理論上講，當網絡層數加深時，網絡的性能會變強，而實際上，在不斷的加深網絡層數后，分類性能不會提高，而是會導致網絡收斂更緩慢，準確率也隨著降低。利用數據增強等方法抑制過擬合后，準確率也不會得到提高，出現梯度消失的現象。因此，盲目的增加網絡層數會適得其反，因此，ResNet(殘差網絡)系列網絡出現了。本次基于Tensorflow2.0實現ResNet50網絡。

1.ResNet50網絡簡介

ResNet50網絡在層數上相比于VGG系列網絡更勝一籌。這是何凱明在2015年提出的一種網絡結構，獲得了ILSVRC-2015分類任務的第一名，同時在ImageNet detection，ImageNet localization，COCO detection和COCO segmentation等任務中均獲得了第一名，在當時可謂是轟動一時。

2.網絡結構

主要分為4個模塊，但是每個模塊中主要包括兩個最基本的塊。

3.創新點

1.引入殘差網絡(跳躍連接)

這個殘差結構實際上就是一個差分方放大器，使得映射F(x)對輸出的變化更加敏感。這個結構不僅改善了網絡越深越難訓練的缺點還加快了模型的收斂速度。（這樣做為什么能夠實現這樣的效果）跳躍鏈接如下所示：

ResNet50網絡主要采用如下兩個模塊：

①左圖為基本的residual block，residual mapping為兩個64通道的3x3卷積，輸入輸出均為64通道，可直接相加。該block主要使用在相對淺層網絡。
② 右圖為針對深層網絡提出的block，稱為“bottleneck” block，主要目的就是為了降維。首先通過一個1x1卷積將256維通道（channel）降到64通道，最后通過一個256通道的1x1卷積恢復。

2.使用1x1卷積核

1x1卷積核在ResNet50網絡中，主要用于升維和降維。這也是一個創新點。

3.使用全局平均池化

在VGG系列中，參數大部分出在了全連接層，而在ResNet網絡中，去除全連接層，而是用全局平均池化代替，大大減少了參數數量，加快了運算速度。

4.網絡實現

def identity_block(input_ten,kernel_size,filters):filters1,filters2,filters3 = filtersx = Conv2D(filters1,(1,1))(input_ten)x = BatchNormalization()(x)x = Activation('relu')(x)x = Conv2D(filters2,kernel_size,padding='same')(x)x = BatchNormalization()(x)x = Activation('relu')(x)x = Conv2D(filters3,(1,1))(x)x = BatchNormalization()(x)x = layers.add([x,input_ten])x = Activation('relu')(x)return x def conv_block(input_ten,kernel_size,filters,strides=(2,2)):filters1,filters2,filters3 = filtersx = Conv2D(filters1,(1,1),strides=strides)(input_ten)x = BatchNormalization()(x)x = Activation('relu')(x)x = Conv2D(filters2,kernel_size,padding='same')(x)x = BatchNormalization()(x)x = Activation('relu')(x)x = Conv2D(filters3,(1,1))(x)x = BatchNormalization()(x)shortcut = Conv2D(filters3,(1,1),strides=strides)(input_ten)shortcut = BatchNormalization()(shortcut)x = layers.add([x,shortcut])x = Activation('relu')(x)return x def ResNet50(nb_class,input_shape):input_ten = Input(shape=input_shape)x = ZeroPadding2D((3,3))(input_ten)x = Conv2D(64,(7,7),strides=(2,2))(x)x = BatchNormalization()(x)x = Activation('relu')(x)x = MaxPooling2D((3,3),strides=(2,2))(x)x = conv_block(x,3,[64,64,256],strides=(1,1))x = identity_block(x,3,[64,64,256])x = identity_block(x,3,[64,64,256])x = conv_block(x,3,[128,128,512])x = identity_block(x,3,[128,128,512])x = identity_block(x,3,[128,128,512])x = identity_block(x,3,[128,128,512])x = conv_block(x,3,[256,256,1024])x = identity_block(x,3,[256,256,1024])x = identity_block(x,3,[256,256,1024])x = identity_block(x,3,[256,256,1024])x = identity_block(x,3,[256,256,1024])x = identity_block(x,3,[256,256,1024])x = conv_block(x,3,[512,512,2048])x = identity_block(x,3,[512,512,2048])x = identity_block(x,3,[512,512,2048])x = AveragePooling2D((7,7))(x)x = tf.keras.layers.Flatten()(x)output_ten = Dense(nb_class,activation='softmax')(x)model = Model(input_ten,output_ten)model.load_weights("resnet50_weights_tf_dim_ordering_tf_kernels.h5")return model model_ResNet50 = ResNet50(24,(img_height,img_width,3)) model_ResNet50.summary() Model: "model_2" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_3 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ zero_padding2d_1 (ZeroPadding2D (None, 230, 230, 3) 0 input_3[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 112, 112, 64) 9472 zero_padding2d_1[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 112, 112, 64) 256 conv2d_66[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 112, 112, 64) 0 batch_normalization_53[0][0] __________________________________________________________________________________________________ max_pooling2d_6 (MaxPooling2D) (None, 55, 55, 64) 0 activation_49[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 55, 55, 64) 4160 max_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 55, 55, 64) 256 conv2d_67[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 55, 55, 64) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 55, 55, 64) 36928 activation_50[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 55, 55, 64) 256 conv2d_68[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 55, 55, 64) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 55, 55, 256) 16640 activation_51[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 55, 55, 256) 16640 max_pooling2d_6[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 55, 55, 256) 1024 conv2d_69[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 55, 55, 256) 1024 conv2d_70[0][0] __________________________________________________________________________________________________ add_16 (Add) (None, 55, 55, 256) 0 batch_normalization_56[0][0] batch_normalization_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 55, 55, 256) 0 add_16[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 55, 55, 64) 16448 activation_52[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 55, 55, 64) 256 conv2d_71[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 55, 55, 64) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 55, 55, 64) 36928 activation_53[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 55, 55, 64) 256 conv2d_72[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 55, 55, 64) 0 batch_normalization_59[0][0] __________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 55, 55, 256) 16640 activation_54[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 55, 55, 256) 1024 conv2d_73[0][0] __________________________________________________________________________________________________ add_17 (Add) (None, 55, 55, 256) 0 batch_normalization_60[0][0] activation_52[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 55, 55, 256) 0 add_17[0][0] __________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 55, 55, 64) 16448 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 55, 55, 64) 256 conv2d_74[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 55, 55, 64) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 55, 55, 64) 36928 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 55, 55, 64) 256 conv2d_75[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 55, 55, 64) 0 batch_normalization_62[0][0] __________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 55, 55, 256) 16640 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 55, 55, 256) 1024 conv2d_76[0][0] __________________________________________________________________________________________________ add_18 (Add) (None, 55, 55, 256) 0 batch_normalization_63[0][0] activation_55[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 55, 55, 256) 0 add_18[0][0] __________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 28, 28, 128) 32896 activation_58[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 28, 28, 128) 512 conv2d_77[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 28, 28, 128) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ conv2d_78 (Conv2D) (None, 28, 28, 128) 147584 activation_59[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 28, 28, 128) 512 conv2d_78[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 28, 28, 128) 0 batch_normalization_65[0][0] __________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 28, 28, 512) 66048 activation_60[0][0] __________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 28, 28, 512) 131584 activation_58[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 28, 28, 512) 2048 conv2d_79[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 28, 28, 512) 2048 conv2d_80[0][0] __________________________________________________________________________________________________ add_19 (Add) (None, 28, 28, 512) 0 batch_normalization_66[0][0] batch_normalization_67[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 28, 28, 512) 0 add_19[0][0] __________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 28, 28, 128) 65664 activation_61[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 28, 28, 128) 512 conv2d_81[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 28, 28, 128) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ conv2d_82 (Conv2D) (None, 28, 28, 128) 147584 activation_62[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 28, 28, 128) 512 conv2d_82[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 28, 28, 128) 0 batch_normalization_69[0][0] __________________________________________________________________________________________________ conv2d_83 (Conv2D) (None, 28, 28, 512) 66048 activation_63[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 28, 28, 512) 2048 conv2d_83[0][0] __________________________________________________________________________________________________ add_20 (Add) (None, 28, 28, 512) 0 batch_normalization_70[0][0] activation_61[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 28, 28, 512) 0 add_20[0][0] __________________________________________________________________________________________________ conv2d_84 (Conv2D) (None, 28, 28, 128) 65664 activation_64[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 28, 28, 128) 512 conv2d_84[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 28, 28, 128) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ conv2d_85 (Conv2D) (None, 28, 28, 128) 147584 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 28, 28, 128) 512 conv2d_85[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 28, 28, 128) 0 batch_normalization_72[0][0] __________________________________________________________________________________________________ conv2d_86 (Conv2D) (None, 28, 28, 512) 66048 activation_66[0][0] __________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 28, 28, 512) 2048 conv2d_86[0][0] __________________________________________________________________________________________________ add_21 (Add) (None, 28, 28, 512) 0 batch_normalization_73[0][0] activation_64[0][0] __________________________________________________________________________________________________ activation_67 (Activation) (None, 28, 28, 512) 0 add_21[0][0] __________________________________________________________________________________________________ conv2d_87 (Conv2D) (None, 28, 28, 128) 65664 activation_67[0][0] __________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 28, 28, 128) 512 conv2d_87[0][0] __________________________________________________________________________________________________ activation_68 (Activation) (None, 28, 28, 128) 0 batch_normalization_74[0][0] __________________________________________________________________________________________________ conv2d_88 (Conv2D) (None, 28, 28, 128) 147584 activation_68[0][0] __________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 28, 28, 128) 512 conv2d_88[0][0] __________________________________________________________________________________________________ activation_69 (Activation) (None, 28, 28, 128) 0 batch_normalization_75[0][0] __________________________________________________________________________________________________ conv2d_89 (Conv2D) (None, 28, 28, 512) 66048 activation_69[0][0] __________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 28, 28, 512) 2048 conv2d_89[0][0] __________________________________________________________________________________________________ add_22 (Add) (None, 28, 28, 512) 0 batch_normalization_76[0][0] activation_67[0][0] __________________________________________________________________________________________________ activation_70 (Activation) (None, 28, 28, 512) 0 add_22[0][0] __________________________________________________________________________________________________ conv2d_90 (Conv2D) (None, 14, 14, 256) 131328 activation_70[0][0] __________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 14, 14, 256) 1024 conv2d_90[0][0] __________________________________________________________________________________________________ activation_71 (Activation) (None, 14, 14, 256) 0 batch_normalization_77[0][0] __________________________________________________________________________________________________ conv2d_91 (Conv2D) (None, 14, 14, 256) 590080 activation_71[0][0] __________________________________________________________________________________________________ batch_normalization_78 (BatchNo (None, 14, 14, 256) 1024 conv2d_91[0][0] __________________________________________________________________________________________________ activation_72 (Activation) (None, 14, 14, 256) 0 batch_normalization_78[0][0] __________________________________________________________________________________________________ conv2d_92 (Conv2D) (None, 14, 14, 1024) 263168 activation_72[0][0] __________________________________________________________________________________________________ conv2d_93 (Conv2D) (None, 14, 14, 1024) 525312 activation_70[0][0] __________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 14, 14, 1024) 4096 conv2d_92[0][0] __________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 14, 14, 1024) 4096 conv2d_93[0][0] __________________________________________________________________________________________________ add_23 (Add) (None, 14, 14, 1024) 0 batch_normalization_79[0][0] batch_normalization_80[0][0] __________________________________________________________________________________________________ activation_73 (Activation) (None, 14, 14, 1024) 0 add_23[0][0] __________________________________________________________________________________________________ conv2d_94 (Conv2D) (None, 14, 14, 256) 262400 activation_73[0][0] __________________________________________________________________________________________________ batch_normalization_81 (BatchNo (None, 14, 14, 256) 1024 conv2d_94[0][0] __________________________________________________________________________________________________ activation_74 (Activation) (None, 14, 14, 256) 0 batch_normalization_81[0][0] __________________________________________________________________________________________________ conv2d_95 (Conv2D) (None, 14, 14, 256) 590080 activation_74[0][0] __________________________________________________________________________________________________ batch_normalization_82 (BatchNo (None, 14, 14, 256) 1024 conv2d_95[0][0] __________________________________________________________________________________________________ activation_75 (Activation) (None, 14, 14, 256) 0 batch_normalization_82[0][0] __________________________________________________________________________________________________ conv2d_96 (Conv2D) (None, 14, 14, 1024) 263168 activation_75[0][0] __________________________________________________________________________________________________ batch_normalization_83 (BatchNo (None, 14, 14, 1024) 4096 conv2d_96[0][0] __________________________________________________________________________________________________ add_24 (Add) (None, 14, 14, 1024) 0 batch_normalization_83[0][0] activation_73[0][0] __________________________________________________________________________________________________ activation_76 (Activation) (None, 14, 14, 1024) 0 add_24[0][0] __________________________________________________________________________________________________ conv2d_97 (Conv2D) (None, 14, 14, 256) 262400 activation_76[0][0] __________________________________________________________________________________________________ batch_normalization_84 (BatchNo (None, 14, 14, 256) 1024 conv2d_97[0][0] __________________________________________________________________________________________________ activation_77 (Activation) (None, 14, 14, 256) 0 batch_normalization_84[0][0] __________________________________________________________________________________________________ conv2d_98 (Conv2D) (None, 14, 14, 256) 590080 activation_77[0][0] __________________________________________________________________________________________________ batch_normalization_85 (BatchNo (None, 14, 14, 256) 1024 conv2d_98[0][0] __________________________________________________________________________________________________ activation_78 (Activation) (None, 14, 14, 256) 0 batch_normalization_85[0][0] __________________________________________________________________________________________________ conv2d_99 (Conv2D) (None, 14, 14, 1024) 263168 activation_78[0][0] __________________________________________________________________________________________________ batch_normalization_86 (BatchNo (None, 14, 14, 1024) 4096 conv2d_99[0][0] __________________________________________________________________________________________________ add_25 (Add) (None, 14, 14, 1024) 0 batch_normalization_86[0][0] activation_76[0][0] __________________________________________________________________________________________________ activation_79 (Activation) (None, 14, 14, 1024) 0 add_25[0][0] __________________________________________________________________________________________________ conv2d_100 (Conv2D) (None, 14, 14, 256) 262400 activation_79[0][0] __________________________________________________________________________________________________ batch_normalization_87 (BatchNo (None, 14, 14, 256) 1024 conv2d_100[0][0] __________________________________________________________________________________________________ activation_80 (Activation) (None, 14, 14, 256) 0 batch_normalization_87[0][0] __________________________________________________________________________________________________ conv2d_101 (Conv2D) (None, 14, 14, 256) 590080 activation_80[0][0] __________________________________________________________________________________________________ batch_normalization_88 (BatchNo (None, 14, 14, 256) 1024 conv2d_101[0][0] __________________________________________________________________________________________________ activation_81 (Activation) (None, 14, 14, 256) 0 batch_normalization_88[0][0] __________________________________________________________________________________________________ conv2d_102 (Conv2D) (None, 14, 14, 1024) 263168 activation_81[0][0] __________________________________________________________________________________________________ batch_normalization_89 (BatchNo (None, 14, 14, 1024) 4096 conv2d_102[0][0] __________________________________________________________________________________________________ add_26 (Add) (None, 14, 14, 1024) 0 batch_normalization_89[0][0] activation_79[0][0] __________________________________________________________________________________________________ activation_82 (Activation) (None, 14, 14, 1024) 0 add_26[0][0] __________________________________________________________________________________________________ conv2d_103 (Conv2D) (None, 14, 14, 256) 262400 activation_82[0][0] __________________________________________________________________________________________________ batch_normalization_90 (BatchNo (None, 14, 14, 256) 1024 conv2d_103[0][0] __________________________________________________________________________________________________ activation_83 (Activation) (None, 14, 14, 256) 0 batch_normalization_90[0][0] __________________________________________________________________________________________________ conv2d_104 (Conv2D) (None, 14, 14, 256) 590080 activation_83[0][0] __________________________________________________________________________________________________ batch_normalization_91 (BatchNo (None, 14, 14, 256) 1024 conv2d_104[0][0] __________________________________________________________________________________________________ activation_84 (Activation) (None, 14, 14, 256) 0 batch_normalization_91[0][0] __________________________________________________________________________________________________ conv2d_105 (Conv2D) (None, 14, 14, 1024) 263168 activation_84[0][0] __________________________________________________________________________________________________ batch_normalization_92 (BatchNo (None, 14, 14, 1024) 4096 conv2d_105[0][0] __________________________________________________________________________________________________ add_27 (Add) (None, 14, 14, 1024) 0 batch_normalization_92[0][0] activation_82[0][0] __________________________________________________________________________________________________ activation_85 (Activation) (None, 14, 14, 1024) 0 add_27[0][0] __________________________________________________________________________________________________ conv2d_106 (Conv2D) (None, 14, 14, 256) 262400 activation_85[0][0] __________________________________________________________________________________________________ batch_normalization_93 (BatchNo (None, 14, 14, 256) 1024 conv2d_106[0][0] __________________________________________________________________________________________________ activation_86 (Activation) (None, 14, 14, 256) 0 batch_normalization_93[0][0] __________________________________________________________________________________________________ conv2d_107 (Conv2D) (None, 14, 14, 256) 590080 activation_86[0][0] __________________________________________________________________________________________________ batch_normalization_94 (BatchNo (None, 14, 14, 256) 1024 conv2d_107[0][0] __________________________________________________________________________________________________ activation_87 (Activation) (None, 14, 14, 256) 0 batch_normalization_94[0][0] __________________________________________________________________________________________________ conv2d_108 (Conv2D) (None, 14, 14, 1024) 263168 activation_87[0][0] __________________________________________________________________________________________________ batch_normalization_95 (BatchNo (None, 14, 14, 1024) 4096 conv2d_108[0][0] __________________________________________________________________________________________________ add_28 (Add) (None, 14, 14, 1024) 0 batch_normalization_95[0][0] activation_85[0][0] __________________________________________________________________________________________________ activation_88 (Activation) (None, 14, 14, 1024) 0 add_28[0][0] __________________________________________________________________________________________________ conv2d_109 (Conv2D) (None, 7, 7, 512) 524800 activation_88[0][0] __________________________________________________________________________________________________ batch_normalization_96 (BatchNo (None, 7, 7, 512) 2048 conv2d_109[0][0] __________________________________________________________________________________________________ activation_89 (Activation) (None, 7, 7, 512) 0 batch_normalization_96[0][0] __________________________________________________________________________________________________ conv2d_110 (Conv2D) (None, 7, 7, 512) 2359808 activation_89[0][0] __________________________________________________________________________________________________ batch_normalization_97 (BatchNo (None, 7, 7, 512) 2048 conv2d_110[0][0] __________________________________________________________________________________________________ activation_90 (Activation) (None, 7, 7, 512) 0 batch_normalization_97[0][0] __________________________________________________________________________________________________ conv2d_111 (Conv2D) (None, 7, 7, 2048) 1050624 activation_90[0][0] __________________________________________________________________________________________________ conv2d_112 (Conv2D) (None, 7, 7, 2048) 2099200 activation_88[0][0] __________________________________________________________________________________________________ batch_normalization_98 (BatchNo (None, 7, 7, 2048) 8192 conv2d_111[0][0] __________________________________________________________________________________________________ batch_normalization_99 (BatchNo (None, 7, 7, 2048) 8192 conv2d_112[0][0] __________________________________________________________________________________________________ add_29 (Add) (None, 7, 7, 2048) 0 batch_normalization_98[0][0] batch_normalization_99[0][0] __________________________________________________________________________________________________ activation_91 (Activation) (None, 7, 7, 2048) 0 add_29[0][0] __________________________________________________________________________________________________ conv2d_113 (Conv2D) (None, 7, 7, 512) 1049088 activation_91[0][0] __________________________________________________________________________________________________ batch_normalization_100 (BatchN (None, 7, 7, 512) 2048 conv2d_113[0][0] __________________________________________________________________________________________________ activation_92 (Activation) (None, 7, 7, 512) 0 batch_normalization_100[0][0] __________________________________________________________________________________________________ conv2d_114 (Conv2D) (None, 7, 7, 512) 2359808 activation_92[0][0] __________________________________________________________________________________________________ batch_normalization_101 (BatchN (None, 7, 7, 512) 2048 conv2d_114[0][0] __________________________________________________________________________________________________ activation_93 (Activation) (None, 7, 7, 512) 0 batch_normalization_101[0][0] __________________________________________________________________________________________________ conv2d_115 (Conv2D) (None, 7, 7, 2048) 1050624 activation_93[0][0] __________________________________________________________________________________________________ batch_normalization_102 (BatchN (None, 7, 7, 2048) 8192 conv2d_115[0][0] __________________________________________________________________________________________________ add_30 (Add) (None, 7, 7, 2048) 0 batch_normalization_102[0][0] activation_91[0][0] __________________________________________________________________________________________________ activation_94 (Activation) (None, 7, 7, 2048) 0 add_30[0][0] __________________________________________________________________________________________________ conv2d_116 (Conv2D) (None, 7, 7, 512) 1049088 activation_94[0][0] __________________________________________________________________________________________________ batch_normalization_103 (BatchN (None, 7, 7, 512) 2048 conv2d_116[0][0] __________________________________________________________________________________________________ activation_95 (Activation) (None, 7, 7, 512) 0 batch_normalization_103[0][0] __________________________________________________________________________________________________ conv2d_117 (Conv2D) (None, 7, 7, 512) 2359808 activation_95[0][0] __________________________________________________________________________________________________ batch_normalization_104 (BatchN (None, 7, 7, 512) 2048 conv2d_117[0][0] __________________________________________________________________________________________________ activation_96 (Activation) (None, 7, 7, 512) 0 batch_normalization_104[0][0] __________________________________________________________________________________________________ conv2d_118 (Conv2D) (None, 7, 7, 2048) 1050624 activation_96[0][0] __________________________________________________________________________________________________ batch_normalization_105 (BatchN (None, 7, 7, 2048) 8192 conv2d_118[0][0] __________________________________________________________________________________________________ add_31 (Add) (None, 7, 7, 2048) 0 batch_normalization_105[0][0] activation_94[0][0] __________________________________________________________________________________________________ activation_97 (Activation) (None, 7, 7, 2048) 0 add_31[0][0] __________________________________________________________________________________________________ average_pooling2d_1 (AveragePoo (None, 1, 1, 2048) 0 activation_97[0][0] __________________________________________________________________________________________________ flatten_2 (Flatten) (None, 2048) 0 average_pooling2d_1[0][0] __________________________________________________________________________________________________ dense_4 (Dense) (None, 24) 49176 flatten_2[0][0] ================================================================================================== Total params: 23,636,888 Trainable params: 23,583,768 Non-trainable params: 53,120 __________________________________________________________________________________________________

相比于VGG16網絡，雖然層數加深，但是訓練參數卻大大減少，甚至比AlexNet網絡還少。得益于去除全連接層，而是用全局平均池化。

努力加油a啊

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