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[==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9932 - loss: 0.0165\n", "Epoch 175/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1168\n", "Epoch 176/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0812\n", "Epoch 177/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9816\n", + "\u001b[1m325/325\u001b[0m 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0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0596\n", "Epoch 185/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9856 - loss: 0.0533\n", "Epoch 186/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9877 - loss: 0.0890\n", "Epoch 187/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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[==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0768\n", "Epoch 192/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0569\n", "Epoch 193/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9924 - loss: 0.0350\n", "Epoch 194/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9822\n", + "\u001b[1m325/325\u001b[0m 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\u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0618\n", "Epoch 205/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0463 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0480\n", "Epoch 206/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0667\n", "Epoch 207/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0741 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9865 - loss: 0.0468\n", "Epoch 208/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0559 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0379\n", "Epoch 209/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0237\n", "Epoch 210/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0247\n", "Epoch 211/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0800\n", "Epoch 212/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0913\n", "Epoch 213/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0268\n", "Epoch 214/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0606 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9950 - loss: 0.0224\n", "Epoch 215/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0853\n", "Epoch 216/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0723 - accuracy: 0.9777\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0475\n", "Epoch 217/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0626\n", "Epoch 218/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0748 - accuracy: 0.9809\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0343\n", "Epoch 219/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0497\n", "Epoch 220/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0674 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0257\n", "Epoch 221/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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[==============================] - 1s 3ms/step - loss: 0.0472 - accuracy: 0.9847\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0523\n", "Epoch 226/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9847\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0175\n", "Epoch 227/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.0624\n", "Epoch 228/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0698 - accuracy: 0.9828\n", + "\u001b[1m325/325\u001b[0m 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[==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0328\n", "Epoch 243/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0214\n", "Epoch 244/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9826 - loss: 0.0636\n", "Epoch 245/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9898 - loss: 0.0216\n", "Epoch 246/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0660\n", "Epoch 247/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0356\n", "Epoch 248/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0553 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 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0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9806 - loss: 0.1203\n", "Epoch 253/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0670\n", "Epoch 254/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0278\n", "Epoch 255/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0645 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0302\n", "Epoch 256/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0429\n", "Epoch 257/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0280\n", "Epoch 258/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0426\n", "Epoch 259/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0653\n", "Epoch 260/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0610 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0462\n", "Epoch 261/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0366 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0592\n", "Epoch 262/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m 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\u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0592\n", "Epoch 290/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0266\n", "Epoch 291/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0394 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0428\n", "Epoch 292/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0495\n", "Epoch 293/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0554 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0449\n", "Epoch 294/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0884 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0592\n", "Epoch 295/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0433\n", "Epoch 296/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0508 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m 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- loss: 0.0627\n", "Epoch 300/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0524 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0190\n", "Epoch 301/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0460 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0931\n", "Epoch 302/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0441 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9829 - loss: 0.0692\n", "Epoch 303/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0212 - accuracy: 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\u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9793 - loss: 0.0820\n", "Epoch 307/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9917 - loss: 0.0270\n", "Epoch 308/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9942 - loss: 0.0189\n", "Epoch 309/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9962 - loss: 0.0088\n", "Epoch 310/500\n", - "394/394 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- loss: 0.0396\n", "Epoch 317/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0328 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0535\n", "Epoch 318/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0477 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9931 - loss: 0.0408\n", "Epoch 319/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0658\n", "Epoch 320/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0295 - accuracy: 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\u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9855 - loss: 0.0484\n", "Epoch 324/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0400\n", "Epoch 325/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0526\n", "Epoch 326/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9801 - loss: 0.0741\n", "Epoch 327/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0256\n", "Epoch 328/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0247\n", "Epoch 329/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0142\n", "Epoch 330/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0422 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0523\n", "Epoch 331/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9911 - loss: 0.0438\n", "Epoch 332/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9948 - loss: 0.0277\n", "Epoch 333/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0409 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0081\n", "Epoch 334/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0370\n", "Epoch 335/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0421\n", "Epoch 336/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0550\n", "Epoch 337/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9904 - loss: 0.0579\n", "Epoch 338/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0451\n", "Epoch 339/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0334\n", "Epoch 340/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9819 - loss: 0.0630\n", "Epoch 341/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0384\n", "Epoch 342/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0580\n", "Epoch 343/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0355\n", "Epoch 344/500\n", - "394/394 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[==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0577\n", "Epoch 362/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0313 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0532\n", "Epoch 363/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9816 - loss: 0.1538\n", "Epoch 364/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m 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0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9949 - loss: 0.0194\n", "Epoch 372/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0262\n", "Epoch 373/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0287\n", "Epoch 374/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0228\n", "Epoch 375/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0169\n", "Epoch 376/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0326\n", "Epoch 377/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0493\n", "Epoch 378/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9905 - loss: 0.0338\n", "Epoch 379/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0298 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9899 - loss: 0.0261\n", "Epoch 380/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9879 - loss: 0.0555\n", "Epoch 381/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m 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0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9864 - loss: 0.0482\n", "Epoch 389/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9894 - loss: 0.0449\n", "Epoch 390/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0171 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 0.0191\n", "Epoch 391/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0196 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9978 - loss: 0.0086\n", "Epoch 392/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0364 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9946 - loss: 0.0604\n", "Epoch 393/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0273 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9826 - loss: 0.0802\n", "Epoch 394/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0492 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9929 - loss: 0.0291\n", "Epoch 395/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 0.0257\n", "Epoch 396/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9941 - loss: 0.0271\n", "Epoch 397/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0630 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9897 - loss: 0.0585\n", "Epoch 398/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9913 - loss: 0.0463\n", "Epoch 399/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0210 - accuracy: 0.9968\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9957 - loss: 0.0137\n", "Epoch 400/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 0.0102\n", "Epoch 401/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9945 - loss: 0.0195\n", "Epoch 402/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0181\n", "Epoch 403/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0232 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0346\n", "Epoch 404/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9937 - loss: 0.0275\n", "Epoch 405/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0329 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9949 - loss: 0.0469\n", "Epoch 406/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0199 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9792 - loss: 0.0668\n", "Epoch 407/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0474 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9903 - loss: 0.0355\n", "Epoch 408/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0154 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - accuracy: 0.9934 - loss: 0.0222\n", "Epoch 409/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9951 - loss: 0.0323\n", "Epoch 410/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9952 - loss: 0.0245\n", "Epoch 411/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0434 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0478\n", "Epoch 412/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0102 - accuracy: 0.9968\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9977 - loss: 0.0135\n", "Epoch 413/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0151 - accuracy: 0.9968\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0486\n", "Epoch 414/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9945 - loss: 0.0316\n", "Epoch 415/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0177 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0134\n", "Epoch 416/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0182 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0336\n", "Epoch 417/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0548 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9934 - loss: 0.0210\n", "Epoch 418/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.0182\n", "Epoch 419/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9975\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0168\n", "Epoch 420/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0466 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0143\n", "Epoch 421/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0328\n", "Epoch 422/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0214\n", "Epoch 423/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0202\n", "Epoch 424/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0385 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0375\n", "Epoch 425/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0403 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0233\n", "Epoch 426/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0312 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0286\n", "Epoch 427/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0357 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0736\n", "Epoch 428/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0462\n", "Epoch 429/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0247 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9840 - loss: 0.0531\n", "Epoch 430/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0550 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0302\n", "Epoch 431/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0380 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0687\n", "Epoch 432/500\n", - "394/394 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- loss: 0.0235\n", "Epoch 439/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0118 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9868 - loss: 0.0337\n", "Epoch 440/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0301\n", "Epoch 441/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0258\n", "Epoch 442/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0333 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0189\n", "Epoch 443/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9914 - loss: 0.0170\n", "Epoch 444/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0611 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0296\n", "Epoch 445/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0274\n", "Epoch 446/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9902 - loss: 0.0442\n", "Epoch 447/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0442\n", "Epoch 448/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0290\n", "Epoch 449/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0272 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0232\n", "Epoch 450/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0285 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9957 - loss: 0.0115\n", "Epoch 451/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0913\n", "Epoch 452/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0159 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9942 - loss: 0.0357\n", "Epoch 453/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0502 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0156\n", "Epoch 454/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0515\n", "Epoch 455/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9909 - loss: 0.0351\n", "Epoch 456/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0302\n", "Epoch 457/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0246 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0288\n", "Epoch 458/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0316 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0135\n", "Epoch 459/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0352\n", "Epoch 460/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0196 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0390\n", "Epoch 461/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0203 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0517\n", "Epoch 462/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9797 - loss: 0.0902\n", "Epoch 463/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0520 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9912 - loss: 0.0169\n", "Epoch 464/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0224\n", "Epoch 465/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0351 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9939 - loss: 0.0285\n", "Epoch 466/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0489\n", "Epoch 467/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0185 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9931 - loss: 0.0314\n", "Epoch 468/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.0072\n", "Epoch 469/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0194 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m 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- loss: 0.0234\n", "Epoch 473/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0561\n", "Epoch 474/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0377\n", "Epoch 475/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0083\n", "Epoch 476/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0157 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0137\n", "Epoch 477/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0569\n", "Epoch 478/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0339 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0388\n", "Epoch 479/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9875 - loss: 0.0265\n", "Epoch 480/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9943 - loss: 0.0349\n", "Epoch 481/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0479\n", "Epoch 482/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0192 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0214\n", "Epoch 483/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0289 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0361\n", "Epoch 484/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0395 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0253\n", "Epoch 485/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0155 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0188\n", "Epoch 486/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9971 - loss: 0.0069\n", "Epoch 487/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0114 - accuracy: 0.9968\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0189\n", "Epoch 488/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0121 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0624\n", "Epoch 489/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0211 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0221\n", "Epoch 490/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0374 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0097\n", "Epoch 491/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0250 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0315\n", "Epoch 492/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0326 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0207\n", "Epoch 493/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0615\n", "Epoch 494/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0141\n", "Epoch 495/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0136 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0557\n", "Epoch 496/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0229\n", "Epoch 497/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9878 - loss: 0.0459\n", "Epoch 498/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9938 - loss: 0.0249\n", "Epoch 499/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0274 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0390\n", "Epoch 500/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9892\n" + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0116\n" ] } ], @@ -1279,7 +1370,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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Model: \"functional_1\"\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┓\n", + "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n", + "┡â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┩\n", + "│ input_layer (InputLayer) │ (None, 32, 20, 3) │ 0 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization │ (None, 32, 20, 3) │ 12 │\n", + "│ (BatchNormalization) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d (Conv2D) │ (None, 32, 20, 32) │ 896 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d (MaxPooling2D) │ (None, 16, 10, 32) │ 0 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_1 (Conv2D) │ (None, 16, 10, 32) │ 9,248 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_1 (MaxPooling2D) │ (None, 8, 5, 32) │ 0 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_2 (Conv2D) │ (None, 8, 5, 32) │ 9,248 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_2 (MaxPooling2D) │ (None, 4, 2, 32) │ 0 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten (Flatten) │ (None, 256) │ 0 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (Dense) │ (None, 256) │ 65,792 │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (Dense) │ (None, 11) │ 2,827 │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n", + "\n" + ], + "text/plain": [ + "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┩\n", + "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m12\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m65,792\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m2,827\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Do not pass these arguments to `fit()`, as they will be ignored.\n", + " self._warn_if_super_not_called()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2822 - loss: 2.2734\n", "Epoch 2/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 1.4881 - accuracy: 0.5226\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.3882 - loss: 1.8318\n", "Epoch 3/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.9700 - accuracy: 0.6910\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5754 - loss: 1.2654\n", "Epoch 4/500\n", - "394/394 [==============================] - 1s 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\u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9856 - loss: 0.0552\n", "Epoch 171/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0709 - accuracy: 0.9822\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0404\n", "Epoch 172/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0538 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0462\n", "Epoch 173/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0685 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0502\n", "Epoch 174/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9932 - loss: 0.0165\n", "Epoch 175/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1168\n", "Epoch 176/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0812\n", "Epoch 177/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9816\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0938\n", "Epoch 178/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0511 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0394\n", "Epoch 179/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9809\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0569\n", "Epoch 180/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0591 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0472\n", "Epoch 181/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0783\n", "Epoch 182/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0449 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0448\n", "Epoch 183/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9828\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9819 - loss: 0.0579\n", "Epoch 184/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0596\n", "Epoch 185/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9856 - loss: 0.0533\n", "Epoch 186/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9877 - loss: 0.0890\n", "Epoch 187/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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0.9809\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0343\n", "Epoch 219/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0497\n", "Epoch 220/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0674 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0257\n", "Epoch 221/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9953 - loss: 0.0221\n", "Epoch 222/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9825 - loss: 0.0648\n", "Epoch 223/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 0.0262\n", "Epoch 224/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9875 - loss: 0.0401\n", "Epoch 225/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0472 - accuracy: 0.9847\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0523\n", "Epoch 226/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9847\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0175\n", "Epoch 227/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.0624\n", "Epoch 228/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0698 - accuracy: 0.9828\n", + "\u001b[1m325/325\u001b[0m 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0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0248\n", "Epoch 236/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0817 - accuracy: 0.9803\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9876 - loss: 0.0304\n", "Epoch 237/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9879 - loss: 0.0423\n", "Epoch 238/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9869 - loss: 0.0492\n", "Epoch 239/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.0580\n", "Epoch 240/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0475 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0473\n", "Epoch 241/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0234 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0531\n", "Epoch 242/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0328\n", "Epoch 243/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0214\n", "Epoch 244/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9826 - loss: 0.0636\n", "Epoch 245/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9898 - loss: 0.0216\n", "Epoch 246/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0660\n", "Epoch 247/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0356\n", "Epoch 248/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0553 - accuracy: 0.9841\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0256\n", "Epoch 249/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0447 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0290\n", "Epoch 250/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0438\n", "Epoch 251/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9847\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9896 - loss: 0.0583\n", "Epoch 252/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9806 - loss: 0.1203\n", "Epoch 253/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0670\n", "Epoch 254/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0278\n", "Epoch 255/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0645 - accuracy: 0.9835\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0302\n", "Epoch 256/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0429\n", "Epoch 257/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0280\n", "Epoch 258/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0426\n", "Epoch 259/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0653\n", "Epoch 260/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0610 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0462\n", "Epoch 261/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0366 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0592\n", "Epoch 262/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9860\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9807 - loss: 0.0897\n", "Epoch 263/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0245\n", "Epoch 264/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0552\n", "Epoch 265/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0201\n", "Epoch 266/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0557\n", "Epoch 267/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9822\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0496\n", "Epoch 268/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0536\n", "Epoch 269/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0695 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0252\n", "Epoch 270/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0325 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9882 - loss: 0.0514\n", "Epoch 271/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0353 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0374\n", "Epoch 272/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9797\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0146\n", "Epoch 273/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.1504\n", "Epoch 274/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0313\n", "Epoch 275/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0284 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9897 - loss: 0.0372\n", "Epoch 276/500\n", - "394/394 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0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9852 - loss: 0.0356\n", "Epoch 304/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0393\n", "Epoch 305/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0221 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9847 - loss: 0.0548\n", "Epoch 306/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0631 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9793 - loss: 0.0820\n", "Epoch 307/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9917 - loss: 0.0270\n", "Epoch 308/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9866\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9942 - loss: 0.0189\n", "Epoch 309/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9962 - loss: 0.0088\n", "Epoch 310/500\n", - "394/394 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- loss: 0.0081\n", "Epoch 334/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0370\n", "Epoch 335/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0421\n", "Epoch 336/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0550\n", "Epoch 337/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9904 - loss: 0.0579\n", "Epoch 338/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0451\n", "Epoch 339/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0334\n", "Epoch 340/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9819 - loss: 0.0630\n", "Epoch 341/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0384\n", "Epoch 342/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0580\n", "Epoch 343/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0355\n", "Epoch 344/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0194\n", "Epoch 345/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0331 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0323\n", "Epoch 346/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0267\n", "Epoch 347/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0348 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0634\n", "Epoch 348/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9873\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0474\n", "Epoch 349/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0292 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9900 - loss: 0.0492\n", "Epoch 350/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 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0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0662\n", "Epoch 355/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0613 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0387\n", "Epoch 356/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9853 - loss: 0.0491\n", "Epoch 357/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0347 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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[==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0577\n", "Epoch 362/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0313 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0532\n", "Epoch 363/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9816 - loss: 0.1538\n", "Epoch 364/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9897 - loss: 0.0727\n", "Epoch 365/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0249\n", "Epoch 366/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0165 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0523\n", "Epoch 367/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9969 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\u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0228\n", "Epoch 375/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0169\n", "Epoch 376/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0326\n", "Epoch 377/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0493\n", "Epoch 378/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9905 - loss: 0.0338\n", "Epoch 379/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0298 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9899 - loss: 0.0261\n", "Epoch 380/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9879 - loss: 0.0555\n", "Epoch 381/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0290\n", "Epoch 382/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0327 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9932 - loss: 0.0283\n", "Epoch 383/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0218 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0360\n", "Epoch 384/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0386 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0545\n", "Epoch 385/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0397\n", "Epoch 386/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0222\n", "Epoch 387/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9879\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0194\n", "Epoch 388/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0252 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9864 - loss: 0.0482\n", "Epoch 389/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9894 - loss: 0.0449\n", "Epoch 390/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0171 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 0.0191\n", "Epoch 391/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0196 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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[==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 0.0257\n", "Epoch 396/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9941 - loss: 0.0271\n", "Epoch 397/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0630 - accuracy: 0.9854\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9897 - loss: 0.0585\n", "Epoch 398/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m 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\u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.0182\n", "Epoch 419/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9975\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0168\n", "Epoch 420/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0466 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0143\n", "Epoch 421/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0328\n", "Epoch 422/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0214\n", "Epoch 423/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9917\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0202\n", "Epoch 424/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0385 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0375\n", "Epoch 425/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0403 - accuracy: 0.9905\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0233\n", "Epoch 426/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0312 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0286\n", "Epoch 427/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0357 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0736\n", "Epoch 428/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0462\n", "Epoch 429/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0247 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9840 - loss: 0.0531\n", "Epoch 430/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0550 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0302\n", "Epoch 431/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0380 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0687\n", "Epoch 432/500\n", - "394/394 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0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0352\n", "Epoch 460/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0196 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0390\n", "Epoch 461/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0203 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0517\n", "Epoch 462/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m 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- loss: 0.0234\n", "Epoch 473/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0561\n", "Epoch 474/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9892\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0377\n", "Epoch 475/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0083\n", "Epoch 476/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0157 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0137\n", "Epoch 477/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0569\n", "Epoch 478/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0339 - accuracy: 0.9924\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0388\n", "Epoch 479/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9875 - loss: 0.0265\n", "Epoch 480/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9943 - loss: 0.0349\n", "Epoch 481/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0479\n", "Epoch 482/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0192 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0214\n", "Epoch 483/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0289 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0361\n", "Epoch 484/500\n", - "394/394 [==============================] - 2s 4ms/step - loss: 0.0395 - accuracy: 0.9886\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0253\n", "Epoch 485/500\n", - "394/394 [==============================] - 1s 4ms/step - loss: 0.0155 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0188\n", "Epoch 486/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9971 - loss: 0.0069\n", "Epoch 487/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0114 - accuracy: 0.9968\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0189\n", "Epoch 488/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0121 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0624\n", "Epoch 489/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0211 - accuracy: 0.9962\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0221\n", "Epoch 490/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0374 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0097\n", "Epoch 491/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0250 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0315\n", "Epoch 492/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0326 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0207\n", "Epoch 493/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0615\n", "Epoch 494/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0141\n", "Epoch 495/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0136 - accuracy: 0.9955\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0557\n", "Epoch 496/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9930\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0229\n", "Epoch 497/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9949\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9878 - loss: 0.0459\n", "Epoch 498/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9898\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9938 - loss: 0.0249\n", "Epoch 499/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0274 - accuracy: 0.9911\n", + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0390\n", "Epoch 500/500\n", - "394/394 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9892\n" + "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0116\n" ] } ], @@ -1279,7 +1370,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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