DL之CNN：利用卷積神經網絡算法(2→2,基于Keras的API-Sequential)利用MNIST(手寫數字圖片識別)數據集實現多分類預測

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設計思路

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輸出結果

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1.10.0 Size of: - Training-set: 55000 - Validation-set: 5000 - Test-set: 10000 Epoch 1/1128/55000 [..............................] - ETA: 15:39 - loss: 2.3021 - acc: 0.0703256/55000 [..............................] - ETA: 13:40 - loss: 2.2876 - acc: 0.1172384/55000 [..............................] - ETA: 14:24 - loss: 2.2780 - acc: 0.1328512/55000 [..............................] - ETA: 13:57 - loss: 2.2613 - acc: 0.1719640/55000 [..............................] - ETA: 13:57 - loss: 2.2414 - acc: 0.1828768/55000 [..............................] - ETA: 13:58 - loss: 2.2207 - acc: 0.2135896/55000 [..............................] - ETA: 14:01 - loss: 2.1926 - acc: 0.24671024/55000 [..............................] - ETA: 13:34 - loss: 2.1645 - acc: 0.27251152/55000 [..............................] - ETA: 13:38 - loss: 2.1341 - acc: 0.29691280/55000 [..............................] - ETA: 13:40 - loss: 2.0999 - acc: 0.32731408/55000 [..............................] - 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: 0.935632/10000 [..............................] - ETA: 22s160/10000 [..............................] - ETA: 8s 288/10000 [..............................] - ETA: 6s416/10000 [>.............................] - ETA: 5s544/10000 [>.............................] - ETA: 5s672/10000 [=>............................] - ETA: 5s800/10000 [=>............................] - ETA: 5s928/10000 [=>............................] - ETA: 4s1056/10000 [==>...........................] - ETA: 4s1184/10000 [==>...........................] - ETA: 4s1312/10000 [==>...........................] - ETA: 4s1440/10000 [===>..........................] - ETA: 4s ……9088/10000 [==========================>...] - ETA: 0s9216/10000 [==========================>...] - ETA: 0s9344/10000 [===========================>..] - ETA: 0s9472/10000 [===========================>..] - ETA: 0s9600/10000 [===========================>..] - ETA: 0s9728/10000 [============================>.] - ETA: 0s9856/10000 [============================>.] - ETA: 0s9984/10000 [============================>.] - 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]]

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設計思路

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核心代碼

后期更新……

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)

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