/step\n","Epoch 272/300\n","118/118 - 2s - loss: 0.6274 - accuracy: 0.7732 - val_loss: 0.6946 - val_accuracy: 0.7489 - 2s/epoch - 20ms/step\n","Epoch 273/300\n","118/118 - 2s - loss: 0.6260 - accuracy: 0.7741 - val_loss: 0.6884 - val_accuracy: 0.7507 - 2s/epoch - 20ms/step\n","Epoch 274/300\n","118/118 - 2s - loss: 0.6248 - accuracy: 0.7744 - val_loss: 0.6905 - val_accuracy: 0.7510 - 2s/epoch - 20ms/step\n","Epoch 275/300\n","118/118 - 2s - loss: 0.6261 - accuracy: 0.7740 - val_loss: 0.6918 - val_accuracy: 0.7480 - 2s/epoch - 20ms/step\n","Epoch 276/300\n","118/118 - 2s - loss: 0.6279 - accuracy: 0.7726 - val_loss: 0.6958 - val_accuracy: 0.7472 - 2s/epoch - 20ms/step\n","Epoch 277/300\n","118/118 - 2s - loss: 0.6238 - accuracy: 0.7735 - val_loss: 0.6936 - val_accuracy: 0.7478 - 2s/epoch - 21ms/step\n","Epoch 278/300\n","118/118 - 2s - loss: 0.6228 - accuracy: 0.7749 - val_loss: 0.6922 - val_accuracy: 0.7474 - 2s/epoch - 20ms/step\n","Epoch 279/300\n","118/118 - 2s - loss: 0.6220 - accuracy: 0.7748 - val_loss: 0.6929 - val_accuracy: 0.7488 - 2s/epoch - 20ms/step\n","Epoch 280/300\n","118/118 - 2s - loss: 0.6225 - accuracy: 0.7745 - val_loss: 0.6922 - val_accuracy: 0.7480 - 2s/epoch - 19ms/step\n","Epoch 281/300\n","118/118 - 2s - loss: 0.6225 - accuracy: 0.7743 - val_loss: 0.6913 - val_accuracy: 0.7504 - 2s/epoch - 20ms/step\n","Epoch 282/300\n","118/118 - 2s - loss: 0.6199 - accuracy: 0.7755 - val_loss: 0.6913 - val_accuracy: 0.7497 - 2s/epoch - 20ms/step\n","Epoch 283/300\n","118/118 - 2s - loss: 0.6192 - accuracy: 0.7764 - val_loss: 0.6890 - val_accuracy: 0.7489 - 2s/epoch - 19ms/step\n","Epoch 284/300\n","118/118 - 2s - loss: 0.6203 - accuracy: 0.7766 - val_loss: 0.6900 - val_accuracy: 0.7476 - 2s/epoch - 20ms/step\n","Epoch 285/300\n","118/118 - 2s - loss: 0.6187 - accuracy: 0.7762 - val_loss: 0.6911 - val_accuracy: 0.7497 - 2s/epoch - 19ms/step\n","Epoch 286/300\n","118/118 - 2s - loss: 0.6185 - accuracy: 0.7772 - val_loss: 0.6872 - val_accuracy: 0.7509 - 2s/epoch - 19ms/step\n","Epoch 287/300\n","118/118 - 2s - loss: 0.6190 - accuracy: 0.7765 - val_loss: 0.6891 - val_accuracy: 0.7506 - 2s/epoch - 20ms/step\n","Epoch 288/300\n","118/118 - 2s - loss: 0.6173 - accuracy: 0.7763 - val_loss: 0.6866 - val_accuracy: 0.7508 - 2s/epoch - 20ms/step\n","Epoch 289/300\n","118/118 - 2s - loss: 0.6161 - accuracy: 0.7768 - val_loss: 0.6857 - val_accuracy: 0.7521 - 2s/epoch - 20ms/step\n","Epoch 290/300\n","118/118 - 2s - loss: 0.6177 - accuracy: 0.7759 - val_loss: 0.6881 - val_accuracy: 0.7474 - 2s/epoch - 20ms/step\n","Epoch 291/300\n","118/118 - 2s - loss: 0.6179 - accuracy: 0.7768 - val_loss: 0.6889 - val_accuracy: 0.7483 - 2s/epoch - 20ms/step\n","Epoch 292/300\n","118/118 - 2s - loss: 0.6167 - accuracy: 0.7769 - val_loss: 0.6886 - val_accuracy: 0.7500 - 2s/epoch - 20ms/step\n","Epoch 293/300\n","118/118 - 2s - loss: 0.6179 - accuracy: 0.7768 - val_loss: 0.6914 - val_accuracy: 0.7507 - 2s/epoch - 20ms/step\n","Epoch 294/300\n","118/118 - 2s - loss: 0.6191 - accuracy: 0.7764 - val_loss: 0.6886 - val_accuracy: 0.7479 - 2s/epoch - 21ms/step\n","Epoch 295/300\n","118/118 - 2s - loss: 0.6190 - accuracy: 0.7767 - val_loss: 0.6884 - val_accuracy: 0.7502 - 2s/epoch - 20ms/step\n","Epoch 296/300\n","118/118 - 2s - loss: 0.6169 - accuracy: 0.7763 - val_loss: 0.6853 - val_accuracy: 0.7525 - 2s/epoch - 20ms/step\n","Epoch 297/300\n","118/118 - 2s - loss: 0.6157 - accuracy: 0.7762 - val_loss: 0.6847 - val_accuracy: 0.7537 - 2s/epoch - 19ms/step\n","Epoch 298/300\n","118/118 - 2s - loss: 0.6151 - accuracy: 0.7758 - val_loss: 0.6824 - val_accuracy: 0.7530 - 2s/epoch - 20ms/step\n","Epoch 299/300\n","118/118 - 2s - loss: 0.6145 - accuracy: 0.7768 - val_loss: 0.6827 - val_accuracy: 0.7552 - 2s/epoch - 20ms/step\n","Epoch 300/300\n","118/118 - 2s - loss: 0.6134 - accuracy: 0.7770 - val_loss: 0.6826 - val_accuracy: 0.7559 - 2s/epoch - 20ms/step\n"]}],"source":["# solution 1: increase weight value\n","\n","#################### YOUR CODE HERE ####################\n","initializer = keras.initializers.RandomNormal(mean=0., stddev=1.)\n","\n","# Đặt initializer ở trên vào các layer có ký hiệu #??\n","model = keras.Sequential()\n","model.add(keras.Input(shape=(784,)))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(128, activation='sigmoid',\n","                             kernel_initializer=initializer))\n","model.add(keras.layers.Dense(10))\n","########################################################\n","\n","# optimizer and loss\n","model.compile(optimizer=keras.optimizers.SGD(), \n","              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n","              metrics=['accuracy'])\n","\n","# training\n","batch_size = 512\n","history = model.fit(X_train, y_train, batch_size,\n","                    validation_data=(X_test, y_test),\n","                    epochs=300, verbose=2)"]},{"cell_type":"code","execution_count":14,"id":"6ced2ddb","metadata":{"id":"6ced2ddb","executionInfo":{"status":"ok","timestamp":1669888249612,"user_tz":-420,"elapsed":14,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":15,"id":"bcfc9fa7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1669888249613,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"},"user_tz":-420},"id":"bcfc9fa7","outputId":"7c4c890f-20f2-4ad4-85f5-dc4965c0fbb3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fc3046eba60>"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(history.history['loss'], label='loss')\n","plt.plot(history.history['val_loss'], label='val_loss')\n","plt.xlabel('iteration')\n","plt.ylabel('Loss')\n","plt.legend()"]},{"cell_type":"code","execution_count":15,"id":"93173344","metadata":{"id":"93173344","executionInfo":{"status":"ok","timestamp":1669888249613,"user_tz":-420,"elapsed":9,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":16,"id":"8bf8cdf5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"elapsed":726,"status":"ok","timestamp":1669888250330,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"},"user_tz":-420},"id":"8bf8cdf5","outputId":"284d31a7-48e8-4e6a-d7c2-0d3780e0cde1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fc3046b5df0>"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(history.history['accuracy'], label='train_accuracy')\n","plt.plot(history.history['val_accuracy'], label='val_accuracy')\n","plt.xlabel('iteration')\n","plt.ylabel('Accuracy')\n","plt.legend()"]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.0"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":true},"gpuClass":"standard"},"nbformat":4,"nbformat_minor":5}