深度学习 Day27——J6ResNeXt-50实战解析

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  • 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
  • 🍖 原作者:K同学啊 | 接辅导、项目定制
  • 🚀 文章来源:K同学的学习圈子

前言

关键字: pytorch实现ResNeXt50详解算法,tensorflow实现ResNeXt50详解算法,ResNeXt50详解

1 我的环境

  • 电脑系统:Windows 11
  • 语言环境:python 3.8.6
  • 编译器:pycharm2020.2.3
  • 深度学习环境:
    torch == 1.9.1+cu111
    torchvision == 0.10.1+cu111
    TensorFlow 2.10.1
  • 显卡:NVIDIA GeForce RTX 4070

2 pytorch实现DenseNet算法

2.1 前期准备

2.1.1 引入库


import torch
import torch.nn as nn
import time
import copy
from torchvision import transforms, datasets
from pathlib import Path
from PIL import Image
import torchsummary as summary
import torch.nn.functional as F
from collections import OrderedDict
import re
import torch.utils.model_zoo as model_zoo
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100  # 分辨率
import warnings

warnings.filterwarnings('ignore')  # 忽略一些warning内容,无需打印

2.1.2 设置GPU(如果设备上支持GPU就使用GPU,否则使用CPU)

"""前期准备-设置GPU"""
# 如果设备上支持GPU就使用GPU,否则使用CPU
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using {} device".format(device))

输出

Using cuda device

2.1.3 导入数据

'''前期工作-导入数据'''
data_dir = r"D:\DeepLearning\data\monkeypox_recognition"
data_dir = Path(data_dir)

data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[-1] for path in data_paths]
print(classeNames)

输出

['Monkeypox', 'Others']

2.1.4 可视化数据

'''前期工作-可视化数据'''
subfolder = Path(data_dir) / "Monkeypox"
image_files = list(p.resolve() for p in subfolder.glob('*') if p.suffix in [".jpg", ".png", ".jpeg"])
plt.figure(figsize=(10, 6))
for i in range(len(image_files[:12])):
    image_file = image_files[i]
    ax = plt.subplot(3, 4, i + 1)
    img = Image.open(str(image_file))
    plt.imshow(img)
    plt.axis("off")
# 显示图片
plt.tight_layout()
plt.show()

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

2.1.4 图像数据变换

'''前期工作-图像数据变换'''
total_datadir = data_dir

# 关于transforms.Compose的更多介绍可以参考:https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    transforms.ToTensor(),  # 将PIL Image或numpy.ndarray转换为tensor,并归一化到[0,1]之间
    transforms.Normalize(  # 标准化处理-->转换为标准正太分布(高斯分布),使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
total_data = datasets.ImageFolder(total_datadir, transform=train_transforms)
print(total_data)
print(total_data.class_to_idx)

输出

Dataset ImageFolder
    Number of datapoints: 2142
    Root location: D:\DeepLearning\data\monkeypox_recognition
    StandardTransform
Transform: Compose(
               Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)
               ToTensor()
               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
           )
{'Monkeypox': 0, 'Others': 1}

2.1.4 划分数据集

'''前期工作-划分数据集'''
train_size = int(0.8 * len(total_data))  # train_size表示训练集大小,通过将总体数据长度的80%转换为整数得到;
test_size = len(total_data) - train_size  # test_size表示测试集大小,是总体数据长度减去训练集大小。
# 使用torch.utils.data.random_split()方法进行数据集划分。该方法将总体数据total_data按照指定的大小比例([train_size, test_size])随机划分为训练集和测试集,
# 并将划分结果分别赋值给train_dataset和test_dataset两个变量。
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print("train_dataset={}\ntest_dataset={}".format(train_dataset, test_dataset))
print("train_size={}\ntest_size={}".format(train_size, test_size))

输出

train_dataset=<torch.utils.data.dataset.Subset object at 0x000002A96E08E0D0>
test_dataset=<torch.utils.data.dataset.Subset object at 0x000002A96E04E640>
train_size=1713
test_size=429

2.1.4 加载数据

'''前期工作-加载数据'''
batch_size = 32

train_dl = torch.utils.data.DataLoader(train_dataset,
                                       batch_size=batch_size,
                                       shuffle=True,
                                       num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
                                      batch_size=batch_size,
                                      shuffle=True,
                                      num_workers=1)

2.1.4 查看数据

'''前期工作-查看数据'''
for X, y in test_dl:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

输出

Shape of X [N, C, H, W]:  torch.Size([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

2.2 搭建ResNeXt50模型

"""构建ResNeXt50网络"""


class BN_Conv2d(nn.Module):
    """
    BN_CONV_RELU
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bias=False):
        super(BN_Conv2d, self).__init__()
        self.seq = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
                      padding=padding, dilation=dilation, groups=groups, bias=bias),
            nn.BatchNorm2d(out_channels)
        )

    def forward(self, x):
        return F.relu(self.seq(x))


class ResNeXt_Block(nn.Module):
    """
    ResNeXt block with group convolutions
    """

    def __init__(self, in_chnls, cardinality, group_depth, stride):
        super(ResNeXt_Block, self).__init__()
        self.group_chnls = cardinality * group_depth
        self.conv1 = BN_Conv2d(in_chnls, self.group_chnls, 1, stride=1, padding=0)
        self.conv2 = BN_Conv2d(self.group_chnls, self.group_chnls, 3, stride=stride, padding=1, groups=cardinality)
        self.conv3 = nn.Conv2d(self.group_chnls, self.group_chnls * 2, 1, stride=1, padding=0)
        self.bn = nn.BatchNorm2d(self.group_chnls * 2)
        self.short_cut = nn.Sequential(
            nn.Conv2d(in_chnls, self.group_chnls * 2, 1, stride, 0, bias=False),
            nn.BatchNorm2d(self.group_chnls * 2)
        )

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.bn(self.conv3(out))
        out += self.short_cut(x)
        return F.relu(out)


class ResNeXt(nn.Module):
    """
    ResNeXt builder
    """

    def __init__(self, layers: object, cardinality, group_depth, num_classes) -> object:
        super(ResNeXt, self).__init__()
        self.cardinality = cardinality
        self.channels = 64
        self.conv1 = BN_Conv2d(3, self.channels, 7, stride=2, padding=3)
        d1 = group_depth
        self.conv2 = self.___make_layers(d1, layers[0], stride=1)
        d2 = d1 * 2
        self.conv3 = self.___make_layers(d2, layers[1], stride=2)
        d3 = d2 * 2
        self.conv4 = self.___make_layers(d3, layers[2], stride=2)
        d4 = d3 * 2
        self.conv5 = self.___make_layers(d4, layers[3], stride=2)
        self.fc = nn.Linear(self.channels, num_classes)  # 224x224 input size

    def ___make_layers(self, d, blocks, stride):
        strides = [stride] + [1] * (blocks - 1)
        layers = []
        for stride in strides:
            layers.append(ResNeXt_Block(self.channels, self.cardinality, d, stride))
            self.channels = self.cardinality * d * 2
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = F.max_pool2d(out, 3, 2, 1)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = F.avg_pool2d(out, 7)
        out = out.view(out.size(0), -1)
        out = F.softmax(self.fc(out), dim=1)
        return out

该模型相比DenseNet的区别是,在最后一个denseblock后增加SE_layer。

# SE_layer
self.features.add_module('SE-module', Squeeze_excitation_layer(num_features))

输出

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 112, 112]           9,408
       BatchNorm2d-2         [-1, 64, 112, 112]             128
         BN_Conv2d-3         [-1, 64, 112, 112]               0
            Conv2d-4          [-1, 128, 56, 56]           8,192
       BatchNorm2d-5          [-1, 128, 56, 56]             256
         BN_Conv2d-6          [-1, 128, 56, 56]               0
            Conv2d-7          [-1, 128, 56, 56]           4,608
       BatchNorm2d-8          [-1, 128, 56, 56]             256
         BN_Conv2d-9          [-1, 128, 56, 56]               0
           Conv2d-10          [-1, 256, 56, 56]          33,024
      BatchNorm2d-11          [-1, 256, 56, 56]             512
           Conv2d-12          [-1, 256, 56, 56]          16,384
      BatchNorm2d-13          [-1, 256, 56, 56]             512
    ResNeXt_Block-14          [-1, 256, 56, 56]               0
           Conv2d-15          [-1, 128, 56, 56]          32,768
      BatchNorm2d-16          [-1, 128, 56, 56]             256
        BN_Conv2d-17          [-1, 128, 56, 56]               0
           Conv2d-18          [-1, 128, 56, 56]           4,608
      BatchNorm2d-19          [-1, 128, 56, 56]             256
        BN_Conv2d-20          [-1, 128, 56, 56]               0
           Conv2d-21          [-1, 256, 56, 56]          33,024
      BatchNorm2d-22          [-1, 256, 56, 56]             512
           Conv2d-23          [-1, 256, 56, 56]          65,536
      BatchNorm2d-24          [-1, 256, 56, 56]             512
    ResNeXt_Block-25          [-1, 256, 56, 56]               0
           Conv2d-26          [-1, 128, 56, 56]          32,768
      BatchNorm2d-27          [-1, 128, 56, 56]             256
        BN_Conv2d-28          [-1, 128, 56, 56]               0
           Conv2d-29          [-1, 128, 56, 56]           4,608
      BatchNorm2d-30          [-1, 128, 56, 56]             256
        BN_Conv2d-31          [-1, 128, 56, 56]               0
           Conv2d-32          [-1, 256, 56, 56]          33,024
      BatchNorm2d-33          [-1, 256, 56, 56]             512
           Conv2d-34          [-1, 256, 56, 56]          65,536
      BatchNorm2d-35          [-1, 256, 56, 56]             512
    ResNeXt_Block-36          [-1, 256, 56, 56]               0
           Conv2d-37          [-1, 256, 56, 56]          65,536
      BatchNorm2d-38          [-1, 256, 56, 56]             512
        BN_Conv2d-39          [-1, 256, 56, 56]               0
           Conv2d-40          [-1, 256, 28, 28]          18,432
      BatchNorm2d-41          [-1, 256, 28, 28]             512
        BN_Conv2d-42          [-1, 256, 28, 28]               0
           Conv2d-43          [-1, 512, 28, 28]         131,584
      BatchNorm2d-44          [-1, 512, 28, 28]           1,024
           Conv2d-45          [-1, 512, 28, 28]         131,072
      BatchNorm2d-46          [-1, 512, 28, 28]           1,024
    ResNeXt_Block-47          [-1, 512, 28, 28]               0
           Conv2d-48          [-1, 256, 28, 28]         131,072
      BatchNorm2d-49          [-1, 256, 28, 28]             512
        BN_Conv2d-50          [-1, 256, 28, 28]               0
           Conv2d-51          [-1, 256, 28, 28]          18,432
      BatchNorm2d-52          [-1, 256, 28, 28]             512
        BN_Conv2d-53          [-1, 256, 28, 28]               0
           Conv2d-54          [-1, 512, 28, 28]         131,584
      BatchNorm2d-55          [-1, 512, 28, 28]           1,024
           Conv2d-56          [-1, 512, 28, 28]         262,144
      BatchNorm2d-57          [-1, 512, 28, 28]           1,024
    ResNeXt_Block-58          [-1, 512, 28, 28]               0
           Conv2d-59          [-1, 256, 28, 28]         131,072
      BatchNorm2d-60          [-1, 256, 28, 28]             512
        BN_Conv2d-61          [-1, 256, 28, 28]               0
           Conv2d-62          [-1, 256, 28, 28]          18,432
      BatchNorm2d-63          [-1, 256, 28, 28]             512
        BN_Conv2d-64          [-1, 256, 28, 28]               0
           Conv2d-65          [-1, 512, 28, 28]         131,584
      BatchNorm2d-66          [-1, 512, 28, 28]           1,024
           Conv2d-67          [-1, 512, 28, 28]         262,144
      BatchNorm2d-68          [-1, 512, 28, 28]           1,024
    ResNeXt_Block-69          [-1, 512, 28, 28]               0
           Conv2d-70          [-1, 256, 28, 28]         131,072
      BatchNorm2d-71          [-1, 256, 28, 28]             512
        BN_Conv2d-72          [-1, 256, 28, 28]               0
           Conv2d-73          [-1, 256, 28, 28]          18,432
      BatchNorm2d-74          [-1, 256, 28, 28]             512
        BN_Conv2d-75          [-1, 256, 28, 28]               0
           Conv2d-76          [-1, 512, 28, 28]         131,584
      BatchNorm2d-77          [-1, 512, 28, 28]           1,024
           Conv2d-78          [-1, 512, 28, 28]         262,144
      BatchNorm2d-79          [-1, 512, 28, 28]           1,024
    ResNeXt_Block-80          [-1, 512, 28, 28]               0
           Conv2d-81          [-1, 512, 28, 28]         262,144
      BatchNorm2d-82          [-1, 512, 28, 28]           1,024
        BN_Conv2d-83          [-1, 512, 28, 28]               0
           Conv2d-84          [-1, 512, 14, 14]          73,728
      BatchNorm2d-85          [-1, 512, 14, 14]           1,024
        BN_Conv2d-86          [-1, 512, 14, 14]               0
           Conv2d-87         [-1, 1024, 14, 14]         525,312
      BatchNorm2d-88         [-1, 1024, 14, 14]           2,048
           Conv2d-89         [-1, 1024, 14, 14]         524,288
      BatchNorm2d-90         [-1, 1024, 14, 14]           2,048
    ResNeXt_Block-91         [-1, 1024, 14, 14]               0
           Conv2d-92          [-1, 512, 14, 14]         524,288
      BatchNorm2d-93          [-1, 512, 14, 14]           1,024
        BN_Conv2d-94          [-1, 512, 14, 14]               0
           Conv2d-95          [-1, 512, 14, 14]          73,728
      BatchNorm2d-96          [-1, 512, 14, 14]           1,024
        BN_Conv2d-97          [-1, 512, 14, 14]               0
           Conv2d-98         [-1, 1024, 14, 14]         525,312
      BatchNorm2d-99         [-1, 1024, 14, 14]           2,048
          Conv2d-100         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-101         [-1, 1024, 14, 14]           2,048
   ResNeXt_Block-102         [-1, 1024, 14, 14]               0
          Conv2d-103          [-1, 512, 14, 14]         524,288
     BatchNorm2d-104          [-1, 512, 14, 14]           1,024
       BN_Conv2d-105          [-1, 512, 14, 14]               0
          Conv2d-106          [-1, 512, 14, 14]          73,728
     BatchNorm2d-107          [-1, 512, 14, 14]           1,024
       BN_Conv2d-108          [-1, 512, 14, 14]               0
          Conv2d-109         [-1, 1024, 14, 14]         525,312
     BatchNorm2d-110         [-1, 1024, 14, 14]           2,048
          Conv2d-111         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-112         [-1, 1024, 14, 14]           2,048
   ResNeXt_Block-113         [-1, 1024, 14, 14]               0
          Conv2d-114          [-1, 512, 14, 14]         524,288
     BatchNorm2d-115          [-1, 512, 14, 14]           1,024
       BN_Conv2d-116          [-1, 512, 14, 14]               0
          Conv2d-117          [-1, 512, 14, 14]          73,728
     BatchNorm2d-118          [-1, 512, 14, 14]           1,024
       BN_Conv2d-119          [-1, 512, 14, 14]               0
          Conv2d-120         [-1, 1024, 14, 14]         525,312
     BatchNorm2d-121         [-1, 1024, 14, 14]           2,048
          Conv2d-122         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-123         [-1, 1024, 14, 14]           2,048
   ResNeXt_Block-124         [-1, 1024, 14, 14]               0
          Conv2d-125          [-1, 512, 14, 14]         524,288
     BatchNorm2d-126          [-1, 512, 14, 14]           1,024
       BN_Conv2d-127          [-1, 512, 14, 14]               0
          Conv2d-128          [-1, 512, 14, 14]          73,728
     BatchNorm2d-129          [-1, 512, 14, 14]           1,024
       BN_Conv2d-130          [-1, 512, 14, 14]               0
          Conv2d-131         [-1, 1024, 14, 14]         525,312
     BatchNorm2d-132         [-1, 1024, 14, 14]           2,048
          Conv2d-133         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-134         [-1, 1024, 14, 14]           2,048
   ResNeXt_Block-135         [-1, 1024, 14, 14]               0
          Conv2d-136          [-1, 512, 14, 14]         524,288
     BatchNorm2d-137          [-1, 512, 14, 14]           1,024
       BN_Conv2d-138          [-1, 512, 14, 14]               0
          Conv2d-139          [-1, 512, 14, 14]          73,728
     BatchNorm2d-140          [-1, 512, 14, 14]           1,024
       BN_Conv2d-141          [-1, 512, 14, 14]               0
          Conv2d-142         [-1, 1024, 14, 14]         525,312
     BatchNorm2d-143         [-1, 1024, 14, 14]           2,048
          Conv2d-144         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-145         [-1, 1024, 14, 14]           2,048
   ResNeXt_Block-146         [-1, 1024, 14, 14]               0
          Conv2d-147         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-148         [-1, 1024, 14, 14]           2,048
       BN_Conv2d-149         [-1, 1024, 14, 14]               0
          Conv2d-150           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-151           [-1, 1024, 7, 7]           2,048
       BN_Conv2d-152           [-1, 1024, 7, 7]               0
          Conv2d-153           [-1, 2048, 7, 7]       2,099,200
     BatchNorm2d-154           [-1, 2048, 7, 7]           4,096
          Conv2d-155           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-156           [-1, 2048, 7, 7]           4,096
   ResNeXt_Block-157           [-1, 2048, 7, 7]               0
          Conv2d-158           [-1, 1024, 7, 7]       2,097,152
     BatchNorm2d-159           [-1, 1024, 7, 7]           2,048
       BN_Conv2d-160           [-1, 1024, 7, 7]               0
          Conv2d-161           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-162           [-1, 1024, 7, 7]           2,048
       BN_Conv2d-163           [-1, 1024, 7, 7]               0
          Conv2d-164           [-1, 2048, 7, 7]       2,099,200
     BatchNorm2d-165           [-1, 2048, 7, 7]           4,096
          Conv2d-166           [-1, 2048, 7, 7]       4,194,304
     BatchNorm2d-167           [-1, 2048, 7, 7]           4,096
   ResNeXt_Block-168           [-1, 2048, 7, 7]               0
          Conv2d-169           [-1, 1024, 7, 7]       2,097,152
     BatchNorm2d-170           [-1, 1024, 7, 7]           2,048
       BN_Conv2d-171           [-1, 1024, 7, 7]               0
          Conv2d-172           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-173           [-1, 1024, 7, 7]           2,048
       BN_Conv2d-174           [-1, 1024, 7, 7]               0
          Conv2d-175           [-1, 2048, 7, 7]       2,099,200
     BatchNorm2d-176           [-1, 2048, 7, 7]           4,096
          Conv2d-177           [-1, 2048, 7, 7]       4,194,304
     BatchNorm2d-178           [-1, 2048, 7, 7]           4,096
   ResNeXt_Block-179           [-1, 2048, 7, 7]               0
          Linear-180                    [-1, 4]           8,196
================================================================
Total params: 37,574,724
Trainable params: 37,574,724
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 379.37
Params size (MB): 143.34
Estimated Total Size (MB): 523.28
----------------------------------------------------------------
None

2.3 训练模型

2.3.1 设置超参数

"""训练模型--设置超参数"""
loss_fn = nn.CrossEntropyLoss()  # 创建损失函数,计算实际输出和真实相差多少,交叉熵损失函数,事实上,它就是做图片分类任务时常用的损失函数
learn_rate = 1e-4  # 学习率
optimizer1 = torch.optim.SGD(model.parameters(), lr=learn_rate)# 作用是定义优化器,用来训练时候优化模型参数;其中,SGD表示随机梯度下降,用于控制实际输出y与真实y之间的相差有多大
optimizer2 = torch.optim.Adam(model.parameters(), lr=learn_rate)  
lr_opt = optimizer2
model_opt = optimizer2
# 调用官方动态学习率接口时使用2
lambda1 = lambda epoch : 0.92 ** (epoch // 4)
# optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(lr_opt, lr_lambda=lambda1) #选定调整方法

2.3.2 编写训练函数

"""训练模型--编写训练函数"""
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小,一共60000张图片
    num_batches = len(dataloader)  # 批次数目,1875(60000/32)

    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率

    for X, y in dataloader:  # 加载数据加载器,得到里面的 X(图片数据)和 y(真实标签)
        X, y = X.to(device), y.to(device) # 用于将数据存到显卡

        # 计算预测误差
        pred = model(X)  # 网络输出
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距,targets为真实值,计算二者差值即为损失

        # 反向传播
        optimizer.zero_grad()  # 清空过往梯度
        loss.backward()  # 反向传播,计算当前梯度
        optimizer.step()  # 根据梯度更新网络参数

        # 记录acc与loss
        train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()

    train_acc /= size
    train_loss /= num_batches

    return train_acc, train_loss

2.3.3 编写测试函数

"""训练模型--编写测试函数"""
# 测试函数和训练函数大致相同,但是由于不进行梯度下降对网络权重进行更新,所以不需要传入优化器
def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)  # 测试集的大小,一共10000张图片
    num_batches = len(dataloader)  # 批次数目,313(10000/32=312.5,向上取整)
    test_loss, test_acc = 0, 0

    # 当不进行训练时,停止梯度更新,节省计算内存消耗
    with torch.no_grad(): # 测试时模型参数不用更新,所以 no_grad,整个模型参数正向推就ok,不反向更新参数
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)

            # 计算loss
            target_pred = model(imgs)
            loss = loss_fn(target_pred, target)

            test_loss += loss.item()
            test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()#统计预测正确的个数

    test_acc /= size
    test_loss /= num_batches

    return test_acc, test_loss

2.3.4 正式训练

"""训练模型--正式训练"""
epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
best_test_acc=0

for epoch in range(epochs):
    milliseconds_t1 = int(time.time() * 1000)

    # 更新学习率(使用自定义学习率时使用)
    # adjust_learning_rate(lr_opt, epoch, learn_rate)

    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, model_opt)
    scheduler.step() # 更新学习率(调用官方动态学习率接口时使用)

    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)

    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)

    # 获取当前的学习率
    lr = lr_opt.state_dict()['param_groups'][0]['lr']

    milliseconds_t2 = int(time.time() * 1000)
    template = ('Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%,Test_loss:{:.3f}, Lr:{:.2E}')
    if best_test_acc < epoch_test_acc:
        best_test_acc = epoch_test_acc
        #备份最好的模型
        best_model = copy.deepcopy(model)
        template = (
            'Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%,Test_loss:{:.3f}, Lr:{:.2E},Update the best model')
    print(
        template.format(epoch + 1, milliseconds_t2-milliseconds_t1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH = './best_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)
print('Done')
Epoch: 1, duration:15650ms, Train_acc:54.8%, Train_loss:1.187, Test_acc:59.9%,Test_loss:1.147, Lr:1.00E-04,Update the best model
Epoch: 2, duration:15311ms, Train_acc:62.2%, Train_loss:1.112, Test_acc:58.7%,Test_loss:1.150, Lr:1.00E-04
Epoch: 3, duration:15336ms, Train_acc:67.3%, Train_loss:1.067, Test_acc:62.9%,Test_loss:1.117, Lr:1.00E-04,Update the best model
Epoch: 4, duration:14853ms, Train_acc:68.0%, Train_loss:1.061, Test_acc:65.0%,Test_loss:1.093, Lr:1.00E-04,Update the best model
Epoch: 5, duration:14930ms, Train_acc:68.0%, Train_loss:1.059, Test_acc:64.6%,Test_loss:1.087, Lr:1.00E-04
Epoch: 6, duration:15118ms, Train_acc:67.2%, Train_loss:1.067, Test_acc:60.1%,Test_loss:1.126, Lr:1.00E-04
Epoch: 7, duration:15024ms, Train_acc:67.8%, Train_loss:1.059, Test_acc:68.5%,Test_loss:1.050, Lr:1.00E-04,Update the best model
Epoch: 8, duration:14973ms, Train_acc:66.9%, Train_loss:1.065, Test_acc:67.6%,Test_loss:1.074, Lr:1.00E-04
Epoch: 9, duration:14902ms, Train_acc:69.3%, Train_loss:1.049, Test_acc:64.1%,Test_loss:1.099, Lr:1.00E-04
Epoch:10, duration:15237ms, Train_acc:70.2%, Train_loss:1.035, Test_acc:71.6%,Test_loss:1.024, Lr:1.00E-04,Update the best model
Epoch:11, duration:14890ms, Train_acc:71.0%, Train_loss:1.029, Test_acc:73.4%,Test_loss:1.010, Lr:1.00E-04,Update the best model
Epoch:12, duration:14951ms, Train_acc:70.5%, Train_loss:1.034, Test_acc:70.2%,Test_loss:1.043, Lr:1.00E-04
Epoch:13, duration:14967ms, Train_acc:72.3%, Train_loss:1.020, Test_acc:71.8%,Test_loss:1.022, Lr:1.00E-04
Epoch:14, duration:14966ms, Train_acc:73.8%, Train_loss:1.004, Test_acc:72.5%,Test_loss:1.017, Lr:1.00E-04
Epoch:15, duration:14886ms, Train_acc:75.5%, Train_loss:0.987, Test_acc:72.3%,Test_loss:1.015, Lr:1.00E-04
Epoch:16, duration:14895ms, Train_acc:72.6%, Train_loss:1.012, Test_acc:72.5%,Test_loss:1.025, Lr:1.00E-04
Epoch:17, duration:15037ms, Train_acc:74.3%, Train_loss:0.994, Test_acc:73.2%,Test_loss:1.016, Lr:1.00E-04
Epoch:18, duration:14797ms, Train_acc:76.5%, Train_loss:0.976, Test_acc:70.6%,Test_loss:1.026, Lr:1.00E-04
Epoch:19, duration:15157ms, Train_acc:72.6%, Train_loss:1.018, Test_acc:72.0%,Test_loss:1.018, Lr:1.00E-04
Epoch:20, duration:14767ms, Train_acc:73.1%, Train_loss:1.009, Test_acc:74.4%,Test_loss:1.003, Lr:1.00E-04,Update the best model



2.4 结果可视化

"""训练模型--结果可视化"""
epochs_range = range(epochs)

plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

2.4 指定图片进行预测

def predict_one_image(image_path, model, transform, classes):
    test_img = Image.open(image_path).convert('RGB')
    plt.imshow(test_img)  # 展示预测的图片
    plt.show()

    test_img = transform(test_img)
    img = test_img.to(device).unsqueeze(0)

    model.eval()
    output = model(img)

    _, pred = torch.max(output, 1)
    pred_class = classes[pred]
    print(f'预测结果是:{pred_class}')
 
# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_location=device))

"""指定图片进行预测"""
classes = list(total_data.class_to_idx)
# 预测训练集中的某张照片
predict_one_image(image_path=str(Path(data_dir) / "Monkeypox/M01_01_01.jpg"),
                  model=model,
                  transform=train_transforms,
                  classes=classes)

输出

预测结果是:Monkeypox

2.6 模型评估

"""模型评估"""
best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
# 查看是否与我们记录的最高准确率一致
print(epoch_test_acc, epoch_test_loss)


输出

0.7435897435897436 0.9976991329874311

3 tensorflow实现DenseNet算法

3.1.引入库

from PIL import Image
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt

# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
import tensorflow as tf
from keras import layers, models, Input
from keras.layers import Input, Activation, BatchNormalization, Flatten
from keras.layers import Dense, Conv2D, MaxPooling2D, ZeroPadding2D, GlobalMaxPooling2D, AveragePooling2D, Flatten, \
    Dropout, BatchNormalization, GlobalAveragePooling2D
from keras.models import Model
from keras import regularizers
from tensorflow import keras
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings('ignore')  # 忽略一些warning内容,无需打印

3.2.设置GPU(如果使用的是CPU可以忽略这步)

'''前期工作-设置GPU(如果使用的是CPU可以忽略这步)'''
# 检查GPU是否可用
print(tf.test.is_built_with_cuda())
gpus = tf.config.list_physical_devices("GPU")
print(gpus)
if gpus:
    gpu0 = gpus[0]  # 如果有多个GPU,仅使用第0个GPU
    tf.config.experimental.set_memory_growth(gpu0, True)  # 设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpu0], "GPU")

执行结果

True
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

3.3.导入数据

'''前期工作-导入数据'''
data_dir = r"D:\DeepLearning\data\monkeypox_recognition"
data_dir = Path(data_dir)

3.4.查看数据

'''前期工作-查看数据'''
image_count = len(list(data_dir.glob('*/*.jpg')))
print("图片总数为:", image_count)
image_list = list(data_dir.glob('Monkeypox/*.jpg'))
image = Image.open(str(image_list[1]))
# 查看图像实例的属性
print(image.format, image.size, image.mode)
plt.imshow(image)
plt.axis("off")
plt.show()

执行结果:

图片总数为: 2142
JPEG (224, 224) RGB

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

3.5.加载数据

'''数据预处理-加载数据'''
batch_size = 32
img_height = 224
img_width = 224
"""
关于image_dataset_from_directory()的详细介绍可以参考文章:https://mtyjkh.blog.csdn.net/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)

运行结果:

Found 2142 files belonging to 2 classes.
Using 1714 files for training.
Found 2142 files belonging to 2 classes.
Using 428 files for validation.
['Monkeypox', 'Others']

3.6.再次检查数据

'''数据预处理-再次检查数据'''
# Image_batch是形状的张量(16, 336, 336, 3)。这是一批形状336x336x3的16张图片(最后一维指的是彩色通道RGB)。
# Label_batch是形状(16,)的张量,这些标签对应16张图片
for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

运行结果

(32, 224, 224, 3)
(32,)

3.7.配置数据集

'''数据预处理-配置数据集'''
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

3.8.可视化数据

'''数据预处理-可视化数据'''
plt.figure(figsize=(10, 5))
for images, labels in train_ds.take(1):
    for i in range(8):
        ax = plt.subplot(2, 4, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]], fontsize=10)
        plt.axis("off")
# 显示图片
plt.show()

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

3.9.构建ResNeXt50网络

"""构建ResNeXt50网络"""

# ----------------------- #
#   groups代表多少组
#   g_channels代表每组的特征图数量
# ----------------------- #
def group_conv2_block(x_0, strides, groups, g_channels):
    g_list = []
    for i in range(groups):
        x = Lambda(lambda x: x[:, :, :, i * g_channels: (i + 1) * g_channels])(x_0)
        x = Conv2D(filters=g_channels, kernel_size=3, strides=strides, padding='same', use_bias=False)(x)
        g_list.append(x)
    x = concatenate(g_list, axis=3)
    x = BatchNormalization(epsilon=1.001e-5)(x)
    x = Activation('relu')(x)
    return x


# 结构快
def block(x, filters, strides=1, groups=32, conv_short=True):
    if conv_short:
        short_cut = Conv2D(filters=filters * 2, kernel_size=1, strides=strides, padding='same')(x)
        short_cut = BatchNormalization(epsilon=1.001e-5)(short_cut)
    else:
        short_cut = x

    # 三层卷积
    x = Conv2D(filters=filters, kernel_size=1, strides=1, padding='same')(x)
    x = BatchNormalization(epsilon=1.001e-5)(x)
    x = Activation('relu')(x)

    g_channels = int(filters / groups)
    x = group_conv2_block(x, strides=strides, groups=groups, g_channels=g_channels)

    x = Conv2D(filters=filters * 2, kernel_size=1, strides=1, padding='same')(x)
    x = BatchNormalization(epsilon=1.001e-5)(x)

    x = Add()([x, short_cut])
    x = Activation('relu')(x)

    return x


def Resnext(inputs, classes):
    x_input = keras.layers.Input(shape=inputs)
    x = ZeroPadding2D((3, 3))(x_input)
    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='valid')(x)
    x = BatchNormalization(epsilon=1.001e-5)(x)
    x = Activation('relu')(x)
    x = ZeroPadding2D((1, 1))(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='valid')(x)

    x = block(x, filters=128, strides=1, conv_short=True)
    x = block(x, filters=128, conv_short=False)
    x = block(x, filters=128, conv_short=False)

    x = block(x, filters=256, strides=2, conv_short=True)
    x = block(x, filters=256, conv_short=False)
    x = block(x, filters=256, conv_short=False)
    x = block(x, filters=256, conv_short=False)

    x = block(x, filters=512, strides=2, conv_short=True)
    x = block(x, filters=512, conv_short=False)
    x = block(x, filters=512, conv_short=False)
    x = block(x, filters=512, conv_short=False)
    x = block(x, filters=512, conv_short=False)
    x = block(x, filters=512, conv_short=False)

    x = block(x, filters=1024, strides=2, conv_short=True)
    x = block(x, filters=1024, conv_short=False)
    x = block(x, filters=1024, conv_short=False)

    x = GlobalAvgPool2D()(x)
    x = Dense(classes, activation='softmax')(x)
    model = keras.models.Model(inputs=[x_input], outputs=[x])
    return model


model = Resnext(inputs= (img_width, img_height, 3),classes=4)
model.summary()

网络结构结果如下:

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv2d (Conv2D)                (None, 112, 112, 64  9408        ['input_1[0][0]']                
                                )                                                                 
                                                                                                  
 batch_normalization (BatchNorm  (None, 112, 112, 64  256        ['conv2d[0][0]']                 
 alization)                     )                                                                 
                                                                                                  
 max_pooling2d (MaxPooling2D)   (None, 56, 56, 64)   0           ['batch_normalization[0][0]']    
                                                                                                  
 batch_normalization_1 (BatchNo  (None, 56, 56, 64)  256         ['max_pooling2d[0][0]']          
 rmalization)                                                                                     
                                                                                                  
 activation (Activation)        (None, 56, 56, 64)   0           ['batch_normalization_1[0][0]']  
                                                                                                  
 conv2d_1 (Conv2D)              (None, 56, 56, 128)  8192        ['activation[0][0]']             
                                                                                                  
 batch_normalization_2 (BatchNo  (None, 56, 56, 128)  512        ['conv2d_1[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 activation_1 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_2[0][0]']  
                                                                                                  
 conv2d_2 (Conv2D)              (None, 56, 56, 32)   36864       ['activation_1[0][0]']           
                                                                                                  
 concatenate (Concatenate)      (None, 56, 56, 96)   0           ['max_pooling2d[0][0]',          
                                                                  'conv2d_2[0][0]']               
                                                                                                  
 batch_normalization_3 (BatchNo  (None, 56, 56, 96)  384         ['concatenate[0][0]']            
 rmalization)                                                                                     
                                                                                                  
 activation_2 (Activation)      (None, 56, 56, 96)   0           ['batch_normalization_3[0][0]']  
                                                                                                  
 conv2d_3 (Conv2D)              (None, 56, 56, 128)  12288       ['activation_2[0][0]']           
                                                                                                  
 batch_normalization_4 (BatchNo  (None, 56, 56, 128)  512        ['conv2d_3[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 activation_3 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_4[0][0]']  
                                                                                                  
 conv2d_4 (Conv2D)              (None, 56, 56, 32)   36864       ['activation_3[0][0]']           
                                                                                                  
 concatenate_1 (Concatenate)    (None, 56, 56, 128)  0           ['concatenate[0][0]',            
                                                                  'conv2d_4[0][0]']               
                                                                                                  
 batch_normalization_5 (BatchNo  (None, 56, 56, 128)  512        ['concatenate_1[0][0]']          
 rmalization)                                                                                     
                                                                                                  
 activation_4 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_5[0][0]']  
                                                                                                  
 conv2d_5 (Conv2D)              (None, 56, 56, 128)  16384       ['activation_4[0][0]']           
                                                                                                  
 batch_normalization_6 (BatchNo  (None, 56, 56, 128)  512        ['conv2d_5[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 activation_5 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_6[0][0]']  
                                                                                                  
 conv2d_6 (Conv2D)              (None, 56, 56, 32)   36864       ['activation_5[0][0]']           
                                                                                                  
 concatenate_2 (Concatenate)    (None, 56, 56, 160)  0           ['concatenate_1[0][0]',          
                                                                  'conv2d_6[0][0]']               
                                                                                                  
 batch_normalization_7 (BatchNo  (None, 56, 56, 160)  640        ['concatenate_2[0][0]']          
 rmalization)                                                                                     
                                                                                                  
 activation_6 (Activation)      (None, 56, 56, 160)  0           ['batch_normalization_7[0][0]']  
                                                                                                  
 conv2d_7 (Conv2D)              (None, 56, 56, 128)  20480       ['activation_6[0][0]']           
                                                                                                  
 batch_normalization_8 (BatchNo  (None, 56, 56, 128)  512        ['conv2d_7[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 activation_7 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_8[0][0]']  
                                                                                                  
 conv2d_8 (Conv2D)              (None, 56, 56, 32)   36864       ['activation_7[0][0]']           
                                                                                                  
 concatenate_3 (Concatenate)    (None, 56, 56, 192)  0           ['concatenate_2[0][0]',          
                                                                  'conv2d_8[0][0]']               
                                                                                                  
 batch_normalization_9 (BatchNo  (None, 56, 56, 192)  768        ['concatenate_3[0][0]']          
 rmalization)                                                                                     
                                                                                                  
 activation_8 (Activation)      (None, 56, 56, 192)  0           ['batch_normalization_9[0][0]']  
                                                                                                  
 conv2d_9 (Conv2D)              (None, 56, 56, 128)  24576       ['activation_8[0][0]']           
                                                                                                  
 batch_normalization_10 (BatchN  (None, 56, 56, 128)  512        ['conv2d_9[0][0]']               
 ormalization)                                                                                    
                                                                                                  
 activation_9 (Activation)      (None, 56, 56, 128)  0           ['batch_normalization_10[0][0]'] 
                                                                                                  
 conv2d_10 (Conv2D)             (None, 56, 56, 32)   36864       ['activation_9[0][0]']           
                                                                                                  
 concatenate_4 (Concatenate)    (None, 56, 56, 224)  0           ['concatenate_3[0][0]',          
                                                                  'conv2d_10[0][0]']              
                                                                                                  
 batch_normalization_11 (BatchN  (None, 56, 56, 224)  896        ['concatenate_4[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_10 (Activation)     (None, 56, 56, 224)  0           ['batch_normalization_11[0][0]'] 
                                                                                                  
 conv2d_11 (Conv2D)             (None, 56, 56, 128)  28672       ['activation_10[0][0]']          
                                                                                                  
 batch_normalization_12 (BatchN  (None, 56, 56, 128)  512        ['conv2d_11[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_11 (Activation)     (None, 56, 56, 128)  0           ['batch_normalization_12[0][0]'] 
                                                                                                  
 conv2d_12 (Conv2D)             (None, 56, 56, 32)   36864       ['activation_11[0][0]']          
                                                                                                  
 concatenate_5 (Concatenate)    (None, 56, 56, 256)  0           ['concatenate_4[0][0]',          
                                                                  'conv2d_12[0][0]']              
                                                                                                  
 batch_normalization_13 (BatchN  (None, 56, 56, 256)  1024       ['concatenate_5[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_12 (Activation)     (None, 56, 56, 256)  0           ['batch_normalization_13[0][0]'] 
                                                                                                  
 conv2d_13 (Conv2D)             (None, 56, 56, 128)  32768       ['activation_12[0][0]']          
                                                                                                  
 average_pooling2d (AveragePool  (None, 28, 28, 128)  0          ['conv2d_13[0][0]']              
 ing2D)                                                                                           
                                                                                                  
 batch_normalization_14 (BatchN  (None, 28, 28, 128)  512        ['average_pooling2d[0][0]']      
 ormalization)                                                                                    
                                                                                                  
 activation_13 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_14[0][0]'] 
                                                                                                  
 conv2d_14 (Conv2D)             (None, 28, 28, 128)  16384       ['activation_13[0][0]']          
                                                                                                  
 batch_normalization_15 (BatchN  (None, 28, 28, 128)  512        ['conv2d_14[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_14 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_15[0][0]'] 
                                                                                                  
 conv2d_15 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_14[0][0]']          
                                                                                                  
 concatenate_6 (Concatenate)    (None, 28, 28, 160)  0           ['average_pooling2d[0][0]',      
                                                                  'conv2d_15[0][0]']              
                                                                                                  
 batch_normalization_16 (BatchN  (None, 28, 28, 160)  640        ['concatenate_6[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_15 (Activation)     (None, 28, 28, 160)  0           ['batch_normalization_16[0][0]'] 
                                                                                                  
 conv2d_16 (Conv2D)             (None, 28, 28, 128)  20480       ['activation_15[0][0]']          
                                                                                                  
 batch_normalization_17 (BatchN  (None, 28, 28, 128)  512        ['conv2d_16[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_16 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_17[0][0]'] 
                                                                                                  
 conv2d_17 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_16[0][0]']          
                                                                                                  
 concatenate_7 (Concatenate)    (None, 28, 28, 192)  0           ['concatenate_6[0][0]',          
                                                                  'conv2d_17[0][0]']              
                                                                                                  
 batch_normalization_18 (BatchN  (None, 28, 28, 192)  768        ['concatenate_7[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_17 (Activation)     (None, 28, 28, 192)  0           ['batch_normalization_18[0][0]'] 
                                                                                                  
 conv2d_18 (Conv2D)             (None, 28, 28, 128)  24576       ['activation_17[0][0]']          
                                                                                                  
 batch_normalization_19 (BatchN  (None, 28, 28, 128)  512        ['conv2d_18[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_18 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_19[0][0]'] 
                                                                                                  
 conv2d_19 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_18[0][0]']          
                                                                                                  
 concatenate_8 (Concatenate)    (None, 28, 28, 224)  0           ['concatenate_7[0][0]',          
                                                                  'conv2d_19[0][0]']              
                                                                                                  
 batch_normalization_20 (BatchN  (None, 28, 28, 224)  896        ['concatenate_8[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_19 (Activation)     (None, 28, 28, 224)  0           ['batch_normalization_20[0][0]'] 
                                                                                                  
 conv2d_20 (Conv2D)             (None, 28, 28, 128)  28672       ['activation_19[0][0]']          
                                                                                                  
 batch_normalization_21 (BatchN  (None, 28, 28, 128)  512        ['conv2d_20[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_20 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_21[0][0]'] 
                                                                                                  
 conv2d_21 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_20[0][0]']          
                                                                                                  
 concatenate_9 (Concatenate)    (None, 28, 28, 256)  0           ['concatenate_8[0][0]',          
                                                                  'conv2d_21[0][0]']              
                                                                                                  
 batch_normalization_22 (BatchN  (None, 28, 28, 256)  1024       ['concatenate_9[0][0]']          
 ormalization)                                                                                    
                                                                                                  
 activation_21 (Activation)     (None, 28, 28, 256)  0           ['batch_normalization_22[0][0]'] 
                                                                                                  
 conv2d_22 (Conv2D)             (None, 28, 28, 128)  32768       ['activation_21[0][0]']          
                                                                                                  
 batch_normalization_23 (BatchN  (None, 28, 28, 128)  512        ['conv2d_22[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_22 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_23[0][0]'] 
                                                                                                  
 conv2d_23 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_22[0][0]']          
                                                                                                  
 concatenate_10 (Concatenate)   (None, 28, 28, 288)  0           ['concatenate_9[0][0]',          
                                                                  'conv2d_23[0][0]']              
                                                                                                  
 batch_normalization_24 (BatchN  (None, 28, 28, 288)  1152       ['concatenate_10[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_23 (Activation)     (None, 28, 28, 288)  0           ['batch_normalization_24[0][0]'] 
                                                                                                  
 conv2d_24 (Conv2D)             (None, 28, 28, 128)  36864       ['activation_23[0][0]']          
                                                                                                  
 batch_normalization_25 (BatchN  (None, 28, 28, 128)  512        ['conv2d_24[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_24 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_25[0][0]'] 
                                                                                                  
 conv2d_25 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_24[0][0]']          
                                                                                                  
 concatenate_11 (Concatenate)   (None, 28, 28, 320)  0           ['concatenate_10[0][0]',         
                                                                  'conv2d_25[0][0]']              
                                                                                                  
 batch_normalization_26 (BatchN  (None, 28, 28, 320)  1280       ['concatenate_11[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_25 (Activation)     (None, 28, 28, 320)  0           ['batch_normalization_26[0][0]'] 
                                                                                                  
 conv2d_26 (Conv2D)             (None, 28, 28, 128)  40960       ['activation_25[0][0]']          
                                                                                                  
 batch_normalization_27 (BatchN  (None, 28, 28, 128)  512        ['conv2d_26[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_26 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_27[0][0]'] 
                                                                                                  
 conv2d_27 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_26[0][0]']          
                                                                                                  
 concatenate_12 (Concatenate)   (None, 28, 28, 352)  0           ['concatenate_11[0][0]',         
                                                                  'conv2d_27[0][0]']              
                                                                                                  
 batch_normalization_28 (BatchN  (None, 28, 28, 352)  1408       ['concatenate_12[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_27 (Activation)     (None, 28, 28, 352)  0           ['batch_normalization_28[0][0]'] 
                                                                                                  
 conv2d_28 (Conv2D)             (None, 28, 28, 128)  45056       ['activation_27[0][0]']          
                                                                                                  
 batch_normalization_29 (BatchN  (None, 28, 28, 128)  512        ['conv2d_28[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_28 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_29[0][0]'] 
                                                                                                  
 conv2d_29 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_28[0][0]']          
                                                                                                  
 concatenate_13 (Concatenate)   (None, 28, 28, 384)  0           ['concatenate_12[0][0]',         
                                                                  'conv2d_29[0][0]']              
                                                                                                  
 batch_normalization_30 (BatchN  (None, 28, 28, 384)  1536       ['concatenate_13[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_29 (Activation)     (None, 28, 28, 384)  0           ['batch_normalization_30[0][0]'] 
                                                                                                  
 conv2d_30 (Conv2D)             (None, 28, 28, 128)  49152       ['activation_29[0][0]']          
                                                                                                  
 batch_normalization_31 (BatchN  (None, 28, 28, 128)  512        ['conv2d_30[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_30 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_31[0][0]'] 
                                                                                                  
 conv2d_31 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_30[0][0]']          
                                                                                                  
 concatenate_14 (Concatenate)   (None, 28, 28, 416)  0           ['concatenate_13[0][0]',         
                                                                  'conv2d_31[0][0]']              
                                                                                                  
 batch_normalization_32 (BatchN  (None, 28, 28, 416)  1664       ['concatenate_14[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_31 (Activation)     (None, 28, 28, 416)  0           ['batch_normalization_32[0][0]'] 
                                                                                                  
 conv2d_32 (Conv2D)             (None, 28, 28, 128)  53248       ['activation_31[0][0]']          
                                                                                                  
 batch_normalization_33 (BatchN  (None, 28, 28, 128)  512        ['conv2d_32[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_32 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_33[0][0]'] 
                                                                                                  
 conv2d_33 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_32[0][0]']          
                                                                                                  
 concatenate_15 (Concatenate)   (None, 28, 28, 448)  0           ['concatenate_14[0][0]',         
                                                                  'conv2d_33[0][0]']              
                                                                                                  
 batch_normalization_34 (BatchN  (None, 28, 28, 448)  1792       ['concatenate_15[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_33 (Activation)     (None, 28, 28, 448)  0           ['batch_normalization_34[0][0]'] 
                                                                                                  
 conv2d_34 (Conv2D)             (None, 28, 28, 128)  57344       ['activation_33[0][0]']          
                                                                                                  
 batch_normalization_35 (BatchN  (None, 28, 28, 128)  512        ['conv2d_34[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_34 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_35[0][0]'] 
                                                                                                  
 conv2d_35 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_34[0][0]']          
                                                                                                  
 concatenate_16 (Concatenate)   (None, 28, 28, 480)  0           ['concatenate_15[0][0]',         
                                                                  'conv2d_35[0][0]']              
                                                                                                  
 batch_normalization_36 (BatchN  (None, 28, 28, 480)  1920       ['concatenate_16[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_35 (Activation)     (None, 28, 28, 480)  0           ['batch_normalization_36[0][0]'] 
                                                                                                  
 conv2d_36 (Conv2D)             (None, 28, 28, 128)  61440       ['activation_35[0][0]']          
                                                                                                  
 batch_normalization_37 (BatchN  (None, 28, 28, 128)  512        ['conv2d_36[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_36 (Activation)     (None, 28, 28, 128)  0           ['batch_normalization_37[0][0]'] 
                                                                                                  
 conv2d_37 (Conv2D)             (None, 28, 28, 32)   36864       ['activation_36[0][0]']          
                                                                                                  
 concatenate_17 (Concatenate)   (None, 28, 28, 512)  0           ['concatenate_16[0][0]',         
                                                                  'conv2d_37[0][0]']              
                                                                                                  
 batch_normalization_38 (BatchN  (None, 28, 28, 512)  2048       ['concatenate_17[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_37 (Activation)     (None, 28, 28, 512)  0           ['batch_normalization_38[0][0]'] 
                                                                                                  
 conv2d_38 (Conv2D)             (None, 28, 28, 256)  131072      ['activation_37[0][0]']          
                                                                                                  
 average_pooling2d_1 (AveragePo  (None, 14, 14, 256)  0          ['conv2d_38[0][0]']              
 oling2D)                                                                                         
                                                                                                  
 batch_normalization_39 (BatchN  (None, 14, 14, 256)  1024       ['average_pooling2d_1[0][0]']    
 ormalization)                                                                                    
                                                                                                  
 activation_38 (Activation)     (None, 14, 14, 256)  0           ['batch_normalization_39[0][0]'] 
                                                                                                  
 conv2d_39 (Conv2D)             (None, 14, 14, 128)  32768       ['activation_38[0][0]']          
                                                                                                  
 batch_normalization_40 (BatchN  (None, 14, 14, 128)  512        ['conv2d_39[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_39 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_40[0][0]'] 
                                                                                                  
 conv2d_40 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_39[0][0]']          
                                                                                                  
 concatenate_18 (Concatenate)   (None, 14, 14, 288)  0           ['average_pooling2d_1[0][0]',    
                                                                  'conv2d_40[0][0]']              
                                                                                                  
 batch_normalization_41 (BatchN  (None, 14, 14, 288)  1152       ['concatenate_18[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_40 (Activation)     (None, 14, 14, 288)  0           ['batch_normalization_41[0][0]'] 
                                                                                                  
 conv2d_41 (Conv2D)             (None, 14, 14, 128)  36864       ['activation_40[0][0]']          
                                                                                                  
 batch_normalization_42 (BatchN  (None, 14, 14, 128)  512        ['conv2d_41[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_41 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_42[0][0]'] 
                                                                                                  
 conv2d_42 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_41[0][0]']          
                                                                                                  
 concatenate_19 (Concatenate)   (None, 14, 14, 320)  0           ['concatenate_18[0][0]',         
                                                                  'conv2d_42[0][0]']              
                                                                                                  
 batch_normalization_43 (BatchN  (None, 14, 14, 320)  1280       ['concatenate_19[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_42 (Activation)     (None, 14, 14, 320)  0           ['batch_normalization_43[0][0]'] 
                                                                                                  
 conv2d_43 (Conv2D)             (None, 14, 14, 128)  40960       ['activation_42[0][0]']          
                                                                                                  
 batch_normalization_44 (BatchN  (None, 14, 14, 128)  512        ['conv2d_43[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_43 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_44[0][0]'] 
                                                                                                  
 conv2d_44 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_43[0][0]']          
                                                                                                  
 concatenate_20 (Concatenate)   (None, 14, 14, 352)  0           ['concatenate_19[0][0]',         
                                                                  'conv2d_44[0][0]']              
                                                                                                  
 batch_normalization_45 (BatchN  (None, 14, 14, 352)  1408       ['concatenate_20[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_44 (Activation)     (None, 14, 14, 352)  0           ['batch_normalization_45[0][0]'] 
                                                                                                  
 conv2d_45 (Conv2D)             (None, 14, 14, 128)  45056       ['activation_44[0][0]']          
                                                                                                  
 batch_normalization_46 (BatchN  (None, 14, 14, 128)  512        ['conv2d_45[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_45 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_46[0][0]'] 
                                                                                                  
 conv2d_46 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_45[0][0]']          
                                                                                                  
 concatenate_21 (Concatenate)   (None, 14, 14, 384)  0           ['concatenate_20[0][0]',         
                                                                  'conv2d_46[0][0]']              
                                                                                                  
 batch_normalization_47 (BatchN  (None, 14, 14, 384)  1536       ['concatenate_21[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_46 (Activation)     (None, 14, 14, 384)  0           ['batch_normalization_47[0][0]'] 
                                                                                                  
 conv2d_47 (Conv2D)             (None, 14, 14, 128)  49152       ['activation_46[0][0]']          
                                                                                                  
 batch_normalization_48 (BatchN  (None, 14, 14, 128)  512        ['conv2d_47[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_47 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_48[0][0]'] 
                                                                                                  
 conv2d_48 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_47[0][0]']          
                                                                                                  
 concatenate_22 (Concatenate)   (None, 14, 14, 416)  0           ['concatenate_21[0][0]',         
                                                                  'conv2d_48[0][0]']              
                                                                                                  
 batch_normalization_49 (BatchN  (None, 14, 14, 416)  1664       ['concatenate_22[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_48 (Activation)     (None, 14, 14, 416)  0           ['batch_normalization_49[0][0]'] 
                                                                                                  
 conv2d_49 (Conv2D)             (None, 14, 14, 128)  53248       ['activation_48[0][0]']          
                                                                                                  
 batch_normalization_50 (BatchN  (None, 14, 14, 128)  512        ['conv2d_49[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_49 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_50[0][0]'] 
                                                                                                  
 conv2d_50 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_49[0][0]']          
                                                                                                  
 concatenate_23 (Concatenate)   (None, 14, 14, 448)  0           ['concatenate_22[0][0]',         
                                                                  'conv2d_50[0][0]']              
                                                                                                  
 batch_normalization_51 (BatchN  (None, 14, 14, 448)  1792       ['concatenate_23[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_50 (Activation)     (None, 14, 14, 448)  0           ['batch_normalization_51[0][0]'] 
                                                                                                  
 conv2d_51 (Conv2D)             (None, 14, 14, 128)  57344       ['activation_50[0][0]']          
                                                                                                  
 batch_normalization_52 (BatchN  (None, 14, 14, 128)  512        ['conv2d_51[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_51 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_52[0][0]'] 
                                                                                                  
 conv2d_52 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_51[0][0]']          
                                                                                                  
 concatenate_24 (Concatenate)   (None, 14, 14, 480)  0           ['concatenate_23[0][0]',         
                                                                  'conv2d_52[0][0]']              
                                                                                                  
 batch_normalization_53 (BatchN  (None, 14, 14, 480)  1920       ['concatenate_24[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_52 (Activation)     (None, 14, 14, 480)  0           ['batch_normalization_53[0][0]'] 
                                                                                                  
 conv2d_53 (Conv2D)             (None, 14, 14, 128)  61440       ['activation_52[0][0]']          
                                                                                                  
 batch_normalization_54 (BatchN  (None, 14, 14, 128)  512        ['conv2d_53[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_53 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_54[0][0]'] 
                                                                                                  
 conv2d_54 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_53[0][0]']          
                                                                                                  
 concatenate_25 (Concatenate)   (None, 14, 14, 512)  0           ['concatenate_24[0][0]',         
                                                                  'conv2d_54[0][0]']              
                                                                                                  
 batch_normalization_55 (BatchN  (None, 14, 14, 512)  2048       ['concatenate_25[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_54 (Activation)     (None, 14, 14, 512)  0           ['batch_normalization_55[0][0]'] 
                                                                                                  
 conv2d_55 (Conv2D)             (None, 14, 14, 128)  65536       ['activation_54[0][0]']          
                                                                                                  
 batch_normalization_56 (BatchN  (None, 14, 14, 128)  512        ['conv2d_55[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_55 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_56[0][0]'] 
                                                                                                  
 conv2d_56 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_55[0][0]']          
                                                                                                  
 concatenate_26 (Concatenate)   (None, 14, 14, 544)  0           ['concatenate_25[0][0]',         
                                                                  'conv2d_56[0][0]']              
                                                                                                  
 batch_normalization_57 (BatchN  (None, 14, 14, 544)  2176       ['concatenate_26[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_56 (Activation)     (None, 14, 14, 544)  0           ['batch_normalization_57[0][0]'] 
                                                                                                  
 conv2d_57 (Conv2D)             (None, 14, 14, 128)  69632       ['activation_56[0][0]']          
                                                                                                  
 batch_normalization_58 (BatchN  (None, 14, 14, 128)  512        ['conv2d_57[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_57 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_58[0][0]'] 
                                                                                                  
 conv2d_58 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_57[0][0]']          
                                                                                                  
 concatenate_27 (Concatenate)   (None, 14, 14, 576)  0           ['concatenate_26[0][0]',         
                                                                  'conv2d_58[0][0]']              
                                                                                                  
 batch_normalization_59 (BatchN  (None, 14, 14, 576)  2304       ['concatenate_27[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_58 (Activation)     (None, 14, 14, 576)  0           ['batch_normalization_59[0][0]'] 
                                                                                                  
 conv2d_59 (Conv2D)             (None, 14, 14, 128)  73728       ['activation_58[0][0]']          
                                                                                                  
 batch_normalization_60 (BatchN  (None, 14, 14, 128)  512        ['conv2d_59[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_59 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_60[0][0]'] 
                                                                                                  
 conv2d_60 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_59[0][0]']          
                                                                                                  
 concatenate_28 (Concatenate)   (None, 14, 14, 608)  0           ['concatenate_27[0][0]',         
                                                                  'conv2d_60[0][0]']              
                                                                                                  
 batch_normalization_61 (BatchN  (None, 14, 14, 608)  2432       ['concatenate_28[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_60 (Activation)     (None, 14, 14, 608)  0           ['batch_normalization_61[0][0]'] 
                                                                                                  
 conv2d_61 (Conv2D)             (None, 14, 14, 128)  77824       ['activation_60[0][0]']          
                                                                                                  
 batch_normalization_62 (BatchN  (None, 14, 14, 128)  512        ['conv2d_61[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_61 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_62[0][0]'] 
                                                                                                  
 conv2d_62 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_61[0][0]']          
                                                                                                  
 concatenate_29 (Concatenate)   (None, 14, 14, 640)  0           ['concatenate_28[0][0]',         
                                                                  'conv2d_62[0][0]']              
                                                                                                  
 batch_normalization_63 (BatchN  (None, 14, 14, 640)  2560       ['concatenate_29[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_62 (Activation)     (None, 14, 14, 640)  0           ['batch_normalization_63[0][0]'] 
                                                                                                  
 conv2d_63 (Conv2D)             (None, 14, 14, 128)  81920       ['activation_62[0][0]']          
                                                                                                  
 batch_normalization_64 (BatchN  (None, 14, 14, 128)  512        ['conv2d_63[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_63 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_64[0][0]'] 
                                                                                                  
 conv2d_64 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_63[0][0]']          
                                                                                                  
 concatenate_30 (Concatenate)   (None, 14, 14, 672)  0           ['concatenate_29[0][0]',         
                                                                  'conv2d_64[0][0]']              
                                                                                                  
 batch_normalization_65 (BatchN  (None, 14, 14, 672)  2688       ['concatenate_30[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_64 (Activation)     (None, 14, 14, 672)  0           ['batch_normalization_65[0][0]'] 
                                                                                                  
 conv2d_65 (Conv2D)             (None, 14, 14, 128)  86016       ['activation_64[0][0]']          
                                                                                                  
 batch_normalization_66 (BatchN  (None, 14, 14, 128)  512        ['conv2d_65[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_65 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_66[0][0]'] 
                                                                                                  
 conv2d_66 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_65[0][0]']          
                                                                                                  
 concatenate_31 (Concatenate)   (None, 14, 14, 704)  0           ['concatenate_30[0][0]',         
                                                                  'conv2d_66[0][0]']              
                                                                                                  
 batch_normalization_67 (BatchN  (None, 14, 14, 704)  2816       ['concatenate_31[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_66 (Activation)     (None, 14, 14, 704)  0           ['batch_normalization_67[0][0]'] 
                                                                                                  
 conv2d_67 (Conv2D)             (None, 14, 14, 128)  90112       ['activation_66[0][0]']          
                                                                                                  
 batch_normalization_68 (BatchN  (None, 14, 14, 128)  512        ['conv2d_67[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_67 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_68[0][0]'] 
                                                                                                  
 conv2d_68 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_67[0][0]']          
                                                                                                  
 concatenate_32 (Concatenate)   (None, 14, 14, 736)  0           ['concatenate_31[0][0]',         
                                                                  'conv2d_68[0][0]']              
                                                                                                  
 batch_normalization_69 (BatchN  (None, 14, 14, 736)  2944       ['concatenate_32[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_68 (Activation)     (None, 14, 14, 736)  0           ['batch_normalization_69[0][0]'] 
                                                                                                  
 conv2d_69 (Conv2D)             (None, 14, 14, 128)  94208       ['activation_68[0][0]']          
                                                                                                  
 batch_normalization_70 (BatchN  (None, 14, 14, 128)  512        ['conv2d_69[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_69 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_70[0][0]'] 
                                                                                                  
 conv2d_70 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_69[0][0]']          
                                                                                                  
 concatenate_33 (Concatenate)   (None, 14, 14, 768)  0           ['concatenate_32[0][0]',         
                                                                  'conv2d_70[0][0]']              
                                                                                                  
 batch_normalization_71 (BatchN  (None, 14, 14, 768)  3072       ['concatenate_33[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_70 (Activation)     (None, 14, 14, 768)  0           ['batch_normalization_71[0][0]'] 
                                                                                                  
 conv2d_71 (Conv2D)             (None, 14, 14, 128)  98304       ['activation_70[0][0]']          
                                                                                                  
 batch_normalization_72 (BatchN  (None, 14, 14, 128)  512        ['conv2d_71[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_71 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_72[0][0]'] 
                                                                                                  
 conv2d_72 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_71[0][0]']          
                                                                                                  
 concatenate_34 (Concatenate)   (None, 14, 14, 800)  0           ['concatenate_33[0][0]',         
                                                                  'conv2d_72[0][0]']              
                                                                                                  
 batch_normalization_73 (BatchN  (None, 14, 14, 800)  3200       ['concatenate_34[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_72 (Activation)     (None, 14, 14, 800)  0           ['batch_normalization_73[0][0]'] 
                                                                                                  
 conv2d_73 (Conv2D)             (None, 14, 14, 128)  102400      ['activation_72[0][0]']          
                                                                                                  
 batch_normalization_74 (BatchN  (None, 14, 14, 128)  512        ['conv2d_73[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_73 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_74[0][0]'] 
                                                                                                  
 conv2d_74 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_73[0][0]']          
                                                                                                  
 concatenate_35 (Concatenate)   (None, 14, 14, 832)  0           ['concatenate_34[0][0]',         
                                                                  'conv2d_74[0][0]']              
                                                                                                  
 batch_normalization_75 (BatchN  (None, 14, 14, 832)  3328       ['concatenate_35[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_74 (Activation)     (None, 14, 14, 832)  0           ['batch_normalization_75[0][0]'] 
                                                                                                  
 conv2d_75 (Conv2D)             (None, 14, 14, 128)  106496      ['activation_74[0][0]']          
                                                                                                  
 batch_normalization_76 (BatchN  (None, 14, 14, 128)  512        ['conv2d_75[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_75 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_76[0][0]'] 
                                                                                                  
 conv2d_76 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_75[0][0]']          
                                                                                                  
 concatenate_36 (Concatenate)   (None, 14, 14, 864)  0           ['concatenate_35[0][0]',         
                                                                  'conv2d_76[0][0]']              
                                                                                                  
 batch_normalization_77 (BatchN  (None, 14, 14, 864)  3456       ['concatenate_36[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_76 (Activation)     (None, 14, 14, 864)  0           ['batch_normalization_77[0][0]'] 
                                                                                                  
 conv2d_77 (Conv2D)             (None, 14, 14, 128)  110592      ['activation_76[0][0]']          
                                                                                                  
 batch_normalization_78 (BatchN  (None, 14, 14, 128)  512        ['conv2d_77[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_77 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_78[0][0]'] 
                                                                                                  
 conv2d_78 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_77[0][0]']          
                                                                                                  
 concatenate_37 (Concatenate)   (None, 14, 14, 896)  0           ['concatenate_36[0][0]',         
                                                                  'conv2d_78[0][0]']              
                                                                                                  
 batch_normalization_79 (BatchN  (None, 14, 14, 896)  3584       ['concatenate_37[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_78 (Activation)     (None, 14, 14, 896)  0           ['batch_normalization_79[0][0]'] 
                                                                                                  
 conv2d_79 (Conv2D)             (None, 14, 14, 128)  114688      ['activation_78[0][0]']          
                                                                                                  
 batch_normalization_80 (BatchN  (None, 14, 14, 128)  512        ['conv2d_79[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_79 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_80[0][0]'] 
                                                                                                  
 conv2d_80 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_79[0][0]']          
                                                                                                  
 concatenate_38 (Concatenate)   (None, 14, 14, 928)  0           ['concatenate_37[0][0]',         
                                                                  'conv2d_80[0][0]']              
                                                                                                  
 batch_normalization_81 (BatchN  (None, 14, 14, 928)  3712       ['concatenate_38[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_80 (Activation)     (None, 14, 14, 928)  0           ['batch_normalization_81[0][0]'] 
                                                                                                  
 conv2d_81 (Conv2D)             (None, 14, 14, 128)  118784      ['activation_80[0][0]']          
                                                                                                  
 batch_normalization_82 (BatchN  (None, 14, 14, 128)  512        ['conv2d_81[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_81 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_82[0][0]'] 
                                                                                                  
 conv2d_82 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_81[0][0]']          
                                                                                                  
 concatenate_39 (Concatenate)   (None, 14, 14, 960)  0           ['concatenate_38[0][0]',         
                                                                  'conv2d_82[0][0]']              
                                                                                                  
 batch_normalization_83 (BatchN  (None, 14, 14, 960)  3840       ['concatenate_39[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_82 (Activation)     (None, 14, 14, 960)  0           ['batch_normalization_83[0][0]'] 
                                                                                                  
 conv2d_83 (Conv2D)             (None, 14, 14, 128)  122880      ['activation_82[0][0]']          
                                                                                                  
 batch_normalization_84 (BatchN  (None, 14, 14, 128)  512        ['conv2d_83[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_83 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_84[0][0]'] 
                                                                                                  
 conv2d_84 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_83[0][0]']          
                                                                                                  
 concatenate_40 (Concatenate)   (None, 14, 14, 992)  0           ['concatenate_39[0][0]',         
                                                                  'conv2d_84[0][0]']              
                                                                                                  
 batch_normalization_85 (BatchN  (None, 14, 14, 992)  3968       ['concatenate_40[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_84 (Activation)     (None, 14, 14, 992)  0           ['batch_normalization_85[0][0]'] 
                                                                                                  
 conv2d_85 (Conv2D)             (None, 14, 14, 128)  126976      ['activation_84[0][0]']          
                                                                                                  
 batch_normalization_86 (BatchN  (None, 14, 14, 128)  512        ['conv2d_85[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_85 (Activation)     (None, 14, 14, 128)  0           ['batch_normalization_86[0][0]'] 
                                                                                                  
 conv2d_86 (Conv2D)             (None, 14, 14, 32)   36864       ['activation_85[0][0]']          
                                                                                                  
 concatenate_41 (Concatenate)   (None, 14, 14, 1024  0           ['concatenate_40[0][0]',         
                                )                                 'conv2d_86[0][0]']              
                                                                                                  
 batch_normalization_87 (BatchN  (None, 14, 14, 1024  4096       ['concatenate_41[0][0]']         
 ormalization)                  )                                                                 
                                                                                                  
 activation_86 (Activation)     (None, 14, 14, 1024  0           ['batch_normalization_87[0][0]'] 
                                )                                                                 
                                                                                                  
 conv2d_87 (Conv2D)             (None, 14, 14, 512)  524288      ['activation_86[0][0]']          
                                                                                                  
 average_pooling2d_2 (AveragePo  (None, 7, 7, 512)   0           ['conv2d_87[0][0]']              
 oling2D)                                                                                         
                                                                                                  
 batch_normalization_88 (BatchN  (None, 7, 7, 512)   2048        ['average_pooling2d_2[0][0]']    
 ormalization)                                                                                    
                                                                                                  
 activation_87 (Activation)     (None, 7, 7, 512)    0           ['batch_normalization_88[0][0]'] 
                                                                                                  
 conv2d_88 (Conv2D)             (None, 7, 7, 128)    65536       ['activation_87[0][0]']          
                                                                                                  
 batch_normalization_89 (BatchN  (None, 7, 7, 128)   512         ['conv2d_88[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_88 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_89[0][0]'] 
                                                                                                  
 conv2d_89 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_88[0][0]']          
                                                                                                  
 concatenate_42 (Concatenate)   (None, 7, 7, 544)    0           ['average_pooling2d_2[0][0]',    
                                                                  'conv2d_89[0][0]']              
                                                                                                  
 batch_normalization_90 (BatchN  (None, 7, 7, 544)   2176        ['concatenate_42[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_89 (Activation)     (None, 7, 7, 544)    0           ['batch_normalization_90[0][0]'] 
                                                                                                  
 conv2d_90 (Conv2D)             (None, 7, 7, 128)    69632       ['activation_89[0][0]']          
                                                                                                  
 batch_normalization_91 (BatchN  (None, 7, 7, 128)   512         ['conv2d_90[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_90 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_91[0][0]'] 
                                                                                                  
 conv2d_91 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_90[0][0]']          
                                                                                                  
 concatenate_43 (Concatenate)   (None, 7, 7, 576)    0           ['concatenate_42[0][0]',         
                                                                  'conv2d_91[0][0]']              
                                                                                                  
 batch_normalization_92 (BatchN  (None, 7, 7, 576)   2304        ['concatenate_43[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_91 (Activation)     (None, 7, 7, 576)    0           ['batch_normalization_92[0][0]'] 
                                                                                                  
 conv2d_92 (Conv2D)             (None, 7, 7, 128)    73728       ['activation_91[0][0]']          
                                                                                                  
 batch_normalization_93 (BatchN  (None, 7, 7, 128)   512         ['conv2d_92[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_92 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_93[0][0]'] 
                                                                                                  
 conv2d_93 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_92[0][0]']          
                                                                                                  
 concatenate_44 (Concatenate)   (None, 7, 7, 608)    0           ['concatenate_43[0][0]',         
                                                                  'conv2d_93[0][0]']              
                                                                                                  
 batch_normalization_94 (BatchN  (None, 7, 7, 608)   2432        ['concatenate_44[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_93 (Activation)     (None, 7, 7, 608)    0           ['batch_normalization_94[0][0]'] 
                                                                                                  
 conv2d_94 (Conv2D)             (None, 7, 7, 128)    77824       ['activation_93[0][0]']          
                                                                                                  
 batch_normalization_95 (BatchN  (None, 7, 7, 128)   512         ['conv2d_94[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_94 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_95[0][0]'] 
                                                                                                  
 conv2d_95 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_94[0][0]']          
                                                                                                  
 concatenate_45 (Concatenate)   (None, 7, 7, 640)    0           ['concatenate_44[0][0]',         
                                                                  'conv2d_95[0][0]']              
                                                                                                  
 batch_normalization_96 (BatchN  (None, 7, 7, 640)   2560        ['concatenate_45[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_95 (Activation)     (None, 7, 7, 640)    0           ['batch_normalization_96[0][0]'] 
                                                                                                  
 conv2d_96 (Conv2D)             (None, 7, 7, 128)    81920       ['activation_95[0][0]']          
                                                                                                  
 batch_normalization_97 (BatchN  (None, 7, 7, 128)   512         ['conv2d_96[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_96 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_97[0][0]'] 
                                                                                                  
 conv2d_97 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_96[0][0]']          
                                                                                                  
 concatenate_46 (Concatenate)   (None, 7, 7, 672)    0           ['concatenate_45[0][0]',         
                                                                  'conv2d_97[0][0]']              
                                                                                                  
 batch_normalization_98 (BatchN  (None, 7, 7, 672)   2688        ['concatenate_46[0][0]']         
 ormalization)                                                                                    
                                                                                                  
 activation_97 (Activation)     (None, 7, 7, 672)    0           ['batch_normalization_98[0][0]'] 
                                                                                                  
 conv2d_98 (Conv2D)             (None, 7, 7, 128)    86016       ['activation_97[0][0]']          
                                                                                                  
 batch_normalization_99 (BatchN  (None, 7, 7, 128)   512         ['conv2d_98[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 activation_98 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_99[0][0]'] 
                                                                                                  
 conv2d_99 (Conv2D)             (None, 7, 7, 32)     36864       ['activation_98[0][0]']          
                                                                                                  
 concatenate_47 (Concatenate)   (None, 7, 7, 704)    0           ['concatenate_46[0][0]',         
                                                                  'conv2d_99[0][0]']              
                                                                                                  
 batch_normalization_100 (Batch  (None, 7, 7, 704)   2816        ['concatenate_47[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_99 (Activation)     (None, 7, 7, 704)    0           ['batch_normalization_100[0][0]']
                                                                                                  
 conv2d_100 (Conv2D)            (None, 7, 7, 128)    90112       ['activation_99[0][0]']          
                                                                                                  
 batch_normalization_101 (Batch  (None, 7, 7, 128)   512         ['conv2d_100[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_100 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_101[0][0]']
                                                                                                  
 conv2d_101 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_100[0][0]']         
                                                                                                  
 concatenate_48 (Concatenate)   (None, 7, 7, 736)    0           ['concatenate_47[0][0]',         
                                                                  'conv2d_101[0][0]']             
                                                                                                  
 batch_normalization_102 (Batch  (None, 7, 7, 736)   2944        ['concatenate_48[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_101 (Activation)    (None, 7, 7, 736)    0           ['batch_normalization_102[0][0]']
                                                                                                  
 conv2d_102 (Conv2D)            (None, 7, 7, 128)    94208       ['activation_101[0][0]']         
                                                                                                  
 batch_normalization_103 (Batch  (None, 7, 7, 128)   512         ['conv2d_102[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_102 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_103[0][0]']
                                                                                                  
 conv2d_103 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_102[0][0]']         
                                                                                                  
 concatenate_49 (Concatenate)   (None, 7, 7, 768)    0           ['concatenate_48[0][0]',         
                                                                  'conv2d_103[0][0]']             
                                                                                                  
 batch_normalization_104 (Batch  (None, 7, 7, 768)   3072        ['concatenate_49[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_103 (Activation)    (None, 7, 7, 768)    0           ['batch_normalization_104[0][0]']
                                                                                                  
 conv2d_104 (Conv2D)            (None, 7, 7, 128)    98304       ['activation_103[0][0]']         
                                                                                                  
 batch_normalization_105 (Batch  (None, 7, 7, 128)   512         ['conv2d_104[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_104 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_105[0][0]']
                                                                                                  
 conv2d_105 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_104[0][0]']         
                                                                                                  
 concatenate_50 (Concatenate)   (None, 7, 7, 800)    0           ['concatenate_49[0][0]',         
                                                                  'conv2d_105[0][0]']             
                                                                                                  
 batch_normalization_106 (Batch  (None, 7, 7, 800)   3200        ['concatenate_50[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_105 (Activation)    (None, 7, 7, 800)    0           ['batch_normalization_106[0][0]']
                                                                                                  
 conv2d_106 (Conv2D)            (None, 7, 7, 128)    102400      ['activation_105[0][0]']         
                                                                                                  
 batch_normalization_107 (Batch  (None, 7, 7, 128)   512         ['conv2d_106[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_106 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_107[0][0]']
                                                                                                  
 conv2d_107 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_106[0][0]']         
                                                                                                  
 concatenate_51 (Concatenate)   (None, 7, 7, 832)    0           ['concatenate_50[0][0]',         
                                                                  'conv2d_107[0][0]']             
                                                                                                  
 batch_normalization_108 (Batch  (None, 7, 7, 832)   3328        ['concatenate_51[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_107 (Activation)    (None, 7, 7, 832)    0           ['batch_normalization_108[0][0]']
                                                                                                  
 conv2d_108 (Conv2D)            (None, 7, 7, 128)    106496      ['activation_107[0][0]']         
                                                                                                  
 batch_normalization_109 (Batch  (None, 7, 7, 128)   512         ['conv2d_108[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_108 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_109[0][0]']
                                                                                                  
 conv2d_109 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_108[0][0]']         
                                                                                                  
 concatenate_52 (Concatenate)   (None, 7, 7, 864)    0           ['concatenate_51[0][0]',         
                                                                  'conv2d_109[0][0]']             
                                                                                                  
 batch_normalization_110 (Batch  (None, 7, 7, 864)   3456        ['concatenate_52[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_109 (Activation)    (None, 7, 7, 864)    0           ['batch_normalization_110[0][0]']
                                                                                                  
 conv2d_110 (Conv2D)            (None, 7, 7, 128)    110592      ['activation_109[0][0]']         
                                                                                                  
 batch_normalization_111 (Batch  (None, 7, 7, 128)   512         ['conv2d_110[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_110 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_111[0][0]']
                                                                                                  
 conv2d_111 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_110[0][0]']         
                                                                                                  
 concatenate_53 (Concatenate)   (None, 7, 7, 896)    0           ['concatenate_52[0][0]',         
                                                                  'conv2d_111[0][0]']             
                                                                                                  
 batch_normalization_112 (Batch  (None, 7, 7, 896)   3584        ['concatenate_53[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_111 (Activation)    (None, 7, 7, 896)    0           ['batch_normalization_112[0][0]']
                                                                                                  
 conv2d_112 (Conv2D)            (None, 7, 7, 128)    114688      ['activation_111[0][0]']         
                                                                                                  
 batch_normalization_113 (Batch  (None, 7, 7, 128)   512         ['conv2d_112[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_112 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_113[0][0]']
                                                                                                  
 conv2d_113 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_112[0][0]']         
                                                                                                  
 concatenate_54 (Concatenate)   (None, 7, 7, 928)    0           ['concatenate_53[0][0]',         
                                                                  'conv2d_113[0][0]']             
                                                                                                  
 batch_normalization_114 (Batch  (None, 7, 7, 928)   3712        ['concatenate_54[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_113 (Activation)    (None, 7, 7, 928)    0           ['batch_normalization_114[0][0]']
                                                                                                  
 conv2d_114 (Conv2D)            (None, 7, 7, 128)    118784      ['activation_113[0][0]']         
                                                                                                  
 batch_normalization_115 (Batch  (None, 7, 7, 128)   512         ['conv2d_114[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_114 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_115[0][0]']
                                                                                                  
 conv2d_115 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_114[0][0]']         
                                                                                                  
 concatenate_55 (Concatenate)   (None, 7, 7, 960)    0           ['concatenate_54[0][0]',         
                                                                  'conv2d_115[0][0]']             
                                                                                                  
 batch_normalization_116 (Batch  (None, 7, 7, 960)   3840        ['concatenate_55[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_115 (Activation)    (None, 7, 7, 960)    0           ['batch_normalization_116[0][0]']
                                                                                                  
 conv2d_116 (Conv2D)            (None, 7, 7, 128)    122880      ['activation_115[0][0]']         
                                                                                                  
 batch_normalization_117 (Batch  (None, 7, 7, 128)   512         ['conv2d_116[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_116 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_117[0][0]']
                                                                                                  
 conv2d_117 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_116[0][0]']         
                                                                                                  
 concatenate_56 (Concatenate)   (None, 7, 7, 992)    0           ['concatenate_55[0][0]',         
                                                                  'conv2d_117[0][0]']             
                                                                                                  
 batch_normalization_118 (Batch  (None, 7, 7, 992)   3968        ['concatenate_56[0][0]']         
 Normalization)                                                                                   
                                                                                                  
 activation_117 (Activation)    (None, 7, 7, 992)    0           ['batch_normalization_118[0][0]']
                                                                                                  
 conv2d_118 (Conv2D)            (None, 7, 7, 128)    126976      ['activation_117[0][0]']         
                                                                                                  
 batch_normalization_119 (Batch  (None, 7, 7, 128)   512         ['conv2d_118[0][0]']             
 Normalization)                                                                                   
                                                                                                  
 activation_118 (Activation)    (None, 7, 7, 128)    0           ['batch_normalization_119[0][0]']
                                                                                                  
 conv2d_119 (Conv2D)            (None, 7, 7, 32)     36864       ['activation_118[0][0]']         
                                                                                                  
 concatenate_57 (Concatenate)   (None, 7, 7, 1024)   0           ['concatenate_56[0][0]',         
                                                                  'conv2d_119[0][0]']             
                                                                                                  
 global_average_pooling2d (Glob  (None, 1024)        0           ['concatenate_57[0][0]']         
 alAveragePooling2D)                                                                              
                                                                                                  
 dense (Dense)                  (None, 16)           16400       ['global_average_pooling2d[0][0]'
                                                                 ]                                
                                                                                                  
 activation_119 (Activation)    (None, 16)           0           ['dense[0][0]']                  
                                                                                                  
 dense_1 (Dense)                (None, 1024)         17408       ['activation_119[0][0]']         
                                                                                                  
 activation_120 (Activation)    (None, 1024)         0           ['dense_1[0][0]']                
                                                                                                  
 reshape (Reshape)              (None, 1, 1, 1024)   0           ['activation_120[0][0]']         
                                                                                                  
 tf.math.multiply (TFOpLambda)  (None, 7, 7, 1024)   0           ['concatenate_57[0][0]',         
                                                                  'reshape[0][0]']                
                                                                                                  
 batch_normalization_120 (Batch  (None, 7, 7, 1024)  4096        ['tf.math.multiply[0][0]']       
 Normalization)                                                                                   
                                                                                                  
 activation_121 (Activation)    (None, 7, 7, 1024)   0           ['batch_normalization_120[0][0]']
                                                                                                  
 global_average_pooling2d_1 (Gl  (None, 1024)        0           ['activation_121[0][0]']         
 obalAveragePooling2D)                                                                            
                                                                                                  
 dense_2 (Dense)                (None, 1000)         1025000     ['global_average_pooling2d_1[0][0
                                                                 ]']                              
                                                                                                  
==================================================================================================
Total params: 8,096,312
Trainable params: 8,012,664
Non-trainable params: 83,648
__________________________________________________________________________________________________

3.10.编译模型

#设置初始学习率
initial_learning_rate = 1e-4
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)
model.compile(optimizer=opt,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])


3.11.训练模型

'''训练模型'''
epochs = 20
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

训练记录如下:

Epoch 1/20
54/54 [==============================] - ETA: 0s - loss: 4.1244 - accuracy: 0.5560
Epoch 1: val_accuracy improved from -inf to 0.07818, saving model to best_model.h5
54/54 [==============================] - 25s 236ms/step - loss: 4.1244 - accuracy: 0.5560 - val_loss: 8.7794 - val_accuracy: 0.0782
Epoch 2/20
54/54 [==============================] - ETA: 0s - loss: 1.3264 - accuracy: 0.6972
Epoch 2: val_accuracy improved from 0.07818 to 0.63477, saving model to best_model.h5
54/54 [==============================] - 12s 214ms/step - loss: 1.3264 - accuracy: 0.6972 - val_loss: 4.7183 - val_accuracy: 0.6348
Epoch 3/20
54/54 [==============================] - ETA: 0s - loss: 0.6500 - accuracy: 0.7515
Epoch 3: val_accuracy did not improve from 0.63477
54/54 [==============================] - 11s 210ms/step - loss: 0.6500 - accuracy: 0.7515 - val_loss: 3.0509 - val_accuracy: 0.5828
Epoch 4/20
54/54 [==============================] - ETA: 0s - loss: 0.4991 - accuracy: 0.8028
Epoch 4: val_accuracy improved from 0.63477 to 0.65811, saving model to best_model.h5
54/54 [==============================] - 12s 217ms/step - loss: 0.4991 - accuracy: 0.8028 - val_loss: 1.5565 - val_accuracy: 0.6581
Epoch 5/20
54/54 [==============================] - ETA: 0s - loss: 0.3937 - accuracy: 0.8448
Epoch 5: val_accuracy did not improve from 0.65811
54/54 [==============================] - 11s 211ms/step - loss: 0.3937 - accuracy: 0.8448 - val_loss: 1.0164 - val_accuracy: 0.6540
Epoch 6/20
54/54 [==============================] - ETA: 0s - loss: 0.3300 - accuracy: 0.8716
Epoch 6: val_accuracy did not improve from 0.65811
54/54 [==============================] - 11s 210ms/step - loss: 0.3300 - accuracy: 0.8716 - val_loss: 0.8846 - val_accuracy: 0.5799
Epoch 7/20
54/54 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8839
Epoch 7: val_accuracy improved from 0.65811 to 0.66219, saving model to best_model.h5
54/54 [==============================] - 12s 221ms/step - loss: 0.2872 - accuracy: 0.8839 - val_loss: 0.8020 - val_accuracy: 0.6622
Epoch 8/20
54/54 [==============================] - ETA: 0s - loss: 0.2339 - accuracy: 0.9090
Epoch 8: val_accuracy improved from 0.66219 to 0.81855, saving model to best_model.h5
54/54 [==============================] - 12s 220ms/step - loss: 0.2339 - accuracy: 0.9090 - val_loss: 0.4418 - val_accuracy: 0.8186
Epoch 9/20
54/54 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9247
Epoch 9: val_accuracy improved from 0.81855 to 0.82555, saving model to best_model.h5
54/54 [==============================] - 12s 222ms/step - loss: 0.2030 - accuracy: 0.9247 - val_loss: 0.4440 - val_accuracy: 0.8256
Epoch 10/20
54/54 [==============================] - ETA: 0s - loss: 0.1891 - accuracy: 0.9259
Epoch 10: val_accuracy did not improve from 0.82555
54/54 [==============================] - 12s 215ms/step - loss: 0.1891 - accuracy: 0.9259 - val_loss: 1.0064 - val_accuracy: 0.7421
Epoch 11/20
54/54 [==============================] - ETA: 0s - loss: 0.1565 - accuracy: 0.9440
Epoch 11: val_accuracy did not improve from 0.82555
54/54 [==============================] - 11s 211ms/step - loss: 0.1565 - accuracy: 0.9440 - val_loss: 0.5438 - val_accuracy: 0.8121
Epoch 12/20
54/54 [==============================] - ETA: 0s - loss: 0.1580 - accuracy: 0.9422
Epoch 12: val_accuracy did not improve from 0.82555
54/54 [==============================] - 11s 210ms/step - loss: 0.1580 - accuracy: 0.9422 - val_loss: 1.1685 - val_accuracy: 0.7100
Epoch 13/20
54/54 [==============================] - ETA: 0s - loss: 0.1100 - accuracy: 0.9650
Epoch 13: val_accuracy improved from 0.82555 to 0.89148, saving model to best_model.h5
54/54 [==============================] - 12s 218ms/step - loss: 0.1100 - accuracy: 0.9650 - val_loss: 0.3890 - val_accuracy: 0.8915
Epoch 14/20
54/54 [==============================] - ETA: 0s - loss: 0.0560 - accuracy: 0.9848
Epoch 14: val_accuracy did not improve from 0.89148
54/54 [==============================] - 11s 213ms/step - loss: 0.0560 - accuracy: 0.9848 - val_loss: 0.4445 - val_accuracy: 0.8676
Epoch 15/20
54/54 [==============================] - ETA: 0s - loss: 0.0700 - accuracy: 0.9772
Epoch 15: val_accuracy did not improve from 0.89148
54/54 [==============================] - 11s 212ms/step - loss: 0.0700 - accuracy: 0.9772 - val_loss: 0.4124 - val_accuracy: 0.8839
Epoch 16/20
54/54 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.9638
Epoch 16: val_accuracy did not improve from 0.89148
54/54 [==============================] - 11s 211ms/step - loss: 0.0994 - accuracy: 0.9638 - val_loss: 0.5568 - val_accuracy: 0.8261
Epoch 17/20
54/54 [==============================] - ETA: 0s - loss: 0.0689 - accuracy: 0.9743
Epoch 17: val_accuracy did not improve from 0.89148
54/54 [==============================] - 11s 214ms/step - loss: 0.0689 - accuracy: 0.9743 - val_loss: 0.5721 - val_accuracy: 0.8436
Epoch 18/20
54/54 [==============================] - ETA: 0s - loss: 0.0355 - accuracy: 0.9924
Epoch 18: val_accuracy improved from 0.89148 to 0.91832, saving model to best_model.h5
54/54 [==============================] - 12s 219ms/step - loss: 0.0355 - accuracy: 0.9924 - val_loss: 0.3478 - val_accuracy: 0.9183
Epoch 19/20
54/54 [==============================] - ETA: 0s - loss: 0.0100 - accuracy: 0.9994
Epoch 19: val_accuracy improved from 0.91832 to 0.94516, saving model to best_model.h5
54/54 [==============================] - 12s 217ms/step - loss: 0.0100 - accuracy: 0.9994 - val_loss: 0.1933 - val_accuracy: 0.9452
Epoch 20/20
54/54 [==============================] - ETA: 0s - loss: 0.0208 - accuracy: 0.9947
Epoch 20: val_accuracy did not improve from 0.94516
54/54 [==============================] - 11s 211ms/step - loss: 0.0208 - accuracy: 0.9947 - val_loss: 0.6098 - val_accuracy: 0.8460

3.12.模型评估

'''模型评估'''
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(len(loss))
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

3.13.图像预测

'''指定图片进行预测'''
# 采用加载的模型(new_model)来看预测结果
plt.figure(figsize=(10, 5))  # 图形的宽为10高为5
plt.suptitle("预测结果展示", fontsize=10)
for images, labels in val_ds.take(1):
    for i in range(8):
        ax = plt.subplot(2, 4, i + 1)

        # 显示图片
        plt.imshow(images[i].numpy().astype("uint8"))

        # 需要给图片增加一个维度
        img_array = tf.expand_dims(images[i], 0)

        # 使用模型预测图片中的人物
        predictions = model.predict(img_array)
        plt.title(class_names[np.argmax(predictions)], fontsize=10)

        plt.axis("off")
plt.show()

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

4 知识点详解

4.1ResNeXt50详解

论文:Aggregated Residual Transformations for Deep Neural Networks.pdf

ResNeXt是由何凯明团队在2017年CVPR会议上提出来的新型图像分类网络。ResNeXt是ResNet的升级版,在ResNet的基础上,引入了cardinality的概念,类似于ResNet,ResNeXt也有ResNeXt-50,ResNeXt-101的版本。

这篇文章介绍了一种用于图像分类的简单而有效的网络架构,称为Aggregated Residual Transformations for Deep Neural Networks。该网络采用了VGG/ResNets的策略,通过重复层来增加深度和宽度,并利用分裂-变换-合并策略以易于扩展的方式进行转换。文章还提出了一个新的维度——“基数”,它是指转换集合的大小,可以在保持复杂性不变的情况下提高分类准确性。作者在ImageNet-1K数据集上进行了实证研究,证明了这种方法的有效性。

下图是ResNet(左)与ResNeXt(右)block的差异。在ResNet中,输入的具有256个通道的特征经过1×1卷积压缩4倍到64个通道,之后3×3的卷积核用于处理特征,经1×1卷积扩大通道数与原特征残差连接后输出。

深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能
ResNeXt也是相同的处理策略,但在ResNeXt中,输入的具有256个通道的特征被分为32个组,每组被压缩64倍到4个通道后进行处理。32个组相加后与原特征残差连接后输出。这里cardinatity指的是一个block中所具有的相同分支的数目。下图为等效模型。
深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能
下图为ResNet50和ResNeXt50(32x4d)的结构对比图。
深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

分组卷积
ResNeXt中采用的分组卷机简单来说就是将特征图分为不同的组,再对每组特征图分别进行卷积,这个操作可以有效的降低计算量。
在分组卷积中,每个卷积核只处理部分通道,比如下图中,红色卷积核只处理红色的通道,绿色卷积核只处理绿色通道,黄色卷积核只处理黄色通道。此时每个卷积核有2个通道,每个卷积核生成一张特征图。
深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能
深度学习 Day27——J6ResNeXt-50实战解析,深度学习,人工智能

4.2 ResNeXt50对比ResNet50V2、DenseNet

4.2.1 网络结构

  ResNet-50v2是ResNet系列中的一个经典模型,由50层卷积层、批量归一化、激活函数和池化层构成。它引入了一种全新的残差块结构,即bottleneck结构,使得网络参数量大幅度降低,同时精度也有所提升。
  DenseNet是一种全新的网络结构,其特点是不同于传统的网络结构,DenseNet中每一层的输出不仅和前一层的输出有关,还和之前所有层的输出有关,这种密集连接的结构可以有效地缓解梯度消失和参数稀疏问题,提高了模型的泛化能力和精度。
  AggResNet(ResNeXt50)则是基于ResNet结构改进而来的新型深度神经网络结构,其特点是采用了聚合残差结构和局部连接结构,同时引入了Random Erasing和Mixup等数据增强和正则化方法,可以进一步提高网络的精度和鲁棒性。

4.2.2 精度和计算量

  在ImageNet数据集上,ResNet-50v2和DenseNet在Top-1和Top-5指标上都取得了优异的性能。与之相比,AggResNet在相同的深度下具有更高的精度,并且在参数量和计算量上都显著降低。同时,在较深的网络结构下,AggResNet的优势更加明显,可以达到更高的精度,而ResNet-50v2和DenseNet则难以继续提高精度。

4.2.3 适用范围

  ResNet-50v2适用于各种图像分类任务,但在一些特定的视觉任务,如目标检测、语义分割等方面的表现可能不如其他模型。
  DenseNet则在各种任务中都具有优异的性能,尤其在目标检测和语义分割等像素级别的任务中表现突出。
  AggResNet则不仅适用于图像分类任务,同时也可以应用于目标检测、语义分割和行人重识别等视觉任务中,并且在这些任务中具有优异的性能。

4 总结

  ResNet-50v2、DenseNet和AggResNet都是非常优秀的深度神经网络结构,它们在不同的任务和场景中都具有不同的优势和适用性。文章来源地址https://www.toymoban.com/news/detail-820039.html

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