【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码

各位同学好,今天和大家分享一下如何使用 Tensorflow 构建 Xception 神经网络模型。

在前面章节中,我已经介绍了很多种轻量化卷积神经网络模型,感兴趣的可以看一下:https://blog.csdn.net/dgvv4/category_11517910.html

Xception 是一种兼顾了准确性和轻量化的算法。如下图所示,横轴表示计算量,纵轴表示准确率。在准确率上,Xception是排在第一梯队的,且在计算速度上,也算是轻量化网络模型。

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第1张图片

Xception 使用了 MobileNetV1 的深度可分离卷积方法,建议大家先学习一下 MobileNetV1:https://blog.csdn.net/dgvv4/article/details/123415708


1. 深度可分离卷积

为了帮助大家更好地掌握 Xception,先简单地复习一下深度可分离卷积的方法。

普通卷积一个卷积核处理所有的通道,输入特征图有多少个通道,卷积核就有几个通道,一个卷积核生成一张特征图。

深度可分离卷积 可理解为 深度卷积 + 逐点卷积
深度卷积只处理长宽方向的空间信息;逐点卷积只处理跨通道方向的信息。能大大减少参数量,提高计算效率

深度卷积: 是一个卷积核只处理一个通道,即每个卷积核只处理自己对应的通道输入特征图有多少个通道就有多少个卷积核。将每个卷积核处理后的特征图堆叠在一起。输入和输出特征图的通道数相同。

由于只处理长宽方向的信息会导致丢失跨通道信息,为了将跨通道的信息补充回来,需要进行逐点卷积。

逐点卷积: 是使用1x1卷积对跨通道维度处理有多少个1x1卷积核就会生成多少个特征图


2. 从 Inception 到 Xception

接下来梳理一下从Inception到Xception网络的核心模块的改进过程,帮助大家对Xception结构有进一步的认识。

首先 InceptionV1 是由9个 BottleNeck-Inception 模块堆叠而成,如下图所示。

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第2张图片


2.1 Inception模块

Inception模块的原理: 将输入的特征图分成四个分支,进行四种不同的处理,再将四种方法处理的结果特征图堆叠起来,输入到下一层。

通过尽可能多的分解和解耦,用不同的尺度、不同的卷积来获取不同层次,不同力度的信息。

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第3张图片


2.2 BottleNeck模块

随着 Inception 模块的输出特征图不断的堆叠,特征图的通道数会越来越多。为了防止特征图越来越多,运算量和参数量爆炸。在 3x3 和 5x5 卷积之前添加了1x1卷积进行降维,控制输出特征图的数量,减少参数量和计算量。左图为Inception模块,右图为BottleNeck模块。

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第4张图片


2.3 Inception 网络的改进过程

(1)首先 InceptionV3 改进了 BottleNeck 模块,将 5x5 卷积分解成两个 3x3 卷积。两层3x3卷积代替一层5x5卷积可以获得相同的感受野,减少参数量,增加非线性,提高模型的表达能力。

(2)将池化层后的1x1卷积换成3x3卷积。

(3)第一层全使用1x1卷积,第二层全使用3x3卷积。

(4)图像输入进来后,先经过一次1x1卷积生成特征图,接下来三个分支都对这个特征图处理。

(5)图像输入后,使用分组卷积对1x1卷积后的特征图处理,不同的卷积核处理不同的通道,各分组之间相互独立

(6)Xception模块,使用深度可分离卷积思想,先逐点卷积,后深度卷积,每个3x3卷积只处理一个通道。逐点卷积和深度卷积的先后次序并太大无影响。

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第5张图片


3. 代码复现

3.1 网络结构图

论文中给出的 Xception 网络模型结构如下图所示

【神经网络】(15) Xception 代码复现,网络解析,附Tensorflow完整代码_第6张图片


3.2 搭建各个卷积模块

(1)标准卷积块

一个标准卷积块由 卷积+批标准化+激活函数 组成

#(1)标准卷积模块
def conv_block(input_tensor, filters, kernel_size, stride):

    # 普通卷积+标准化+激活函数
    x = layers.Conv2D(filters = filters,  # 输出特征图个数
                      kernel_size = kernel_size,  # 卷积size
                      strides = stride,  # 步长
                      padding = 'same',  # 步长=1输出特征图size不变,步长=2特征图长宽减半
                      use_bias = False)(input_tensor)  # 有BN层就不需要偏置
    
    x = layers.BatchNormalization()(x)  # 批标准化

    x = layers.ReLU()(x)  # relu激活函数

    return x  # 返回标准卷积的输出特征图

(2)残差块

按结构图所示,构建一个残差单元,由 两个深度可分离卷积+最大池化+残差边 组成

#(2)深度可分离卷积模块
def sep_conv_block(input_tensor, filters, kernel_size):

    # 激活函数
    x = layers.ReLU()(input_tensor)

    # 深度可分离卷积函数,包含了(深度卷积+逐点卷积)
    x = layers.SeparableConvolution2D(filters = filters,  # 逐点卷积的卷积核个数,输出特征图个数
                                      kernel_size = kernel_size,  # 深度卷积的卷积核size
                                      strides = 1,  # 深度卷积的步长
                                      padding = 'same',  # 卷积过程中特征图size不变
                                      use_bias = False)(x)  # 有BN层就不要偏置
    
    return x  # 返回输出特征图


#(3)一个残差单元
def res_block(input_tensor, filters):

    # ① 残差边
    residual = layers.Conv2D(filters,  # 输出图像的通道数
                              kernel_size = (1,1),  # 卷积核size
                              strides = 2)(input_tensor)  # 使输入和输出的size相同

    residual = layers.BatchNormalization()(residual)  # 批标准化

    # ② 卷积块
    x = sep_conv_block(input_tensor, filters, kernel_size=(3,3))
    x = sep_conv_block(x, filters, kernel_size=(3,3))
    x = layers.MaxPooling2D(pool_size=(3,3), strides=2, padding='same')(x)

    # ③ 输入输出叠加,残差连接
    output = layers.Add()([residual, x])

    return output

3.3 完整代码展示

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Model, layers

#(1)标准卷积模块
def conv_block(input_tensor, filters, kernel_size, stride):

    # 普通卷积+标准化+激活函数
    x = layers.Conv2D(filters = filters,  # 输出特征图个数
                      kernel_size = kernel_size,  # 卷积size
                      strides = stride,  # 步长
                      padding = 'same',  # 步长=1输出特征图size不变,步长=2特征图长宽减半
                      use_bias = False)(input_tensor)  # 有BN层就不需要偏置
    
    x = layers.BatchNormalization()(x)  # 批标准化

    x = layers.ReLU()(x)  # relu激活函数

    return x  # 返回标准卷积的输出特征图


#(2)深度可分离卷积模块
def sep_conv_block(input_tensor, filters, kernel_size):

    # 激活函数
    x = layers.ReLU()(input_tensor)

    # 深度可分离卷积函数,包含了(深度卷积+逐点卷积)
    x = layers.SeparableConvolution2D(filters = filters,  # 逐点卷积的卷积核个数,输出特征图个数
                                      kernel_size = kernel_size,  # 深度卷积的卷积核size
                                      strides = 1,  # 深度卷积的步长
                                      padding = 'same',  # 卷积过程中特征图size不变
                                      use_bias = False)(x)  # 有BN层就不要偏置
    
    return x  # 返回输出特征图


#(3)一个残差单元
def res_block(input_tensor, filters):

    # ① 残差边
    residual = layers.Conv2D(filters,  # 输出图像的通道数
                              kernel_size = (1,1),  # 卷积核size
                              strides = 2)(input_tensor)  # 使输入和输出的size相同

    residual = layers.BatchNormalization()(residual)  # 批标准化

    # ② 卷积块
    x = sep_conv_block(input_tensor, filters, kernel_size=(3,3))
    x = sep_conv_block(x, filters, kernel_size=(3,3))
    x = layers.MaxPooling2D(pool_size=(3,3), strides=2, padding='same')(x)

    # ③ 输入输出叠加,残差连接
    output = layers.Add()([residual, x])

    return output


#(4)Middle Flow模块
def middle_flow(x, filters):

    # 该模块循环8次
    for _ in range(8): 

        # 残差边
        residual = x
        # 三个深度可分离卷积块
        x = sep_conv_block(x, filters, kernel_size=(3,3))
        x = sep_conv_block(x, filters, kernel_size=(3,3))
        x = sep_conv_block(x, filters, kernel_size=(3,3))
        # 叠加残差边
        x = layers.Add()([residual, x])

    return x


#(5)主干网络
def xception(input_shape, classes):

    # 构建输入
    inputs = keras.Input(shape=input_shape)

    # [299,299,3]==>[149,149,32]
    x = conv_block(inputs, filters=32, kernel_size=(3,3), stride=2)  # 标准卷积块
    # [149,149,32]==>[149,149,64]
    x = conv_block(x, filters=64, kernel_size=(3,3), stride=1)

    # [149,149,64]==>[75,75,128]
    # 残差边
    residual = layers.Conv2D(filters=128, kernel_size=(1,1), strides=2, 
                             padding='same', use_bias=False)(x)
    residual = layers.BatchNormalization()(residual)
    # 卷积块[149,149,64]==>[149,149,128]
    x = layers.SeparableConv2D(128, kernel_size=(3,3), strides=1, padding='same',use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    # [149,149,128]==>[149,149,128]
    x = sep_conv_block(x, filters=128, kernel_size=(3,3))
    # [149,149,128]==>[75,75,128]
    x = layers.MaxPooling2D(pool_size=(3,3), strides=2, padding='same')(x)

    # [75,75,128]==>[38,38,256]
    x = res_block(x, filters=256)
    # [38,38,256]==>[19,19,728]
    x = res_block(x, filters=728)
    # [19,19,728]==>[19,19,728]
    x = middle_flow(x, filters=728)
    
    # 残差边模块[19,19,728]==>[10,10,1024]
    residual = layers.Conv2D(filters=1024, kernel_size=(1,1), 
                             strides=2, use_bias=False, padding='same')(x) 
    
    residual = layers.BatchNormalization()(residual)  # 批标准化

    # 卷积块[19,19,728]==>[19,19,728]
    x = sep_conv_block(x, filters=728, kernel_size=(3,3))
    # [19,19,728]==>[19,19,1024]
    x = sep_conv_block(x, filters=1024, kernel_size=(3,3))
    # [19,19,1024]==>[10,10,1024]
    x = layers.MaxPooling2D(pool_size=(3,3), strides=2, padding='same')(x)

    # 叠加残差边[10,10,1024]
    x = layers.Add()([residual, x])

    # [10,10,1024]==>[10,10,1536]
    x = layers.SeparableConv2D(1536, (3,3), padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    
    # [10,10,1536]==>[10,10,2048]
    x = layers.SeparableConv2D(2048, (3,3), padding='same', use_bias=False)(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)

    # [10,10,2048]==>[None,2048]
    x = layers.GlobalAveragePooling2D()(x)

    # [None,2048]==>[None,classes]
    outputs = layers.Dense(classes)(x)  # logits层不做softmax

    # 构建模型
    model = Model(inputs, outputs)

    return model


#(6)接收网络模型
if __name__ == '__main__':

    model = xception(input_shape=[299,299,3], classes=1000)

    model.summary()  # 查看网络模型结构

3.4 查看网络架构

通过 model.summary() 查看网络模型框架,网络参数量2千多万

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 150, 150, 32) 864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 150, 150, 32) 128         conv2d[0][0]                     
__________________________________________________________________________________________________
re_lu (ReLU)                    (None, 150, 150, 32) 0           batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 150, 150, 64) 18432       re_lu[0][0]                      
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 150, 150, 64) 256         conv2d_1[0][0]                   
__________________________________________________________________________________________________
re_lu_1 (ReLU)                  (None, 150, 150, 64) 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
separable_conv2d (SeparableConv (None, 150, 150, 128 8768        re_lu_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 150, 150, 128 512         separable_conv2d[0][0]           
__________________________________________________________________________________________________
re_lu_2 (ReLU)                  (None, 150, 150, 128 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
separable_conv2d_1 (SeparableCo (None, 150, 150, 128 17536       re_lu_2[0][0]                    
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 75, 75, 128)  0           separable_conv2d_1[0][0]         
__________________________________________________________________________________________________
re_lu_3 (ReLU)                  (None, 75, 75, 128)  0           max_pooling2d[0][0]              
__________________________________________________________________________________________________
separable_conv2d_2 (SeparableCo (None, 75, 75, 256)  33920       re_lu_3[0][0]                    
__________________________________________________________________________________________________
re_lu_4 (ReLU)                  (None, 75, 75, 256)  0           separable_conv2d_2[0][0]         
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 38, 38, 256)  33024       max_pooling2d[0][0]              
__________________________________________________________________________________________________
separable_conv2d_3 (SeparableCo (None, 75, 75, 256)  67840       re_lu_4[0][0]                    
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 38, 38, 256)  1024        conv2d_3[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 38, 38, 256)  0           separable_conv2d_3[0][0]         
__________________________________________________________________________________________________
add (Add)                       (None, 38, 38, 256)  0           batch_normalization_4[0][0]      
                                                                 max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
re_lu_5 (ReLU)                  (None, 38, 38, 256)  0           add[0][0]                        
__________________________________________________________________________________________________
separable_conv2d_4 (SeparableCo (None, 38, 38, 728)  188672      re_lu_5[0][0]                    
__________________________________________________________________________________________________
re_lu_6 (ReLU)                  (None, 38, 38, 728)  0           separable_conv2d_4[0][0]         
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 19, 19, 728)  187096      add[0][0]                        
__________________________________________________________________________________________________
separable_conv2d_5 (SeparableCo (None, 38, 38, 728)  536536      re_lu_6[0][0]                    
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 19, 19, 728)  2912        conv2d_4[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 19, 19, 728)  0           separable_conv2d_5[0][0]         
__________________________________________________________________________________________________
add_1 (Add)                     (None, 19, 19, 728)  0           batch_normalization_5[0][0]      
                                                                 max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
re_lu_7 (ReLU)                  (None, 19, 19, 728)  0           add_1[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_6 (SeparableCo (None, 19, 19, 728)  536536      re_lu_7[0][0]                    
__________________________________________________________________________________________________
re_lu_8 (ReLU)                  (None, 19, 19, 728)  0           separable_conv2d_6[0][0]         
__________________________________________________________________________________________________
separable_conv2d_7 (SeparableCo (None, 19, 19, 728)  536536      re_lu_8[0][0]                    
__________________________________________________________________________________________________
re_lu_9 (ReLU)                  (None, 19, 19, 728)  0           separable_conv2d_7[0][0]         
__________________________________________________________________________________________________
separable_conv2d_8 (SeparableCo (None, 19, 19, 728)  536536      re_lu_9[0][0]                    
__________________________________________________________________________________________________
add_2 (Add)                     (None, 19, 19, 728)  0           add_1[0][0]                      
                                                                 separable_conv2d_8[0][0]         
__________________________________________________________________________________________________
re_lu_10 (ReLU)                 (None, 19, 19, 728)  0           add_2[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_9 (SeparableCo (None, 19, 19, 728)  536536      re_lu_10[0][0]                   
__________________________________________________________________________________________________
re_lu_11 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_9[0][0]         
__________________________________________________________________________________________________
separable_conv2d_10 (SeparableC (None, 19, 19, 728)  536536      re_lu_11[0][0]                   
__________________________________________________________________________________________________
re_lu_12 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_10[0][0]        
__________________________________________________________________________________________________
separable_conv2d_11 (SeparableC (None, 19, 19, 728)  536536      re_lu_12[0][0]                   
__________________________________________________________________________________________________
add_3 (Add)                     (None, 19, 19, 728)  0           add_2[0][0]                      
                                                                 separable_conv2d_11[0][0]        
__________________________________________________________________________________________________
re_lu_13 (ReLU)                 (None, 19, 19, 728)  0           add_3[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_12 (SeparableC (None, 19, 19, 728)  536536      re_lu_13[0][0]                   
__________________________________________________________________________________________________
re_lu_14 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_12[0][0]        
__________________________________________________________________________________________________
separable_conv2d_13 (SeparableC (None, 19, 19, 728)  536536      re_lu_14[0][0]                   
__________________________________________________________________________________________________
re_lu_15 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_13[0][0]        
__________________________________________________________________________________________________
separable_conv2d_14 (SeparableC (None, 19, 19, 728)  536536      re_lu_15[0][0]                   
__________________________________________________________________________________________________
add_4 (Add)                     (None, 19, 19, 728)  0           add_3[0][0]                      
                                                                 separable_conv2d_14[0][0]        
__________________________________________________________________________________________________
re_lu_16 (ReLU)                 (None, 19, 19, 728)  0           add_4[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_15 (SeparableC (None, 19, 19, 728)  536536      re_lu_16[0][0]                   
__________________________________________________________________________________________________
re_lu_17 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_15[0][0]        
__________________________________________________________________________________________________
separable_conv2d_16 (SeparableC (None, 19, 19, 728)  536536      re_lu_17[0][0]                   
__________________________________________________________________________________________________
re_lu_18 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_16[0][0]        
__________________________________________________________________________________________________
separable_conv2d_17 (SeparableC (None, 19, 19, 728)  536536      re_lu_18[0][0]                   
__________________________________________________________________________________________________
add_5 (Add)                     (None, 19, 19, 728)  0           add_4[0][0]                      
                                                                 separable_conv2d_17[0][0]        
__________________________________________________________________________________________________
re_lu_19 (ReLU)                 (None, 19, 19, 728)  0           add_5[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_18 (SeparableC (None, 19, 19, 728)  536536      re_lu_19[0][0]                   
__________________________________________________________________________________________________
re_lu_20 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_18[0][0]        
__________________________________________________________________________________________________
separable_conv2d_19 (SeparableC (None, 19, 19, 728)  536536      re_lu_20[0][0]                   
__________________________________________________________________________________________________
re_lu_21 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_19[0][0]        
__________________________________________________________________________________________________
separable_conv2d_20 (SeparableC (None, 19, 19, 728)  536536      re_lu_21[0][0]                   
__________________________________________________________________________________________________
add_6 (Add)                     (None, 19, 19, 728)  0           add_5[0][0]                      
                                                                 separable_conv2d_20[0][0]        
__________________________________________________________________________________________________
re_lu_22 (ReLU)                 (None, 19, 19, 728)  0           add_6[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_21 (SeparableC (None, 19, 19, 728)  536536      re_lu_22[0][0]                   
__________________________________________________________________________________________________
re_lu_23 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_21[0][0]        
__________________________________________________________________________________________________
separable_conv2d_22 (SeparableC (None, 19, 19, 728)  536536      re_lu_23[0][0]                   
__________________________________________________________________________________________________
re_lu_24 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_22[0][0]        
__________________________________________________________________________________________________
separable_conv2d_23 (SeparableC (None, 19, 19, 728)  536536      re_lu_24[0][0]                   
__________________________________________________________________________________________________
add_7 (Add)                     (None, 19, 19, 728)  0           add_6[0][0]                      
                                                                 separable_conv2d_23[0][0]        
__________________________________________________________________________________________________
re_lu_25 (ReLU)                 (None, 19, 19, 728)  0           add_7[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_24 (SeparableC (None, 19, 19, 728)  536536      re_lu_25[0][0]                   
__________________________________________________________________________________________________
re_lu_26 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_24[0][0]        
__________________________________________________________________________________________________
separable_conv2d_25 (SeparableC (None, 19, 19, 728)  536536      re_lu_26[0][0]                   
__________________________________________________________________________________________________
re_lu_27 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_25[0][0]        
__________________________________________________________________________________________________
separable_conv2d_26 (SeparableC (None, 19, 19, 728)  536536      re_lu_27[0][0]                   
__________________________________________________________________________________________________
add_8 (Add)                     (None, 19, 19, 728)  0           add_7[0][0]                      
                                                                 separable_conv2d_26[0][0]        
__________________________________________________________________________________________________
re_lu_28 (ReLU)                 (None, 19, 19, 728)  0           add_8[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_27 (SeparableC (None, 19, 19, 728)  536536      re_lu_28[0][0]                   
__________________________________________________________________________________________________
re_lu_29 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_27[0][0]        
__________________________________________________________________________________________________
separable_conv2d_28 (SeparableC (None, 19, 19, 728)  536536      re_lu_29[0][0]                   
__________________________________________________________________________________________________
re_lu_30 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_28[0][0]        
__________________________________________________________________________________________________
separable_conv2d_29 (SeparableC (None, 19, 19, 728)  536536      re_lu_30[0][0]                   
__________________________________________________________________________________________________
add_9 (Add)                     (None, 19, 19, 728)  0           add_8[0][0]                      
                                                                 separable_conv2d_29[0][0]        
__________________________________________________________________________________________________
re_lu_31 (ReLU)                 (None, 19, 19, 728)  0           add_9[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_30 (SeparableC (None, 19, 19, 728)  536536      re_lu_31[0][0]                   
__________________________________________________________________________________________________
re_lu_32 (ReLU)                 (None, 19, 19, 728)  0           separable_conv2d_30[0][0]        
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 10, 10, 1024) 745472      add_9[0][0]                      
__________________________________________________________________________________________________
separable_conv2d_31 (SeparableC (None, 19, 19, 1024) 752024      re_lu_32[0][0]                   
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 10, 10, 1024) 4096        conv2d_5[0][0]                   
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 10, 10, 1024) 0           separable_conv2d_31[0][0]        
__________________________________________________________________________________________________
add_10 (Add)                    (None, 10, 10, 1024) 0           batch_normalization_6[0][0]      
                                                                 max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
separable_conv2d_32 (SeparableC (None, 10, 10, 1536) 1582080     add_10[0][0]                     
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 10, 10, 1536) 6144        separable_conv2d_32[0][0]        
__________________________________________________________________________________________________
re_lu_33 (ReLU)                 (None, 10, 10, 1536) 0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
separable_conv2d_33 (SeparableC (None, 10, 10, 2048) 3159552     re_lu_33[0][0]                   
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 10, 10, 2048) 8192        separable_conv2d_33[0][0]        
__________________________________________________________________________________________________
re_lu_34 (ReLU)                 (None, 10, 10, 2048) 0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 2048)         0           re_lu_34[0][0]                   
__________________________________________________________________________________________________
dense (Dense)                   (None, 1000)         2049000     global_average_pooling2d[0][0]   
==================================================================================================
Total params: 22,817,480
Trainable params: 22,805,848
Non-trainable params: 11,632
__________________________________________________________________________________________________

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