keras 脚本 设置神经网络对 MNIST 数字图像数据进行分类.rar

Mon Apr 22 14:21:04 2019 mnist: python version: 3.6.8 keras version: 2.2.4 Neural network to classify the MNIST digit image data. Number of training items = 60000 Training data shape = (60000,28,28) Sample training labels: [5 0 4 ... 5 6 8] Number of testing items = 10000 Training data shape = (60000,28,28) Sample testing labels: [7 2 1 ... 4 5 6] Epoch 1/5 128/60000 [..............................] - ETA: 53s - loss: 2.3559 - acc: 0.1172 2048/60000 [>.............................] - ETA: 4s - loss: 1.0528 - acc: 0.6938 3968/60000 [>.............................] - ETA: 3s - loss: 0.7874 - acc: 0.7719 5888/60000 [=>............................] - ETA: 2s - loss: 0.6668 - acc: 0.8062 7808/60000 [==>...........................] - ETA: 2s - loss: 0.5965 - acc: 0.8259 9728/60000 [===>..........................] - ETA: 1s - loss: 0.5456 - acc: 0.8399 11520/60000 [====>.........................] - ETA: 1s - loss: 0.5153 - acc: 0.8493 13312/60000 [=====>........................] - ETA: 1s - loss: 0.4869 - acc: 0.8567 15232/60000 [======>.......................] - ETA: 1s - loss: 0.4625 - acc: 0.8638 17152/60000 [=======>......................] - 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2s 30us/step - loss: 0.2598 - acc: 0.9241 Epoch 2/5 128/60000 [..............................] - ETA: 2s - loss: 0.0663 - acc: 0.9766 2048/60000 [>.............................] - ETA: 1s - loss: 0.1286 - acc: 0.9604 3712/60000 [>.............................] - ETA: 1s - loss: 0.1333 - acc: 0.9596 5504/60000 [=>............................] - ETA: 1s - loss: 0.1269 - acc: 0.9624 7168/60000 [==>...........................] - ETA: 1s - loss: 0.1232 - acc: 0.9626 8832/60000 [===>..........................] - ETA: 1s - loss: 0.1216 - acc: 0.9649 10496/60000 [====>.........................] - ETA: 1s - loss: 0.1247 - acc: 0.9638 12032/60000 [=====>........................] - ETA: 1s - loss: 0.1247 - acc: 0.9638 13824/60000 [=====>........................] - ETA: 1s - loss: 0.1218 - acc: 0.9646 15360/60000 [======>.......................] - ETA: 1s - loss: 0.1219 - acc: 0.9646 17280/60000 [=======>......................] - ETA: 1s - loss: 0.1201 - acc: 0.9651 19200/60000 [========>.....................] - ETA: 1s - loss: 0.1193 - acc: 0.9654 21120/60000 [=========>....................] - ETA: 1s - loss: 0.1174 - acc: 0.9660 23040/60000 [==========>...................] - ETA: 1s - loss: 0.1161 - acc: 0.9663 24832/60000 [===========>..................] - ETA: 1s - loss: 0.1152 - acc: 0.9665 26752/60000 [============>.................] - ETA: 0s - loss: 0.1147 - acc: 0.9666 28416/60000 [=============>................] - ETA: 0s - loss: 0.1139 - acc: 0.9666 30208/60000 [==============>...............] - ETA: 0s - loss: 0.1132 - acc: 0.9669 32000/60000 [===============>..............] - ETA: 0s - loss: 0.1125 - acc: 0.9670 33920/60000 [===============>..............] - ETA: 0s - loss: 0.1114 - acc: 0.9671 35840/60000 [================>.............] - ETA: 0s - loss: 0.1102 - acc: 0.9676 37760/60000 [=================>............] - ETA: 0s - loss: 0.1111 - acc: 0.9673 39552/60000 [==================>...........] - ETA: 0s - loss: 0.1110 - acc: 0.9672 41216/60000 [===================>..........] - ETA: 0s - loss: 0.1101 - acc: 0.9676 43136/60000 [====================>.........] - ETA: 0s - loss: 0.1100 - acc: 0.9674 45056/60000 [=====================>........] - ETA: 0s - loss: 0.1091 - acc: 0.9675 46976/60000 [======================>.......] - ETA: 0s - loss: 0.1083 - acc: 0.9678 48896/60000 [=======================>......] - ETA: 0s - loss: 0.1087 - acc: 0.9678 50688/60000 [========================>.....] - ETA: 0s - loss: 0.1089 - acc: 0.9678 52352/60000 [=========================>....] - ETA: 0s - loss: 0.1081 - acc: 0.9680 54144/60000 [==========================>...] - ETA: 0s - loss: 0.1071 - acc: 0.9683 56064/60000 [===========================>..] - ETA: 0s - loss: 0.1071 - acc: 0.9684 57856/60000 [===========================>..] - ETA: 0s - loss: 0.1062 - acc: 0.9686 59776/60000 [============================>.] - ETA: 0s - loss: 0.1057 - acc: 0.9687 60000/60000 [==============================] - 2s 29us/step - loss: 0.1059 - acc: 0.9687 Epoch 3/5 128/60000 [..............................] - ETA: 2s - loss: 0.0573 - acc: 0.9766 1920/60000 [..............................] - ETA: 1s - loss: 0.0553 - acc: 0.9812 3584/60000 [>.............................] - ETA: 1s - loss: 0.0575 - acc: 0.9816 5376/60000 [=>............................] - ETA: 1s - loss: 0.0639 - acc: 0.9801 7296/60000 [==>...........................] - ETA: 1s - loss: 0.0646 - acc: 0.9807 9216/60000 [===>..........................] - ETA: 1s - loss: 0.0647 - acc: 0.9803 11136/60000 [====>.........................] - ETA: 1s - loss: 0.0645 - acc: 0.9802 13056/60000 [=====>........................] - ETA: 1s - loss: 0.0683 - acc: 0.9795 14848/60000 [======>.......................] - ETA: 1s - loss: 0.0707 - acc: 0.9791 16512/60000 [=======>......................] - ETA: 1s - loss: 0.0703 - acc: 0.9790 18304/60000 [========>.....................] - ETA: 1s - loss: 0.0709 - acc: 0.9789 20224/60000 [=========>....................] - ETA: 1s - loss: 0.0706 - acc: 0.9791 22144/60000 [==========>...................] - 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