diff --git a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb b/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb index 59ec5787..9577537c 100644 --- a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb +++ b/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb @@ -24,7 +24,7 @@ "source": [ "########### Basic Parameters for Running: ################################\n", " \n", - "TFlite_Version = \"1700\" \n", + "TFlite_Version = \"1701\" \n", "TFlite_MainType = \"dig-class11\"\n", "TFlite_Size = \"s2\"\n", "Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n", @@ -82,8 +82,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1554, 32, 20, 3)\n", - "(1554, 11)\n" + "(1573, 32, 20, 3)\n", + "(1573, 11)\n" ] } ], @@ -238,1382 +238,1005 @@ "output_type": "stream", "text": [ "Epoch 1/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 2.1558 - accuracy: 0.3224\n", + "394/394 [==============================] - 1s 3ms/step - loss: 2.1516 - accuracy: 0.3223\n", "Epoch 2/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 1.5740 - accuracy: 0.4562\n", + "394/394 [==============================] - 1s 3ms/step - loss: 1.4881 - accuracy: 0.5226\n", "Epoch 3/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 1.0740 - accuracy: 0.6634\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.9700 - accuracy: 0.6910\n", "Epoch 4/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.8498 - accuracy: 0.7265\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.8070 - accuracy: 0.7451\n", "Epoch 5/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.6561 - accuracy: 0.7928\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.6602 - accuracy: 0.7991\n", "Epoch 6/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.6543 - accuracy: 0.8063\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.5955 - accuracy: 0.8258\n", "Epoch 7/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.4950 - accuracy: 0.8571\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.5233 - accuracy: 0.8474\n", "Epoch 8/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.5020 - accuracy: 0.8417\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.4445 - accuracy: 0.8652\n", "Epoch 9/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.4557 - accuracy: 0.8655\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.4406 - accuracy: 0.8735\n", "Epoch 10/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.4191 - accuracy: 0.8848\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.4012 - accuracy: 0.8887\n", "Epoch 11/500\n", - 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0.3208 - accuracy: 0.9027\n", "Epoch 16/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.3275 - accuracy: 0.9060\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.9256\n", "Epoch 17/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.3092 - accuracy: 0.9131\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2488 - accuracy: 0.9186\n", "Epoch 18/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2600 - accuracy: 0.9311\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2749 - accuracy: 0.9199\n", "Epoch 19/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2793 - accuracy: 0.9157\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2611 - accuracy: 0.9193\n", "Epoch 20/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2562 - accuracy: 0.9234\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2224 - accuracy: 0.9193\n", "Epoch 21/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2564 - accuracy: 0.9305\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2185 - accuracy: 0.9421\n", "Epoch 22/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1953 - accuracy: 0.9414\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2420 - accuracy: 0.9224\n", "Epoch 23/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1991 - accuracy: 0.9376\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2117 - accuracy: 0.9307\n", "Epoch 24/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2127 - accuracy: 0.9382\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2134 - accuracy: 0.9358\n", "Epoch 25/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2177 - accuracy: 0.9453\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2233 - accuracy: 0.9402\n", "Epoch 26/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2221 - accuracy: 0.9408\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2151 - accuracy: 0.9307\n", "Epoch 27/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1757 - accuracy: 0.9524\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.2009 - accuracy: 0.9421\n", "Epoch 28/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2054 - accuracy: 0.9421\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1893 - accuracy: 0.9390\n", "Epoch 29/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.2037 - accuracy: 0.9472\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1961 - accuracy: 0.9415\n", "Epoch 30/500\n", - 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[==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9593\n", "Epoch 40/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1590 - accuracy: 0.9556\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1369 - accuracy: 0.9631\n", "Epoch 41/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1736 - accuracy: 0.9466\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9574\n", "Epoch 42/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1444 - accuracy: 0.9633\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1526 - accuracy: 0.9574\n", "Epoch 43/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1304 - accuracy: 0.9653\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1262 - accuracy: 0.9650\n", "Epoch 44/500\n", - "389/389 [==============================] - 1s 3ms/step 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[==============================] - 1s 3ms/step - loss: 0.0860 - accuracy: 0.9695\n", "Epoch 59/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1235 - accuracy: 0.9659\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1186 - accuracy: 0.9701\n", "Epoch 60/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1100 - accuracy: 0.9653\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1105 - accuracy: 0.9638\n", "Epoch 61/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1245 - accuracy: 0.9665\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1149 - accuracy: 0.9733\n", "Epoch 62/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1234 - accuracy: 0.9672\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1056 - accuracy: 0.9727\n", "Epoch 63/500\n", - "389/389 [==============================] - 1s 3ms/step 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3ms/step - loss: 0.1301 - accuracy: 0.9723\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0688 - accuracy: 0.9771\n", "Epoch 102/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1086 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0929 - accuracy: 0.9739\n", "Epoch 103/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0853 - accuracy: 0.9762\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0740 - accuracy: 0.9828\n", "Epoch 104/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0661 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0888 - accuracy: 0.9777\n", "Epoch 105/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n", "Epoch 106/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0902 - accuracy: 0.9762\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0800 - accuracy: 0.9809\n", "Epoch 107/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0649 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0834 - accuracy: 0.9790\n", "Epoch 108/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0907 - accuracy: 0.9730\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n", "Epoch 109/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1153 - accuracy: 0.9755\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9790\n", "Epoch 110/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1236 - accuracy: 0.9749\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0701 - accuracy: 0.9822\n", "Epoch 111/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0928 - accuracy: 0.9710\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9797\n", "Epoch 112/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1099 - accuracy: 0.9736\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0934 - accuracy: 0.9733\n", "Epoch 113/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0422 - accuracy: 0.9917\n", "Epoch 114/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0711 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1159 - accuracy: 0.9765\n", "Epoch 115/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0684 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9822\n", "Epoch 116/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0644 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1053 - accuracy: 0.9752\n", "Epoch 117/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0667 - accuracy: 0.9794\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0595 - accuracy: 0.9828\n", "Epoch 118/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0829 - accuracy: 0.9775\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1004 - accuracy: 0.9688\n", "Epoch 119/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0810 - accuracy: 0.9794\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0747 - accuracy: 0.9784\n", "Epoch 120/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0658 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0942 - accuracy: 0.9809\n", "Epoch 121/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0820 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0587 - accuracy: 0.9860\n", "Epoch 122/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0825 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0854 - accuracy: 0.9714\n", "Epoch 123/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0883 - accuracy: 0.9775\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9784\n", "Epoch 124/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0550 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0968 - accuracy: 0.9746\n", "Epoch 125/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9866\n", "Epoch 126/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0613 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0764 - accuracy: 0.9771\n", "Epoch 127/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0981 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9797\n", "Epoch 128/500\n", - "389/389 [==============================] - 1s 4ms/step - loss: 0.0681 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9873\n", "Epoch 129/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1018 - accuracy: 0.9723\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9708\n", "Epoch 130/500\n", - "389/389 [==============================] - 1s 4ms/step - loss: 0.0666 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9816\n", "Epoch 131/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0958 - accuracy: 0.9797\n", "Epoch 132/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0671 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0657 - accuracy: 0.9835\n", "Epoch 133/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0534 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9854\n", "Epoch 134/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0833 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0846 - accuracy: 0.9765\n", "Epoch 135/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0716 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0726 - accuracy: 0.9758\n", "Epoch 136/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0647 - accuracy: 0.9768\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9816\n", "Epoch 137/500\n", - "389/389 [==============================] - 1s 4ms/step - loss: 0.0905 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9816\n", "Epoch 138/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0860 - accuracy: 0.9755\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9854\n", "Epoch 139/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0585 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0546 - accuracy: 0.9822\n", "Epoch 140/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0565 - accuracy: 0.9841\n", "Epoch 141/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0797 - accuracy: 0.9768\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.1083 - accuracy: 0.9720\n", "Epoch 142/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0966 - accuracy: 0.9755\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9835\n", "Epoch 143/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0835 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0743 - accuracy: 0.9797\n", "Epoch 144/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0628 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0753 - accuracy: 0.9816\n", "Epoch 145/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0690 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0504 - accuracy: 0.9905\n", "Epoch 146/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0809 - accuracy: 0.9775\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9854\n", "Epoch 147/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0651 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0463 - accuracy: 0.9879\n", "Epoch 148/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9803\n", "Epoch 149/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0425 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9860\n", "Epoch 150/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0780 - accuracy: 0.9841\n", "Epoch 151/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0593 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0662 - accuracy: 0.9835\n", "Epoch 152/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0739 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0714 - accuracy: 0.9847\n", "Epoch 153/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0512 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n", "Epoch 154/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0845 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0793 - accuracy: 0.9777\n", "Epoch 155/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0686 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n", "Epoch 156/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0821 - accuracy: 0.9794\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0636 - accuracy: 0.9841\n", "Epoch 157/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0791 - accuracy: 0.9781\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0745 - accuracy: 0.9847\n", "Epoch 158/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0638 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0616 - accuracy: 0.9828\n", "Epoch 159/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0767 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0360 - accuracy: 0.9886\n", "Epoch 160/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0455 - accuracy: 0.9892\n", "Epoch 161/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9835\n", "Epoch 162/500\n", - "389/389 [==============================] - 2s 4ms/step - loss: 0.0533 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9898\n", "Epoch 163/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0706 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9860\n", "Epoch 164/500\n", - "389/389 [==============================] - 2s 4ms/step - loss: 0.0766 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9873\n", "Epoch 165/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0482 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n", "Epoch 166/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0724 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0614 - accuracy: 0.9866\n", "Epoch 167/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0367 - accuracy: 0.9892\n", "Epoch 168/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0516 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0599 - accuracy: 0.9847\n", "Epoch 169/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0697 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0718 - accuracy: 0.9847\n", "Epoch 170/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0304 - accuracy: 0.9898\n", "Epoch 171/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0805 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0709 - accuracy: 0.9822\n", "Epoch 172/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0589 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0538 - accuracy: 0.9860\n", "Epoch 173/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0685 - accuracy: 0.9860\n", "Epoch 174/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0708 - accuracy: 0.9794\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9841\n", "Epoch 175/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0839 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9854\n", "Epoch 176/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0719 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9854\n", "Epoch 177/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0625 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9816\n", "Epoch 178/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0385 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0511 - accuracy: 0.9873\n", "Epoch 179/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0655 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9809\n", "Epoch 180/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0591 - accuracy: 0.9860\n", "Epoch 181/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0474 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9860\n", "Epoch 182/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0486 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0449 - accuracy: 0.9835\n", "Epoch 183/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0426 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9828\n", "Epoch 184/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0591 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9879\n", "Epoch 185/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0579 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9860\n", "Epoch 186/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0835 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9835\n", "Epoch 187/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0482 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9860\n", "Epoch 188/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0794 - accuracy: 0.9841\n", "Epoch 189/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0433 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9886\n", "Epoch 190/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9826\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0822 - accuracy: 0.9784\n", "Epoch 191/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0681 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9898\n", "Epoch 192/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9911\n", "Epoch 193/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0582 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9854\n", "Epoch 194/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0351 - accuracy: 0.9949\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9822\n", "Epoch 195/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0562 - accuracy: 0.9833\n", + "394/394 [==============================] - 2s 4ms/step - loss: 0.0511 - accuracy: 0.9866\n", "Epoch 196/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9879\n", "Epoch 197/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0417 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0384 - accuracy: 0.9917\n", "Epoch 198/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0675 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9873\n", "Epoch 199/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0430 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n", "Epoch 200/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0748 - accuracy: 0.9826\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0843 - accuracy: 0.9828\n", "Epoch 201/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0679 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9860\n", "Epoch 202/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0424 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9866\n", "Epoch 203/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9847\n", "Epoch 204/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0593 - accuracy: 0.9826\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0667 - accuracy: 0.9847\n", "Epoch 205/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0685 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0463 - accuracy: 0.9841\n", "Epoch 206/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0444 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9835\n", "Epoch 207/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0902 - accuracy: 0.9801\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0741 - accuracy: 0.9860\n", "Epoch 208/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0587 - accuracy: 0.9813\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0559 - accuracy: 0.9841\n", "Epoch 209/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0665 - accuracy: 0.9788\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9873\n", "Epoch 210/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0704 - accuracy: 0.9775\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9873\n", "Epoch 211/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0791 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9873\n", "Epoch 212/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0513 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9892\n", "Epoch 213/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9873\n", "Epoch 214/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0398 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0606 - accuracy: 0.9866\n", "Epoch 215/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0722 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9860\n", "Epoch 216/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0412 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0723 - accuracy: 0.9777\n", "Epoch 217/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0479 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9911\n", "Epoch 218/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0413 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0748 - accuracy: 0.9809\n", "Epoch 219/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0897 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9866\n", "Epoch 220/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0674 - accuracy: 0.9841\n", "Epoch 221/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0444 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9911\n", "Epoch 222/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9879\n", "Epoch 223/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9892\n", "Epoch 224/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0647 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9886\n", "Epoch 225/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0617 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0472 - accuracy: 0.9847\n", "Epoch 226/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9847\n", "Epoch 227/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9820\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.9905\n", "Epoch 228/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0623 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0698 - accuracy: 0.9828\n", "Epoch 229/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0270 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9917\n", "Epoch 230/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0316 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9847\n", "Epoch 231/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0337 - accuracy: 0.9898\n", "Epoch 232/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0651 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0469 - accuracy: 0.9892\n", "Epoch 233/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0732 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0355 - accuracy: 0.9905\n", "Epoch 234/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9905\n", "Epoch 235/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0667 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9873\n", "Epoch 236/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0817 - accuracy: 0.9803\n", "Epoch 237/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0282 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9873\n", "Epoch 238/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0183 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9866\n", "Epoch 239/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9911\n", "Epoch 240/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0475 - accuracy: 0.9886\n", "Epoch 241/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0234 - accuracy: 0.9911\n", "Epoch 242/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0557 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9854\n", "Epoch 243/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0478 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9879\n", "Epoch 244/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0476 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9911\n", "Epoch 245/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0682 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9892\n", "Epoch 246/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9873\n", "Epoch 247/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0487 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n", "Epoch 248/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0358 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0553 - accuracy: 0.9841\n", "Epoch 249/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0447 - accuracy: 0.9886\n", "Epoch 250/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0424 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9905\n", "Epoch 251/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0384 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9847\n", "Epoch 252/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0301 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9905\n", "Epoch 253/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0450 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9911\n", "Epoch 254/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0636 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9879\n", "Epoch 255/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0381 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0645 - accuracy: 0.9835\n", "Epoch 256/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0551 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9898\n", "Epoch 257/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9892\n", "Epoch 258/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0563 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9886\n", "Epoch 259/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9911\n", "Epoch 260/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0394 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0610 - accuracy: 0.9860\n", "Epoch 261/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0366 - accuracy: 0.9911\n", "Epoch 262/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0609 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9860\n", "Epoch 263/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0470 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n", "Epoch 264/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0304 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9898\n", "Epoch 265/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0813 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9924\n", "Epoch 266/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9866\n", "Epoch 267/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0334 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9822\n", "Epoch 268/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0433 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9898\n", "Epoch 269/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0238 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0695 - accuracy: 0.9866\n", "Epoch 270/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0325 - accuracy: 0.9898\n", "Epoch 271/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0280 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0353 - accuracy: 0.9911\n", "Epoch 272/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0603 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9797\n", "Epoch 273/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0633 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9886\n", "Epoch 274/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0515 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9911\n", "Epoch 275/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0455 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0284 - accuracy: 0.9930\n", "Epoch 276/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0547 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9911\n", "Epoch 277/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0488 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9860\n", "Epoch 278/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0753 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0514 - accuracy: 0.9866\n", "Epoch 279/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0541 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9911\n", "Epoch 280/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0415 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0459 - accuracy: 0.9898\n", "Epoch 281/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0322 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0490 - accuracy: 0.9835\n", "Epoch 282/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0271 - accuracy: 0.9924\n", "Epoch 283/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0648 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.9873\n", "Epoch 284/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0411 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0570 - accuracy: 0.9905\n", "Epoch 285/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9905\n", "Epoch 286/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0340 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n", "Epoch 287/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0588 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9835\n", "Epoch 288/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n", "Epoch 289/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0569 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9892\n", "Epoch 290/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0275 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9886\n", "Epoch 291/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0364 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0394 - accuracy: 0.9873\n", "Epoch 292/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0408 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n", "Epoch 293/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0624 - accuracy: 0.9858\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0554 - accuracy: 0.9860\n", "Epoch 294/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0884 - accuracy: 0.9835\n", "Epoch 295/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0451 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9892\n", "Epoch 296/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0549 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0508 - accuracy: 0.9886\n", "Epoch 297/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0542 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0648 - accuracy: 0.9854\n", "Epoch 298/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0611 - accuracy: 0.9807\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9879\n", "Epoch 299/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0726 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9873\n", "Epoch 300/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0524 - accuracy: 0.9905\n", "Epoch 301/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0231 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0460 - accuracy: 0.9892\n", "Epoch 302/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0402 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0441 - accuracy: 0.9866\n", "Epoch 303/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0212 - accuracy: 0.9949\n", "Epoch 304/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0373 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9873\n", "Epoch 305/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9833\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0221 - accuracy: 0.9905\n", "Epoch 306/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0424 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0631 - accuracy: 0.9886\n", "Epoch 307/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0314 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9886\n", "Epoch 308/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9866\n", "Epoch 309/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0415 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9886\n", "Epoch 310/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9898\n", "Epoch 311/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0429 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9905\n", "Epoch 312/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0294 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0213 - accuracy: 0.9962\n", "Epoch 313/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0367 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9892\n", "Epoch 314/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9879\n", "Epoch 315/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0421 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0383 - accuracy: 0.9905\n", "Epoch 316/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0470 - accuracy: 0.9898\n", "Epoch 317/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0439 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0328 - accuracy: 0.9905\n", "Epoch 318/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0471 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0477 - accuracy: 0.9898\n", "Epoch 319/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0334 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9898\n", "Epoch 320/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0380 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0295 - accuracy: 0.9930\n", "Epoch 321/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0195 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0491 - accuracy: 0.9898\n", "Epoch 322/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0330 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0249 - accuracy: 0.9930\n", "Epoch 323/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0299 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9886\n", "Epoch 324/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0343 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9917\n", "Epoch 325/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0641 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9879\n", "Epoch 326/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0524 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.9924\n", "Epoch 327/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0387 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9854\n", "Epoch 328/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0405 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9892\n", "Epoch 329/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0521 - accuracy: 0.9910\n", + "394/394 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9892\n", "Epoch 330/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0422 - accuracy: 0.9873\n", "Epoch 331/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.9936\n", "Epoch 332/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9905\n", "Epoch 333/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0345 - accuracy: 0.9910\n", + "394/394 [==============================] - 2s 4ms/step - loss: 0.0409 - accuracy: 0.9886\n", "Epoch 334/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0718 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9879\n", "Epoch 335/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9873\n", "Epoch 336/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0307 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9873\n", "Epoch 337/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9905\n", "Epoch 338/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0343 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.9892\n", "Epoch 339/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0486 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n", "Epoch 340/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0155 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n", "Epoch 341/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0572 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9917\n", "Epoch 342/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0412 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9917\n", "Epoch 343/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0390 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9905\n", "Epoch 344/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0199 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9898\n", "Epoch 345/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0232 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0331 - accuracy: 0.9917\n", "Epoch 346/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0652 - accuracy: 0.9839\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9892\n", "Epoch 347/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0268 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0348 - accuracy: 0.9911\n", "Epoch 348/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0476 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9873\n", "Epoch 349/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0463 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0292 - accuracy: 0.9936\n", "Epoch 350/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0547 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9892\n", "Epoch 351/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9930\n", "Epoch 352/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0428 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9905\n", "Epoch 353/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.1115 - accuracy: 0.9846\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0299 - accuracy: 0.9924\n", "Epoch 354/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0258 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0368 - accuracy: 0.9943\n", "Epoch 355/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0320 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0613 - accuracy: 0.9898\n", "Epoch 356/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0193 - accuracy: 0.9955\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9892\n", "Epoch 357/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0347 - accuracy: 0.9936\n", "Epoch 358/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0484 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9892\n", "Epoch 359/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0255 - accuracy: 0.9949\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9936\n", "Epoch 360/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0258 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0699 - accuracy: 0.9879\n", "Epoch 361/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0646 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", "Epoch 362/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0352 - accuracy: 0.9891\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0313 - accuracy: 0.9898\n", "Epoch 363/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n", "Epoch 364/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0450 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9924\n", "Epoch 365/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0178 - accuracy: 0.9949\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9924\n", "Epoch 366/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0218 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0165 - accuracy: 0.9949\n", "Epoch 367/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0296 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9892\n", "Epoch 368/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0111 - accuracy: 0.9955\n", "Epoch 369/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0582 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n", "Epoch 370/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0389 - accuracy: 0.9924\n", "Epoch 371/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0261 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9917\n", "Epoch 372/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0242 - accuracy: 0.9942\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9924\n", "Epoch 373/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0607 - accuracy: 0.9871\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9892\n", "Epoch 374/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9892\n", "Epoch 375/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9898\n", "Epoch 376/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0534 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9898\n", "Epoch 377/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0412 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9930\n", "Epoch 378/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0531 - accuracy: 0.9897\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9917\n", "Epoch 379/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0298 - accuracy: 0.9911\n", "Epoch 380/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0379 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n", "Epoch 381/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0344 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9892\n", "Epoch 382/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0301 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0327 - accuracy: 0.9892\n", "Epoch 383/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0218 - accuracy: 0.9911\n", "Epoch 384/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0377 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0386 - accuracy: 0.9905\n", "Epoch 385/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0185 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9898\n", "Epoch 386/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0419 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9936\n", "Epoch 387/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0220 - accuracy: 0.9936\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9879\n", "Epoch 388/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0297 - accuracy: 0.9949\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0252 - accuracy: 0.9949\n", "Epoch 389/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0326 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9905\n", "Epoch 390/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0596 - accuracy: 0.9852\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0171 - accuracy: 0.9962\n", "Epoch 391/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9884\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0196 - accuracy: 0.9943\n", "Epoch 392/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0364 - accuracy: 0.9917\n", "Epoch 393/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9903\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0273 - accuracy: 0.9898\n", "Epoch 394/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0390 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0492 - accuracy: 0.9873\n", "Epoch 395/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0217 - accuracy: 0.9968\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9930\n", "Epoch 396/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0286 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9962\n", "Epoch 397/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0561 - accuracy: 0.9865\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0630 - accuracy: 0.9854\n", "Epoch 398/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9929\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9917\n", "Epoch 399/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0228 - accuracy: 0.9923\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0210 - accuracy: 0.9968\n", "Epoch 400/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0408 - accuracy: 0.9910\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9886\n", "Epoch 401/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0422 - accuracy: 0.9878\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9866\n", "Epoch 402/500\n", - "389/389 [==============================] - 1s 3ms/step - loss: 0.0419 - accuracy: 0.9916\n", + "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9905\n", "Epoch 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[==============================] - 3s 8ms/step - loss: 0.0175 - accuracy: 0.9947\n", - "Epoch 474/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0297 - accuracy: 0.9901\n", - "Epoch 475/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0251 - accuracy: 0.9915\n", - "Epoch 476/500\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "381/381 [==============================] - 3s 7ms/step - loss: 0.0754 - accuracy: 0.9895\n", - "Epoch 477/500\n", - "381/381 [==============================] - 3s 7ms/step - loss: 0.0196 - accuracy: 0.9954\n", - "Epoch 478/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0122 - accuracy: 0.9947\n", - "Epoch 479/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0356 - accuracy: 0.9934\n", - "Epoch 480/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0445 - accuracy: 0.9941\n", - "Epoch 481/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0225 - accuracy: 0.9941\n", - "Epoch 482/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0282 - accuracy: 0.9954\n", - "Epoch 483/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0137 - accuracy: 0.9961\n", - "Epoch 484/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0360 - accuracy: 0.9934\n", - "Epoch 485/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0196 - accuracy: 0.9947\n", - "Epoch 486/500\n", - "381/381 [==============================] - 4s 9ms/step - loss: 0.0245 - accuracy: 0.9947\n", - "Epoch 487/500\n", - "381/381 [==============================] - 4s 9ms/step - loss: 0.0276 - accuracy: 0.9941\n", - "Epoch 488/500\n", - "381/381 [==============================] - 4s 9ms/step - loss: 0.0306 - accuracy: 0.9928\n", - "Epoch 489/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0470 - accuracy: 0.9915\n", - "Epoch 490/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0471 - accuracy: 0.9915\n", - "Epoch 491/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0264 - accuracy: 0.9947\n", - "Epoch 492/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0391 - accuracy: 0.9921\n", - "Epoch 493/500\n", - "381/381 [==============================] - 3s 9ms/step - loss: 0.0406 - accuracy: 0.9941\n", - "Epoch 494/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0274 - accuracy: 0.9915\n", - "Epoch 495/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0267 - accuracy: 0.9895\n", - "Epoch 496/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0239 - accuracy: 0.9954\n", - "Epoch 497/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0305 - accuracy: 0.9921\n", - "Epoch 498/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0511 - accuracy: 0.9921\n", - "Epoch 499/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0321 - accuracy: 0.9947\n", - "Epoch 500/500\n", - "381/381 [==============================] - 3s 8ms/step - loss: 0.0346 - accuracy: 0.9908\n" + "394/394 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9892\n" ] } ], @@ -1656,7 +1279,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -1809,14 +1432,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpxwkpxwin\\assets\n" + "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpxwkpxwin\\assets\n" + "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n" ] }, { @@ -1855,14 +1478,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmphtias004\\assets\n" + "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmphtias004\\assets\n", + "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n", "C:\\Users\\Muell\\anaconda3\\envs\\py39-td-opencv\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n", " warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n" ] @@ -1871,7 +1494,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "dig-class11_1700_s2_q.tflite\n" + "dig-class11_1701_s2_q.tflite\n" ] }, { diff --git a/README.md b/README.md index 6b8e71c4..1f4b1b49 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,10 @@ The readout is used in a water meter measurement system. +#### 17.0.1 New Images (2024-03-25) + +* New images (LCD) + #### 16.0.0 New Images (2023-01-13) * New images (red on black) diff --git a/Versions.md b/Versions.md index 0cd64651..b3d773d7 100644 --- a/Versions.md +++ b/Versions.md @@ -1,8 +1,4 @@ ## Version -#### 17.0.2 Current Version (204-03-25) - -* New images for digits - #### 13.3.0 Current Version (2021-11-23) * New images for digits