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import osimport numpy as npimport tensorflow as tfimport randomimport seaborn as snsimport matplotlib.pyplot as pltfrom keras.models import Sequential, Modelfrom keras.layers import Dense, Dropout, Activation, Flatten, Inputfrom keras.layers.convolutional import Conv2D, MaxPooling2Dfrom keras.optimizers import RMSprop, Adam, SGDfrom keras.preprocessing import imagefrom keras.preprocessing.image import ImageDataGeneratorfrom keras.utils import np_utilsfrom sklearn.model_selection import train_test_split

图片预处理

def read_and_process_image(data_dir,width=32, height=32, channels=3, preprocess=False):        train_classes= [data_dir +  i for i in os.listdir(data_dir) ]    train_images = []    for train_class in train_classes:        train_images= train_images + [train_class + "/" + i for i in os.listdir(train_class)]        random.shuffle(train_images)        def read_image(file_path, preprocess):        img = image.load_img(file_path, target_size=(height, width))        x = image.img_to_array(img)        x = np.expand_dims(x, axis=0)        # if preprocess:            # x = preprocess_input(x)        return x        def prep_data(images, proprocess):        count = len(images)        data = np.ndarray((count, height, width, channels), dtype = np.float32)                for i, image_file in enumerate(images):            image = read_image(image_file, preprocess)            data[i] = image                return data        def read_labels(file_path):        labels = []        for i in file_path:            if 'airplane' in i:                label = 0            elif 'automobile' in i:                label = 1            elif 'bird' in i:                label = 2            elif 'cat' in i:                label = 3            elif 'deer' in i:                label = 4            elif 'dog' in i:                label = 5            elif 'frog' in i:                label = 6            elif 'horse' in i:                label = 7            elif 'ship' in i:                label = 8            elif 'truck' in i:                label = 9            labels.append(label)                return labels        X = prep_data(train_images, preprocess)    labels = read_labels(train_images)        assert X.shape[0] == len(labels)        print("Train shape: {}".format(X.shape))        return X, labels

读取训练集，以及测试集

# 读取训练集图片WIDTH = 32HEIGHT = 32CHANNELS = 3X, y = read_and_process_image('D:/Python Project/cifar-10/train/',width=WIDTH, height=HEIGHT, channels=CHANNELS)# 读取测试集图片WIDTH = 32HEIGHT = 32CHANNELS = 3test_X, test_y = read_and_process_image('D:/Python Project/cifar-10/test/',width=WIDTH, height=HEIGHT, channels=CHANNELS)# 统计ysns.countplot(y)# 统计ysns.countplot(test_y)

one-hot编码

train_y = np_utils.to_categorical(y)test_y = np_utils.to_categorical(test_y)

显示图片

# 显示图片def show_picture(X, idx):    plt.figure(figsize=(10,5), frameon=True)    img = X[idx,:,:,::-1]    img = img/255    plt.imshow(img)    plt.show()for idx in range(0,3):    show_picture(X, idx)

定义模型

num_classes=10model = Sequential()model.add(Conv2D(32 ,3 ,input_shape=(HEIGHT,WIDTH,CHANNELS),activation='relu',padding='same'))model.add(Conv2D(32 ,3 ,activation='relu',padding='same'))model.add(MaxPooling2D(pool_size=2))model.add(Conv2D(64 ,3 ,activation='relu',padding='same'))model.add(Conv2D(64 ,3 ,activation='relu',padding='same'))model.add(MaxPooling2D(pool_size=2))model.add(Conv2D(128 ,3 ,activation='relu',padding='same'))model.add(Conv2D(128 ,3 ,activation='relu',padding='same'))model.add(MaxPooling2D(pool_size=2))model.add(Conv2D(256 ,3 ,activation='relu',padding='same'))model.add(Conv2D(256 ,3 ,activation='relu',padding='same'))model.add(MaxPooling2D(pool_size=2))model.add(Flatten())model.add(Dense(256, activation='relu'))model.add(Dropout(0.5))model.add(Dense(256, activation='relu'))model.add(Dropout(0.5))model.add(Dense(num_classes, activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])model.summary()

训练模型

history = model.fit(X,train_y, validation_data=(test_X, test_y),epochs=20,batch_size=100,verbose=True)score = model.evaluate(test_X, test_y, verbose=0)print("Large CNN Error: %.2f%%" %(100-score[1]*100))

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