@toc
参考论文：Densely Connected Convolutional Networks作者：Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
==CVPR2017最佳论文奖==
1、DenseNet简介
DenseNet:以前馈方式将每一层连接到其他每一层。对于具有L层的传统卷积网络有L个连接(每一层与其后续层之间有一个连接)，而DenseNet有$\frac{L(L+1)}{2}$个连接。
对于每一层，所有前面层的特征图都被用作输入，它自己的特征图被用作所有后续层的输入。
DenseNet有几个引人注目的优势：
- 缓解了梯度消失问题，加强了特征传播，鼓励特征重用，并大大减少了参数量。
官方代码和预训练模型：https://github.com/liuzhuang13/DenseNet
图 1：增长率为 k = 4 的 5 层密集块。每一层都将所有前面的特征图作为输入。
在本文中，我们提出了一种架构，将这种见解提炼成一个简单的连接模式：为了确保网络中各层之间的最大信息流，我们将所有层（具有匹配的特征图大小）直接相互连接。为了保持前馈特性，每一层都从所有前面的层获得额外的输入，并将其自己的特征图传递给所有后续层。图 1 示意性地说明了这种布局。至关重要的是，与 ResNets 相比，我们在将特征传递到层之前从不通过求和来组合特征。相反，我们通过连接(Concatenate操作)它们来组合特征。
因此，第 ℓ 层有 ℓ 输入，由所有先前卷积块的特征图组成。它自己的特征图被传递到所有 L−ℓ 后续层。这在 L 层网络中引入了 L(L+1)2 个连接，而不是传统架构中的仅 L 个连接。由于其密集的连接模式，我们将我们的方法称为密集卷积网络（DenseNet）。
除了更好的参数效率外，DenseNets 的一大优势是它们改进了整个网络的信息流和梯度，这使得它们易于训练。每一层都可以直接访问损失函数和原始输入信号的梯度，从而实现隐式深度监督 [20]。这有助于训练更深层次的网络架构。此外，我们还观察到密集连接具有正则化效果，可以减少对较小训练集大小的任务的过度拟合。
当然，他自己的论文介绍中肯定要吹一波自己的模型很牛逼,可以理解。
2、DenseNet与ResNet的主要区别
DenseNets 不是从极深或极宽的架构中汲取表征能力，而是通过特征重用利用网络的潜力，产生易于训练且参数效率高的浓缩模型。连接不同层学习的特征图增加了后续层输入的变化并提高了效率。这构成了 DenseNets 和 ResNets 之间的主要区别。与同样连接来自不同层的特征的 Inception 网络 相比，DenseNets 更简单、更高效。
3、DenseNets
==这部分是重点，因为论文后面所有的参数都是通过这部分得到的，仔细看下面加粗的部分，这就是后面代码中的超参数和网络结构搭建方法，重点中的重点，要不你都看不懂代码中参数是为什么那样写的。==
考虑通过卷积网络传递的单个图像 x0。该网络由 L 层组成，每一层都实现了一个非线性变换 Hℓ(·)，其中 ℓ 对层进行索引。 Hℓ(·) 可以是批量归一化 (BN) [14]、整流线性单元 (ReLU) [6]、池化 [19] 或卷积 (Conv) 等操作的复合函数。我们将第 ℓ 层的输出表示为 xℓ。
传统的卷积前馈网络将第 ℓ 层的输出作为输入连接到第 (ℓ + 1) 层 [16]，从而产生以下层转换：xℓ = Hℓ(xℓ−1)。
ResNets [11] 添加了一个Skip Connection，可以绕过具有恒等函数的非线性变换：
$$ xℓ = Hℓ(xℓ−1) + xℓ−1. $$
3.1 ResNets
ResNets 的一个优点是梯度可以直接通过恒等函数从后面的层流到前面的层。但是，恒等函数和 Hℓ 的输出是通过求和组合的，这可能会阻碍网络中的信息流。
3.2 Dense connectivity(密集连接)
Dense connectivity,为了进一步改善层之间的信息流，我们提出了一种不同的连接模式：我们引入了从任何层到所有后续层的直接连接。图 1 示意性地说明了生成的 DenseNet 的布局。因此，第ℓ层接收所有前面层的特征图，x0，...。 . . , xℓ−1，作为输入：
$$ xℓ = Hℓ([x0, x1, . . . , xℓ−1]), $$
其中 [x0, x1, . . . , xℓ−1] 是指在第 0 . . . , ℓ − 1层中生成的特征图的串联，。由于其密集的连接性，我们将此网络架构称为密集卷积网络（DenseNet）
3.3 Composite function(复合函数)
我们将 Hℓ(·) 定义为三个连续操作的复合函数：批量归一化 (BN) ，然后是整流线性单元 (ReLU) 和 3 × 3 卷积 (Conv ）。
3.4 Pooling layers(池化层)
方程式(2)中使用的连接操作。 当特征图的大小发生变化时是不可行的。然而，卷积网络的一个重要部分是下采样层，它会改变特征图的大小。为了便于在我们的架构中进行下采样，我们将网络划分为多个密集连接的密集块；参见图 2。我们将块之间的层称为过渡层，它们进行卷积和池化。我们实验中使用的过渡层由一个批量归一化层和一个 1×1 卷积层和一个 2×2 平均池化层组成。
3.5 Growth rate(增长率)
如果每个函数 Hℓ 产生 k 个特征图，则第 ℓ 层有 k0 + k × (ℓ − 1) 个输入特征图，其中 k0 是输入层中的通道数。 DenseNet 与现有网络架构的一个重要区别是 DenseNet 可以有非常窄的层，例如 k = 12。我们将超参数 k 称为网络的增长率。
3.6 Bottleneck layers(瓶颈层)
尽管每一层只产生 k 个输出特征图，但它通常有更多的输入。在 [36, 11] 中已经注意到，可以在每个 3×3 卷积之前引入一个 1×1 卷积作为瓶颈层，以减少输入特征图的数量，从而提高计算效率。我们发现这种设计对 DenseNet 特别有效，我们将我们的网络称为具有这样一个瓶颈层的网络，即 Hℓ 的 BN-ReLU-Conv(1×1)-BN-ReLU-Conv(3×3) 版本，如DenseNet-B。在我们的实验中，我们让每个 1×1 卷积产生 4k 个特征图。
3.7 Compression(压缩)
为了进一步提高模型的紧凑性，我们可以减少过渡层的特征图数量。如果一个密集块包含 m 个特征图，我们让下面的过渡层生成 ⌊θm⌋ 输出特征图，其中 0 < θ ≤ 1 称为压缩因子。当 θ = 1 时，跨过渡层的特征图数量保持不变。我们将 θ < 1 的 DenseNet 称为 DenseNet-C，我们在实验中设置 θ = 0.5。当同时使用 θ < 1 的瓶颈层和过渡层时，我们将我们的模型称为 DenseNet-BC。
3.8 Implementation Details(实施细节)
在除 ImageNet 之外的所有数据集上，我们实验中使用的 DenseNet 具有三个密集块，每个块具有相同的层数。在进入第一个密集块之前，对输入图像执行具有 16 个（或 DenseNet-BC 增长率的两倍）输出通道的卷积。对于内核大小为 3×3 的卷积层，输入的每一边都用一个像素补零，以保持特征图大小固定。我们使用 1×1 卷积和 2×2 平均池化作为两个连续密集块之间的过渡层。在最后一个密集块结束时，执行全局平均池化，然后附加一个 softmax 分类器。三个密集块中的特征图大小分别为 32×32、16×16 和 8×8。我们用配置 {L = 40, k = 12}, {L = 100, k = 12} 和 {L = 100, k = 24} 来试验基本的 DenseNet 结构。对于 DenseNetBC，对配置为 {L = 100, k = 12},{L = 250, k = 24} 和 {L = 190, k = 40} 的网络进行评估。
L为网络层数，k为网络增长率。
在 ImageNet 上的实验中，我们在 224×224 输入图像上使用具有 4 个密集块的 DenseNet-BC 结构。==初始卷积层包含 2k 个大小为 7×7 的卷积，步长为 2==；所有其他层中的特征图的数量也来自设置 k。
3.9 training
表 2：CIFAR 和 SVHN 数据集的错误率 (%)。 k 表示网络的增长率。超过所有竞争方法的结果为粗体，整体最佳结果为蓝色。 “+”表示标准数据增强。 ∗ 表示我们自己运行的结果。没有数据增强的 DenseNets 的所有结果（C10、C100、SVHN）都是使用 Dropout 获得的。 DenseNets 在使用比 ResNet 更少的参数的同时实现了更低的错误率。如果没有数据增强，DenseNet 的性能会大大提高。
表 3：ImageNet 验证集上的前 1 和前 5 错误率，采用单裁剪（10 裁剪）测试。
图 3：ImageNet 验证数据集上 DenseNets 和 ResNets top-1 错误率（单裁剪测试）的比较，作为学习参数（左）和测试期间 FLOP（右）的函数。
4、网络结构
表 1：ImageNet 的 DenseNet 架构。前 3 个网络的增长率为 k = 32，对于 DenseNet-161，k = 48。请注意，==表中显示的每个“conv”层都对应于序列 BN-ReLU-Conv==。
5、DenseNet121代码复现
5.1 Dense Block
Dense Block的每一个密集层：$BN+ReLU+1*1Conv+BN+ReLU+3*3Conv$
每一层产生k个特征图，原论文中让每个1*1个卷积产生4k个特征图
growth_rate:网络的增长率，原论文中超参数名称为k
论文中对内核大小为3*3的卷积层，输入的每一边都用一个像素补零，以保持特征图大小固定。(代码中设置padding='same'即可)
import tensorflow as tf
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,ReLU
from tensorflow.keras.layers import Concatenate,AveragePooling2D,Input,ZeroPadding2D
from tensorflow.keras.layers import MaxPooling2D,GlobalAveragePooling2D,Dense
from tensorflow.keras.models import Model
from plot_model import plot_model# Bottleneck layers
# BN+ReLU+1*1Conv+BN+ReLU+3*3Conv
# 每一层产生k个特征图，原论文中让每个1*1个卷积产生4k个特征图
# growth_rate:网络的增长率，原论文中超参数名称为k
def conv_block(x,growth_rate,name):
x1=BatchNormalization(name=name+'_0_bn')(x)
x1=ReLU(name=name+'_0_relu')(x1)
x1=Conv2D(filters=4*growth_rate,
kernel_size=(1,1),
use_bias=False,
name=name+'_1_conv')(x1)
x1=BatchNormalization(name=name+'_1_bn')(x1)
x1=ReLU(name=name+'_1_relu')(x1)
# 论文原话：对内核大小为3*3的卷积层，输入的每一边都用一个像素补零，以保持特征图大小固定。
x1=Conv2D(growth_rate,
kernel_size=(3,3),
padding='same',
use_bias=False,
name=name+'_2_conv')(x1)
# 将前面所有层的特征堆叠后传到下一层
out=Concatenate(axis=-1,name=name+'_concat')([x,x1])
return out
'''
x: input tensor.
blocks: integer, the number of building blocks.
name: string, block label.
'''
# DenseNet
def dense_block(x,blocks,name):
for i in range(blocks):
x=conv_block(x,32,name=name+'_block'+str(i+1))
return x5.2 Transition Layer(过渡层)
过渡层：$BN+1*1Conv+2*2AvgPool$
reduction：压缩因子，原论文中名称为$\theta$,设置为0.5。
# 论文原话：使用1*1卷积和2*2平均池化作为两个连续密集块之间的过渡层
# 过渡层：BN+1*1Conv+2*2AvgPool
# reduction：压缩因子，原论文中设置为0.5
def transition_block(x,reduction,name):
x=BatchNormalization(name=name+'_bn')(x)
x=ReLU(name=name+'_relu')(x)
x=Conv2D(filters=int(x.shape[-1]*reduction),
kernel_size=(1,1),
use_bias=False,
name=name+'_conv')(x)
x=AveragePooling2D(pool_size=(2,2),strides=2,name=name+'_pool')(x)
return x5.3 DensNet121网络骨干
几种网络结构的参数设置如下：
DenseNet121(k=32):blocks=[6,12,24,16]
DenseNet169(k=32):blocks=[6,12,32,32]
DenseNet201(k=32):blocks=[6,12,48,32]
DenseNet161(k=48):blocks=[6,12,36,24]
def DenseNet(blocks,include_top=True,weights='imagenet',intput_shape=None,classes=1000):
img_input=Input(shape=intput_shape)
x=ZeroPadding2D(padding=((3,3),(3,3)))(img_input)
# 论文原话：初始卷积层包含2k个大小为7*7的卷积，步长为2(k=32)
# 其实这里完全可以去掉这两个ZeroPadding2D，直接给这里的卷积和池化设置padding='same'即可，
# 但是我看谷歌的源码中没用same padding,所以这里我也没用。
x=Conv2D(filters=64,kernel_size=(7,7),strides=2,use_bias=False,name='conv1/conv')(x)
x=BatchNormalization(name='conv1/bn')(x)
x=ReLU(name='conv1/relu')(x)
x=ZeroPadding2D(padding=((1,1),(1,1)))(x)
x=MaxPooling2D(pool_size=(3,3),strides=2,name='pool1')(x)
x=dense_block(x,blocks[0],name='conv2')
x=transition_block(x,0.5,name='pool2')
x=dense_block(x,blocks[1],name='conv3')
x=transition_block(x,0.5,name='pool3')
x=dense_block(x,blocks[2],name='conv4')
x=transition_block(x,0.5,name='pool4')
x=dense_block(x,blocks[3],name='conv5')
x=BatchNormalization(name='bn')(x)
x=ReLU(name='relu')(x)
x=GlobalAveragePooling2D(name='global_avg_pool')(x)
x=Dense(classes,activation='softmax')(x)
model=Model(img_input,x)
return model5.4 构建网络并查看结构
if __name__ == '__main__':
densenet121=DenseNet(blocks=[6,12,24,16],intput_shape=(224,224,3))
densenet121.summary()
plot_model(densenet121,to_file='img/DenseNet121.png')Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D) (None, 230, 230, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv1/conv (Conv2D) (None, 112, 112, 64) 9408 zero_padding2d[0][0]
__________________________________________________________________________________________________
conv1/bn (BatchNormalization) (None, 112, 112, 64) 256 conv1/conv[0][0]
__________________________________________________________________________________________________
conv1/relu (ReLU) (None, 112, 112, 64) 0 conv1/bn[0][0]
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 114, 114, 64) 0 conv1/relu[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 56, 56, 64) 0 zero_padding2d_1[0][0]
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 56, 56, 64) 256 pool1[0][0]
__________________________________________________________________________________________________
conv2_block1_0_relu (ReLU) (None, 56, 56, 64) 0 conv2_block1_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D) (None, 56, 56, 128) 8192 conv2_block1_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_concat (Concatenat (None, 56, 56, 96) 0 pool1[0][0]
conv2_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_0_bn (BatchNormali (None, 56, 56, 96) 384 conv2_block1_concat[0][0]
__________________________________________________________________________________________________
conv2_block2_0_relu (ReLU) (None, 56, 56, 96) 0 conv2_block2_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D) (None, 56, 56, 128) 12288 conv2_block2_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_concat (Concatenat (None, 56, 56, 128) 0 conv2_block1_concat[0][0]
conv2_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_0_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block2_concat[0][0]
__________________________________________________________________________________________________
conv2_block3_0_relu (ReLU) (None, 56, 56, 128) 0 conv2_block3_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D) (None, 56, 56, 128) 16384 conv2_block3_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_concat (Concatenat (None, 56, 56, 160) 0 conv2_block2_concat[0][0]
conv2_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block4_0_bn (BatchNormali (None, 56, 56, 160) 640 conv2_block3_concat[0][0]
__________________________________________________________________________________________________
conv2_block4_0_relu (ReLU) (None, 56, 56, 160) 0 conv2_block4_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block4_1_conv (Conv2D) (None, 56, 56, 128) 20480 conv2_block4_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block4_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block4_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block4_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block4_concat (Concatenat (None, 56, 56, 192) 0 conv2_block3_concat[0][0]
conv2_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block5_0_bn (BatchNormali (None, 56, 56, 192) 768 conv2_block4_concat[0][0]
__________________________________________________________________________________________________
conv2_block5_0_relu (ReLU) (None, 56, 56, 192) 0 conv2_block5_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block5_1_conv (Conv2D) (None, 56, 56, 128) 24576 conv2_block5_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block5_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block5_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block5_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block5_concat (Concatenat (None, 56, 56, 224) 0 conv2_block4_concat[0][0]
conv2_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block6_0_bn (BatchNormali (None, 56, 56, 224) 896 conv2_block5_concat[0][0]
__________________________________________________________________________________________________
conv2_block6_0_relu (ReLU) (None, 56, 56, 224) 0 conv2_block6_0_bn[0][0]
__________________________________________________________________________________________________
conv2_block6_1_conv (Conv2D) (None, 56, 56, 128) 28672 conv2_block6_0_relu[0][0]
__________________________________________________________________________________________________
conv2_block6_1_bn (BatchNormali (None, 56, 56, 128) 512 conv2_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block6_1_relu (ReLU) (None, 56, 56, 128) 0 conv2_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block6_2_conv (Conv2D) (None, 56, 56, 32) 36864 conv2_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block6_concat (Concatenat (None, 56, 56, 256) 0 conv2_block5_concat[0][0]
conv2_block6_2_conv[0][0]
__________________________________________________________________________________________________
pool2_bn (BatchNormalization) (None, 56, 56, 256) 1024 conv2_block6_concat[0][0]
__________________________________________________________________________________________________
pool2_relu (ReLU) (None, 56, 56, 256) 0 pool2_bn[0][0]
__________________________________________________________________________________________________
pool2_conv (Conv2D) (None, 56, 56, 128) 32768 pool2_relu[0][0]
__________________________________________________________________________________________________
pool2_pool (AveragePooling2D) (None, 28, 28, 128) 0 pool2_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 28, 28, 128) 512 pool2_pool[0][0]
__________________________________________________________________________________________________
conv3_block1_0_relu (ReLU) (None, 28, 28, 128) 0 conv3_block1_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 16384 conv3_block1_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_concat (Concatenat (None, 28, 28, 160) 0 pool2_pool[0][0]
conv3_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_0_bn (BatchNormali (None, 28, 28, 160) 640 conv3_block1_concat[0][0]
__________________________________________________________________________________________________
conv3_block2_0_relu (ReLU) (None, 28, 28, 160) 0 conv3_block2_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 20480 conv3_block2_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_concat (Concatenat (None, 28, 28, 192) 0 conv3_block1_concat[0][0]
conv3_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_0_bn (BatchNormali (None, 28, 28, 192) 768 conv3_block2_concat[0][0]
__________________________________________________________________________________________________
conv3_block3_0_relu (ReLU) (None, 28, 28, 192) 0 conv3_block3_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 24576 conv3_block3_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_concat (Concatenat (None, 28, 28, 224) 0 conv3_block2_concat[0][0]
conv3_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_0_bn (BatchNormali (None, 28, 28, 224) 896 conv3_block3_concat[0][0]
__________________________________________________________________________________________________
conv3_block4_0_relu (ReLU) (None, 28, 28, 224) 0 conv3_block4_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 28672 conv3_block4_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_concat (Concatenat (None, 28, 28, 256) 0 conv3_block3_concat[0][0]
conv3_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block5_0_bn (BatchNormali (None, 28, 28, 256) 1024 conv3_block4_concat[0][0]
__________________________________________________________________________________________________
conv3_block5_0_relu (ReLU) (None, 28, 28, 256) 0 conv3_block5_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block5_1_conv (Conv2D) (None, 28, 28, 128) 32768 conv3_block5_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block5_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block5_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block5_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block5_concat (Concatenat (None, 28, 28, 288) 0 conv3_block4_concat[0][0]
conv3_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block6_0_bn (BatchNormali (None, 28, 28, 288) 1152 conv3_block5_concat[0][0]
__________________________________________________________________________________________________
conv3_block6_0_relu (ReLU) (None, 28, 28, 288) 0 conv3_block6_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block6_1_conv (Conv2D) (None, 28, 28, 128) 36864 conv3_block6_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block6_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block6_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block6_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block6_concat (Concatenat (None, 28, 28, 320) 0 conv3_block5_concat[0][0]
conv3_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block7_0_bn (BatchNormali (None, 28, 28, 320) 1280 conv3_block6_concat[0][0]
__________________________________________________________________________________________________
conv3_block7_0_relu (ReLU) (None, 28, 28, 320) 0 conv3_block7_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block7_1_conv (Conv2D) (None, 28, 28, 128) 40960 conv3_block7_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block7_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block7_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block7_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block7_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block7_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block7_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block7_concat (Concatenat (None, 28, 28, 352) 0 conv3_block6_concat[0][0]
conv3_block7_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block8_0_bn (BatchNormali (None, 28, 28, 352) 1408 conv3_block7_concat[0][0]
__________________________________________________________________________________________________
conv3_block8_0_relu (ReLU) (None, 28, 28, 352) 0 conv3_block8_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block8_1_conv (Conv2D) (None, 28, 28, 128) 45056 conv3_block8_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block8_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block8_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block8_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block8_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block8_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block8_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block8_concat (Concatenat (None, 28, 28, 384) 0 conv3_block7_concat[0][0]
conv3_block8_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block9_0_bn (BatchNormali (None, 28, 28, 384) 1536 conv3_block8_concat[0][0]
__________________________________________________________________________________________________
conv3_block9_0_relu (ReLU) (None, 28, 28, 384) 0 conv3_block9_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block9_1_conv (Conv2D) (None, 28, 28, 128) 49152 conv3_block9_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block9_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block9_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block9_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block9_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block9_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block9_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block9_concat (Concatenat (None, 28, 28, 416) 0 conv3_block8_concat[0][0]
conv3_block9_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block10_0_bn (BatchNormal (None, 28, 28, 416) 1664 conv3_block9_concat[0][0]
__________________________________________________________________________________________________
conv3_block10_0_relu (ReLU) (None, 28, 28, 416) 0 conv3_block10_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block10_1_conv (Conv2D) (None, 28, 28, 128) 53248 conv3_block10_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block10_1_bn (BatchNormal (None, 28, 28, 128) 512 conv3_block10_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block10_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block10_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block10_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block10_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block10_concat (Concatena (None, 28, 28, 448) 0 conv3_block9_concat[0][0]
conv3_block10_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block11_0_bn (BatchNormal (None, 28, 28, 448) 1792 conv3_block10_concat[0][0]
__________________________________________________________________________________________________
conv3_block11_0_relu (ReLU) (None, 28, 28, 448) 0 conv3_block11_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block11_1_conv (Conv2D) (None, 28, 28, 128) 57344 conv3_block11_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block11_1_bn (BatchNormal (None, 28, 28, 128) 512 conv3_block11_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block11_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block11_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block11_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block11_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block11_concat (Concatena (None, 28, 28, 480) 0 conv3_block10_concat[0][0]
conv3_block11_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block12_0_bn (BatchNormal (None, 28, 28, 480) 1920 conv3_block11_concat[0][0]
__________________________________________________________________________________________________
conv3_block12_0_relu (ReLU) (None, 28, 28, 480) 0 conv3_block12_0_bn[0][0]
__________________________________________________________________________________________________
conv3_block12_1_conv (Conv2D) (None, 28, 28, 128) 61440 conv3_block12_0_relu[0][0]
__________________________________________________________________________________________________
conv3_block12_1_bn (BatchNormal (None, 28, 28, 128) 512 conv3_block12_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block12_1_relu (ReLU) (None, 28, 28, 128) 0 conv3_block12_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block12_2_conv (Conv2D) (None, 28, 28, 32) 36864 conv3_block12_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block12_concat (Concatena (None, 28, 28, 512) 0 conv3_block11_concat[0][0]
conv3_block12_2_conv[0][0]
__________________________________________________________________________________________________
pool3_bn (BatchNormalization) (None, 28, 28, 512) 2048 conv3_block12_concat[0][0]
__________________________________________________________________________________________________
pool3_relu (ReLU) (None, 28, 28, 512) 0 pool3_bn[0][0]
__________________________________________________________________________________________________
pool3_conv (Conv2D) (None, 28, 28, 256) 131072 pool3_relu[0][0]
__________________________________________________________________________________________________
pool3_pool (AveragePooling2D) (None, 14, 14, 256) 0 pool3_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 14, 14, 256) 1024 pool3_pool[0][0]
__________________________________________________________________________________________________
conv4_block1_0_relu (ReLU) (None, 14, 14, 256) 0 conv4_block1_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D) (None, 14, 14, 128) 32768 conv4_block1_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_concat (Concatenat (None, 14, 14, 288) 0 pool3_pool[0][0]
conv4_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_0_bn (BatchNormali (None, 14, 14, 288) 1152 conv4_block1_concat[0][0]
__________________________________________________________________________________________________
conv4_block2_0_relu (ReLU) (None, 14, 14, 288) 0 conv4_block2_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D) (None, 14, 14, 128) 36864 conv4_block2_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_concat (Concatenat (None, 14, 14, 320) 0 conv4_block1_concat[0][0]
conv4_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_0_bn (BatchNormali (None, 14, 14, 320) 1280 conv4_block2_concat[0][0]
__________________________________________________________________________________________________
conv4_block3_0_relu (ReLU) (None, 14, 14, 320) 0 conv4_block3_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D) (None, 14, 14, 128) 40960 conv4_block3_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_concat (Concatenat (None, 14, 14, 352) 0 conv4_block2_concat[0][0]
conv4_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_0_bn (BatchNormali (None, 14, 14, 352) 1408 conv4_block3_concat[0][0]
__________________________________________________________________________________________________
conv4_block4_0_relu (ReLU) (None, 14, 14, 352) 0 conv4_block4_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D) (None, 14, 14, 128) 45056 conv4_block4_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_concat (Concatenat (None, 14, 14, 384) 0 conv4_block3_concat[0][0]
conv4_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_0_bn (BatchNormali (None, 14, 14, 384) 1536 conv4_block4_concat[0][0]
__________________________________________________________________________________________________
conv4_block5_0_relu (ReLU) (None, 14, 14, 384) 0 conv4_block5_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D) (None, 14, 14, 128) 49152 conv4_block5_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_concat (Concatenat (None, 14, 14, 416) 0 conv4_block4_concat[0][0]
conv4_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_0_bn (BatchNormali (None, 14, 14, 416) 1664 conv4_block5_concat[0][0]
__________________________________________________________________________________________________
conv4_block6_0_relu (ReLU) (None, 14, 14, 416) 0 conv4_block6_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D) (None, 14, 14, 128) 53248 conv4_block6_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_concat (Concatenat (None, 14, 14, 448) 0 conv4_block5_concat[0][0]
conv4_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block7_0_bn (BatchNormali (None, 14, 14, 448) 1792 conv4_block6_concat[0][0]
__________________________________________________________________________________________________
conv4_block7_0_relu (ReLU) (None, 14, 14, 448) 0 conv4_block7_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block7_1_conv (Conv2D) (None, 14, 14, 128) 57344 conv4_block7_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block7_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block7_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block7_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block7_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block7_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block7_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block7_concat (Concatenat (None, 14, 14, 480) 0 conv4_block6_concat[0][0]
conv4_block7_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block8_0_bn (BatchNormali (None, 14, 14, 480) 1920 conv4_block7_concat[0][0]
__________________________________________________________________________________________________
conv4_block8_0_relu (ReLU) (None, 14, 14, 480) 0 conv4_block8_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block8_1_conv (Conv2D) (None, 14, 14, 128) 61440 conv4_block8_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block8_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block8_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block8_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block8_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block8_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block8_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block8_concat (Concatenat (None, 14, 14, 512) 0 conv4_block7_concat[0][0]
conv4_block8_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block9_0_bn (BatchNormali (None, 14, 14, 512) 2048 conv4_block8_concat[0][0]
__________________________________________________________________________________________________
conv4_block9_0_relu (ReLU) (None, 14, 14, 512) 0 conv4_block9_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block9_1_conv (Conv2D) (None, 14, 14, 128) 65536 conv4_block9_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block9_1_bn (BatchNormali (None, 14, 14, 128) 512 conv4_block9_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block9_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block9_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block9_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block9_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block9_concat (Concatenat (None, 14, 14, 544) 0 conv4_block8_concat[0][0]
conv4_block9_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block10_0_bn (BatchNormal (None, 14, 14, 544) 2176 conv4_block9_concat[0][0]
__________________________________________________________________________________________________
conv4_block10_0_relu (ReLU) (None, 14, 14, 544) 0 conv4_block10_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block10_1_conv (Conv2D) (None, 14, 14, 128) 69632 conv4_block10_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block10_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block10_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block10_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block10_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block10_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block10_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block10_concat (Concatena (None, 14, 14, 576) 0 conv4_block9_concat[0][0]
conv4_block10_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block11_0_bn (BatchNormal (None, 14, 14, 576) 2304 conv4_block10_concat[0][0]
__________________________________________________________________________________________________
conv4_block11_0_relu (ReLU) (None, 14, 14, 576) 0 conv4_block11_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block11_1_conv (Conv2D) (None, 14, 14, 128) 73728 conv4_block11_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block11_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block11_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block11_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block11_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block11_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block11_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block11_concat (Concatena (None, 14, 14, 608) 0 conv4_block10_concat[0][0]
conv4_block11_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block12_0_bn (BatchNormal (None, 14, 14, 608) 2432 conv4_block11_concat[0][0]
__________________________________________________________________________________________________
conv4_block12_0_relu (ReLU) (None, 14, 14, 608) 0 conv4_block12_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block12_1_conv (Conv2D) (None, 14, 14, 128) 77824 conv4_block12_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block12_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block12_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block12_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block12_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block12_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block12_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block12_concat (Concatena (None, 14, 14, 640) 0 conv4_block11_concat[0][0]
conv4_block12_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block13_0_bn (BatchNormal (None, 14, 14, 640) 2560 conv4_block12_concat[0][0]
__________________________________________________________________________________________________
conv4_block13_0_relu (ReLU) (None, 14, 14, 640) 0 conv4_block13_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block13_1_conv (Conv2D) (None, 14, 14, 128) 81920 conv4_block13_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block13_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block13_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block13_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block13_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block13_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block13_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block13_concat (Concatena (None, 14, 14, 672) 0 conv4_block12_concat[0][0]
conv4_block13_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block14_0_bn (BatchNormal (None, 14, 14, 672) 2688 conv4_block13_concat[0][0]
__________________________________________________________________________________________________
conv4_block14_0_relu (ReLU) (None, 14, 14, 672) 0 conv4_block14_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block14_1_conv (Conv2D) (None, 14, 14, 128) 86016 conv4_block14_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block14_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block14_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block14_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block14_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block14_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block14_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block14_concat (Concatena (None, 14, 14, 704) 0 conv4_block13_concat[0][0]
conv4_block14_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block15_0_bn (BatchNormal (None, 14, 14, 704) 2816 conv4_block14_concat[0][0]
__________________________________________________________________________________________________
conv4_block15_0_relu (ReLU) (None, 14, 14, 704) 0 conv4_block15_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block15_1_conv (Conv2D) (None, 14, 14, 128) 90112 conv4_block15_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block15_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block15_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block15_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block15_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block15_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block15_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block15_concat (Concatena (None, 14, 14, 736) 0 conv4_block14_concat[0][0]
conv4_block15_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block16_0_bn (BatchNormal (None, 14, 14, 736) 2944 conv4_block15_concat[0][0]
__________________________________________________________________________________________________
conv4_block16_0_relu (ReLU) (None, 14, 14, 736) 0 conv4_block16_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block16_1_conv (Conv2D) (None, 14, 14, 128) 94208 conv4_block16_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block16_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block16_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block16_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block16_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block16_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block16_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block16_concat (Concatena (None, 14, 14, 768) 0 conv4_block15_concat[0][0]
conv4_block16_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block17_0_bn (BatchNormal (None, 14, 14, 768) 3072 conv4_block16_concat[0][0]
__________________________________________________________________________________________________
conv4_block17_0_relu (ReLU) (None, 14, 14, 768) 0 conv4_block17_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block17_1_conv (Conv2D) (None, 14, 14, 128) 98304 conv4_block17_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block17_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block17_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block17_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block17_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block17_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block17_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block17_concat (Concatena (None, 14, 14, 800) 0 conv4_block16_concat[0][0]
conv4_block17_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block18_0_bn (BatchNormal (None, 14, 14, 800) 3200 conv4_block17_concat[0][0]
__________________________________________________________________________________________________
conv4_block18_0_relu (ReLU) (None, 14, 14, 800) 0 conv4_block18_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block18_1_conv (Conv2D) (None, 14, 14, 128) 102400 conv4_block18_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block18_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block18_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block18_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block18_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block18_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block18_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block18_concat (Concatena (None, 14, 14, 832) 0 conv4_block17_concat[0][0]
conv4_block18_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block19_0_bn (BatchNormal (None, 14, 14, 832) 3328 conv4_block18_concat[0][0]
__________________________________________________________________________________________________
conv4_block19_0_relu (ReLU) (None, 14, 14, 832) 0 conv4_block19_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block19_1_conv (Conv2D) (None, 14, 14, 128) 106496 conv4_block19_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block19_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block19_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block19_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block19_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block19_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block19_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block19_concat (Concatena (None, 14, 14, 864) 0 conv4_block18_concat[0][0]
conv4_block19_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block20_0_bn (BatchNormal (None, 14, 14, 864) 3456 conv4_block19_concat[0][0]
__________________________________________________________________________________________________
conv4_block20_0_relu (ReLU) (None, 14, 14, 864) 0 conv4_block20_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block20_1_conv (Conv2D) (None, 14, 14, 128) 110592 conv4_block20_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block20_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block20_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block20_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block20_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block20_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block20_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block20_concat (Concatena (None, 14, 14, 896) 0 conv4_block19_concat[0][0]
conv4_block20_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block21_0_bn (BatchNormal (None, 14, 14, 896) 3584 conv4_block20_concat[0][0]
__________________________________________________________________________________________________
conv4_block21_0_relu (ReLU) (None, 14, 14, 896) 0 conv4_block21_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block21_1_conv (Conv2D) (None, 14, 14, 128) 114688 conv4_block21_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block21_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block21_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block21_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block21_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block21_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block21_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block21_concat (Concatena (None, 14, 14, 928) 0 conv4_block20_concat[0][0]
conv4_block21_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block22_0_bn (BatchNormal (None, 14, 14, 928) 3712 conv4_block21_concat[0][0]
__________________________________________________________________________________________________
conv4_block22_0_relu (ReLU) (None, 14, 14, 928) 0 conv4_block22_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block22_1_conv (Conv2D) (None, 14, 14, 128) 118784 conv4_block22_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block22_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block22_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block22_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block22_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block22_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block22_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block22_concat (Concatena (None, 14, 14, 960) 0 conv4_block21_concat[0][0]
conv4_block22_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block23_0_bn (BatchNormal (None, 14, 14, 960) 3840 conv4_block22_concat[0][0]
__________________________________________________________________________________________________
conv4_block23_0_relu (ReLU) (None, 14, 14, 960) 0 conv4_block23_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block23_1_conv (Conv2D) (None, 14, 14, 128) 122880 conv4_block23_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block23_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block23_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block23_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block23_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block23_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block23_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block23_concat (Concatena (None, 14, 14, 992) 0 conv4_block22_concat[0][0]
conv4_block23_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block24_0_bn (BatchNormal (None, 14, 14, 992) 3968 conv4_block23_concat[0][0]
__________________________________________________________________________________________________
conv4_block24_0_relu (ReLU) (None, 14, 14, 992) 0 conv4_block24_0_bn[0][0]
__________________________________________________________________________________________________
conv4_block24_1_conv (Conv2D) (None, 14, 14, 128) 126976 conv4_block24_0_relu[0][0]
__________________________________________________________________________________________________
conv4_block24_1_bn (BatchNormal (None, 14, 14, 128) 512 conv4_block24_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block24_1_relu (ReLU) (None, 14, 14, 128) 0 conv4_block24_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block24_2_conv (Conv2D) (None, 14, 14, 32) 36864 conv4_block24_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block24_concat (Concatena (None, 14, 14, 1024) 0 conv4_block23_concat[0][0]
conv4_block24_2_conv[0][0]
__________________________________________________________________________________________________
pool4_bn (BatchNormalization) (None, 14, 14, 1024) 4096 conv4_block24_concat[0][0]
__________________________________________________________________________________________________
pool4_relu (ReLU) (None, 14, 14, 1024) 0 pool4_bn[0][0]
__________________________________________________________________________________________________
pool4_conv (Conv2D) (None, 14, 14, 512) 524288 pool4_relu[0][0]
__________________________________________________________________________________________________
pool4_pool (AveragePooling2D) (None, 7, 7, 512) 0 pool4_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 7, 7, 512) 2048 pool4_pool[0][0]
__________________________________________________________________________________________________
conv5_block1_0_relu (ReLU) (None, 7, 7, 512) 0 conv5_block1_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D) (None, 7, 7, 128) 65536 conv5_block1_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_concat (Concatenat (None, 7, 7, 544) 0 pool4_pool[0][0]
conv5_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_0_bn (BatchNormali (None, 7, 7, 544) 2176 conv5_block1_concat[0][0]
__________________________________________________________________________________________________
conv5_block2_0_relu (ReLU) (None, 7, 7, 544) 0 conv5_block2_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D) (None, 7, 7, 128) 69632 conv5_block2_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_concat (Concatenat (None, 7, 7, 576) 0 conv5_block1_concat[0][0]
conv5_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_0_bn (BatchNormali (None, 7, 7, 576) 2304 conv5_block2_concat[0][0]
__________________________________________________________________________________________________
conv5_block3_0_relu (ReLU) (None, 7, 7, 576) 0 conv5_block3_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D) (None, 7, 7, 128) 73728 conv5_block3_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_concat (Concatenat (None, 7, 7, 608) 0 conv5_block2_concat[0][0]
conv5_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block4_0_bn (BatchNormali (None, 7, 7, 608) 2432 conv5_block3_concat[0][0]
__________________________________________________________________________________________________
conv5_block4_0_relu (ReLU) (None, 7, 7, 608) 0 conv5_block4_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block4_1_conv (Conv2D) (None, 7, 7, 128) 77824 conv5_block4_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block4_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block4_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block4_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block4_concat (Concatenat (None, 7, 7, 640) 0 conv5_block3_concat[0][0]
conv5_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block5_0_bn (BatchNormali (None, 7, 7, 640) 2560 conv5_block4_concat[0][0]
__________________________________________________________________________________________________
conv5_block5_0_relu (ReLU) (None, 7, 7, 640) 0 conv5_block5_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block5_1_conv (Conv2D) (None, 7, 7, 128) 81920 conv5_block5_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block5_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block5_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block5_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block5_concat (Concatenat (None, 7, 7, 672) 0 conv5_block4_concat[0][0]
conv5_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block6_0_bn (BatchNormali (None, 7, 7, 672) 2688 conv5_block5_concat[0][0]
__________________________________________________________________________________________________
conv5_block6_0_relu (ReLU) (None, 7, 7, 672) 0 conv5_block6_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block6_1_conv (Conv2D) (None, 7, 7, 128) 86016 conv5_block6_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block6_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block6_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block6_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block6_concat (Concatenat (None, 7, 7, 704) 0 conv5_block5_concat[0][0]
conv5_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block7_0_bn (BatchNormali (None, 7, 7, 704) 2816 conv5_block6_concat[0][0]
__________________________________________________________________________________________________
conv5_block7_0_relu (ReLU) (None, 7, 7, 704) 0 conv5_block7_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block7_1_conv (Conv2D) (None, 7, 7, 128) 90112 conv5_block7_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block7_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block7_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block7_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block7_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block7_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block7_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block7_concat (Concatenat (None, 7, 7, 736) 0 conv5_block6_concat[0][0]
conv5_block7_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block8_0_bn (BatchNormali (None, 7, 7, 736) 2944 conv5_block7_concat[0][0]
__________________________________________________________________________________________________
conv5_block8_0_relu (ReLU) (None, 7, 7, 736) 0 conv5_block8_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block8_1_conv (Conv2D) (None, 7, 7, 128) 94208 conv5_block8_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block8_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block8_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block8_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block8_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block8_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block8_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block8_concat (Concatenat (None, 7, 7, 768) 0 conv5_block7_concat[0][0]
conv5_block8_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block9_0_bn (BatchNormali (None, 7, 7, 768) 3072 conv5_block8_concat[0][0]
__________________________________________________________________________________________________
conv5_block9_0_relu (ReLU) (None, 7, 7, 768) 0 conv5_block9_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block9_1_conv (Conv2D) (None, 7, 7, 128) 98304 conv5_block9_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block9_1_bn (BatchNormali (None, 7, 7, 128) 512 conv5_block9_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block9_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block9_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block9_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block9_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block9_concat (Concatenat (None, 7, 7, 800) 0 conv5_block8_concat[0][0]
conv5_block9_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block10_0_bn (BatchNormal (None, 7, 7, 800) 3200 conv5_block9_concat[0][0]
__________________________________________________________________________________________________
conv5_block10_0_relu (ReLU) (None, 7, 7, 800) 0 conv5_block10_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block10_1_conv (Conv2D) (None, 7, 7, 128) 102400 conv5_block10_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block10_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block10_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block10_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block10_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block10_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block10_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block10_concat (Concatena (None, 7, 7, 832) 0 conv5_block9_concat[0][0]
conv5_block10_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block11_0_bn (BatchNormal (None, 7, 7, 832) 3328 conv5_block10_concat[0][0]
__________________________________________________________________________________________________
conv5_block11_0_relu (ReLU) (None, 7, 7, 832) 0 conv5_block11_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block11_1_conv (Conv2D) (None, 7, 7, 128) 106496 conv5_block11_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block11_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block11_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block11_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block11_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block11_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block11_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block11_concat (Concatena (None, 7, 7, 864) 0 conv5_block10_concat[0][0]
conv5_block11_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block12_0_bn (BatchNormal (None, 7, 7, 864) 3456 conv5_block11_concat[0][0]
__________________________________________________________________________________________________
conv5_block12_0_relu (ReLU) (None, 7, 7, 864) 0 conv5_block12_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block12_1_conv (Conv2D) (None, 7, 7, 128) 110592 conv5_block12_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block12_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block12_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block12_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block12_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block12_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block12_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block12_concat (Concatena (None, 7, 7, 896) 0 conv5_block11_concat[0][0]
conv5_block12_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block13_0_bn (BatchNormal (None, 7, 7, 896) 3584 conv5_block12_concat[0][0]
__________________________________________________________________________________________________
conv5_block13_0_relu (ReLU) (None, 7, 7, 896) 0 conv5_block13_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block13_1_conv (Conv2D) (None, 7, 7, 128) 114688 conv5_block13_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block13_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block13_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block13_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block13_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block13_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block13_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block13_concat (Concatena (None, 7, 7, 928) 0 conv5_block12_concat[0][0]
conv5_block13_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block14_0_bn (BatchNormal (None, 7, 7, 928) 3712 conv5_block13_concat[0][0]
__________________________________________________________________________________________________
conv5_block14_0_relu (ReLU) (None, 7, 7, 928) 0 conv5_block14_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block14_1_conv (Conv2D) (None, 7, 7, 128) 118784 conv5_block14_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block14_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block14_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block14_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block14_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block14_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block14_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block14_concat (Concatena (None, 7, 7, 960) 0 conv5_block13_concat[0][0]
conv5_block14_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block15_0_bn (BatchNormal (None, 7, 7, 960) 3840 conv5_block14_concat[0][0]
__________________________________________________________________________________________________
conv5_block15_0_relu (ReLU) (None, 7, 7, 960) 0 conv5_block15_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block15_1_conv (Conv2D) (None, 7, 7, 128) 122880 conv5_block15_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block15_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block15_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block15_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block15_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block15_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block15_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block15_concat (Concatena (None, 7, 7, 992) 0 conv5_block14_concat[0][0]
conv5_block15_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block16_0_bn (BatchNormal (None, 7, 7, 992) 3968 conv5_block15_concat[0][0]
__________________________________________________________________________________________________
conv5_block16_0_relu (ReLU) (None, 7, 7, 992) 0 conv5_block16_0_bn[0][0]
__________________________________________________________________________________________________
conv5_block16_1_conv (Conv2D) (None, 7, 7, 128) 126976 conv5_block16_0_relu[0][0]
__________________________________________________________________________________________________
conv5_block16_1_bn (BatchNormal (None, 7, 7, 128) 512 conv5_block16_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block16_1_relu (ReLU) (None, 7, 7, 128) 0 conv5_block16_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block16_2_conv (Conv2D) (None, 7, 7, 32) 36864 conv5_block16_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block16_concat (Concatena (None, 7, 7, 1024) 0 conv5_block15_concat[0][0]
conv5_block16_2_conv[0][0]
__________________________________________________________________________________________________
bn (BatchNormalization) (None, 7, 7, 1024) 4096 conv5_block16_concat[0][0]
__________________________________________________________________________________________________
relu (ReLU) (None, 7, 7, 1024) 0 bn[0][0]
__________________________________________________________________________________________________
global_avg_pool (GlobalAverageP (None, 1024) 0 relu[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 1000) 1025000 global_avg_pool[0][0]
==================================================================================================
Total params: 8,062,504
Trainable params: 7,978,856
Non-trainable params: 83,648
References
Huang G , Liu Z , Laurens V , et al. Densely Connected Convolutional Networks[C]// IEEE Computer Society. IEEE Computer Society, 2016.
Ioffe S , Szegedy C . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[C]// JMLR.org. JMLR.org, 2015.
X. Glorot, A. Bordes, and Y. Bengio. Deep sparse rectifier neural networks. In AISTATS, 2011. 3
https://github.com/liuzhuang13/DenseNet
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/applications/DenseNet121
DenseNet 天气图片四分类（权重迁移学习），附Tensorflow完整代码
引用打的不全，翻译可能有点蹩脚，不过今天这个模型是对照着源码搭建的，以后还是尽量要看源码。还有一点就是搭建模型的时候最好把那个模型的论文看一下，要不很多给定的超参数你不知道为什么那样写，这些超参数论文中都是通过实验给出来的经验值，看过论文你就知道我在说什么了。
