输出结果
1.10.0
Size of:
- Training-set: 55000
- Validation-set: 5000
- Test-set: 10000
Epoch 1/1
128/55000 [..............................] - ETA: 15:39 - loss: 2.3021 - acc: 0.0703
256/55000 [..............................] - ETA: 13:40 - loss: 2.2876 - acc: 0.1172
384/55000 [..............................] - ETA: 14:24 - loss: 2.2780 - acc: 0.1328
512/55000 [..............................] - ETA: 13:57 - loss: 2.2613 - acc: 0.1719
640/55000 [..............................] - ETA: 13:57 - loss: 2.2414 - acc: 0.1828
768/55000 [..............................] - ETA: 13:58 - loss: 2.2207 - acc: 0.2135
896/55000 [..............................] - ETA: 14:01 - loss: 2.1926 - acc: 0.2467
1024/55000 [..............................] - ETA: 13:34 - loss: 2.1645 - acc: 0.2725
1152/55000 [..............................] - ETA: 13:38 - loss: 2.1341 - acc: 0.2969
1280/55000 [..............................] - ETA: 13:40 - loss: 2.0999 - acc: 0.3273
1408/55000 [..............................] - ETA: 13:37 - loss: 2.0555 - acc: 0.3629
……
54016/55000 [============================>.] - ETA: 15s - loss: 0.2200 - acc: 0.9350
54144/55000 [============================>.] - ETA: 13s - loss: 0.2198 - acc: 0.9350
54272/55000 [============================>.] - ETA: 11s - loss: 0.2194 - acc: 0.9351
54400/55000 [============================>.] - ETA: 9s - loss: 0.2191 - acc: 0.9352
54528/55000 [============================>.] - ETA: 7s - loss: 0.2189 - acc: 0.9352
54656/55000 [============================>.] - ETA: 5s - loss: 0.2185 - acc: 0.9354
54784/55000 [============================>.] - ETA: 3s - loss: 0.2182 - acc: 0.9354
54912/55000 [============================>.] - ETA: 1s - loss: 0.2180 - acc: 0.9355
55000/55000 [==============================] - 863s 16ms/step - loss: 0.2177 - acc: 0.9356
32/10000 [..............................] - ETA: 22s
160/10000 [..............................] - ETA: 8s
288/10000 [..............................] - ETA: 6s
416/10000 [>.............................] - ETA: 5s
544/10000 [>.............................] - ETA: 5s
672/10000 [=>............................] - ETA: 5s
800/10000 [=>............................] - ETA: 5s
928/10000 [=>............................] - ETA: 4s
1056/10000 [==>...........................] - ETA: 4s
1184/10000 [==>...........................] - ETA: 4s
1312/10000 [==>...........................] - ETA: 4s
1440/10000 [===>..........................] - ETA: 4s
……
9088/10000 [==========================>...] - ETA: 0s
9216/10000 [==========================>...] - ETA: 0s
9344/10000 [===========================>..] - ETA: 0s
9472/10000 [===========================>..] - ETA: 0s
9600/10000 [===========================>..] - ETA: 0s
9728/10000 [============================>.] - ETA: 0s
9856/10000 [============================>.] - ETA: 0s
9984/10000 [============================>.] - ETA: 0s
10000/10000 [==============================] - 5s 489us/step
loss 0.060937872195523234
acc 0.9803
acc: 98.03%
[[ 963 0 0 1 1 0 4 1 4 6]
[ 0 1128 0 2 0 1 2 0 2 0]
[ 2 9 1006 1 1 0 0 3 10 0]
[ 1 0 2 995 0 3 0 5 2 2]
[ 0 1 0 0 977 0 0 1 0 3]
[ 2 0 0 7 0 874 3 1 1 4]
[ 2 3 0 0 6 1 943 0 3 0]
[ 0 5 7 3 1 1 0 990 1 20]
[ 4 1 3 3 2 1 7 2 944 7]
[ 4 6 0 4 9 1 0 1 1 983]]
设计思路
核心代码
result = model.evaluate(x=data.x_test,
y=data.y_test)
for name, value in zip(model.metrics_names, result):
print(name, value)
print("{0}: {1:.2%}".format(model.metrics_names[1], result[1]))
y_pred = model.predict(x=data.x_test)
cls_pred = np.argmax(y_pred, axis=1)
plot_example_errors(cls_pred)
plot_confusion_matrix(cls_pred)
images = data.x_test[0:9]
cls_true = data.y_test_cls[0:9]
y_pred = model.predict(x=images)
cls_pred = np.argmax(y_pred, axis=1)
title = 'MNIST(Sequential Model): plot predicted example, resl VS predict'
plot_images(title, images=images,
cls_true=cls_true,
cls_pred=cls_pred)
