偽代碼：

graph TD A(導入相應庫) --> Z[模型參數設置以及其它配置] Z --> B[生成訓練集,測試集,驗證集的三個迭代器] B --> C[identity_block函數的編寫] C --> D[bottleneck_block函數的編寫] D --> F[根據resnet網絡構建表來構建網絡] F --> G[模型訓練與驗證] G --> H[模型保存] H --> I(模型在測試集上測試)

代碼：

from keras.models import Model from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \Activation, ZeroPadding2D from keras.layers import add, Flatten from keras.utils import plot_model from keras.metrics import top_k_categorical_accuracy from keras.preprocessing.image import ImageDataGenerator from keras.models import load_model import os# Global Constants NB_CLASS=20 IM_WIDTH=224 IM_HEIGHT=224 train_root='your train dataset' vaildation_root='your val dataset test_root='your test dataset' batch_size=32 EPOCH=60# train data train_datagen = ImageDataGenerator(width_shift_range=0.1,height_shift_range=0.1,shear_range=0.1,zoom_range=0.1,horizontal_flip=True,rescale=1./255 ) train_generator = train_datagen.flow_from_directory(train_root,target_size=(IM_WIDTH, IM_HEIGHT),batch_size=batch_size,shuffle=True )# vaild data vaild_datagen = ImageDataGenerator(width_shift_range=0.1,height_shift_range=0.1,shear_range=0.1,zoom_range=0.1,horizontal_flip=True,rescale=1./255 ) vaild_generator = train_datagen.flow_from_directory(vaildation_root,target_size=(IM_WIDTH, IM_HEIGHT),batch_size=batch_size, )# test data test_datagen = ImageDataGenerator(rescale=1./255 ) test_generator = train_datagen.flow_from_directory(test_root,target_size=(IM_WIDTH, IM_HEIGHT),batch_size=batch_size, )def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None):if name is not None:bn_name = name + '_bn'conv_name = name + '_conv'else:bn_name = Noneconv_name = Nonex = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)x = BatchNormalization(axis=3, name=bn_name)(x)return xdef identity_Block(inpt, nb_filter, kernel_size, strides=(1, 1), with_conv_shortcut=False):x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same')x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same')if with_conv_shortcut:shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size)x = add([x, shortcut])return xelse:x = add([x, inpt])return xdef bottleneck_Block(inpt,nb_filters,strides=(1,1),with_conv_shortcut=False):k1,k2,k3=nb_filtersx = Conv2d_BN(inpt, nb_filter=k1, kernel_size=1, strides=strides, padding='same')x = Conv2d_BN(x, nb_filter=k2, kernel_size=3, padding='same')x = Conv2d_BN(x, nb_filter=k3, kernel_size=1, padding='same')if with_conv_shortcut:shortcut = Conv2d_BN(inpt, nb_filter=k3, strides=strides, kernel_size=1)x = add([x, shortcut])return xelse:x = add([x, inpt])return xdef resnet_34(width,height,channel,classes):inpt = Input(shape=(width, height, channel))x = ZeroPadding2D((3, 3))(inpt)#conv1x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)#conv2_xx = identity_Block(x, nb_filter=64, kernel_size=(3, 3))x = identity_Block(x, nb_filter=64, kernel_size=(3, 3))x = identity_Block(x, nb_filter=64, kernel_size=(3, 3))#conv3_xx = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))#conv4_xx = identity_Block(x, nb_filter=256, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))#conv5_xx = identity_Block(x, nb_filter=512, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)x = identity_Block(x, nb_filter=512, kernel_size=(3, 3))x = identity_Block(x, nb_filter=512, kernel_size=(3, 3))x = AveragePooling2D(pool_size=(7, 7))(x)x = Flatten()(x)x = Dense(classes, activation='softmax')(x)model = Model(inputs=inpt, outputs=x)return modeldef resnet_50(width,height,channel,classes):inpt = Input(shape=(width, height, channel))x = ZeroPadding2D((3, 3))(inpt)x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)#conv2_xx = bottleneck_Block(x, nb_filters=[64,64,256],strides=(1,1),with_conv_shortcut=True)x = bottleneck_Block(x, nb_filters=[64,64,256])x = bottleneck_Block(x, nb_filters=[64,64,256])#conv3_xx = bottleneck_Block(x, nb_filters=[128, 128, 512],strides=(2,2),with_conv_shortcut=True)x = bottleneck_Block(x, nb_filters=[128, 128, 512])x = bottleneck_Block(x, nb_filters=[128, 128, 512])x = bottleneck_Block(x, nb_filters=[128, 128, 512])#conv4_xx = bottleneck_Block(x, nb_filters=[256, 256, 1024],strides=(2,2),with_conv_shortcut=True)x = bottleneck_Block(x, nb_filters=[256, 256, 1024])x = bottleneck_Block(x, nb_filters=[256, 256, 1024])x = bottleneck_Block(x, nb_filters=[256, 256, 1024])x = bottleneck_Block(x, nb_filters=[256, 256, 1024])x = bottleneck_Block(x, nb_filters=[256, 256, 1024])#conv5_xx = bottleneck_Block(x, nb_filters=[512, 512, 2048], strides=(2, 2), with_conv_shortcut=True)x = bottleneck_Block(x, nb_filters=[512, 512, 2048])x = bottleneck_Block(x, nb_filters=[512, 512, 2048])x = AveragePooling2D(pool_size=(7, 7))(x)x = Flatten()(x)x = Dense(classes, activation='softmax')(x)model = Model(inputs=inpt, outputs=x)return modeldef acc_top2(y_true, y_pred):return top_k_categorical_accuracy(y_true, y_pred, k=2)def check_print():# Create a Keras Modelmodel = resnet_50(IM_WIDTH,IM_HEIGHT,3,NB_CLASS)model.summary()# Save a PNG of the Model Buildplot_model(model, to_file='resnet.png')model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc',top_k_categorical_accuracy])print 'Model Compiled'return modelif __name__ == '__main__':if os.path.exists('resnet_50.h5'):model=load_model('resnet_50.h5')else:model=check_print()model.fit_generator(train_generator,validation_data=vaild_generator,epochs=EPOCH,steps_per_epoch=train_generator.n/batch_size,validation_steps=vaild_generator.n/batch_size)model.save('resnet_50.h5')loss,acc,top_acc=model.evaluate_generator(test_generator, steps=test_generator.n / batch_size)print 'Test result:loss:%f,acc:%f,top_acc:%f' % (loss, acc, top_acc)

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