案例：? ??

????基于mnist數(shù)據(jù)集，建立mlp模型，實現(xiàn)0-9數(shù)字的十分類任務(wù)：

????1.實現(xiàn)mnist數(shù)據(jù)載入，可視化圖形數(shù)字

??? 2.完成數(shù)據(jù)預(yù)處理：圖像數(shù)據(jù)維度轉(zhuǎn)換與歸一化、輸出結(jié)果格式轉(zhuǎn)換

????3.計算模型在預(yù)測數(shù)據(jù)集的準(zhǔn)確率

??? 4.模型結(jié)構(gòu)：兩層隱藏層，每層有392個神經(jīng)元

mnist數(shù)據(jù)集介紹

????機(jī)器學(xué)習(xí)領(lǐng)域中非常經(jīng)典的一個數(shù)據(jù)集，由60000個訓(xùn)練樣本和10000個測試樣本組成，每個樣本都是一個28*28像素的灰度手寫數(shù)字圖片。一共4個文件，訓(xùn)練集、訓(xùn)練集標(biāo)簽、測試集、測試集標(biāo)簽。

# 加載mnist數(shù)據(jù)from?keras.datasets?import?mnist(X_train,y_train),(X_test,y_test)?=?mnist.load_data()

but.....網(wǎng)絡(luò)不行(*^_^*)

#下載mnist.npz文件本地加載import numpy as npf = np.load('mnist.npz')X_train, y_train = f['x_train'], f['y_train']X_test, y_test = f['x_test'], f['y_test']f.close()#查看數(shù)據(jù)維度print(type(X_train),X_train.shape) (60000, 28, 28)print(type(X_test),X_test.shape) (10000, 28, 28)#可視化訓(xùn)練集第一張圖片img1 = X_train[0]%matplotlib inlinefrom matplotlib import pyplot as pltfig1 = plt.figure(figsize=(3,3))plt.imshow(img1)plt.title('image size: 28 X 28')plt.show()

#查看計算機(jī)中的格式img1array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170,
253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253,
253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253,
253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190,
253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35,
241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39,
148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221,
253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253,
253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253,
195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133,
11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8)#輸入數(shù)據(jù)格式化feature_size = img1.shape[0]*img1.shape[1]X_train_format = X_train.reshape(X_train.shape[0],feature_size)X_test_format?=?X_test.reshape(X_test.shape[0],feature_size)print(X_train_format.shape)(60000, 784)#輸入數(shù)據(jù)歸一化X_train_normal = X_train_format/255X_test_normal = X_test_format/255print(X_train_normal[0])[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
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0. 0. 0.01176471 0.07058824 0.07058824 0.07058824
0.49411765 0.53333333 0.68627451 0.10196078 0.65098039 1.
0.96862745 0.49803922 0. 0. 0. 0.
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0. 0. 0.11764706 0.14117647 0.36862745 0.60392157
0.66666667 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686
0.88235294 0.6745098 0.99215686 0.94901961 0.76470588 0.25098039
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0.93333333 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686
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0.32156863 0.21960784 0.15294118 0. 0. 0.
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0.99215686 0.99215686 0.99215686 0.99215686 0.77647059 0.71372549
0.96862745 0.94509804 0. 0. 0. 0.
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0.99215686 0.80392157 0.04313725 0. 0.16862745 0.60392157
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0.74509804 0.99215686 0.2745098 0. 0. 0.
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0. 0. 0. 0. 0.1372549 0.94509804
0.88235294 0.62745098 0.42352941 0.00392157 0. 0.
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0.31764706 0.94117647 0.99215686
0.99215686 0.46666667 0.09803922 0. 0. 0.
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.17647059 0.72941176 0.99215686 0.99215686
0.58823529 0.10588235 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.0627451 0.36470588 0.98823529 0.99215686 0.73333333
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.97647059 0.99215686 0.97647059 0.25098039 0.
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.18039216 0.50980392 0.71764706 0.99215686
0.99215686 0.81176471 0.00784314 0. 0. 0.
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0. 0. 0. 0. 0.15294118 0.58039216
0.89803922 0.99215686 0.99215686 0.99215686 0.98039216 0.71372549
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.09411765 0.44705882 0.86666667 0.99215686 0.99215686 0.99215686
0.99215686 0.78823529 0.30588235 0. 0. 0.
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0. 0. 0.09019608 0.25882353 0.83529412 0.99215686
0.99215686 0.99215686 0.99215686 0.77647059 0.31764706 0.00784314
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.07058824 0.67058824
0.85882353 0.99215686 0.99215686 0.99215686 0.99215686 0.76470588
0.31372549 0.03529412 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0.
0.21568627 0.6745098 0.88627451 0.99215686 0.99215686 0.99215686
0.99215686 0.95686275 0.52156863 0.04313725 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.53333333 0.99215686
0.99215686 0.99215686 0.83137255 0.52941176 0.51764706 0.0627451
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]#輸出數(shù)據(jù)(標(biāo)簽)格式化from keras.utils import to_categoricaly_train_format = to_categorical(y_train)y_test_format = to_categorical(y_test)print(y_train_format[0])[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]#查看輸入數(shù)據(jù)，輸出數(shù)據(jù)維度print(X_train_normal.shape,y_train_format.shape)(60000, 784) (60000, 10)

#建立模型from keras.models import Sequentialfrom?keras.layers?import?Dense,?Activationmlp = Sequential()mlp.add(Dense(units=392,activation='relu',input_dim=784))mlp.add(Dense(units=392,activation='relu'))mlp.add(Dense(units=10,activation='softmax'))mlp.summary()

#模型相關(guān)設(shè)置mlp.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['categorical_accuracy'])#訓(xùn)練模型mlp.fit(X_train_normal,y_train_format,epochs=10)

#模型預(yù)測y_train_predict = mlp.predict_classes(X_train_normal)print(type(y_train_predict))print(y_train_predict[0:10])[5 0 4 1 9 2 1 3 1 4]from sklearn.metrics import accuracy_scoreaccuracy_train = accuracy_score(y_train,y_train_predict)print(accuracy_train)0.9972666666666666y_test_predict = mlp.predict_classes(X_test_normal)accuracy_test = accuracy_score(y_test,y_test_predict)print(accuracy_test)0.9807img2 = X_test[100]fig2 = plt.figure(figsize=(3,3))plt.imshow(img2)plt.title(y_test_predict[100])plt.show()

# coding:utf-8import matplotlib as mlpfont2 = {'family' : 'SimHei','weight' : 'normal','size' : 20,}mlp.rcParams['font.family'] = 'SimHei'mlp.rcParams['axes.unicode_minus'] = Falsea = [i for i in range(1,10)]fig4 = plt.figure(figsize=(5,5))for i in a: plt.subplot(3,3,i) plt.tight_layout() plt.imshow(X_test[i]) plt.title('predict:{}'.format(y_test_predict[i]),font2) plt.xticks([]) plt.yticks([])

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