深度学习 Day23——J3DenseNet算法实战与解析

🍨 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

文章目录

前言
1 我的环境
2 pytorch实现DenseNet算法
- 2.1 前期准备
- - 2.1.1 引入库
  - 2.1.2 设置GPU（如果设备上支持GPU就使用GPU,否则使用CPU）
  - 2.1.3 导入数据
  - 2.1.4 可视化数据
  - 2.1.4 图像数据变换
  - 2.1.4 划分数据集
  - 2.1.4 加载数据
  - 2.1.4 查看数据
- 2.2 搭建densenet121模型
- 2.3 训练模型
- - 2.3.1 设置超参数
  - 2.3.2 编写训练函数
  - 2.3.3 编写测试函数
  - 2.3.4 正式训练
- 2.4 结果可视化
- 2.4 指定图片进行预测
- 2.6 模型评估
3 tensorflow实现DenseNet算法
- 3.1.引入库
- 3.2.设置GPU（如果使用的是CPU可以忽略这步）
- 3.3.导入数据
- 3.4.查看数据
- 3.5.加载数据
- 3.6.再次检查数据
- 3.7.配置数据集
- 3.8.可视化数据
- 3.9.构建DenseNet网络
- 3.10.编译模型
- 3.11.训练模型
- 3.12.模型评估
- 3.13.图像预测
4 知识点详解
- 4.1 DenseNet算法详解
- - 4.1.1 前言
  - 4.1.2 设计理念
  - 4.1.2.1 标准神经网络
  - 4.1.2.2 ResNet
  - 4.1.2.3 DenseNet
  - 4.1.3 网络结构
  - 4.1.4 效果对比
  - 4.1.5 使用Pytroch实现DenseNet121
总结

前言

关键字： pytorch实现DenseNet算法，tensorflow实现DenseNet算法，DenseNet算法详解

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 warningswarnings.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CPUdevice = 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\bird\bird_photos"
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

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

2.1.4 可视化数据

'''前期工作-可视化数据'''
subfolder = Path(data_dir) / "Cockatoo"
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()

在这里插入图片描述

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 ImageFolderNumber of datapoints: 565Root location: D:\DeepLearning\data\bird\bird_photosStandardTransform
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]))
{'Bananaquit': 0, 'Black Skimmer': 1, 'Black Throated Bushtiti': 2, 'Cockatoo': 3}

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 0x000001309DFA26D0>
test_dataset=<torch.utils.data.dataset.Subset object at 0x000001309DFA2760>
train_size=452
test_size=113

2.1.4 加载数据

'''前期工作-加载数据'''
batch_size = 32train_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densenet121模型

"""构建DenseNet网络"""
# 这里我们采用了Pytorch的框架来实现DenseNet，
# 首先实现DenseBlock中的内部结构，这里是BN+ReLU+1×1Conv+BN+ReLU+3×3Conv结构，最后也加入dropout层用于训练过程。
class _DenseLayer(nn.Sequential):"""Basic unit of DenseBlock (using bottleneck layer) """def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):super(_DenseLayer, self).__init__()self.add_module('norm1', nn.BatchNorm2d(num_input_features)),self.add_module('relu1', nn.ReLU(inplace=True)),self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate,kernel_size=1, stride=1, bias=False)),self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),self.add_module('relu2', nn.ReLU(inplace=True)),self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,kernel_size=3, stride=1, padding=1, bias=False)),self.drop_rate = drop_ratedef forward(self, x):new_features = super(_DenseLayer, self).forward(x)if self.drop_rate > 0:new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)return torch.cat([x, new_features], 1)# 实现DenseBlock模块，内部是密集连接方式（输入特征数线性增长）：
class _DenseBlock(nn.Sequential):"""DenseBlock """def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):super(_DenseBlock, self).__init__()for i in range(num_layers):layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)self.add_module('denselayer%d' % (i + 1), layer)# 实现Transition层，它主要是一个卷积层和一个池化层：
class _Transition(nn.Sequential):def __init__(self, num_input_features, num_output_features):super(_Transition, self).__init__()self.add_module('norm', nn.BatchNorm2d(num_input_features))self.add_module('relu', nn.ReLU(inplace=True))self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,kernel_size=1, stride=1, bias=False))self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))# 最后我们实现DenseNet网络：
class DenseNet(nn.Module):r"""Densenet-BC model class, based on`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`Args:growth_rate (int) - how many filters to add each layer (`k` in paper)block_config (list of 3 or 4 ints) - how many layers in each pooling blocknum_init_features (int) - the number of filters to learn in the first convolution layerbn_size (int) - multiplicative factor for number of bottle neck layers(i.e. bn_size * k features in the bottleneck layer)drop_rate (float) - dropout rate after each dense layernum_classes (int) - number of classification classes"""def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),num_init_features=24, bn_size=4, compression=0.5, drop_rate=0,num_classes=1000):super(DenseNet, self).__init__()# First Conv2dself.features = nn.Sequential(OrderedDict([('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),('norm0', nn.BatchNorm2d(num_init_features)),('relu0', nn.ReLU(inplace=True)),('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))]))# Each denseblocknum_features = num_init_featuresfor i, num_layers in enumerate(block_config):block = _DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)self.features.add_module('denseblock%d' % (i + 1), block)num_features += num_layers * growth_rateif i != len(block_config) - 1:transition = _Transition(num_input_features=num_features,num_output_features=int(num_features * compression))self.features.add_module('transition%d' % (i + 1), transition)num_features = int(num_features * compression)# Final bn+reluself.features.add_module('norm5', nn.BatchNorm2d(num_features))self.features.add_module('relu5', nn.ReLU(inplace=True))# classification layerself.classifier = nn.Linear(num_features, num_classes)# params initializationfor m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):features = self.features(x)out = F.avg_pool2d(features, 7, stride=1).view(features.size(0), -1)out = self.classifier(out)return outmodel_urls = {'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth','densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth','densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth','densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth'}def densenet121(pretrained=False, **kwargs):"""DenseNet121"""model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),	**kwargs)if pretrained:# '.'s are no longer allowed in module names, but pervious _DenseLayer# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.# They are also in the checkpoints in model_urls. This pattern is used# to find such keys.pattern = re.compile(r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')state_dict = model_zoo.load_url(model_urls['densenet121'])for key in list(state_dict.keys()):res = pattern.match(key)if res:new_key = res.group(1) + res.group(2)state_dict[new_key] = state_dict[key]del state_dict[key]model.load_state_dict(state_dict)return model"""搭建densenet121模型"""
# model = densenet121().to(device)  
model = densenet121(True).to(device)  # 使用预训练模型
print(model)
print(summary.summary(model, (3, 224, 224)))  # 查看模型的参数量以及相关指标

输出

DenseNet((features): Sequential((conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu0): ReLU(inplace=True)(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(denseblock1): _DenseBlock((denselayer1): _DenseLayer((norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): _DenseLayer((norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): _DenseLayer((norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): _DenseLayer((norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): _DenseLayer((norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): _DenseLayer((norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition1): _Transition((norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock2): _DenseBlock((denselayer1): _DenseLayer((norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): _DenseLayer((norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): _DenseLayer((norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): _DenseLayer((norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): _DenseLayer((norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): _DenseLayer((norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): _DenseLayer((norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): _DenseLayer((norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): _DenseLayer((norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): _DenseLayer((norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): _DenseLayer((norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): _DenseLayer((norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition2): _Transition((norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock3): _DenseBlock((denselayer1): _DenseLayer((norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): _DenseLayer((norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): _DenseLayer((norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): _DenseLayer((norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): _DenseLayer((norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): _DenseLayer((norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): _DenseLayer((norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): _DenseLayer((norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): _DenseLayer((norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): _DenseLayer((norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): _DenseLayer((norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): _DenseLayer((norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer13): _DenseLayer((norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer14): _DenseLayer((norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer15): _DenseLayer((norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer16): _DenseLayer((norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer17): _DenseLayer((norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer18): _DenseLayer((norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer19): _DenseLayer((norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer20): _DenseLayer((norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer21): _DenseLayer((norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer22): _DenseLayer((norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer23): _DenseLayer((norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer24): _DenseLayer((norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(transition3): _Transition((norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock4): _DenseBlock((denselayer1): _DenseLayer((norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer2): _DenseLayer((norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer3): _DenseLayer((norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer4): _DenseLayer((norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer5): _DenseLayer((norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer6): _DenseLayer((norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer7): _DenseLayer((norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer8): _DenseLayer((norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer9): _DenseLayer((norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer10): _DenseLayer((norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer11): _DenseLayer((norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer12): _DenseLayer((norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer13): _DenseLayer((norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer14): _DenseLayer((norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer15): _DenseLayer((norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))(denselayer16): _DenseLayer((norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu1): ReLU(inplace=True)(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu2): ReLU(inplace=True)(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)))(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu5): ReLU(inplace=True))(classifier): Linear(in_features=1024, out_features=1000, bias=True)
)
----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,408BatchNorm2d-2         [-1, 64, 112, 112]             128ReLU-3         [-1, 64, 112, 112]               0MaxPool2d-4           [-1, 64, 56, 56]               0BatchNorm2d-5           [-1, 64, 56, 56]             128ReLU-6           [-1, 64, 56, 56]               0Conv2d-7          [-1, 128, 56, 56]           8,192BatchNorm2d-8          [-1, 128, 56, 56]             256ReLU-9          [-1, 128, 56, 56]               0Conv2d-10           [-1, 32, 56, 56]          36,864BatchNorm2d-11           [-1, 96, 56, 56]             192ReLU-12           [-1, 96, 56, 56]               0Conv2d-13          [-1, 128, 56, 56]          12,288BatchNorm2d-14          [-1, 128, 56, 56]             256ReLU-15          [-1, 128, 56, 56]               0Conv2d-16           [-1, 32, 56, 56]          36,864BatchNorm2d-17          [-1, 128, 56, 56]             256ReLU-18          [-1, 128, 56, 56]               0Conv2d-19          [-1, 128, 56, 56]          16,384BatchNorm2d-20          [-1, 128, 56, 56]             256ReLU-21          [-1, 128, 56, 56]               0Conv2d-22           [-1, 32, 56, 56]          36,864BatchNorm2d-23          [-1, 160, 56, 56]             320ReLU-24          [-1, 160, 56, 56]               0Conv2d-25          [-1, 128, 56, 56]          20,480BatchNorm2d-26          [-1, 128, 56, 56]             256ReLU-27          [-1, 128, 56, 56]               0Conv2d-28           [-1, 32, 56, 56]          36,864BatchNorm2d-29          [-1, 192, 56, 56]             384ReLU-30          [-1, 192, 56, 56]               0Conv2d-31          [-1, 128, 56, 56]          24,576BatchNorm2d-32          [-1, 128, 56, 56]             256ReLU-33          [-1, 128, 56, 56]               0Conv2d-34           [-1, 32, 56, 56]          36,864BatchNorm2d-35          [-1, 224, 56, 56]             448ReLU-36          [-1, 224, 56, 56]               0Conv2d-37          [-1, 128, 56, 56]          28,672BatchNorm2d-38          [-1, 128, 56, 56]             256ReLU-39          [-1, 128, 56, 56]               0Conv2d-40           [-1, 32, 56, 56]          36,864BatchNorm2d-41          [-1, 256, 56, 56]             512ReLU-42          [-1, 256, 56, 56]               0Conv2d-43          [-1, 128, 56, 56]          32,768AvgPool2d-44          [-1, 128, 28, 28]               0BatchNorm2d-45          [-1, 128, 28, 28]             256ReLU-46          [-1, 128, 28, 28]               0Conv2d-47          [-1, 128, 28, 28]          16,384BatchNorm2d-48          [-1, 128, 28, 28]             256ReLU-49          [-1, 128, 28, 28]               0Conv2d-50           [-1, 32, 28, 28]          36,864BatchNorm2d-51          [-1, 160, 28, 28]             320ReLU-52          [-1, 160, 28, 28]               0Conv2d-53          [-1, 128, 28, 28]          20,480BatchNorm2d-54          [-1, 128, 28, 28]             256ReLU-55          [-1, 128, 28, 28]               0Conv2d-56           [-1, 32, 28, 28]          36,864BatchNorm2d-57          [-1, 192, 28, 28]             384ReLU-58          [-1, 192, 28, 28]               0Conv2d-59          [-1, 128, 28, 28]          24,576BatchNorm2d-60          [-1, 128, 28, 28]             256ReLU-61          [-1, 128, 28, 28]               0Conv2d-62           [-1, 32, 28, 28]          36,864BatchNorm2d-63          [-1, 224, 28, 28]             448ReLU-64          [-1, 224, 28, 28]               0Conv2d-65          [-1, 128, 28, 28]          28,672BatchNorm2d-66          [-1, 128, 28, 28]             256ReLU-67          [-1, 128, 28, 28]               0Conv2d-68           [-1, 32, 28, 28]          36,864BatchNorm2d-69          [-1, 256, 28, 28]             512ReLU-70          [-1, 256, 28, 28]               0Conv2d-71          [-1, 128, 28, 28]          32,768BatchNorm2d-72          [-1, 128, 28, 28]             256ReLU-73          [-1, 128, 28, 28]               0Conv2d-74           [-1, 32, 28, 28]          36,864BatchNorm2d-75          [-1, 288, 28, 28]             576ReLU-76          [-1, 288, 28, 28]               0Conv2d-77          [-1, 128, 28, 28]          36,864BatchNorm2d-78          [-1, 128, 28, 28]             256ReLU-79          [-1, 128, 28, 28]               0Conv2d-80           [-1, 32, 28, 28]          36,864BatchNorm2d-81          [-1, 320, 28, 28]             640ReLU-82          [-1, 320, 28, 28]               0Conv2d-83          [-1, 128, 28, 28]          40,960BatchNorm2d-84          [-1, 128, 28, 28]             256ReLU-85          [-1, 128, 28, 28]               0Conv2d-86           [-1, 32, 28, 28]          36,864BatchNorm2d-87          [-1, 352, 28, 28]             704ReLU-88          [-1, 352, 28, 28]               0Conv2d-89          [-1, 128, 28, 28]          45,056BatchNorm2d-90          [-1, 128, 28, 28]             256ReLU-91          [-1, 128, 28, 28]               0Conv2d-92           [-1, 32, 28, 28]          36,864BatchNorm2d-93          [-1, 384, 28, 28]             768ReLU-94          [-1, 384, 28, 28]               0Conv2d-95          [-1, 128, 28, 28]          49,152BatchNorm2d-96          [-1, 128, 28, 28]             256ReLU-97          [-1, 128, 28, 28]               0Conv2d-98           [-1, 32, 28, 28]          36,864BatchNorm2d-99          [-1, 416, 28, 28]             832ReLU-100          [-1, 416, 28, 28]               0Conv2d-101          [-1, 128, 28, 28]          53,248BatchNorm2d-102          [-1, 128, 28, 28]             256ReLU-103          [-1, 128, 28, 28]               0Conv2d-104           [-1, 32, 28, 28]          36,864BatchNorm2d-105          [-1, 448, 28, 28]             896ReLU-106          [-1, 448, 28, 28]               0Conv2d-107          [-1, 128, 28, 28]          57,344BatchNorm2d-108          [-1, 128, 28, 28]             256ReLU-109          [-1, 128, 28, 28]               0Conv2d-110           [-1, 32, 28, 28]          36,864BatchNorm2d-111          [-1, 480, 28, 28]             960ReLU-112          [-1, 480, 28, 28]               0Conv2d-113          [-1, 128, 28, 28]          61,440BatchNorm2d-114          [-1, 128, 28, 28]             256ReLU-115          [-1, 128, 28, 28]               0Conv2d-116           [-1, 32, 28, 28]          36,864BatchNorm2d-117          [-1, 512, 28, 28]           1,024ReLU-118          [-1, 512, 28, 28]               0Conv2d-119          [-1, 256, 28, 28]         131,072AvgPool2d-120          [-1, 256, 14, 14]               0BatchNorm2d-121          [-1, 256, 14, 14]             512ReLU-122          [-1, 256, 14, 14]               0Conv2d-123          [-1, 128, 14, 14]          32,768BatchNorm2d-124          [-1, 128, 14, 14]             256ReLU-125          [-1, 128, 14, 14]               0Conv2d-126           [-1, 32, 14, 14]          36,864BatchNorm2d-127          [-1, 288, 14, 14]             576ReLU-128          [-1, 288, 14, 14]               0Conv2d-129          [-1, 128, 14, 14]          36,864BatchNorm2d-130          [-1, 128, 14, 14]             256ReLU-131          [-1, 128, 14, 14]               0Conv2d-132           [-1, 32, 14, 14]          36,864BatchNorm2d-133          [-1, 320, 14, 14]             640ReLU-134          [-1, 320, 14, 14]               0Conv2d-135          [-1, 128, 14, 14]          40,960BatchNorm2d-136          [-1, 128, 14, 14]             256ReLU-137          [-1, 128, 14, 14]               0Conv2d-138           [-1, 32, 14, 14]          36,864BatchNorm2d-139          [-1, 352, 14, 14]             704ReLU-140          [-1, 352, 14, 14]               0Conv2d-141          [-1, 128, 14, 14]          45,056BatchNorm2d-142          [-1, 128, 14, 14]             256ReLU-143          [-1, 128, 14, 14]               0Conv2d-144           [-1, 32, 14, 14]          36,864BatchNorm2d-145          [-1, 384, 14, 14]             768ReLU-146          [-1, 384, 14, 14]               0Conv2d-147          [-1, 128, 14, 14]          49,152BatchNorm2d-148          [-1, 128, 14, 14]             256ReLU-149          [-1, 128, 14, 14]               0Conv2d-150           [-1, 32, 14, 14]          36,864BatchNorm2d-151          [-1, 416, 14, 14]             832ReLU-152          [-1, 416, 14, 14]               0Conv2d-153          [-1, 128, 14, 14]          53,248BatchNorm2d-154          [-1, 128, 14, 14]             256ReLU-155          [-1, 128, 14, 14]               0Conv2d-156           [-1, 32, 14, 14]          36,864BatchNorm2d-157          [-1, 448, 14, 14]             896ReLU-158          [-1, 448, 14, 14]               0Conv2d-159          [-1, 128, 14, 14]          57,344BatchNorm2d-160          [-1, 128, 14, 14]             256ReLU-161          [-1, 128, 14, 14]               0Conv2d-162           [-1, 32, 14, 14]          36,864BatchNorm2d-163          [-1, 480, 14, 14]             960ReLU-164          [-1, 480, 14, 14]               0Conv2d-165          [-1, 128, 14, 14]          61,440BatchNorm2d-166          [-1, 128, 14, 14]             256ReLU-167          [-1, 128, 14, 14]               0Conv2d-168           [-1, 32, 14, 14]          36,864BatchNorm2d-169          [-1, 512, 14, 14]           1,024ReLU-170          [-1, 512, 14, 14]               0Conv2d-171          [-1, 128, 14, 14]          65,536BatchNorm2d-172          [-1, 128, 14, 14]             256ReLU-173          [-1, 128, 14, 14]               0Conv2d-174           [-1, 32, 14, 14]          36,864BatchNorm2d-175          [-1, 544, 14, 14]           1,088ReLU-176          [-1, 544, 14, 14]               0Conv2d-177          [-1, 128, 14, 14]          69,632BatchNorm2d-178          [-1, 128, 14, 14]             256ReLU-179          [-1, 128, 14, 14]               0Conv2d-180           [-1, 32, 14, 14]          36,864BatchNorm2d-181          [-1, 576, 14, 14]           1,152ReLU-182          [-1, 576, 14, 14]               0Conv2d-183          [-1, 128, 14, 14]          73,728BatchNorm2d-184          [-1, 128, 14, 14]             256ReLU-185          [-1, 128, 14, 14]               0Conv2d-186           [-1, 32, 14, 14]          36,864BatchNorm2d-187          [-1, 608, 14, 14]           1,216ReLU-188          [-1, 608, 14, 14]               0Conv2d-189          [-1, 128, 14, 14]          77,824BatchNorm2d-190          [-1, 128, 14, 14]             256ReLU-191          [-1, 128, 14, 14]               0Conv2d-192           [-1, 32, 14, 14]          36,864BatchNorm2d-193          [-1, 640, 14, 14]           1,280ReLU-194          [-1, 640, 14, 14]               0Conv2d-195          [-1, 128, 14, 14]          81,920BatchNorm2d-196          [-1, 128, 14, 14]             256ReLU-197          [-1, 128, 14, 14]               0Conv2d-198           [-1, 32, 14, 14]          36,864BatchNorm2d-199          [-1, 672, 14, 14]           1,344ReLU-200          [-1, 672, 14, 14]               0Conv2d-201          [-1, 128, 14, 14]          86,016BatchNorm2d-202          [-1, 128, 14, 14]             256ReLU-203          [-1, 128, 14, 14]               0Conv2d-204           [-1, 32, 14, 14]          36,864BatchNorm2d-205          [-1, 704, 14, 14]           1,408ReLU-206          [-1, 704, 14, 14]               0Conv2d-207          [-1, 128, 14, 14]          90,112BatchNorm2d-208          [-1, 128, 14, 14]             256ReLU-209          [-1, 128, 14, 14]               0Conv2d-210           [-1, 32, 14, 14]          36,864BatchNorm2d-211          [-1, 736, 14, 14]           1,472ReLU-212          [-1, 736, 14, 14]               0Conv2d-213          [-1, 128, 14, 14]          94,208BatchNorm2d-214          [-1, 128, 14, 14]             256ReLU-215          [-1, 128, 14, 14]               0Conv2d-216           [-1, 32, 14, 14]          36,864BatchNorm2d-217          [-1, 768, 14, 14]           1,536ReLU-218          [-1, 768, 14, 14]               0Conv2d-219          [-1, 128, 14, 14]          98,304BatchNorm2d-220          [-1, 128, 14, 14]             256ReLU-221          [-1, 128, 14, 14]               0Conv2d-222           [-1, 32, 14, 14]          36,864BatchNorm2d-223          [-1, 800, 14, 14]           1,600ReLU-224          [-1, 800, 14, 14]               0Conv2d-225          [-1, 128, 14, 14]         102,400BatchNorm2d-226          [-1, 128, 14, 14]             256ReLU-227          [-1, 128, 14, 14]               0Conv2d-228           [-1, 32, 14, 14]          36,864BatchNorm2d-229          [-1, 832, 14, 14]           1,664ReLU-230          [-1, 832, 14, 14]               0Conv2d-231          [-1, 128, 14, 14]         106,496BatchNorm2d-232          [-1, 128, 14, 14]             256ReLU-233          [-1, 128, 14, 14]               0Conv2d-234           [-1, 32, 14, 14]          36,864BatchNorm2d-235          [-1, 864, 14, 14]           1,728ReLU-236          [-1, 864, 14, 14]               0Conv2d-237          [-1, 128, 14, 14]         110,592BatchNorm2d-238          [-1, 128, 14, 14]             256ReLU-239          [-1, 128, 14, 14]               0Conv2d-240           [-1, 32, 14, 14]          36,864BatchNorm2d-241          [-1, 896, 14, 14]           1,792ReLU-242          [-1, 896, 14, 14]               0Conv2d-243          [-1, 128, 14, 14]         114,688BatchNorm2d-244          [-1, 128, 14, 14]             256ReLU-245          [-1, 128, 14, 14]               0Conv2d-246           [-1, 32, 14, 14]          36,864BatchNorm2d-247          [-1, 928, 14, 14]           1,856ReLU-248          [-1, 928, 14, 14]               0Conv2d-249          [-1, 128, 14, 14]         118,784BatchNorm2d-250          [-1, 128, 14, 14]             256ReLU-251          [-1, 128, 14, 14]               0Conv2d-252           [-1, 32, 14, 14]          36,864BatchNorm2d-253          [-1, 960, 14, 14]           1,920ReLU-254          [-1, 960, 14, 14]               0Conv2d-255          [-1, 128, 14, 14]         122,880BatchNorm2d-256          [-1, 128, 14, 14]             256ReLU-257          [-1, 128, 14, 14]               0Conv2d-258           [-1, 32, 14, 14]          36,864BatchNorm2d-259          [-1, 992, 14, 14]           1,984ReLU-260          [-1, 992, 14, 14]               0Conv2d-261          [-1, 128, 14, 14]         126,976BatchNorm2d-262          [-1, 128, 14, 14]             256ReLU-263          [-1, 128, 14, 14]               0Conv2d-264           [-1, 32, 14, 14]          36,864BatchNorm2d-265         [-1, 1024, 14, 14]           2,048ReLU-266         [-1, 1024, 14, 14]               0Conv2d-267          [-1, 512, 14, 14]         524,288AvgPool2d-268            [-1, 512, 7, 7]               0BatchNorm2d-269            [-1, 512, 7, 7]           1,024ReLU-270            [-1, 512, 7, 7]               0Conv2d-271            [-1, 128, 7, 7]          65,536BatchNorm2d-272            [-1, 128, 7, 7]             256ReLU-273            [-1, 128, 7, 7]               0Conv2d-274             [-1, 32, 7, 7]          36,864BatchNorm2d-275            [-1, 544, 7, 7]           1,088ReLU-276            [-1, 544, 7, 7]               0Conv2d-277            [-1, 128, 7, 7]          69,632BatchNorm2d-278            [-1, 128, 7, 7]             256ReLU-279            [-1, 128, 7, 7]               0Conv2d-280             [-1, 32, 7, 7]          36,864BatchNorm2d-281            [-1, 576, 7, 7]           1,152ReLU-282            [-1, 576, 7, 7]               0Conv2d-283            [-1, 128, 7, 7]          73,728BatchNorm2d-284            [-1, 128, 7, 7]             256ReLU-285            [-1, 128, 7, 7]               0Conv2d-286             [-1, 32, 7, 7]          36,864BatchNorm2d-287            [-1, 608, 7, 7]           1,216ReLU-288            [-1, 608, 7, 7]               0Conv2d-289            [-1, 128, 7, 7]          77,824BatchNorm2d-290            [-1, 128, 7, 7]             256ReLU-291            [-1, 128, 7, 7]               0Conv2d-292             [-1, 32, 7, 7]          36,864BatchNorm2d-293            [-1, 640, 7, 7]           1,280ReLU-294            [-1, 640, 7, 7]               0Conv2d-295            [-1, 128, 7, 7]          81,920BatchNorm2d-296            [-1, 128, 7, 7]             256ReLU-297            [-1, 128, 7, 7]               0Conv2d-298             [-1, 32, 7, 7]          36,864BatchNorm2d-299            [-1, 672, 7, 7]           1,344ReLU-300            [-1, 672, 7, 7]               0Conv2d-301            [-1, 128, 7, 7]          86,016BatchNorm2d-302            [-1, 128, 7, 7]             256ReLU-303            [-1, 128, 7, 7]               0Conv2d-304             [-1, 32, 7, 7]          36,864BatchNorm2d-305            [-1, 704, 7, 7]           1,408ReLU-306            [-1, 704, 7, 7]               0Conv2d-307            [-1, 128, 7, 7]          90,112BatchNorm2d-308            [-1, 128, 7, 7]             256ReLU-309            [-1, 128, 7, 7]               0Conv2d-310             [-1, 32, 7, 7]          36,864BatchNorm2d-311            [-1, 736, 7, 7]           1,472ReLU-312            [-1, 736, 7, 7]               0Conv2d-313            [-1, 128, 7, 7]          94,208BatchNorm2d-314            [-1, 128, 7, 7]             256ReLU-315            [-1, 128, 7, 7]               0Conv2d-316             [-1, 32, 7, 7]          36,864BatchNorm2d-317            [-1, 768, 7, 7]           1,536ReLU-318            [-1, 768, 7, 7]               0Conv2d-319            [-1, 128, 7, 7]          98,304BatchNorm2d-320            [-1, 128, 7, 7]             256ReLU-321            [-1, 128, 7, 7]               0Conv2d-322             [-1, 32, 7, 7]          36,864BatchNorm2d-323            [-1, 800, 7, 7]           1,600ReLU-324            [-1, 800, 7, 7]               0Conv2d-325            [-1, 128, 7, 7]         102,400BatchNorm2d-326            [-1, 128, 7, 7]             256ReLU-327            [-1, 128, 7, 7]               0Conv2d-328             [-1, 32, 7, 7]          36,864BatchNorm2d-329            [-1, 832, 7, 7]           1,664ReLU-330            [-1, 832, 7, 7]               0Conv2d-331            [-1, 128, 7, 7]         106,496BatchNorm2d-332            [-1, 128, 7, 7]             256ReLU-333            [-1, 128, 7, 7]               0Conv2d-334             [-1, 32, 7, 7]          36,864BatchNorm2d-335            [-1, 864, 7, 7]           1,728ReLU-336            [-1, 864, 7, 7]               0Conv2d-337            [-1, 128, 7, 7]         110,592BatchNorm2d-338            [-1, 128, 7, 7]             256ReLU-339            [-1, 128, 7, 7]               0Conv2d-340             [-1, 32, 7, 7]          36,864BatchNorm2d-341            [-1, 896, 7, 7]           1,792ReLU-342            [-1, 896, 7, 7]               0Conv2d-343            [-1, 128, 7, 7]         114,688BatchNorm2d-344            [-1, 128, 7, 7]             256ReLU-345            [-1, 128, 7, 7]               0Conv2d-346             [-1, 32, 7, 7]          36,864BatchNorm2d-347            [-1, 928, 7, 7]           1,856ReLU-348            [-1, 928, 7, 7]               0Conv2d-349            [-1, 128, 7, 7]         118,784BatchNorm2d-350            [-1, 128, 7, 7]             256ReLU-351            [-1, 128, 7, 7]               0Conv2d-352             [-1, 32, 7, 7]          36,864BatchNorm2d-353            [-1, 960, 7, 7]           1,920ReLU-354            [-1, 960, 7, 7]               0Conv2d-355            [-1, 128, 7, 7]         122,880BatchNorm2d-356            [-1, 128, 7, 7]             256ReLU-357            [-1, 128, 7, 7]               0Conv2d-358             [-1, 32, 7, 7]          36,864BatchNorm2d-359            [-1, 992, 7, 7]           1,984ReLU-360            [-1, 992, 7, 7]               0Conv2d-361            [-1, 128, 7, 7]         126,976BatchNorm2d-362            [-1, 128, 7, 7]             256ReLU-363            [-1, 128, 7, 7]               0Conv2d-364             [-1, 32, 7, 7]          36,864BatchNorm2d-365           [-1, 1024, 7, 7]           2,048ReLU-366           [-1, 1024, 7, 7]               0Linear-367                 [-1, 1000]       1,025,000
================================================================
Total params: 7,978,856
Trainable params: 7,978,856
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.58
Params size (MB): 30.44
Estimated Total Size (MB): 325.59
----------------------------------------------------------------

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losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn 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losstarget_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 /= sizetest_loss /= num_batchesreturn test_acc, test_loss

2.3.4 正式训练

"""训练模型--正式训练"""
epochs = 10
train_loss = []
train_acc = []
test_loss = []
test_acc = []
best_test_acc=0for 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')

输出最高精度为Test_acc:100%

Epoch: 1, duration:5339ms, Train_acc:32.5%, Train_loss:4.717, Test_acc:94.7%，Test_loss:0.666, Lr:1.00E-04,Update the best model
Epoch: 2, duration:4585ms, Train_acc:98.5%, Train_loss:0.255, Test_acc:98.2%，Test_loss:0.120, Lr:1.00E-04,Update the best model
Epoch: 3, duration:4651ms, Train_acc:100.0%, Train_loss:0.037, Test_acc:99.1%，Test_loss:0.057, Lr:1.00E-04,Update the best model
Epoch: 4, duration:4610ms, Train_acc:99.8%, Train_loss:0.039, Test_acc:100.0%，Test_loss:0.040, Lr:1.00E-04,Update the best model
Epoch: 5, duration:4520ms, Train_acc:99.8%, Train_loss:0.032, Test_acc:100.0%，Test_loss:0.047, Lr:1.00E-04
Epoch: 6, duration:4528ms, Train_acc:100.0%, Train_loss:0.055, Test_acc:100.0%，Test_loss:0.038, Lr:1.00E-04
Epoch: 7, duration:4541ms, Train_acc:100.0%, Train_loss:0.021, Test_acc:100.0%，Test_loss:0.022, Lr:1.00E-04
Epoch: 8, duration:4568ms, Train_acc:100.0%, Train_loss:0.066, Test_acc:100.0%，Test_loss:0.018, Lr:1.00E-04
Epoch: 9, duration:4515ms, Train_acc:99.8%, Train_loss:0.084, Test_acc:100.0%，Test_loss:0.022, Lr:1.00E-04
Epoch:10, duration:4602ms, Train_acc:99.6%, Train_loss:0.136, Test_acc:100.0%，Test_loss:0.028, Lr:1.00E-04

这里使用了预训练模型，效果特别好

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()

在这里插入图片描述

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) / "Cockatoo/001.jpg"),model=model,transform=train_transforms,classes=classes)

在这里插入图片描述

输出

预测结果是：Cockatoo

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

1.0 0.05105462612118572

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 warningswarnings.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GPUtf.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\bird\bird_photos"
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('Bananaquit/*.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： 565
JPEG (224, 224) RGB

在这里插入图片描述

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 565 files belonging to 4 classes.
Using 452 files for training.
Found 565 files belonging to 4 classes.
Using 113 files for validation.
['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

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()

在这里插入图片描述

3.9.构建DenseNet网络

"""构建DenseNet网络"""
def conv_fn(x, growth_rate):x1 = keras.layers.BatchNormalization()(x)x1 = keras.layers.Activation('relu')(x1)x1 = keras.layers.Conv2D(4 * growth_rate, 1, 1, padding="same", use_bias=False)(x1)x1 = keras.layers.BatchNormalization()(x1)x1 = keras.layers.Activation("relu")(x1)x1 = keras.layers.Conv2D(growth_rate, 3, 1, padding="same", use_bias=False)(x1)return keras.layers.Concatenate(axis=3)([x, x1])def dense_block(x, block, growth_rate=32):for i in range(block):x = conv_fn(x, growth_rate)return xk = keras.backend
def trans_block(x, theta):x1 = keras.layers.BatchNormalization()(x)x1 = keras.layers.Activation("relu")(x1)x1 = keras.layers.Conv2D(int(k.int_shape(x)[3] * theta), 1, 1, use_bias=False)(x1)x1 = keras.layers.AveragePooling2D(pool_size=(2, 2), strides=2, padding="valid")(x1)return x1def densenet(input_shape, block, n_classes=1000):# 56*56*64x_input = keras.layers.Input(shape=input_shape)x = keras.layers.Conv2D(64, kernel_size=(7, 7), strides=2, padding="same", use_bias=False)(x_input)x = keras.layers.BatchNormalization()(x)x = keras.layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)x = dense_block(x, block[0])x = trans_block(x, 0.5)  # 28*28x = dense_block(x, block[1])x = trans_block(x, 0.5)  # 14*14x = dense_block(x, block[2])x = trans_block(x, 0.5)  # 7*7x = dense_block(x, block[3])x = keras.layers.BatchNormalization()(x)x = keras.layers.Activation("relu")(x)x = keras.layers.GlobalAveragePooling2D()(x)outputs = keras.layers.Dense(n_classes, activation="softmax")(x)model = keras.models.Model(inputs=[x_input], outputs=[outputs])return modelmodel_121 = densenet([224, 224, 3], [6, 12, 24, 16])  # DenseNet-121
model_169 = densenet([224, 224, 3], [6, 12, 32, 32])  # DenseNet-169
model_201 = densenet([224, 224, 3], [6, 12, 48, 32])  # DenseNet-201
model_269 = densenet([224, 224, 3], [6, 12, 64, 48])  # DenseNet-269
model = model_121
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]']             batch_normalization_120 (Batch  (None, 7, 7, 1024)  4096        ['concatenate_57[0][0]']         Normalization)                                                                                   activation_119 (Activation)    (None, 7, 7, 1024)   0           ['batch_normalization_120[0][0]']global_average_pooling2d (Glob  (None, 1024)        0           ['activation_119[0][0]']         alAveragePooling2D)                                                                              dense (Dense)                  (None, 1000)         1025000     ['global_average_pooling2d[0][0]']                                ==================================================================================================
Total params: 8,062,504
Trainable params: 7,978,856
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 = 10
history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)

训练记录如下：

Epoch 1/10
15/15 [==============================] - ETA: 0s - loss: 5.1516 - accuracy: 0.4735
Epoch 1: val_accuracy improved from -inf to 0.30310, saving model to best_model.h5
15/15 [==============================] - 19s 345ms/step - loss: 5.1516 - accuracy: 0.4735 - val_loss: 5.6926 - val_accuracy: 0.3031
Epoch 2/10
15/15 [==============================] - ETA: 0s - loss: 2.6124 - accuracy: 0.7102
Epoch 2: val_accuracy did not improve from 0.30310
15/15 [==============================] - 3s 211ms/step - loss: 2.6124 - accuracy: 0.7102 - val_loss: 5.7346 - val_accuracy: 0.3009
Epoch 3/10
15/15 [==============================] - ETA: 0s - loss: 1.3227 - accuracy: 0.7987
Epoch 3: val_accuracy improved from 0.30310 to 0.35841, saving model to best_model.h5
15/15 [==============================] - 3s 234ms/step - loss: 1.3227 - accuracy: 0.7987 - val_loss: 5.5666 - val_accuracy: 0.3584
Epoch 4/10
14/15 [===========================>..] - ETA: 0s - loss: 0.7132 - accuracy: 0.8862
Epoch 4: val_accuracy improved from 0.35841 to 0.43584, saving model to best_model.h5
15/15 [==============================] - 3s 230ms/step - loss: 0.7095 - accuracy: 0.8872 - val_loss: 5.0263 - val_accuracy: 0.4358
Epoch 5/10
15/15 [==============================] - ETA: 0s - loss: 0.4048 - accuracy: 0.9248
Epoch 5: val_accuracy improved from 0.43584 to 0.52434, saving model to best_model.h5
15/15 [==============================] - 3s 233ms/step - loss: 0.4048 - accuracy: 0.9248 - val_loss: 4.4517 - val_accuracy: 0.5243
Epoch 6/10
15/15 [==============================] - ETA: 0s - loss: 0.2979 - accuracy: 0.9425
Epoch 6: val_accuracy improved from 0.52434 to 0.63274, saving model to best_model.h5
15/15 [==============================] - 3s 234ms/step - loss: 0.2979 - accuracy: 0.9425 - val_loss: 3.6860 - val_accuracy: 0.6327
Epoch 7/10
15/15 [==============================] - ETA: 0s - loss: 0.2562 - accuracy: 0.9336
Epoch 7: val_accuracy did not improve from 0.63274
15/15 [==============================] - 3s 206ms/step - loss: 0.2562 - accuracy: 0.9336 - val_loss: 3.1774 - val_accuracy: 0.4823
Epoch 8/10
14/15 [===========================>..] - ETA: 0s - loss: 0.1304 - accuracy: 0.9777
Epoch 8: val_accuracy did not improve from 0.63274
15/15 [==============================] - 3s 206ms/step - loss: 0.1313 - accuracy: 0.9757 - val_loss: 2.8684 - val_accuracy: 0.3562
Epoch 9/10
15/15 [==============================] - ETA: 0s - loss: 0.1086 - accuracy: 0.9801
Epoch 9: val_accuracy did not improve from 0.63274
15/15 [==============================] - 3s 204ms/step - loss: 0.1086 - accuracy: 0.9801 - val_loss: 2.4839 - val_accuracy: 0.4049
Epoch 10/10
15/15 [==============================] - ETA: 0s - loss: 0.0784 - accuracy: 0.9889
Epoch 10: val_accuracy did not improve from 0.63274
15/15 [==============================] - 3s 204ms/step - loss: 0.0784 - accuracy: 0.9889 - val_loss: 2.3421 - val_accuracy: 0.3982

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()

在这里插入图片描述

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()

在这里插入图片描述

4 知识点详解

4.1 DenseNet算法详解

4.1.1 前言

在计算机视觉领域，卷积神经网络（CNN）已经成为最主流的方法，比如GoogleNet，VGG-16，Incepetion等模型。CNN史上的一个里程碑事件是ResNet模型的出现，ResNet可以训练出更深的CNN模型，从而实现更高的准确率。ResNet模型的核心是通过建立前面层与后面层之间的“短路连接”（shortcut, skip connection），进而训练出更深的CNN网络。

DenseNet模型的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的紧密连接（dense connection），它的名称也是由此而来。DenseNet的另一大特色是通过特征在channel上的的连接来实现特征重用（feature reuse）。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能，DenseNet也因此斩获CVPR2017的最佳论文奖。
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图1 Dense模块（5-layer,growth rate of k=4）

其中DenseNet论文原文地址为：https://arxiv.org/pdf/1608.06993v5.pdf

4.1.2 设计理念

相比ResNet，DenseNet提出了一个更激进的密集连接机制：即互相连接所有的层，具体来说就是每个层都会接受前面所有层作为额外的输入。

图3为ResNet网络的残差连接机制，作为对比，图4为DenseNet的密集连接机制。可以看到，ResNet是每个层与前面的某层（一般是2~4层）短路连接在一起，连接方式是通过元素相加。而在DenseNet中，每个层都会与前面所有层在channel维度上链接（concat）在一起（即元素叠加），并作为下一层的输入。

对于一个 $L$ 层的网络，DenseNet共包含 $\frac{L(L+1)}{2}$ 个连接，相比ResNet，这是一种密集连接。而且DenseNet是直接concat来自不同层的特征图，这可以实现特征重用，提升效率，这一特点是DenseNet与ResNet最主要的区别。

4.1.2.1 标准神经网络

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图2 标准的神经网络传播过程

图2是一个标准的神经网络传播过程示意图，输入和输出的公式是 $X_l=H_l(X_{l-1})$ ，其中 $H_l$ 是一个组合函数，通常包括BN、ReLu、Pooling、Conv等操作， $X_{l-1}$ 是第 $l$ 层的输入的特征图（来自于l-1层的输出）, $X_l$ 是第 $l$ 层的输出的特征图。

4.1.2.2 ResNet

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图3 ResNet网络的短路连接机制（+代表元素级相加操作）

图3是ResNet的网络连接机制，由图可知是跨层相加，输入和输出的公式是 $X_l=H_l(X_{l-1})+X_{l-1}$

4.1.2.3 DenseNet

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图4 DenseNet网络的密集连接机制（其中C代表层级的concat操作）

图4为DenseNet的连接机制，采用跨通道的concat的形式连接，会连接前面所有层作为输入，输入和输出的公式是 $X_l=H_l(X_0,X_1,...,X_{l-1})$ 。这里要注意所有层的输入都来源于前面所有层在channel维度的concat，以下动图形象表示这一操作。
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图5 DenseNet前向过程

4.1.3 网络结构

网络的具体实现细节如图6所示。

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图6 DenseNet的网络结构

CNN网络一般要经过Pooling或者stride>1的Conv来降低特征图的大小，而DenseNet的密集连接方式需要特征图大小保持一致。为了解决这个问题，DenseNet网络中使用DenseBlock+Transition的结构，其中DenseBlock是包含很多层的模块，每个层的特征图大小相同，层与层之间采用密集连接方式。而Transition层是连接两个相邻的DenseBlock，并且通过Pooling使特征图大小降低。图7给出了DenseNet的网络结构，它共包含4个DenseBlock，各个DenseBlock之间通过Transition层连接在一起。

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图7 使用DenseBlock+Transition的DenseNet网络

在DenseBlock中，各个层的特征图大小一致，可以在channel维度上连接。DenseBlock中的非线性组合函数 $H(\cdot)$ 的是BN+ReLU+3x3Conv的结构，如图8所示。另外，与ResNet不同，所有DenseBlock中各个层卷积之后均输出k个特征图，即得到的特征图的channel数为k，或者说采用k个卷积核。k在DenseNet称为growth rate，这是一个超参数。一般情况下使用较小的k（比如12），就可以得到较佳的性能。假定输入层的特征图的channel数为 $k_0$ ，那么 $l$ 层输入的channel数为 $k_0+k_{(1,2,...,l-1)}$ ，因此随着层数的增加，尽管设定的较小，DenseBlock的输入会非常多，不过这是由于特征重用所造成的，每个层仅有个k特征是自己独有的。

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图8 DenseBlock中的非线性转换结构

由于后面层的输入会非常大，DenseBlock内部采用bottleneck层来减少计算量，主要是原有的结构中增加1x1Conv，如图9所示，即BN+ReLU+1x1Conv+BN+ReLU+3x3Conv，称为DenseNet-B结构。其中1x1Conv得到4k个特征图，它起到的作用是降低特征数量，从而提升计算效率。

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图9 使用bottleneck层的DenseBlock结构

对于Trasition层，它主要是连接两个相邻的DenseBlock，并且降低特征图大小。Transition层包括一个1x1的卷积和2x2的AvgPooling，结构为BN+ReLU+1x1Conv+2x2AvgPooling。另外，Transition层可以起到压缩模型的作用。假定Transition层的上接DenseBlock得到特征图channels数为 $m$ ，Transition层可以产生个 $\lfloor\theta_m\rfloor$ 特征（通过卷积层），其中 $\theta\in(0,1]$ 是压缩系数（compression rate）。当 $\theta<=1$ 时，特征个数经过Transition层没有变化，即无压缩，而当压缩系数小于1时，这种结构称为DenseNet-C，文中使用 $\theta=0.5$ 。对于使用bootleneck层的DenseBlock结构和压缩系数小于1的Transition组合机构称为DenseNet-BC。

对于ImageNet数据集，图片输入大小为224x224，网络结构采用包含4个DenseBlock的DenseNet-BC，其首先是一个stride=2的7x7卷积层，然后是一个stride=2的3x3MaxPooling层，后面才进入DenseBlock。ImageNet数据集所采用的网络配置如表1所示：

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表1 ImageNet数据集上所采用的DenseNet结构

4.1.4 效果对比

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图10 在CIFA-10数据集上ResNet vs DenseNet

4.1.5 使用Pytroch实现DenseNet121

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图11 DenseNet121网络架构

图11为DenseNet121的具体网络结构，它与表1中的DenseNet121相对应。左边是整个DenseNet121的网络结构，其中粉色为DenseBlock，最右侧为其详细结构，灰色为Transition，中间为其详细结构。
这里我们采用Pytorch框架来实现DenseNet，首先实现DenseBlock中的内部结构，这里是BN+ReLU+1x1Conv+BN+ReLU+3x3Conv结构，最后也加入dropout层以用于训练过程。
选择不同网络参数，就可以实现不同深度的DenseNet。

DenseNet121网络结构图
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总结

通过本文的学习，分别采用pytorch和tensorflow框架实现desennet算法，发现采用pytorch可以实现很高的识别率，而tenserflow只能达到50%左右，经过查阅相关资料，发现有可能是tensorflow框架中BatchNormalization层有问题，测试数据采用训练数据，发现在屏蔽BatchNormalization层后，测试精度与训练精度差不多，而保留BatchNormalization层，测试精度就很低，可以确定就是BatchNormalization的原因。之前就有看到tensorflow 2.3版本BN层有问题，而tensorflow 1.x则正常。因此在需要使用tensorflow 搭建网络时，尽量选择1.x版本，本人未对该结果进行验证。