一、構(gòu)建CNN模型

from keras import layersfrom keras import modelsmodel = models.Sequential()model.add(layers.Conv2D(32, (3, 3),           activation='relu',           input_shape=(28, 28, 1))          )model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.Flatten())model.add(layers.Dropout(0.25))model.add(layers.Dense(64, activation='relu'))model.add(layers.Dense(10,?activation='softmax'))

卷積神經(jīng)網(wǎng)絡(luò)接收形狀為(image_height, image_width, image_channels)的輸入張量（不包括批量維度）。本例中設(shè)置卷積神經(jīng)網(wǎng)絡(luò)處理大小為(28, 28, 1) 的輸入張量，這正是MNIST 圖像的格式。我們向第一層傳入?yún)?shù)input_shape=(28, 28, 1) 來完成此設(shè)置。我們來看一下目前卷積神經(jīng)網(wǎng)絡(luò)的架構(gòu)。

model.summary()Model: "sequential"_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================conv2d (Conv2D)              (None, 26, 26, 32)        320       _________________________________________________________________max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         _________________________________________________________________conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     _________________________________________________________________max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         _________________________________________________________________conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     _________________________________________________________________flatten (Flatten)            (None, 576)               0         _________________________________________________________________dropout (Dropout)            (None, 576)               0         _________________________________________________________________dense (Dense)                (None, 64)                36928     _________________________________________________________________dense_1 (Dense)              (None, 10)                650       =================================================================Total params: 93,322Trainable params: 93,322Non-trainable params: 0_________________________________________________________________

二、圖片下載與查看

from keras.datasets import mnistfrom keras.utils    import to_categoricalfrom keras          import datasets(train_images,train_labels), (test_images,test_labels)  = mnist.load_data()dir(datasets)#可以看看內(nèi)置的所有數(shù)據(jù)集

查看圖片

import matplotlib.pyplot as pltplt.imshow(train_images[200] , cmap=plt.cm.binary)plt.show()

三、模型訓(xùn)練&準(zhǔn)確率評估

train_images = train_images.reshape((60000, 28, 28, 1))train_images = train_images.astype('float32') / 255train_labels = to_categorical(train_labels)

test_images  = test_images.reshape((10000, 28, 28, 1))test_images  = test_images.astype('float32') / 255test_labels  = to_categorical(test_labels)

model.compile(optimizer='rmsprop',              loss='categorical_crossentropy',              metrics=['accuracy'])model.fit(train_images, train_labels, epochs=5, batch_size=64)

test_loss, test_acc = model.evaluate(test_images, test_labels)test_acc 0.9914000034332275

#看看預(yù)測的到底準(zhǔn)不準(zhǔn)呢

y_pred = model.predict(test_images)import numpy as nppred = np.argmax(y_pred, axis=1)import matplotlib.pyplot as plt#看看第2990個數(shù)字是啥，我們預(yù)測的是8，看看圖片也是8，挺準(zhǔn)的steps = 2990print('pred: ',pred[steps])pred:  8plt.imshow(test_images[steps] , cmap=plt.cm.binary)plt.show()

四、預(yù)測自己的手寫數(shù)字

模型訓(xùn)練好了，準(zhǔn)確率挺高，但是實際有沒有用，還需要用自己的數(shù)據(jù)進(jìn)行測試，打開自己在畫圖板里面隨便寫幾個數(shù)字，然后單個截圖保存后進(jìn)行預(yù)測。

分別截圖后保存成img3、img4、img5......，下面進(jìn)行預(yù)處理，處理成和模型訓(xùn)練一樣的數(shù)據(jù)才能預(yù)測。

from keras.preprocessing.image import load_img,img_to_arrayimport matplotlib.pyplot as pltimport numpy as nppath = 'C:/Users/伍正祥/Desktop/img5.jpg'#讀取圖片、調(diào)整圖片大小，轉(zhuǎn)換成灰度 help(load_img)img = load_img(path, target_size=(28, 28),color_mode="grayscale")#255-為了調(diào)成白底，系統(tǒng)灰度轉(zhuǎn)換自動給處理成黑底了img = 255-img_to_array(img)#查看自己加載的圖片plt.imshow(img , cmap=plt.cm.binary)plt.show()#進(jìn)行數(shù)據(jù)預(yù)測img = img.astype('float32')/255img = img.reshape((1, 28, 28, 1))y_pred = model.predict(img)print('預(yù)測數(shù)字：',np.argmax(y_pred, axis=1)[0]