[hint]Solution3_BetterOptimizer.ipynb

{"cells":[{"cell_type":"markdown","source":["## Load Data"],"metadata":{"id":"syncB6SrV7ca"},"id":"syncB6SrV7ca"},{"cell_type":"code","execution_count":1,"id":"e175eeac","metadata":{"id":"e175eeac","outputId":"b3bbd889-ec42-4943-daa2-57c606278424","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669892415988,"user_tz":-420,"elapsed":5985,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","29515/29515 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26421880/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","5148/5148 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4422102/4422102 [==============================] - 0s 0us/step\n","(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":"f068e1ea"},"id":"f068e1ea"},{"cell_type":"code","execution_count":2,"id":"8aabf29a","metadata":{"id":"8aabf29a","outputId":"3286214a-6045-4b68-fcea-f1e4ccb5bd2e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669892415988,"user_tz":-420,"elapsed":9,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"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","source":["## Better Optimizer"],"metadata":{"id":"c3167cdb"},"id":"c3167cdb"},{"cell_type":"code","execution_count":3,"id":"c46b65da","metadata":{"id":"c46b65da","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669893040386,"user_tz":-420,"elapsed":624404,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}},"outputId":"5b736799-e33d-43e1-94fd-e21f4d8ea483"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/300\n","118/118 - 4s - loss: 2.2993 - accuracy: 0.1172 - val_loss: 2.0502 - val_accuracy: 0.1973 - 4s/epoch - 36ms/step\n","Epoch 2/300\n","118/118 - 2s - loss: 1.5978 - accuracy: 0.3135 - val_loss: 1.3034 - val_accuracy: 0.4230 - 2s/epoch - 18ms/step\n","Epoch 3/300\n","118/118 - 2s - loss: 1.1969 - accuracy: 0.4649 - val_loss: 1.1467 - val_accuracy: 0.4961 - 2s/epoch - 17ms/step\n","Epoch 4/300\n","118/118 - 2s - loss: 1.0780 - accuracy: 0.5248 - val_loss: 1.0832 - val_accuracy: 0.5217 - 2s/epoch - 17ms/step\n","Epoch 5/300\n","118/118 - 2s - loss: 1.0449 - accuracy: 0.5445 - val_loss: 1.0200 - val_accuracy: 0.5595 - 2s/epoch - 19ms/step\n","Epoch 6/300\n","118/118 - 2s - loss: 0.9688 - accuracy: 0.6018 - val_loss: 1.0133 - val_accuracy: 0.6117 - 2s/epoch - 17ms/step\n","Epoch 7/300\n","118/118 - 2s - loss: 0.9076 - accuracy: 0.6348 - val_loss: 0.9239 - val_accuracy: 0.6238 - 2s/epoch - 17ms/step\n","Epoch 8/300\n","118/118 - 2s - loss: 0.9077 - accuracy: 0.6198 - val_loss: 0.9110 - val_accuracy: 0.6369 - 2s/epoch - 17ms/step\n","Epoch 9/300\n","118/118 - 2s - loss: 0.8839 - accuracy: 0.6410 - val_loss: 0.9086 - val_accuracy: 0.6474 - 2s/epoch - 17ms/step\n","Epoch 10/300\n","118/118 - 2s - loss: 0.8639 - accuracy: 0.6544 - val_loss: 0.8553 - val_accuracy: 0.6692 - 2s/epoch - 17ms/step\n","Epoch 11/300\n","118/118 - 2s - loss: 0.8266 - accuracy: 0.6676 - val_loss: 0.8636 - val_accuracy: 0.6517 - 2s/epoch - 17ms/step\n","Epoch 12/300\n","118/118 - 2s - loss: 0.8295 - accuracy: 0.6648 - val_loss: 0.8317 - val_accuracy: 0.6717 - 2s/epoch - 17ms/step\n","Epoch 13/300\n","118/118 - 2s - loss: 0.8185 - accuracy: 0.6700 - val_loss: 0.8553 - val_accuracy: 0.6696 - 2s/epoch - 17ms/step\n","Epoch 14/300\n","118/118 - 2s - loss: 0.7985 - accuracy: 0.6802 - val_loss: 0.8071 - val_accuracy: 0.6755 - 2s/epoch - 17ms/step\n","Epoch 15/300\n","118/118 - 2s - loss: 0.7820 - accuracy: 0.6956 - val_loss: 0.8482 - val_accuracy: 0.7018 - 2s/epoch - 17ms/step\n","Epoch 16/300\n","118/118 - 2s - loss: 0.7927 - accuracy: 0.6938 - val_loss: 0.8252 - val_accuracy: 0.6764 - 2s/epoch - 17ms/step\n","Epoch 17/300\n","118/118 - 2s - loss: 0.7803 - accuracy: 0.7051 - val_loss: 0.8179 - val_accuracy: 0.6870 - 2s/epoch - 17ms/step\n","Epoch 18/300\n","118/118 - 2s - loss: 0.7765 - accuracy: 0.7077 - val_loss: 0.8354 - val_accuracy: 0.7164 - 2s/epoch - 17ms/step\n","Epoch 19/300\n","118/118 - 2s - loss: 0.7730 - accuracy: 0.7116 - val_loss: 0.7688 - val_accuracy: 0.7358 - 2s/epoch - 17ms/step\n","Epoch 20/300\n","118/118 - 2s - loss: 0.7530 - accuracy: 0.7296 - val_loss: 0.7386 - val_accuracy: 0.7406 - 2s/epoch - 17ms/step\n","Epoch 21/300\n","118/118 - 2s - loss: 0.7311 - accuracy: 0.7374 - val_loss: 0.7915 - val_accuracy: 0.7019 - 2s/epoch - 17ms/step\n","Epoch 22/300\n","118/118 - 2s - loss: 0.7698 - accuracy: 0.7256 - val_loss: 0.7590 - val_accuracy: 0.7301 - 2s/epoch - 17ms/step\n","Epoch 23/300\n","118/118 - 2s - loss: 0.7291 - accuracy: 0.7385 - val_loss: 0.7696 - val_accuracy: 0.7282 - 2s/epoch - 17ms/step\n","Epoch 24/300\n","118/118 - 2s - loss: 0.7357 - accuracy: 0.7350 - val_loss: 0.7525 - val_accuracy: 0.7370 - 2s/epoch - 17ms/step\n","Epoch 25/300\n","118/118 - 2s - loss: 0.7377 - accuracy: 0.7321 - val_loss: 0.7859 - val_accuracy: 0.7150 - 2s/epoch - 17ms/step\n","Epoch 26/300\n","118/118 - 2s - loss: 0.7303 - accuracy: 0.7435 - val_loss: 0.7294 - val_accuracy: 0.7514 - 2s/epoch - 17ms/step\n","Epoch 27/300\n","118/118 - 2s - loss: 0.7443 - accuracy: 0.7285 - val_loss: 0.7384 - val_accuracy: 0.7366 - 2s/epoch - 17ms/step\n","Epoch 28/300\n","118/118 - 2s - loss: 0.7610 - accuracy: 0.7194 - val_loss: 0.7918 - val_accuracy: 0.7308 - 2s/epoch - 17ms/step\n","Epoch 29/300\n","118/118 - 2s - loss: 0.7168 - accuracy: 0.7399 - val_loss: 0.7355 - val_accuracy: 0.7302 - 2s/epoch - 17ms/step\n","Epoch 30/300\n","118/118 - 2s - loss: 0.7411 - accuracy: 0.7236 - val_loss: 0.7519 - val_accuracy: 0.7342 - 2s/epoch - 17ms/step\n","Epoch 31/300\n","118/118 - 2s - loss: 0.7336 - accuracy: 0.7301 - val_loss: 0.7641 - val_accuracy: 0.7190 - 2s/epoch - 17ms/step\n","Epoch 32/300\n","118/118 - 2s - loss: 0.7295 - accuracy: 0.7243 - val_loss: 0.7515 - val_accuracy: 0.7183 - 2s/epoch - 17ms/step\n","Epoch 33/300\n","118/118 - 2s - loss: 0.7732 - accuracy: 0.7038 - val_loss: 0.7936 - val_accuracy: 0.7035 - 2s/epoch - 17ms/step\n","Epoch 34/300\n","118/118 - 2s - loss: 0.7509 - accuracy: 0.7135 - val_loss: 0.7630 - val_accuracy: 0.7047 - 2s/epoch - 17ms/step\n","Epoch 35/300\n","118/118 - 2s - loss: 0.7822 - accuracy: 0.7021 - val_loss: 0.7676 - val_accuracy: 0.7092 - 2s/epoch - 17ms/step\n","Epoch 36/300\n","118/118 - 2s - loss: 0.7131 - accuracy: 0.7357 - val_loss: 0.7634 - val_accuracy: 0.7221 - 2s/epoch - 17ms/step\n","Epoch 37/300\n","118/118 - 2s - loss: 0.6966 - accuracy: 0.7454 - val_loss: 0.7386 - val_accuracy: 0.7255 - 2s/epoch - 17ms/step\n","Epoch 38/300\n","118/118 - 2s - loss: 0.7256 - accuracy: 0.7270 - val_loss: 0.7520 - val_accuracy: 0.7132 - 2s/epoch - 17ms/step\n","Epoch 39/300\n","118/118 - 2s - loss: 0.7217 - accuracy: 0.7142 - val_loss: 0.6902 - val_accuracy: 0.7500 - 2s/epoch - 17ms/step\n","Epoch 40/300\n","118/118 - 2s - loss: 0.6854 - accuracy: 0.7425 - val_loss: 0.6945 - val_accuracy: 0.7487 - 2s/epoch - 17ms/step\n","Epoch 41/300\n","118/118 - 2s - loss: 0.6799 - accuracy: 0.7462 - val_loss: 0.7033 - val_accuracy: 0.7364 - 2s/epoch - 17ms/step\n","Epoch 42/300\n","118/118 - 2s - loss: 0.6948 - accuracy: 0.7409 - val_loss: 0.7561 - val_accuracy: 0.7482 - 2s/epoch - 17ms/step\n","Epoch 43/300\n","118/118 - 2s - loss: 0.6753 - accuracy: 0.7514 - val_loss: 0.7105 - val_accuracy: 0.7378 - 2s/epoch - 17ms/step\n","Epoch 44/300\n","118/118 - 2s - loss: 0.6846 - accuracy: 0.7512 - val_loss: 0.7150 - val_accuracy: 0.6967 - 2s/epoch - 17ms/step\n","Epoch 45/300\n","118/118 - 2s - loss: 0.6746 - accuracy: 0.7493 - val_loss: 0.7096 - val_accuracy: 0.7481 - 2s/epoch - 17ms/step\n","Epoch 46/300\n","118/118 - 2s - loss: 0.6823 - accuracy: 0.7524 - val_loss: 0.7494 - val_accuracy: 0.7270 - 2s/epoch - 17ms/step\n","Epoch 47/300\n","118/118 - 2s - loss: 0.6922 - accuracy: 0.7448 - val_loss: 0.7084 - val_accuracy: 0.7483 - 2s/epoch - 17ms/step\n","Epoch 48/300\n","118/118 - 2s - loss: 0.7146 - accuracy: 0.7325 - val_loss: 0.8001 - val_accuracy: 0.6572 - 2s/epoch - 17ms/step\n","Epoch 49/300\n","118/118 - 2s - loss: 0.7036 - accuracy: 0.7405 - val_loss: 0.7545 - val_accuracy: 0.7159 - 2s/epoch - 17ms/step\n","Epoch 50/300\n","118/118 - 2s - loss: 0.7163 - accuracy: 0.7206 - val_loss: 0.7451 - val_accuracy: 0.7331 - 2s/epoch - 17ms/step\n","Epoch 51/300\n","118/118 - 2s - loss: 0.6914 - accuracy: 0.7454 - val_loss: 0.7219 - val_accuracy: 0.7263 - 2s/epoch - 17ms/step\n","Epoch 52/300\n","118/118 - 2s - loss: 0.7409 - accuracy: 0.7356 - val_loss: 0.7312 - val_accuracy: 0.7565 - 2s/epoch - 17ms/step\n","Epoch 53/300\n","118/118 - 2s - loss: 0.7164 - accuracy: 0.7528 - val_loss: 0.7285 - val_accuracy: 0.7496 - 2s/epoch - 17ms/step\n","Epoch 54/300\n","118/118 - 2s - loss: 0.6989 - accuracy: 0.7524 - val_loss: 0.6998 - val_accuracy: 0.7534 - 2s/epoch - 17ms/step\n","Epoch 55/300\n","118/118 - 2s - loss: 0.6649 - accuracy: 0.7601 - val_loss: 0.6759 - val_accuracy: 0.7574 - 2s/epoch - 17ms/step\n","Epoch 56/300\n","118/118 - 2s - loss: 0.6515 - accuracy: 0.7643 - val_loss: 0.6986 - val_accuracy: 0.7521 - 2s/epoch - 17ms/step\n","Epoch 57/300\n","118/118 - 2s - loss: 0.6687 - accuracy: 0.7538 - val_loss: 0.7154 - val_accuracy: 0.7425 - 2s/epoch - 17ms/step\n","Epoch 58/300\n","118/118 - 2s - loss: 0.6890 - accuracy: 0.7417 - val_loss: 0.6751 - val_accuracy: 0.7570 - 2s/epoch - 17ms/step\n","Epoch 59/300\n","118/118 - 2s - loss: 0.7143 - accuracy: 0.7418 - val_loss: 0.7848 - val_accuracy: 0.7362 - 2s/epoch - 17ms/step\n","Epoch 60/300\n","118/118 - 2s - loss: 0.7862 - accuracy: 0.7100 - val_loss: 0.9200 - val_accuracy: 0.6269 - 2s/epoch - 17ms/step\n","Epoch 61/300\n","118/118 - 2s - loss: 0.8112 - accuracy: 0.6897 - val_loss: 0.8091 - val_accuracy: 0.6995 - 2s/epoch - 17ms/step\n","Epoch 62/300\n","118/118 - 2s - loss: 0.7716 - accuracy: 0.7257 - val_loss: 0.7571 - val_accuracy: 0.7217 - 2s/epoch - 17ms/step\n","Epoch 63/300\n","118/118 - 2s - loss: 0.7505 - accuracy: 0.7277 - val_loss: 0.7423 - val_accuracy: 0.7289 - 2s/epoch - 17ms/step\n","Epoch 64/300\n","118/118 - 2s - loss: 0.6912 - accuracy: 0.7489 - val_loss: 0.7277 - val_accuracy: 0.7341 - 2s/epoch - 17ms/step\n","Epoch 65/300\n","118/118 - 2s - loss: 0.6975 - accuracy: 0.7403 - val_loss: 0.7241 - val_accuracy: 0.7342 - 2s/epoch - 17ms/step\n","Epoch 66/300\n","118/118 - 2s - loss: 0.7074 - accuracy: 0.7332 - val_loss: 0.7412 - val_accuracy: 0.7302 - 2s/epoch - 17ms/step\n","Epoch 67/300\n","118/118 - 2s - loss: 0.6639 - accuracy: 0.7557 - val_loss: 0.6909 - val_accuracy: 0.7544 - 2s/epoch - 17ms/step\n","Epoch 68/300\n","118/118 - 2s - loss: 0.6591 - accuracy: 0.7671 - val_loss: 0.6725 - val_accuracy: 0.7562 - 2s/epoch - 17ms/step\n","Epoch 69/300\n","118/118 - 2s - loss: 0.6636 - accuracy: 0.7674 - val_loss: 0.7503 - val_accuracy: 0.7411 - 2s/epoch - 17ms/step\n","Epoch 70/300\n","118/118 - 2s - loss: 0.6317 - accuracy: 0.7726 - val_loss: 0.6617 - val_accuracy: 0.7706 - 2s/epoch - 17ms/step\n","Epoch 71/300\n","118/118 - 2s - loss: 0.7169 - accuracy: 0.7441 - val_loss: 0.7727 - val_accuracy: 0.6890 - 2s/epoch - 17ms/step\n","Epoch 72/300\n","118/118 - 2s - loss: 0.7267 - accuracy: 0.7376 - val_loss: 0.6896 - val_accuracy: 0.7560 - 2s/epoch - 17ms/step\n","Epoch 73/300\n","118/118 - 2s - loss: 0.6904 - accuracy: 0.7430 - val_loss: 0.7151 - val_accuracy: 0.7148 - 2s/epoch - 17ms/step\n","Epoch 74/300\n","118/118 - 2s - loss: 0.6822 - accuracy: 0.7478 - val_loss: 0.7478 - val_accuracy: 0.7245 - 2s/epoch - 17ms/step\n","Epoch 75/300\n","118/118 - 2s - loss: 0.6956 - accuracy: 0.7543 - val_loss: 0.6969 - val_accuracy: 0.7596 - 2s/epoch - 17ms/step\n","Epoch 76/300\n","118/118 - 2s - loss: 0.6781 - accuracy: 0.7525 - val_loss: 0.6791 - val_accuracy: 0.7520 - 2s/epoch - 17ms/step\n","Epoch 77/300\n","118/118 - 2s - loss: 0.6612 - accuracy: 0.7492 - val_loss: 0.7218 - val_accuracy: 0.7260 - 2s/epoch - 17ms/step\n","Epoch 78/300\n","118/118 - 2s - loss: 0.6636 - accuracy: 0.7492 - val_loss: 0.7130 - val_accuracy: 0.7205 - 2s/epoch - 17ms/step\n","Epoch 79/300\n","118/118 - 2s - loss: 0.6646 - accuracy: 0.7497 - val_loss: 0.6774 - val_accuracy: 0.7451 - 2s/epoch - 17ms/step\n","Epoch 80/300\n","118/118 - 2s - loss: 0.6651 - accuracy: 0.7528 - val_loss: 0.7053 - val_accuracy: 0.7303 - 2s/epoch - 17ms/step\n","Epoch 81/300\n","118/118 - 2s - loss: 0.7034 - accuracy: 0.7406 - val_loss: 0.7179 - val_accuracy: 0.7432 - 2s/epoch - 17ms/step\n","Epoch 82/300\n","118/118 - 2s - loss: 0.6856 - accuracy: 0.7483 - val_loss: 0.7567 - val_accuracy: 0.7039 - 2s/epoch - 17ms/step\n","Epoch 83/300\n","118/118 - 2s - loss: 0.7127 - accuracy: 0.7328 - val_loss: 0.7483 - val_accuracy: 0.7449 - 2s/epoch - 17ms/step\n","Epoch 84/300\n","118/118 - 2s - loss: 0.6545 - accuracy: 0.7651 - val_loss: 0.6676 - val_accuracy: 0.7676 - 2s/epoch - 17ms/step\n","Epoch 85/300\n","118/118 - 2s - loss: 0.6577 - accuracy: 0.7611 - val_loss: 0.7821 - val_accuracy: 0.6992 - 2s/epoch - 17ms/step\n","Epoch 86/300\n","118/118 - 2s - loss: 0.6828 - accuracy: 0.7507 - val_loss: 0.6901 - val_accuracy: 0.7499 - 2s/epoch - 17ms/step\n","Epoch 87/300\n","118/118 - 2s - loss: 0.6735 - accuracy: 0.7535 - val_loss: 0.6860 - val_accuracy: 0.7476 - 2s/epoch - 17ms/step\n","Epoch 88/300\n","118/118 - 2s - loss: 0.6894 - accuracy: 0.7459 - val_loss: 0.7268 - val_accuracy: 0.7292 - 2s/epoch - 17ms/step\n","Epoch 89/300\n","118/118 - 2s - loss: 0.6680 - accuracy: 0.7556 - val_loss: 0.6799 - val_accuracy: 0.7573 - 2s/epoch - 18ms/step\n","Epoch 90/300\n","118/118 - 2s - loss: 0.6530 - accuracy: 0.7670 - val_loss: 0.6678 - val_accuracy: 0.7709 - 2s/epoch - 17ms/step\n","Epoch 91/300\n","118/118 - 2s - loss: 0.6570 - accuracy: 0.7668 - val_loss: 0.7460 - val_accuracy: 0.7339 - 2s/epoch - 17ms/step\n","Epoch 92/300\n","118/118 - 2s - loss: 0.6818 - accuracy: 0.7461 - val_loss: 0.6879 - val_accuracy: 0.7537 - 2s/epoch - 17ms/step\n","Epoch 93/300\n","118/118 - 2s - loss: 0.6840 - accuracy: 0.7440 - val_loss: 0.8542 - val_accuracy: 0.6633 - 2s/epoch - 17ms/step\n","Epoch 94/300\n","118/118 - 2s - loss: 0.6582 - accuracy: 0.7630 - val_loss: 0.6855 - val_accuracy: 0.7555 - 2s/epoch - 17ms/step\n","Epoch 95/300\n","118/118 - 2s - loss: 0.6742 - accuracy: 0.7556 - val_loss: 0.6995 - val_accuracy: 0.7425 - 2s/epoch - 17ms/step\n","Epoch 96/300\n","118/118 - 2s - loss: 0.6691 - accuracy: 0.7543 - val_loss: 0.7054 - val_accuracy: 0.7350 - 2s/epoch - 17ms/step\n","Epoch 97/300\n","118/118 - 2s - loss: 0.6822 - accuracy: 0.7502 - val_loss: 0.7007 - val_accuracy: 0.7487 - 2s/epoch - 17ms/step\n","Epoch 98/300\n","118/118 - 2s - loss: 0.6842 - accuracy: 0.7577 - val_loss: 0.6895 - val_accuracy: 0.7593 - 2s/epoch - 17ms/step\n","Epoch 99/300\n","118/118 - 2s - loss: 0.6759 - accuracy: 0.7629 - val_loss: 0.7009 - val_accuracy: 0.7496 - 2s/epoch - 17ms/step\n","Epoch 100/300\n","118/118 - 2s - loss: 0.6498 - accuracy: 0.7713 - val_loss: 0.6985 - val_accuracy: 0.7489 - 2s/epoch - 17ms/step\n","Epoch 101/300\n","118/118 - 2s - loss: 0.6747 - accuracy: 0.7533 - val_loss: 0.7002 - val_accuracy: 0.7385 - 2s/epoch - 17ms/step\n","Epoch 102/300\n","118/118 - 2s - loss: 0.6545 - accuracy: 0.7600 - val_loss: 0.6954 - val_accuracy: 0.7498 - 2s/epoch - 17ms/step\n","Epoch 103/300\n","118/118 - 2s - loss: 0.6818 - accuracy: 0.7485 - val_loss: 0.6980 - val_accuracy: 0.7337 - 2s/epoch - 17ms/step\n","Epoch 104/300\n","118/118 - 2s - loss: 0.6668 - accuracy: 0.7476 - val_loss: 0.7269 - val_accuracy: 0.7085 - 2s/epoch - 17ms/step\n","Epoch 105/300\n","118/118 - 2s - loss: 0.6782 - accuracy: 0.7340 - val_loss: 0.6854 - val_accuracy: 0.7493 - 2s/epoch - 17ms/step\n","Epoch 106/300\n","118/118 - 2s - loss: 0.6517 - accuracy: 0.7631 - val_loss: 0.6760 - val_accuracy: 0.7588 - 2s/epoch - 17ms/step\n","Epoch 107/300\n","118/118 - 2s - loss: 0.6368 - accuracy: 0.7709 - val_loss: 0.6610 - val_accuracy: 0.7609 - 2s/epoch - 17ms/step\n","Epoch 108/300\n","118/118 - 2s - loss: 0.6431 - accuracy: 0.7672 - val_loss: 0.7380 - val_accuracy: 0.7278 - 2s/epoch - 17ms/step\n","Epoch 109/300\n","118/118 - 2s - loss: 0.6905 - accuracy: 0.7466 - val_loss: 0.6796 - val_accuracy: 0.7529 - 2s/epoch - 17ms/step\n","Epoch 110/300\n","118/118 - 2s - loss: 0.6667 - accuracy: 0.7584 - val_loss: 0.7316 - val_accuracy: 0.7377 - 2s/epoch - 17ms/step\n","Epoch 111/300\n","118/118 - 2s - loss: 0.7780 - accuracy: 0.7062 - val_loss: 0.7454 - val_accuracy: 0.7049 - 2s/epoch - 17ms/step\n","Epoch 112/300\n","118/118 - 2s - loss: 0.7061 - accuracy: 0.7190 - val_loss: 0.7116 - val_accuracy: 0.7083 - 2s/epoch - 17ms/step\n","Epoch 113/300\n","118/118 - 2s - loss: 0.6703 - accuracy: 0.7446 - val_loss: 0.6693 - val_accuracy: 0.7455 - 2s/epoch - 17ms/step\n","Epoch 114/300\n","118/118 - 2s - loss: 0.6270 - accuracy: 0.7747 - val_loss: 0.6403 - val_accuracy: 0.7775 - 2s/epoch - 17ms/step\n","Epoch 115/300\n","118/118 - 2s - loss: 0.6371 - accuracy: 0.7714 - val_loss: 0.6665 - val_accuracy: 0.7691 - 2s/epoch - 17ms/step\n","Epoch 116/300\n","118/118 - 2s - loss: 0.6518 - accuracy: 0.7699 - val_loss: 0.6677 - val_accuracy: 0.7577 - 2s/epoch - 17ms/step\n","Epoch 117/300\n","118/118 - 2s - loss: 0.6265 - accuracy: 0.7688 - val_loss: 0.6545 - val_accuracy: 0.7662 - 2s/epoch - 17ms/step\n","Epoch 118/300\n","118/118 - 2s - loss: 0.6425 - accuracy: 0.7675 - val_loss: 0.6613 - val_accuracy: 0.7604 - 2s/epoch - 17ms/step\n","Epoch 119/300\n","118/118 - 2s - loss: 0.6428 - accuracy: 0.7633 - val_loss: 0.6760 - val_accuracy: 0.7443 - 2s/epoch - 17ms/step\n","Epoch 120/300\n","118/118 - 2s - loss: 0.6233 - accuracy: 0.7731 - val_loss: 0.6542 - val_accuracy: 0.7655 - 2s/epoch - 17ms/step\n","Epoch 121/300\n","118/118 - 2s - loss: 0.6233 - accuracy: 0.7738 - val_loss: 0.6400 - val_accuracy: 0.7737 - 2s/epoch - 17ms/step\n","Epoch 122/300\n","118/118 - 2s - loss: 0.6075 - accuracy: 0.7749 - val_loss: 0.6360 - val_accuracy: 0.7667 - 2s/epoch - 17ms/step\n","Epoch 123/300\n","118/118 - 2s - loss: 0.6073 - accuracy: 0.7839 - val_loss: 0.6337 - val_accuracy: 0.7760 - 2s/epoch - 17ms/step\n","Epoch 124/300\n","118/118 - 2s - loss: 0.6275 - accuracy: 0.7660 - val_loss: 0.6460 - val_accuracy: 0.7628 - 2s/epoch - 17ms/step\n","Epoch 125/300\n","118/118 - 2s - loss: 0.6238 - accuracy: 0.7731 - val_loss: 0.6366 - val_accuracy: 0.7772 - 2s/epoch - 17ms/step\n","Epoch 126/300\n","118/118 - 2s - loss: 0.6224 - accuracy: 0.7778 - val_loss: 0.6583 - val_accuracy: 0.7633 - 2s/epoch - 17ms/step\n","Epoch 127/300\n","118/118 - 2s - loss: 0.6407 - accuracy: 0.7677 - val_loss: 0.6941 - val_accuracy: 0.7516 - 2s/epoch - 17ms/step\n","Epoch 128/300\n","118/118 - 2s - loss: 0.6559 - accuracy: 0.7649 - val_loss: 0.7673 - val_accuracy: 0.7452 - 2s/epoch - 17ms/step\n","Epoch 129/300\n","118/118 - 2s - loss: 0.6553 - accuracy: 0.7653 - val_loss: 0.6508 - val_accuracy: 0.7695 - 2s/epoch - 17ms/step\n","Epoch 130/300\n","118/118 - 2s - loss: 0.6230 - accuracy: 0.7791 - val_loss: 0.6785 - val_accuracy: 0.7495 - 2s/epoch - 17ms/step\n","Epoch 131/300\n","118/118 - 2s - loss: 0.6008 - accuracy: 0.7832 - val_loss: 0.6462 - val_accuracy: 0.7576 - 2s/epoch - 17ms/step\n","Epoch 132/300\n","118/118 - 2s - loss: 0.6013 - accuracy: 0.7792 - val_loss: 0.6096 - val_accuracy: 0.7848 - 2s/epoch - 17ms/step\n","Epoch 133/300\n","118/118 - 2s - loss: 0.6034 - accuracy: 0.7843 - val_loss: 0.6127 - val_accuracy: 0.7771 - 2s/epoch - 18ms/step\n","Epoch 134/300\n","118/118 - 2s - loss: 0.5985 - accuracy: 0.7830 - val_loss: 0.6932 - val_accuracy: 0.7576 - 2s/epoch - 17ms/step\n","Epoch 135/300\n","118/118 - 2s - loss: 0.6120 - accuracy: 0.7757 - val_loss: 0.6427 - val_accuracy: 0.7652 - 2s/epoch - 17ms/step\n","Epoch 136/300\n","118/118 - 2s - loss: 0.6365 - accuracy: 0.7627 - val_loss: 0.6938 - val_accuracy: 0.7365 - 2s/epoch - 17ms/step\n","Epoch 137/300\n","118/118 - 2s - loss: 0.6322 - accuracy: 0.7730 - val_loss: 0.6468 - val_accuracy: 0.7733 - 2s/epoch - 17ms/step\n","Epoch 138/300\n","118/118 - 2s - loss: 0.6219 - accuracy: 0.7796 - val_loss: 0.6958 - val_accuracy: 0.7554 - 2s/epoch - 17ms/step\n","Epoch 139/300\n","118/118 - 2s - loss: 0.6219 - accuracy: 0.7854 - val_loss: 0.6746 - val_accuracy: 0.7599 - 2s/epoch - 17ms/step\n","Epoch 140/300\n","118/118 - 2s - loss: 0.6159 - accuracy: 0.7865 - val_loss: 0.6633 - val_accuracy: 0.7728 - 2s/epoch - 17ms/step\n","Epoch 141/300\n","118/118 - 2s - loss: 0.6335 - accuracy: 0.7764 - val_loss: 0.6419 - val_accuracy: 0.7755 - 2s/epoch - 17ms/step\n","Epoch 142/300\n","118/118 - 3s - loss: 0.6114 - accuracy: 0.7832 - val_loss: 0.6286 - val_accuracy: 0.7752 - 3s/epoch - 23ms/step\n","Epoch 143/300\n","118/118 - 2s - loss: 0.5969 - accuracy: 0.7897 - val_loss: 0.6358 - val_accuracy: 0.7778 - 2s/epoch - 17ms/step\n","Epoch 144/300\n","118/118 - 2s - loss: 0.6164 - accuracy: 0.7763 - val_loss: 0.6355 - val_accuracy: 0.7677 - 2s/epoch - 17ms/step\n","Epoch 145/300\n","118/118 - 2s - loss: 0.5912 - accuracy: 0.7862 - val_loss: 0.6254 - val_accuracy: 0.7729 - 2s/epoch - 17ms/step\n","Epoch 146/300\n","118/118 - 2s - loss: 0.6304 - accuracy: 0.7738 - val_loss: 1.6503 - val_accuracy: 0.4075 - 2s/epoch - 17ms/step\n","Epoch 147/300\n","118/118 - 2s - loss: 0.6684 - accuracy: 0.7642 - val_loss: 0.6687 - val_accuracy: 0.7634 - 2s/epoch - 17ms/step\n","Epoch 148/300\n","118/118 - 2s - loss: 0.6123 - accuracy: 0.7821 - val_loss: 0.6482 - val_accuracy: 0.7700 - 2s/epoch - 17ms/step\n","Epoch 149/300\n","118/118 - 2s - loss: 0.6229 - accuracy: 0.7762 - val_loss: 0.6819 - val_accuracy: 0.7668 - 2s/epoch - 17ms/step\n","Epoch 150/300\n","118/118 - 2s - loss: 0.6229 - accuracy: 0.7745 - val_loss: 0.6430 - val_accuracy: 0.7679 - 2s/epoch - 17ms/step\n","Epoch 151/300\n","118/118 - 2s - loss: 0.6063 - accuracy: 0.7821 - val_loss: 0.6369 - val_accuracy: 0.7724 - 2s/epoch - 17ms/step\n","Epoch 152/300\n","118/118 - 2s - loss: 0.5872 - accuracy: 0.7875 - val_loss: 0.6435 - val_accuracy: 0.7749 - 2s/epoch - 17ms/step\n","Epoch 153/300\n","118/118 - 2s - loss: 0.6017 - accuracy: 0.7808 - val_loss: 0.6893 - val_accuracy: 0.7525 - 2s/epoch - 17ms/step\n","Epoch 154/300\n","118/118 - 2s - loss: 0.6391 - accuracy: 0.7664 - val_loss: 0.7355 - val_accuracy: 0.7284 - 2s/epoch - 17ms/step\n","Epoch 155/300\n","118/118 - 2s - loss: 0.6174 - accuracy: 0.7829 - val_loss: 0.6899 - val_accuracy: 0.7656 - 2s/epoch - 17ms/step\n","Epoch 156/300\n","118/118 - 2s - loss: 0.6508 - accuracy: 0.7664 - val_loss: 0.6534 - val_accuracy: 0.7725 - 2s/epoch - 17ms/step\n","Epoch 157/300\n","118/118 - 2s - loss: 0.6594 - accuracy: 0.7736 - val_loss: 0.6763 - val_accuracy: 0.7671 - 2s/epoch - 17ms/step\n","Epoch 158/300\n","118/118 - 2s - loss: 0.6875 - accuracy: 0.7612 - val_loss: 0.7203 - val_accuracy: 0.7457 - 2s/epoch - 17ms/step\n","Epoch 159/300\n","118/118 - 2s - loss: 0.6297 - accuracy: 0.7787 - val_loss: 0.6836 - val_accuracy: 0.7591 - 2s/epoch - 17ms/step\n","Epoch 160/300\n","118/118 - 2s - loss: 0.6357 - accuracy: 0.7745 - val_loss: 0.6951 - val_accuracy: 0.7578 - 2s/epoch - 17ms/step\n","Epoch 161/300\n","118/118 - 2s - loss: 0.6701 - accuracy: 0.7544 - val_loss: 0.6978 - val_accuracy: 0.7528 - 2s/epoch - 17ms/step\n","Epoch 162/300\n","118/118 - 2s - loss: 0.6477 - accuracy: 0.7675 - val_loss: 0.7596 - val_accuracy: 0.7386 - 2s/epoch - 17ms/step\n","Epoch 163/300\n","118/118 - 2s - loss: 0.6889 - accuracy: 0.7575 - val_loss: 0.6519 - val_accuracy: 0.7784 - 2s/epoch - 17ms/step\n","Epoch 164/300\n","118/118 - 2s - loss: 0.6346 - accuracy: 0.7783 - val_loss: 0.7004 - val_accuracy: 0.7521 - 2s/epoch - 17ms/step\n","Epoch 165/300\n","118/118 - 2s - loss: 0.6530 - accuracy: 0.7727 - val_loss: 0.6610 - val_accuracy: 0.7744 - 2s/epoch - 17ms/step\n","Epoch 166/300\n","118/118 - 2s - loss: 0.6231 - accuracy: 0.7813 - val_loss: 0.6678 - val_accuracy: 0.7652 - 2s/epoch - 17ms/step\n","Epoch 167/300\n","118/118 - 2s - loss: 0.6283 - accuracy: 0.7785 - val_loss: 0.6828 - val_accuracy: 0.7507 - 2s/epoch - 17ms/step\n","Epoch 168/300\n","118/118 - 2s - loss: 0.6379 - accuracy: 0.7707 - val_loss: 0.6555 - val_accuracy: 0.7702 - 2s/epoch - 17ms/step\n","Epoch 169/300\n","118/118 - 2s - loss: 0.6287 - accuracy: 0.7768 - val_loss: 0.6788 - val_accuracy: 0.7630 - 2s/epoch - 17ms/step\n","Epoch 170/300\n","118/118 - 2s - loss: 0.6125 - accuracy: 0.7832 - val_loss: 0.6311 - val_accuracy: 0.7717 - 2s/epoch - 17ms/step\n","Epoch 171/300\n","118/118 - 2s - loss: 0.6044 - accuracy: 0.7824 - val_loss: 0.6388 - val_accuracy: 0.7632 - 2s/epoch - 17ms/step\n","Epoch 172/300\n","118/118 - 2s - loss: 0.7736 - accuracy: 0.7176 - val_loss: 1.1646 - val_accuracy: 0.5422 - 2s/epoch - 17ms/step\n","Epoch 173/300\n","118/118 - 2s - loss: 0.8393 - accuracy: 0.6631 - val_loss: 0.7870 - val_accuracy: 0.6792 - 2s/epoch - 17ms/step\n","Epoch 174/300\n","118/118 - 2s - loss: 0.7363 - accuracy: 0.7405 - val_loss: 0.7439 - val_accuracy: 0.7178 - 2s/epoch - 17ms/step\n","Epoch 175/300\n","118/118 - 2s - loss: 0.6792 - accuracy: 0.7571 - val_loss: 0.6942 - val_accuracy: 0.7505 - 2s/epoch - 17ms/step\n","Epoch 176/300\n","118/118 - 2s - loss: 0.6559 - accuracy: 0.7653 - val_loss: 0.7325 - val_accuracy: 0.7159 - 2s/epoch - 17ms/step\n","Epoch 177/300\n","118/118 - 2s - loss: 0.6323 - accuracy: 0.7814 - val_loss: 0.6978 - val_accuracy: 0.7610 - 2s/epoch - 17ms/step\n","Epoch 178/300\n","118/118 - 2s - loss: 0.6724 - accuracy: 0.7676 - val_loss: 0.6927 - val_accuracy: 0.7561 - 2s/epoch - 17ms/step\n","Epoch 179/300\n","118/118 - 2s - loss: 0.6435 - accuracy: 0.7797 - val_loss: 0.6536 - val_accuracy: 0.7747 - 2s/epoch - 17ms/step\n","Epoch 180/300\n","118/118 - 2s - loss: 0.6269 - accuracy: 0.7808 - val_loss: 0.6382 - val_accuracy: 0.7740 - 2s/epoch - 17ms/step\n","Epoch 181/300\n","118/118 - 2s - loss: 0.6552 - accuracy: 0.7688 - val_loss: 0.7379 - val_accuracy: 0.7113 - 2s/epoch - 17ms/step\n","Epoch 182/300\n","118/118 - 2s - loss: 0.6522 - accuracy: 0.7688 - val_loss: 0.6541 - val_accuracy: 0.7739 - 2s/epoch - 17ms/step\n","Epoch 183/300\n","118/118 - 2s - loss: 0.6493 - accuracy: 0.7752 - val_loss: 0.6604 - val_accuracy: 0.7729 - 2s/epoch - 17ms/step\n","Epoch 184/300\n","118/118 - 2s - loss: 0.6465 - accuracy: 0.7763 - val_loss: 0.6989 - val_accuracy: 0.7602 - 2s/epoch - 17ms/step\n","Epoch 185/300\n","118/118 - 2s - loss: 0.6865 - accuracy: 0.7546 - val_loss: 0.7566 - val_accuracy: 0.7260 - 2s/epoch - 17ms/step\n","Epoch 186/300\n","118/118 - 2s - loss: 0.6554 - accuracy: 0.7679 - val_loss: 0.6787 - val_accuracy: 0.7717 - 2s/epoch - 17ms/step\n","Epoch 187/300\n","118/118 - 2s - loss: 0.6389 - accuracy: 0.7732 - val_loss: 0.6983 - val_accuracy: 0.7624 - 2s/epoch - 17ms/step\n","Epoch 188/300\n","118/118 - 2s - loss: 0.6552 - accuracy: 0.7668 - val_loss: 0.6872 - val_accuracy: 0.7587 - 2s/epoch - 17ms/step\n","Epoch 189/300\n","118/118 - 2s - loss: 0.6399 - accuracy: 0.7689 - val_loss: 0.6697 - val_accuracy: 0.7523 - 2s/epoch - 17ms/step\n","Epoch 190/300\n","118/118 - 2s - loss: 0.6332 - accuracy: 0.7747 - val_loss: 0.6646 - val_accuracy: 0.7566 - 2s/epoch - 17ms/step\n","Epoch 191/300\n","118/118 - 2s - loss: 0.6302 - accuracy: 0.7790 - val_loss: 0.6903 - val_accuracy: 0.7598 - 2s/epoch - 18ms/step\n","Epoch 192/300\n","118/118 - 2s - loss: 0.6583 - accuracy: 0.7703 - val_loss: 0.6866 - val_accuracy: 0.7767 - 2s/epoch - 17ms/step\n","Epoch 193/300\n","118/118 - 2s - loss: 0.6587 - accuracy: 0.7787 - val_loss: 0.7212 - val_accuracy: 0.7697 - 2s/epoch - 17ms/step\n","Epoch 194/300\n","118/118 - 2s - loss: 0.6642 - accuracy: 0.7751 - val_loss: 0.7270 - val_accuracy: 0.7635 - 2s/epoch - 17ms/step\n","Epoch 195/300\n","118/118 - 2s - loss: 0.6891 - accuracy: 0.7654 - val_loss: 0.7029 - val_accuracy: 0.7637 - 2s/epoch - 17ms/step\n","Epoch 196/300\n","118/118 - 2s - loss: 0.6776 - accuracy: 0.7728 - val_loss: 1.2504 - val_accuracy: 0.4849 - 2s/epoch - 17ms/step\n","Epoch 197/300\n","118/118 - 2s - loss: 0.7600 - accuracy: 0.7351 - val_loss: 0.7105 - val_accuracy: 0.7639 - 2s/epoch - 17ms/step\n","Epoch 198/300\n","118/118 - 2s - loss: 0.6867 - accuracy: 0.7723 - val_loss: 0.6879 - val_accuracy: 0.7694 - 2s/epoch - 17ms/step\n","Epoch 199/300\n","118/118 - 2s - loss: 0.6644 - accuracy: 0.7719 - val_loss: 0.6785 - val_accuracy: 0.7694 - 2s/epoch - 17ms/step\n","Epoch 200/300\n","118/118 - 2s - loss: 0.6573 - accuracy: 0.7719 - val_loss: 0.6857 - val_accuracy: 0.7667 - 2s/epoch - 17ms/step\n","Epoch 201/300\n","118/118 - 2s - loss: 0.6638 - accuracy: 0.7708 - val_loss: 0.6676 - val_accuracy: 0.7671 - 2s/epoch - 17ms/step\n","Epoch 202/300\n","118/118 - 2s - loss: 0.6504 - accuracy: 0.7710 - val_loss: 0.6740 - val_accuracy: 0.7677 - 2s/epoch - 17ms/step\n","Epoch 203/300\n","118/118 - 2s - loss: 0.6493 - accuracy: 0.7785 - val_loss: 0.6995 - val_accuracy: 0.7636 - 2s/epoch - 18ms/step\n","Epoch 204/300\n","118/118 - 2s - loss: 0.6882 - accuracy: 0.7644 - val_loss: 0.6787 - val_accuracy: 0.7581 - 2s/epoch - 17ms/step\n","Epoch 205/300\n","118/118 - 2s - loss: 0.6428 - accuracy: 0.7712 - val_loss: 0.6610 - val_accuracy: 0.7703 - 2s/epoch - 17ms/step\n","Epoch 206/300\n","118/118 - 2s - loss: 0.6382 - accuracy: 0.7865 - val_loss: 0.7545 - val_accuracy: 0.7344 - 2s/epoch - 17ms/step\n","Epoch 207/300\n","118/118 - 2s - loss: 0.6633 - accuracy: 0.7658 - val_loss: 0.6973 - val_accuracy: 0.7567 - 2s/epoch - 17ms/step\n","Epoch 208/300\n","118/118 - 2s - loss: 0.8194 - accuracy: 0.7148 - val_loss: 0.6985 - val_accuracy: 0.7668 - 2s/epoch - 17ms/step\n","Epoch 209/300\n","118/118 - 2s - loss: 0.6660 - accuracy: 0.7715 - val_loss: 0.6831 - val_accuracy: 0.7649 - 2s/epoch - 17ms/step\n","Epoch 210/300\n","118/118 - 2s - loss: 0.6413 - accuracy: 0.7800 - val_loss: 0.6556 - val_accuracy: 0.7708 - 2s/epoch - 17ms/step\n","Epoch 211/300\n","118/118 - 2s - loss: 0.6364 - accuracy: 0.7812 - val_loss: 0.7243 - val_accuracy: 0.7433 - 2s/epoch - 17ms/step\n","Epoch 212/300\n","118/118 - 2s - loss: 0.6515 - accuracy: 0.7706 - val_loss: 0.6621 - val_accuracy: 0.7754 - 2s/epoch - 17ms/step\n","Epoch 213/300\n","118/118 - 2s - loss: 0.7483 - accuracy: 0.7262 - val_loss: 0.8432 - val_accuracy: 0.6911 - 2s/epoch - 17ms/step\n","Epoch 214/300\n","118/118 - 2s - loss: 0.7218 - accuracy: 0.7507 - val_loss: 0.6912 - val_accuracy: 0.7706 - 2s/epoch - 17ms/step\n","Epoch 215/300\n","118/118 - 2s - loss: 0.6483 - accuracy: 0.7863 - val_loss: 0.6740 - val_accuracy: 0.7807 - 2s/epoch - 17ms/step\n","Epoch 216/300\n","118/118 - 2s - loss: 0.6368 - accuracy: 0.7874 - val_loss: 0.6805 - val_accuracy: 0.7649 - 2s/epoch - 17ms/step\n","Epoch 217/300\n","118/118 - 2s - loss: 0.6466 - accuracy: 0.7780 - val_loss: 0.6578 - val_accuracy: 0.7838 - 2s/epoch - 17ms/step\n","Epoch 218/300\n","118/118 - 2s - loss: 0.6460 - accuracy: 0.7811 - val_loss: 0.6747 - val_accuracy: 0.7728 - 2s/epoch - 17ms/step\n","Epoch 219/300\n","118/118 - 2s - loss: 0.7594 - accuracy: 0.7229 - val_loss: 0.7036 - val_accuracy: 0.7437 - 2s/epoch - 17ms/step\n","Epoch 220/300\n","118/118 - 2s - loss: 0.6654 - accuracy: 0.7643 - val_loss: 0.7139 - val_accuracy: 0.7497 - 2s/epoch - 17ms/step\n","Epoch 221/300\n","118/118 - 2s - loss: 0.7407 - accuracy: 0.7274 - val_loss: 0.9070 - val_accuracy: 0.6537 - 2s/epoch - 17ms/step\n","Epoch 222/300\n","118/118 - 2s - loss: 0.7186 - accuracy: 0.7431 - val_loss: 0.7019 - val_accuracy: 0.7672 - 2s/epoch - 17ms/step\n","Epoch 223/300\n","118/118 - 2s - loss: 0.6881 - accuracy: 0.7683 - val_loss: 0.7281 - val_accuracy: 0.7559 - 2s/epoch - 17ms/step\n","Epoch 224/300\n","118/118 - 2s - loss: 0.6746 - accuracy: 0.7727 - val_loss: 0.6842 - val_accuracy: 0.7638 - 2s/epoch - 17ms/step\n","Epoch 225/300\n","118/118 - 2s - loss: 0.6670 - accuracy: 0.7729 - val_loss: 0.6919 - val_accuracy: 0.7598 - 2s/epoch - 17ms/step\n","Epoch 226/300\n","118/118 - 2s - loss: 0.6851 - accuracy: 0.7455 - val_loss: 0.6900 - val_accuracy: 0.7461 - 2s/epoch - 17ms/step\n","Epoch 227/300\n","118/118 - 2s - loss: 0.6756 - accuracy: 0.7557 - val_loss: 0.6587 - val_accuracy: 0.7614 - 2s/epoch - 17ms/step\n","Epoch 228/300\n","118/118 - 2s - loss: 0.6960 - accuracy: 0.7537 - val_loss: 0.6975 - val_accuracy: 0.7463 - 2s/epoch - 17ms/step\n","Epoch 229/300\n","118/118 - 2s - loss: 0.6878 - accuracy: 0.7569 - val_loss: 0.6735 - val_accuracy: 0.7665 - 2s/epoch - 17ms/step\n","Epoch 230/300\n","118/118 - 2s - loss: 0.6473 - accuracy: 0.7704 - val_loss: 0.6815 - val_accuracy: 0.7616 - 2s/epoch - 17ms/step\n","Epoch 231/300\n","118/118 - 2s - loss: 0.6780 - accuracy: 0.7681 - val_loss: 0.6612 - val_accuracy: 0.7779 - 2s/epoch - 17ms/step\n","Epoch 232/300\n","118/118 - 2s - loss: 0.6399 - accuracy: 0.7815 - val_loss: 0.6521 - val_accuracy: 0.7767 - 2s/epoch - 17ms/step\n","Epoch 233/300\n","118/118 - 2s - loss: 0.6391 - accuracy: 0.7778 - val_loss: 0.6663 - val_accuracy: 0.7634 - 2s/epoch - 17ms/step\n","Epoch 234/300\n","118/118 - 2s - loss: 0.6622 - accuracy: 0.7688 - val_loss: 0.6958 - val_accuracy: 0.7412 - 2s/epoch - 17ms/step\n","Epoch 235/300\n","118/118 - 2s - loss: 0.6997 - accuracy: 0.7370 - val_loss: 0.7385 - val_accuracy: 0.7172 - 2s/epoch - 18ms/step\n","Epoch 236/300\n","118/118 - 2s - loss: 0.8571 - accuracy: 0.6780 - val_loss: 0.8182 - val_accuracy: 0.6753 - 2s/epoch - 17ms/step\n","Epoch 237/300\n","118/118 - 2s - loss: 0.7644 - accuracy: 0.7322 - val_loss: 0.7438 - val_accuracy: 0.7569 - 2s/epoch - 17ms/step\n","Epoch 238/300\n","118/118 - 2s - loss: 0.7341 - accuracy: 0.7551 - val_loss: 0.7526 - val_accuracy: 0.7446 - 2s/epoch - 17ms/step\n","Epoch 239/300\n","118/118 - 2s - loss: 0.7221 - accuracy: 0.7628 - val_loss: 0.7242 - val_accuracy: 0.7684 - 2s/epoch - 17ms/step\n","Epoch 240/300\n","118/118 - 2s - loss: 0.7024 - accuracy: 0.7675 - val_loss: 0.7080 - val_accuracy: 0.7687 - 2s/epoch - 17ms/step\n","Epoch 241/300\n","118/118 - 2s - loss: 0.7038 - accuracy: 0.7738 - val_loss: 0.6943 - val_accuracy: 0.7753 - 2s/epoch - 17ms/step\n","Epoch 242/300\n","118/118 - 2s - loss: 0.6798 - accuracy: 0.7747 - val_loss: 0.6861 - val_accuracy: 0.7736 - 2s/epoch - 17ms/step\n","Epoch 243/300\n","118/118 - 2s - loss: 0.6763 - accuracy: 0.7787 - val_loss: 0.7114 - val_accuracy: 0.7687 - 2s/epoch - 17ms/step\n","Epoch 244/300\n","118/118 - 2s - loss: 0.6867 - accuracy: 0.7750 - val_loss: 0.7019 - val_accuracy: 0.7713 - 2s/epoch - 17ms/step\n","Epoch 245/300\n","118/118 - 2s - loss: 0.6738 - accuracy: 0.7821 - val_loss: 0.6811 - val_accuracy: 0.7717 - 2s/epoch - 17ms/step\n","Epoch 246/300\n","118/118 - 2s - loss: 0.6584 - accuracy: 0.7783 - val_loss: 0.6882 - val_accuracy: 0.7717 - 2s/epoch - 17ms/step\n","Epoch 247/300\n","118/118 - 2s - loss: 0.6863 - accuracy: 0.7676 - val_loss: 0.8465 - val_accuracy: 0.6868 - 2s/epoch - 17ms/step\n","Epoch 248/300\n","118/118 - 2s - loss: 0.6784 - accuracy: 0.7708 - val_loss: 0.7033 - val_accuracy: 0.7543 - 2s/epoch - 17ms/step\n","Epoch 249/300\n","118/118 - 2s - loss: 0.7061 - accuracy: 0.7528 - val_loss: 0.7526 - val_accuracy: 0.7239 - 2s/epoch - 17ms/step\n","Epoch 250/300\n","118/118 - 2s - loss: 0.6929 - accuracy: 0.7573 - val_loss: 0.7466 - val_accuracy: 0.7426 - 2s/epoch - 17ms/step\n","Epoch 251/300\n","118/118 - 2s - loss: 0.7173 - accuracy: 0.7504 - val_loss: 0.7594 - val_accuracy: 0.7306 - 2s/epoch - 18ms/step\n","Epoch 252/300\n","118/118 - 3s - loss: 0.6694 - accuracy: 0.7668 - val_loss: 0.7028 - val_accuracy: 0.7635 - 3s/epoch - 23ms/step\n","Epoch 253/300\n","118/118 - 2s - loss: 0.6468 - accuracy: 0.7777 - val_loss: 0.6864 - val_accuracy: 0.7604 - 2s/epoch - 17ms/step\n","Epoch 254/300\n","118/118 - 2s - loss: 0.6542 - accuracy: 0.7663 - val_loss: 0.6449 - val_accuracy: 0.7628 - 2s/epoch - 17ms/step\n","Epoch 255/300\n","118/118 - 2s - loss: 0.6273 - accuracy: 0.7816 - val_loss: 0.6798 - val_accuracy: 0.7497 - 2s/epoch - 17ms/step\n","Epoch 256/300\n","118/118 - 2s - loss: 0.6250 - accuracy: 0.7782 - val_loss: 0.6386 - val_accuracy: 0.7733 - 2s/epoch - 17ms/step\n","Epoch 257/300\n","118/118 - 2s - loss: 0.6388 - accuracy: 0.7757 - val_loss: 0.6905 - val_accuracy: 0.7546 - 2s/epoch - 17ms/step\n","Epoch 258/300\n","118/118 - 2s - loss: 0.7061 - accuracy: 0.7526 - val_loss: 0.6897 - val_accuracy: 0.7714 - 2s/epoch - 17ms/step\n","Epoch 259/300\n","118/118 - 2s - loss: 0.6447 - accuracy: 0.7749 - val_loss: 0.7009 - val_accuracy: 0.7676 - 2s/epoch - 17ms/step\n","Epoch 260/300\n","118/118 - 2s - loss: 0.6401 - accuracy: 0.7761 - val_loss: 0.6580 - val_accuracy: 0.7627 - 2s/epoch - 17ms/step\n","Epoch 261/300\n","118/118 - 2s - loss: 0.6505 - accuracy: 0.7617 - val_loss: 0.7187 - val_accuracy: 0.7261 - 2s/epoch - 17ms/step\n","Epoch 262/300\n","118/118 - 2s - loss: 0.7594 - accuracy: 0.7078 - val_loss: 0.7626 - val_accuracy: 0.7050 - 2s/epoch - 17ms/step\n","Epoch 263/300\n","118/118 - 2s - loss: 0.7213 - accuracy: 0.7324 - val_loss: 0.7992 - val_accuracy: 0.7082 - 2s/epoch - 17ms/step\n","Epoch 264/300\n","118/118 - 2s - loss: 0.8488 - accuracy: 0.6964 - val_loss: 0.9116 - val_accuracy: 0.6808 - 2s/epoch - 17ms/step\n","Epoch 265/300\n","118/118 - 2s - loss: 0.8312 - accuracy: 0.6921 - val_loss: 0.8034 - val_accuracy: 0.6893 - 2s/epoch - 17ms/step\n","Epoch 266/300\n","118/118 - 2s - loss: 0.7711 - accuracy: 0.7096 - val_loss: 0.7854 - val_accuracy: 0.6923 - 2s/epoch - 17ms/step\n","Epoch 267/300\n","118/118 - 2s - loss: 0.7441 - accuracy: 0.7130 - val_loss: 0.7559 - val_accuracy: 0.7089 - 2s/epoch - 17ms/step\n","Epoch 268/300\n","118/118 - 2s - loss: 0.7861 - accuracy: 0.6948 - val_loss: 0.7638 - val_accuracy: 0.7064 - 2s/epoch - 17ms/step\n","Epoch 269/300\n","118/118 - 2s - loss: 0.7512 - accuracy: 0.7069 - val_loss: 0.7830 - val_accuracy: 0.7085 - 2s/epoch - 17ms/step\n","Epoch 270/300\n","118/118 - 2s - loss: 0.7590 - accuracy: 0.7086 - val_loss: 0.7988 - val_accuracy: 0.6983 - 2s/epoch - 17ms/step\n","Epoch 271/300\n","118/118 - 2s - loss: 0.7420 - accuracy: 0.7060 - val_loss: 0.7545 - val_accuracy: 0.7063 - 2s/epoch - 17ms/step\n","Epoch 272/300\n","118/118 - 2s - loss: 0.8050 - accuracy: 0.6821 - val_loss: 0.8014 - val_accuracy: 0.6754 - 2s/epoch - 17ms/step\n","Epoch 273/300\n","118/118 - 2s - loss: 0.7189 - accuracy: 0.7232 - val_loss: 0.7512 - val_accuracy: 0.7087 - 2s/epoch - 17ms/step\n","Epoch 274/300\n","118/118 - 2s - loss: 0.8446 - accuracy: 0.6664 - val_loss: 1.0004 - val_accuracy: 0.6681 - 2s/epoch - 17ms/step\n","Epoch 275/300\n","118/118 - 2s - loss: 0.9331 - accuracy: 0.6610 - val_loss: 0.9199 - val_accuracy: 0.6792 - 2s/epoch - 17ms/step\n","Epoch 276/300\n","118/118 - 2s - loss: 0.8911 - accuracy: 0.6794 - val_loss: 0.8776 - val_accuracy: 0.6815 - 2s/epoch - 17ms/step\n","Epoch 277/300\n","118/118 - 2s - loss: 0.8375 - accuracy: 0.6929 - val_loss: 0.8434 - val_accuracy: 0.7074 - 2s/epoch - 17ms/step\n","Epoch 278/300\n","118/118 - 2s - loss: 0.7995 - accuracy: 0.7113 - val_loss: 0.8039 - val_accuracy: 0.7026 - 2s/epoch - 17ms/step\n","Epoch 279/300\n","118/118 - 2s - loss: 0.7724 - accuracy: 0.7179 - val_loss: 0.7880 - val_accuracy: 0.7142 - 2s/epoch - 17ms/step\n","Epoch 280/300\n","118/118 - 2s - loss: 0.7647 - accuracy: 0.7151 - val_loss: 0.7655 - val_accuracy: 0.7138 - 2s/epoch - 17ms/step\n","Epoch 281/300\n","118/118 - 2s - loss: 0.7455 - accuracy: 0.7204 - val_loss: 0.7519 - val_accuracy: 0.7162 - 2s/epoch - 17ms/step\n","Epoch 282/300\n","118/118 - 2s - loss: 0.7357 - accuracy: 0.7167 - val_loss: 0.7513 - val_accuracy: 0.7031 - 2s/epoch - 17ms/step\n","Epoch 283/300\n","118/118 - 2s - loss: 0.7327 - accuracy: 0.7213 - val_loss: 0.7476 - val_accuracy: 0.7164 - 2s/epoch - 17ms/step\n","Epoch 284/300\n","118/118 - 2s - loss: 0.7247 - accuracy: 0.7213 - val_loss: 0.7387 - val_accuracy: 0.7170 - 2s/epoch - 17ms/step\n","Epoch 285/300\n","118/118 - 2s - loss: 0.7216 - accuracy: 0.7242 - val_loss: 0.7429 - val_accuracy: 0.7195 - 2s/epoch - 17ms/step\n","Epoch 286/300\n","118/118 - 2s - loss: 0.7092 - accuracy: 0.7265 - val_loss: 0.7588 - val_accuracy: 0.7075 - 2s/epoch - 17ms/step\n","Epoch 287/300\n","118/118 - 2s - loss: 0.7369 - accuracy: 0.7101 - val_loss: 0.7341 - val_accuracy: 0.7032 - 2s/epoch - 17ms/step\n","Epoch 288/300\n","118/118 - 2s - loss: 0.7130 - accuracy: 0.7209 - val_loss: 0.7343 - val_accuracy: 0.7186 - 2s/epoch - 17ms/step\n","Epoch 289/300\n","118/118 - 2s - loss: 0.7310 - accuracy: 0.7196 - val_loss: 0.7701 - val_accuracy: 0.7101 - 2s/epoch - 17ms/step\n","Epoch 290/300\n","118/118 - 2s - loss: 0.7593 - accuracy: 0.7034 - val_loss: 0.7472 - val_accuracy: 0.7161 - 2s/epoch - 17ms/step\n","Epoch 291/300\n","118/118 - 2s - loss: 0.7195 - accuracy: 0.7255 - val_loss: 0.7743 - val_accuracy: 0.7033 - 2s/epoch - 17ms/step\n","Epoch 292/300\n","118/118 - 2s - loss: 0.7348 - accuracy: 0.7174 - val_loss: 0.7533 - val_accuracy: 0.7167 - 2s/epoch - 17ms/step\n","Epoch 293/300\n","118/118 - 2s - loss: 0.7238 - accuracy: 0.7218 - val_loss: 0.7417 - val_accuracy: 0.7170 - 2s/epoch - 17ms/step\n","Epoch 294/300\n","118/118 - 2s - loss: 0.7206 - accuracy: 0.7219 - val_loss: 0.7509 - val_accuracy: 0.7172 - 2s/epoch - 17ms/step\n","Epoch 295/300\n","118/118 - 2s - loss: 0.7138 - accuracy: 0.7226 - val_loss: 0.7380 - val_accuracy: 0.7192 - 2s/epoch - 17ms/step\n","Epoch 296/300\n","118/118 - 2s - loss: 0.7078 - accuracy: 0.7218 - val_loss: 0.7224 - val_accuracy: 0.7196 - 2s/epoch - 17ms/step\n","Epoch 297/300\n","118/118 - 2s - loss: 0.7024 - accuracy: 0.7223 - val_loss: 0.7284 - val_accuracy: 0.7168 - 2s/epoch - 17ms/step\n","Epoch 298/300\n","118/118 - 2s - loss: 0.7080 - accuracy: 0.7208 - val_loss: 0.7326 - val_accuracy: 0.7195 - 2s/epoch - 17ms/step\n","Epoch 299/300\n","118/118 - 2s - loss: 0.7035 - accuracy: 0.7165 - val_loss: 0.7252 - val_accuracy: 0.7176 - 2s/epoch - 17ms/step\n","Epoch 300/300\n","118/118 - 2s - loss: 0.7106 - accuracy: 0.7162 - val_loss: 0.7363 - val_accuracy: 0.7020 - 2s/epoch - 17ms/step\n"]}],"source":["# solution 3: Better optimizer\n","initializer = tf.keras.initializers.RandomNormal()\n","\n","# create model\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","#################### YOUR CODE HERE ####################\n","# Các bạn thực hiện code để khi model compile sẽ \n","# dùng optimizer là Adam\n","model.compile(optimizer=tf.keras.optimizers.Adam(),\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":3,"id":"4da831e7","metadata":{"id":"4da831e7","executionInfo":{"status":"ok","timestamp":1669893040387,"user_tz":-420,"elapsed":14,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":4,"id":"248dd07e","metadata":{"id":"248dd07e","outputId":"b72d5114-3030-4871-88b9-58222a0bcd50","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1669893040387,"user_tz":-420,"elapsed":12,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fd3d8050760>"]},"metadata":{},"execution_count":4},{"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":4,"id":"8e35d7eb","metadata":{"id":"8e35d7eb","executionInfo":{"status":"ok","timestamp":1669893040388,"user_tz":-420,"elapsed":10,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":5,"id":"4b997d5b","metadata":{"id":"4b997d5b","outputId":"f5caf9e1-454e-473a-b06b-d0895e8d08e1","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1669893041142,"user_tz":-420,"elapsed":764,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fd3d7ffebe0>"]},"metadata":{},"execution_count":5},{"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['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()"]},{"cell_type":"code","execution_count":5,"id":"8623181e","metadata":{"id":"8623181e","executionInfo":{"status":"ok","timestamp":1669893041142,"user_tz":-420,"elapsed":8,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]},{"cell_type":"code","execution_count":5,"id":"c84dafda","metadata":{"id":"c84dafda","executionInfo":{"status":"ok","timestamp":1669893041143,"user_tz":-420,"elapsed":8,"user":{"displayName":"Thanh Hùng","userId":"08335145449228425002"}}},"outputs":[],"source":[]}],"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":false},"gpuClass":"standard"},"nbformat":4,"nbformat_minor":5}