常德建设网站多少钱,爱站网排名,微信crm系统软件,学网站开发工程师难学吗#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 | 接辅导、项目定制#x1f680; 文章来源#xff1a;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… 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CPU2.1.3 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ResNeXt50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ResNeXt50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4.1ResNeXt50详解4.2 ResNeXt50对比ResNet50V2、DenseNet4.2.1 网络结构4.2.2 精度和计算量4.2.3 适用范围 4 总结 前言
关键字 pytorch实现ResNeXt50详解算法tensorflow实现ResNeXt50详解算法ResNeXt50详解
1 我的环境
电脑系统Windows 11语言环境python 3.8.6编译器pycharm2020.2.3深度学习环境 torch 1.9.1cu111 torchvision 0.10.1cu111 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 device2.1.3 导入数据
前期工作-导入数据
data_dir rD:\DeepLearning\data\monkeypox_recognition
data_dir Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(\\)[-1] for path in data_paths]
print(classeNames)输出
[Monkeypox, Others]
2.1.4 可视化数据
前期工作-可视化数据
subfolder Path(data_dir) / Monkeypox
image_files list(p.resolve() for p in subfolder.glob(*) if p.suffix in [.jpg, .png, .jpeg])
plt.figure(figsize(10, 6))
for i in range(len(image_files[:12])):image_file image_files[i]ax plt.subplot(3, 4, i 1)img Image.open(str(image_file))plt.imshow(img)plt.axis(off)
# 显示图片
plt.tight_layout()
plt.show()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, transformtrain_transforms)
print(total_data)
print(total_data.class_to_idx)输出
Dataset ImageFolderNumber of datapoints: 2142Root location: D:\DeepLearning\data\monkeypox_recognitionStandardTransform
Transform: Compose(Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiasNone)ToTensor()Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]))
{Monkeypox: 0, Others: 1}
2.1.4 划分数据集
前期工作-划分数据集
train_size int(0.8 * len(total_data)) # train_size表示训练集大小通过将总体数据长度的80%转换为整数得到
test_size len(total_data) - train_size # test_size表示测试集大小是总体数据长度减去训练集大小。
# 使用torch.utils.data.random_split()方法进行数据集划分。该方法将总体数据total_data按照指定的大小比例[train_size, test_size]随机划分为训练集和测试集
# 并将划分结果分别赋值给train_dataset和test_dataset两个变量。
train_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset{}\ntest_dataset{}.format(train_dataset, test_dataset))
print(train_size{}\ntest_size{}.format(train_size, test_size))输出
train_datasettorch.utils.data.dataset.Subset object at 0x000002A96E08E0D0
test_datasettorch.utils.data.dataset.Subset object at 0x000002A96E04E640
train_size1713
test_size4292.1.4 加载数据
前期工作-加载数据
batch_size 32train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)
test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)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.int642.2 搭建ResNeXt50模型
构建ResNeXt50网络class BN_Conv2d(nn.Module):BN_CONV_RELUdef __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation1, groups1, biasFalse):super(BN_Conv2d, self).__init__()self.seq nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_sizekernel_size, stridestride,paddingpadding, dilationdilation, groupsgroups, biasbias),nn.BatchNorm2d(out_channels))def forward(self, x):return F.relu(self.seq(x))class ResNeXt_Block(nn.Module):ResNeXt block with group convolutionsdef __init__(self, in_chnls, cardinality, group_depth, stride):super(ResNeXt_Block, self).__init__()self.group_chnls cardinality * group_depthself.conv1 BN_Conv2d(in_chnls, self.group_chnls, 1, stride1, padding0)self.conv2 BN_Conv2d(self.group_chnls, self.group_chnls, 3, stridestride, padding1, groupscardinality)self.conv3 nn.Conv2d(self.group_chnls, self.group_chnls * 2, 1, stride1, padding0)self.bn nn.BatchNorm2d(self.group_chnls * 2)self.short_cut nn.Sequential(nn.Conv2d(in_chnls, self.group_chnls * 2, 1, stride, 0, biasFalse),nn.BatchNorm2d(self.group_chnls * 2))def forward(self, x):out self.conv1(x)out self.conv2(out)out self.bn(self.conv3(out))out self.short_cut(x)return F.relu(out)class ResNeXt(nn.Module):ResNeXt builderdef __init__(self, layers: object, cardinality, group_depth, num_classes) - object:super(ResNeXt, self).__init__()self.cardinality cardinalityself.channels 64self.conv1 BN_Conv2d(3, self.channels, 7, stride2, padding3)d1 group_depthself.conv2 self.___make_layers(d1, layers[0], stride1)d2 d1 * 2self.conv3 self.___make_layers(d2, layers[1], stride2)d3 d2 * 2self.conv4 self.___make_layers(d3, layers[2], stride2)d4 d3 * 2self.conv5 self.___make_layers(d4, layers[3], stride2)self.fc nn.Linear(self.channels, num_classes) # 224x224 input sizedef ___make_layers(self, d, blocks, stride):strides [stride] [1] * (blocks - 1)layers []for stride in strides:layers.append(ResNeXt_Block(self.channels, self.cardinality, d, stride))self.channels self.cardinality * d * 2return nn.Sequential(*layers)def forward(self, x):out self.conv1(x)out F.max_pool2d(out, 3, 2, 1)out self.conv2(out)out self.conv3(out)out self.conv4(out)out self.conv5(out)out F.avg_pool2d(out, 7)out out.view(out.size(0), -1)out F.softmax(self.fc(out), dim1)return out
该模型相比DenseNet的区别是在最后一个denseblock后增加SE_layer。
# SE_layer
self.features.add_module(SE-module, Squeeze_excitation_layer(num_features))输出
----------------------------------------------------------------Layer (type) Output Shape Param #
Conv2d-1 [-1, 64, 112, 112] 9,408BatchNorm2d-2 [-1, 64, 112, 112] 128BN_Conv2d-3 [-1, 64, 112, 112] 0Conv2d-4 [-1, 128, 56, 56] 8,192BatchNorm2d-5 [-1, 128, 56, 56] 256BN_Conv2d-6 [-1, 128, 56, 56] 0Conv2d-7 [-1, 128, 56, 56] 4,608BatchNorm2d-8 [-1, 128, 56, 56] 256BN_Conv2d-9 [-1, 128, 56, 56] 0Conv2d-10 [-1, 256, 56, 56] 33,024BatchNorm2d-11 [-1, 256, 56, 56] 512Conv2d-12 [-1, 256, 56, 56] 16,384BatchNorm2d-13 [-1, 256, 56, 56] 512ResNeXt_Block-14 [-1, 256, 56, 56] 0Conv2d-15 [-1, 128, 56, 56] 32,768BatchNorm2d-16 [-1, 128, 56, 56] 256BN_Conv2d-17 [-1, 128, 56, 56] 0Conv2d-18 [-1, 128, 56, 56] 4,608BatchNorm2d-19 [-1, 128, 56, 56] 256BN_Conv2d-20 [-1, 128, 56, 56] 0Conv2d-21 [-1, 256, 56, 56] 33,024BatchNorm2d-22 [-1, 256, 56, 56] 512Conv2d-23 [-1, 256, 56, 56] 65,536BatchNorm2d-24 [-1, 256, 56, 56] 512ResNeXt_Block-25 [-1, 256, 56, 56] 0Conv2d-26 [-1, 128, 56, 56] 32,768BatchNorm2d-27 [-1, 128, 56, 56] 256BN_Conv2d-28 [-1, 128, 56, 56] 0Conv2d-29 [-1, 128, 56, 56] 4,608BatchNorm2d-30 [-1, 128, 56, 56] 256BN_Conv2d-31 [-1, 128, 56, 56] 0Conv2d-32 [-1, 256, 56, 56] 33,024BatchNorm2d-33 [-1, 256, 56, 56] 512Conv2d-34 [-1, 256, 56, 56] 65,536BatchNorm2d-35 [-1, 256, 56, 56] 512ResNeXt_Block-36 [-1, 256, 56, 56] 0Conv2d-37 [-1, 256, 56, 56] 65,536BatchNorm2d-38 [-1, 256, 56, 56] 512BN_Conv2d-39 [-1, 256, 56, 56] 0Conv2d-40 [-1, 256, 28, 28] 18,432BatchNorm2d-41 [-1, 256, 28, 28] 512BN_Conv2d-42 [-1, 256, 28, 28] 0Conv2d-43 [-1, 512, 28, 28] 131,584BatchNorm2d-44 [-1, 512, 28, 28] 1,024Conv2d-45 [-1, 512, 28, 28] 131,072BatchNorm2d-46 [-1, 512, 28, 28] 1,024ResNeXt_Block-47 [-1, 512, 28, 28] 0Conv2d-48 [-1, 256, 28, 28] 131,072BatchNorm2d-49 [-1, 256, 28, 28] 512BN_Conv2d-50 [-1, 256, 28, 28] 0Conv2d-51 [-1, 256, 28, 28] 18,432BatchNorm2d-52 [-1, 256, 28, 28] 512BN_Conv2d-53 [-1, 256, 28, 28] 0Conv2d-54 [-1, 512, 28, 28] 131,584BatchNorm2d-55 [-1, 512, 28, 28] 1,024Conv2d-56 [-1, 512, 28, 28] 262,144BatchNorm2d-57 [-1, 512, 28, 28] 1,024ResNeXt_Block-58 [-1, 512, 28, 28] 0Conv2d-59 [-1, 256, 28, 28] 131,072BatchNorm2d-60 [-1, 256, 28, 28] 512BN_Conv2d-61 [-1, 256, 28, 28] 0Conv2d-62 [-1, 256, 28, 28] 18,432BatchNorm2d-63 [-1, 256, 28, 28] 512BN_Conv2d-64 [-1, 256, 28, 28] 0Conv2d-65 [-1, 512, 28, 28] 131,584BatchNorm2d-66 [-1, 512, 28, 28] 1,024Conv2d-67 [-1, 512, 28, 28] 262,144BatchNorm2d-68 [-1, 512, 28, 28] 1,024ResNeXt_Block-69 [-1, 512, 28, 28] 0Conv2d-70 [-1, 256, 28, 28] 131,072BatchNorm2d-71 [-1, 256, 28, 28] 512BN_Conv2d-72 [-1, 256, 28, 28] 0Conv2d-73 [-1, 256, 28, 28] 18,432BatchNorm2d-74 [-1, 256, 28, 28] 512BN_Conv2d-75 [-1, 256, 28, 28] 0Conv2d-76 [-1, 512, 28, 28] 131,584BatchNorm2d-77 [-1, 512, 28, 28] 1,024Conv2d-78 [-1, 512, 28, 28] 262,144BatchNorm2d-79 [-1, 512, 28, 28] 1,024ResNeXt_Block-80 [-1, 512, 28, 28] 0Conv2d-81 [-1, 512, 28, 28] 262,144BatchNorm2d-82 [-1, 512, 28, 28] 1,024BN_Conv2d-83 [-1, 512, 28, 28] 0Conv2d-84 [-1, 512, 14, 14] 73,728BatchNorm2d-85 [-1, 512, 14, 14] 1,024BN_Conv2d-86 [-1, 512, 14, 14] 0Conv2d-87 [-1, 1024, 14, 14] 525,312BatchNorm2d-88 [-1, 1024, 14, 14] 2,048Conv2d-89 [-1, 1024, 14, 14] 524,288BatchNorm2d-90 [-1, 1024, 14, 14] 2,048ResNeXt_Block-91 [-1, 1024, 14, 14] 0Conv2d-92 [-1, 512, 14, 14] 524,288BatchNorm2d-93 [-1, 512, 14, 14] 1,024BN_Conv2d-94 [-1, 512, 14, 14] 0Conv2d-95 [-1, 512, 14, 14] 73,728BatchNorm2d-96 [-1, 512, 14, 14] 1,024BN_Conv2d-97 [-1, 512, 14, 14] 0Conv2d-98 [-1, 1024, 14, 14] 525,312BatchNorm2d-99 [-1, 1024, 14, 14] 2,048Conv2d-100 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-101 [-1, 1024, 14, 14] 2,048ResNeXt_Block-102 [-1, 1024, 14, 14] 0Conv2d-103 [-1, 512, 14, 14] 524,288BatchNorm2d-104 [-1, 512, 14, 14] 1,024BN_Conv2d-105 [-1, 512, 14, 14] 0Conv2d-106 [-1, 512, 14, 14] 73,728BatchNorm2d-107 [-1, 512, 14, 14] 1,024BN_Conv2d-108 [-1, 512, 14, 14] 0Conv2d-109 [-1, 1024, 14, 14] 525,312BatchNorm2d-110 [-1, 1024, 14, 14] 2,048Conv2d-111 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-112 [-1, 1024, 14, 14] 2,048ResNeXt_Block-113 [-1, 1024, 14, 14] 0Conv2d-114 [-1, 512, 14, 14] 524,288BatchNorm2d-115 [-1, 512, 14, 14] 1,024BN_Conv2d-116 [-1, 512, 14, 14] 0Conv2d-117 [-1, 512, 14, 14] 73,728BatchNorm2d-118 [-1, 512, 14, 14] 1,024BN_Conv2d-119 [-1, 512, 14, 14] 0Conv2d-120 [-1, 1024, 14, 14] 525,312BatchNorm2d-121 [-1, 1024, 14, 14] 2,048Conv2d-122 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-123 [-1, 1024, 14, 14] 2,048ResNeXt_Block-124 [-1, 1024, 14, 14] 0Conv2d-125 [-1, 512, 14, 14] 524,288BatchNorm2d-126 [-1, 512, 14, 14] 1,024BN_Conv2d-127 [-1, 512, 14, 14] 0Conv2d-128 [-1, 512, 14, 14] 73,728BatchNorm2d-129 [-1, 512, 14, 14] 1,024BN_Conv2d-130 [-1, 512, 14, 14] 0Conv2d-131 [-1, 1024, 14, 14] 525,312BatchNorm2d-132 [-1, 1024, 14, 14] 2,048Conv2d-133 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-134 [-1, 1024, 14, 14] 2,048ResNeXt_Block-135 [-1, 1024, 14, 14] 0Conv2d-136 [-1, 512, 14, 14] 524,288BatchNorm2d-137 [-1, 512, 14, 14] 1,024BN_Conv2d-138 [-1, 512, 14, 14] 0Conv2d-139 [-1, 512, 14, 14] 73,728BatchNorm2d-140 [-1, 512, 14, 14] 1,024BN_Conv2d-141 [-1, 512, 14, 14] 0Conv2d-142 [-1, 1024, 14, 14] 525,312BatchNorm2d-143 [-1, 1024, 14, 14] 2,048Conv2d-144 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-145 [-1, 1024, 14, 14] 2,048ResNeXt_Block-146 [-1, 1024, 14, 14] 0Conv2d-147 [-1, 1024, 14, 14] 1,048,576BatchNorm2d-148 [-1, 1024, 14, 14] 2,048BN_Conv2d-149 [-1, 1024, 14, 14] 0Conv2d-150 [-1, 1024, 7, 7] 294,912BatchNorm2d-151 [-1, 1024, 7, 7] 2,048BN_Conv2d-152 [-1, 1024, 7, 7] 0Conv2d-153 [-1, 2048, 7, 7] 2,099,200BatchNorm2d-154 [-1, 2048, 7, 7] 4,096Conv2d-155 [-1, 2048, 7, 7] 2,097,152BatchNorm2d-156 [-1, 2048, 7, 7] 4,096ResNeXt_Block-157 [-1, 2048, 7, 7] 0Conv2d-158 [-1, 1024, 7, 7] 2,097,152BatchNorm2d-159 [-1, 1024, 7, 7] 2,048BN_Conv2d-160 [-1, 1024, 7, 7] 0Conv2d-161 [-1, 1024, 7, 7] 294,912BatchNorm2d-162 [-1, 1024, 7, 7] 2,048BN_Conv2d-163 [-1, 1024, 7, 7] 0Conv2d-164 [-1, 2048, 7, 7] 2,099,200BatchNorm2d-165 [-1, 2048, 7, 7] 4,096Conv2d-166 [-1, 2048, 7, 7] 4,194,304BatchNorm2d-167 [-1, 2048, 7, 7] 4,096ResNeXt_Block-168 [-1, 2048, 7, 7] 0Conv2d-169 [-1, 1024, 7, 7] 2,097,152BatchNorm2d-170 [-1, 1024, 7, 7] 2,048BN_Conv2d-171 [-1, 1024, 7, 7] 0Conv2d-172 [-1, 1024, 7, 7] 294,912BatchNorm2d-173 [-1, 1024, 7, 7] 2,048BN_Conv2d-174 [-1, 1024, 7, 7] 0Conv2d-175 [-1, 2048, 7, 7] 2,099,200BatchNorm2d-176 [-1, 2048, 7, 7] 4,096Conv2d-177 [-1, 2048, 7, 7] 4,194,304BatchNorm2d-178 [-1, 2048, 7, 7] 4,096ResNeXt_Block-179 [-1, 2048, 7, 7] 0Linear-180 [-1, 4] 8,196Total params: 37,574,724
Trainable params: 37,574,724
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 379.37
Params size (MB): 143.34
Estimated Total Size (MB): 523.28
----------------------------------------------------------------
None
2.3 训练模型
2.3.1 设置超参数
训练模型--设置超参数
loss_fn nn.CrossEntropyLoss() # 创建损失函数计算实际输出和真实相差多少交叉熵损失函数事实上它就是做图片分类任务时常用的损失函数
learn_rate 1e-4 # 学习率
optimizer1 torch.optim.SGD(model.parameters(), lrlearn_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(), lrlearn_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(), lrlearn_rate)
scheduler torch.optim.lr_scheduler.LambdaLR(lr_opt, lr_lambdalambda1) #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187560000/32train_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_loss2.3.3 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31310000/32312.5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 20
train_loss []
train_acc []
test_loss []
test_acc []
best_test_acc0for epoch in range(epochs):milliseconds_t1 int(time.time() * 1000)# 更新学习率使用自定义学习率时使用# adjust_learning_rate(lr_opt, epoch, learn_rate)model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, model_opt)scheduler.step() # 更新学习率调用官方动态学习率接口时使用model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr lr_opt.state_dict()[param_groups][0][lr]milliseconds_t2 int(time.time() * 1000)template (Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f}, Lr:{:.2E})if best_test_acc epoch_test_acc:best_test_acc epoch_test_acc#备份最好的模型best_model copy.deepcopy(model)template (Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f}, Lr:{:.2E},Update the best model)print(template.format(epoch 1, milliseconds_t2-milliseconds_t1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH ./best_model.pth # 保存的参数文件名
torch.save(model.state_dict(), PATH)
print(Done)Epoch: 1, duration:15650ms, Train_acc:54.8%, Train_loss:1.187, Test_acc:59.9%Test_loss:1.147, Lr:1.00E-04,Update the best model
Epoch: 2, duration:15311ms, Train_acc:62.2%, Train_loss:1.112, Test_acc:58.7%Test_loss:1.150, Lr:1.00E-04
Epoch: 3, duration:15336ms, Train_acc:67.3%, Train_loss:1.067, Test_acc:62.9%Test_loss:1.117, Lr:1.00E-04,Update the best model
Epoch: 4, duration:14853ms, Train_acc:68.0%, Train_loss:1.061, Test_acc:65.0%Test_loss:1.093, Lr:1.00E-04,Update the best model
Epoch: 5, duration:14930ms, Train_acc:68.0%, Train_loss:1.059, Test_acc:64.6%Test_loss:1.087, Lr:1.00E-04
Epoch: 6, duration:15118ms, Train_acc:67.2%, Train_loss:1.067, Test_acc:60.1%Test_loss:1.126, Lr:1.00E-04
Epoch: 7, duration:15024ms, Train_acc:67.8%, Train_loss:1.059, Test_acc:68.5%Test_loss:1.050, Lr:1.00E-04,Update the best model
Epoch: 8, duration:14973ms, Train_acc:66.9%, Train_loss:1.065, Test_acc:67.6%Test_loss:1.074, Lr:1.00E-04
Epoch: 9, duration:14902ms, Train_acc:69.3%, Train_loss:1.049, Test_acc:64.1%Test_loss:1.099, Lr:1.00E-04
Epoch:10, duration:15237ms, Train_acc:70.2%, Train_loss:1.035, Test_acc:71.6%Test_loss:1.024, Lr:1.00E-04,Update the best model
Epoch:11, duration:14890ms, Train_acc:71.0%, Train_loss:1.029, Test_acc:73.4%Test_loss:1.010, Lr:1.00E-04,Update the best model
Epoch:12, duration:14951ms, Train_acc:70.5%, Train_loss:1.034, Test_acc:70.2%Test_loss:1.043, Lr:1.00E-04
Epoch:13, duration:14967ms, Train_acc:72.3%, Train_loss:1.020, Test_acc:71.8%Test_loss:1.022, Lr:1.00E-04
Epoch:14, duration:14966ms, Train_acc:73.8%, Train_loss:1.004, Test_acc:72.5%Test_loss:1.017, Lr:1.00E-04
Epoch:15, duration:14886ms, Train_acc:75.5%, Train_loss:0.987, Test_acc:72.3%Test_loss:1.015, Lr:1.00E-04
Epoch:16, duration:14895ms, Train_acc:72.6%, Train_loss:1.012, Test_acc:72.5%Test_loss:1.025, Lr:1.00E-04
Epoch:17, duration:15037ms, Train_acc:74.3%, Train_loss:0.994, Test_acc:73.2%Test_loss:1.016, Lr:1.00E-04
Epoch:18, duration:14797ms, Train_acc:76.5%, Train_loss:0.976, Test_acc:70.6%Test_loss:1.026, Lr:1.00E-04
Epoch:19, duration:15157ms, Train_acc:72.6%, Train_loss:1.018, Test_acc:72.0%Test_loss:1.018, Lr:1.00E-04
Epoch:20, duration:14767ms, Train_acc:73.1%, Train_loss:1.009, Test_acc:74.4%Test_loss:1.003, Lr:1.00E-04,Update the best model
2.4 结果可视化
训练模型--结果可视化
epochs_range range(epochs)plt.figure(figsize(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, labelTraining Accuracy)
plt.plot(epochs_range, test_acc, labelTest Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, labelTraining Loss)
plt.plot(epochs_range, test_loss, labelTest Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show()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_locationdevice))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_pathstr(Path(data_dir) / Monkeypox/M01_01_01.jpg),modelmodel,transformtrain_transforms,classesclasses)输出
预测结果是Monkeypox
2.6 模型评估
模型评估
best_model.eval()
epoch_test_acc, epoch_test_loss test(test_dl, best_model, loss_fn)
# 查看是否与我们记录的最高准确率一致
print(epoch_test_acc, epoch_test_loss)
输出
0.7435897435897436 0.9976991329874311
3 tensorflow实现DenseNet算法
3.1.引入库
from PIL import Image
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt# 支持中文
plt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签
plt.rcParams[axes.unicode_minus] False # 用来正常显示负号
import tensorflow as tf
from keras import layers, models, Input
from keras.layers import Input, Activation, BatchNormalization, Flatten
from keras.layers import Dense, Conv2D, MaxPooling2D, ZeroPadding2D, GlobalMaxPooling2D, AveragePooling2D, Flatten, \Dropout, BatchNormalization, GlobalAveragePooling2D
from keras.models import Model
from keras import regularizers
from tensorflow import keras
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import 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_typeGPU)]3.3.导入数据
前期工作-导入数据
data_dir rD:\DeepLearning\data\monkeypox_recognition
data_dir Path(data_dir)3.4.查看数据
前期工作-查看数据
image_count len(list(data_dir.glob(*/*.jpg)))
print(图片总数为, image_count)
image_list list(data_dir.glob(Monkeypox/*.jpg))
image Image.open(str(image_list[1]))
# 查看图像实例的属性
print(image.format, image.size, image.mode)
plt.imshow(image)
plt.axis(off)
plt.show()执行结果
图片总数为 2142
JPEG (224, 224) RGB 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/117018789train_ds tf.keras.preprocessing.image_dataset_from_directory(data_dir,validation_split0.2,subsettraining,seed123,image_size(img_height, img_width),batch_sizebatch_size)
val_ds tf.keras.preprocessing.image_dataset_from_directory(data_dir,validation_split0.2,subsetvalidation,seed123,image_size(img_height, img_width),batch_sizebatch_size)
class_names train_ds.class_names
print(class_names)运行结果
Found 2142 files belonging to 2 classes.
Using 1714 files for training.
Found 2142 files belonging to 2 classes.
Using 428 files for validation.
[Monkeypox, Others]3.6.再次检查数据
数据预处理-再次检查数据
# Image_batch是形状的张量(16, 336, 336, 3)。这是一批形状336x336x3的16张图片最后一维指的是彩色通道RGB。
# Label_batch是形状16的张量这些标签对应16张图片
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break运行结果
(32, 224, 224, 3)
(32,)3.7.配置数据集
数据预处理-配置数据集
AUTOTUNE tf.data.AUTOTUNE
train_ds train_ds.cache().shuffle(1000).prefetch(buffer_sizeAUTOTUNE)
val_ds val_ds.cache().prefetch(buffer_sizeAUTOTUNE)
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]], fontsize10)plt.axis(off)
# 显示图片
plt.show() 3.9.构建ResNeXt50网络
构建ResNeXt50网络# ----------------------- #
# groups代表多少组
# g_channels代表每组的特征图数量
# ----------------------- #
def group_conv2_block(x_0, strides, groups, g_channels):g_list []for i in range(groups):x Lambda(lambda x: x[:, :, :, i * g_channels: (i 1) * g_channels])(x_0)x Conv2D(filtersg_channels, kernel_size3, stridesstrides, paddingsame, use_biasFalse)(x)g_list.append(x)x concatenate(g_list, axis3)x BatchNormalization(epsilon1.001e-5)(x)x Activation(relu)(x)return x# 结构快
def block(x, filters, strides1, groups32, conv_shortTrue):if conv_short:short_cut Conv2D(filtersfilters * 2, kernel_size1, stridesstrides, paddingsame)(x)short_cut BatchNormalization(epsilon1.001e-5)(short_cut)else:short_cut x# 三层卷积x Conv2D(filtersfilters, kernel_size1, strides1, paddingsame)(x)x BatchNormalization(epsilon1.001e-5)(x)x Activation(relu)(x)g_channels int(filters / groups)x group_conv2_block(x, stridesstrides, groupsgroups, g_channelsg_channels)x Conv2D(filtersfilters * 2, kernel_size1, strides1, paddingsame)(x)x BatchNormalization(epsilon1.001e-5)(x)x Add()([x, short_cut])x Activation(relu)(x)return xdef Resnext(inputs, classes):x_input keras.layers.Input(shapeinputs)x ZeroPadding2D((3, 3))(x_input)x Conv2D(filters64, kernel_size7, strides2, paddingvalid)(x)x BatchNormalization(epsilon1.001e-5)(x)x Activation(relu)(x)x ZeroPadding2D((1, 1))(x)x MaxPool2D(pool_size3, strides2, paddingvalid)(x)x block(x, filters128, strides1, conv_shortTrue)x block(x, filters128, conv_shortFalse)x block(x, filters128, conv_shortFalse)x block(x, filters256, strides2, conv_shortTrue)x block(x, filters256, conv_shortFalse)x block(x, filters256, conv_shortFalse)x block(x, filters256, conv_shortFalse)x block(x, filters512, strides2, conv_shortTrue)x block(x, filters512, conv_shortFalse)x block(x, filters512, conv_shortFalse)x block(x, filters512, conv_shortFalse)x block(x, filters512, conv_shortFalse)x block(x, filters512, conv_shortFalse)x block(x, filters1024, strides2, conv_shortTrue)x block(x, filters1024, conv_shortFalse)x block(x, filters1024, conv_shortFalse)x GlobalAvgPool2D()(x)x Dense(classes, activationsoftmax)(x)model keras.models.Model(inputs[x_input], outputs[x])return modelmodel Resnext(inputs (img_width, img_height, 3),classes4)
model.summary()
网络结构结果如下
Model: model
__________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to
input_1 (InputLayer) [(None, 224, 224, 3 0 [] )] conv2d (Conv2D) (None, 112, 112, 64 9408 [input_1[0][0]] ) batch_normalization (BatchNorm (None, 112, 112, 64 256 [conv2d[0][0]] alization) ) max_pooling2d (MaxPooling2D) (None, 56, 56, 64) 0 [batch_normalization[0][0]] batch_normalization_1 (BatchNo (None, 56, 56, 64) 256 [max_pooling2d[0][0]] rmalization) activation (Activation) (None, 56, 56, 64) 0 [batch_normalization_1[0][0]] conv2d_1 (Conv2D) (None, 56, 56, 128) 8192 [activation[0][0]] batch_normalization_2 (BatchNo (None, 56, 56, 128) 512 [conv2d_1[0][0]] rmalization) activation_1 (Activation) (None, 56, 56, 128) 0 [batch_normalization_2[0][0]] conv2d_2 (Conv2D) (None, 56, 56, 32) 36864 [activation_1[0][0]] concatenate (Concatenate) (None, 56, 56, 96) 0 [max_pooling2d[0][0], conv2d_2[0][0]] batch_normalization_3 (BatchNo (None, 56, 56, 96) 384 [concatenate[0][0]] rmalization) activation_2 (Activation) (None, 56, 56, 96) 0 [batch_normalization_3[0][0]] conv2d_3 (Conv2D) (None, 56, 56, 128) 12288 [activation_2[0][0]] batch_normalization_4 (BatchNo (None, 56, 56, 128) 512 [conv2d_3[0][0]] rmalization) activation_3 (Activation) (None, 56, 56, 128) 0 [batch_normalization_4[0][0]] conv2d_4 (Conv2D) (None, 56, 56, 32) 36864 [activation_3[0][0]] concatenate_1 (Concatenate) (None, 56, 56, 128) 0 [concatenate[0][0], conv2d_4[0][0]] batch_normalization_5 (BatchNo (None, 56, 56, 128) 512 [concatenate_1[0][0]] rmalization) activation_4 (Activation) (None, 56, 56, 128) 0 [batch_normalization_5[0][0]] conv2d_5 (Conv2D) (None, 56, 56, 128) 16384 [activation_4[0][0]] batch_normalization_6 (BatchNo (None, 56, 56, 128) 512 [conv2d_5[0][0]] rmalization) activation_5 (Activation) (None, 56, 56, 128) 0 [batch_normalization_6[0][0]] conv2d_6 (Conv2D) (None, 56, 56, 32) 36864 [activation_5[0][0]] concatenate_2 (Concatenate) (None, 56, 56, 160) 0 [concatenate_1[0][0], conv2d_6[0][0]] batch_normalization_7 (BatchNo (None, 56, 56, 160) 640 [concatenate_2[0][0]] rmalization) activation_6 (Activation) (None, 56, 56, 160) 0 [batch_normalization_7[0][0]] conv2d_7 (Conv2D) (None, 56, 56, 128) 20480 [activation_6[0][0]] batch_normalization_8 (BatchNo (None, 56, 56, 128) 512 [conv2d_7[0][0]] rmalization) activation_7 (Activation) (None, 56, 56, 128) 0 [batch_normalization_8[0][0]] conv2d_8 (Conv2D) (None, 56, 56, 32) 36864 [activation_7[0][0]] concatenate_3 (Concatenate) (None, 56, 56, 192) 0 [concatenate_2[0][0], conv2d_8[0][0]] batch_normalization_9 (BatchNo (None, 56, 56, 192) 768 [concatenate_3[0][0]] rmalization) activation_8 (Activation) (None, 56, 56, 192) 0 [batch_normalization_9[0][0]] conv2d_9 (Conv2D) (None, 56, 56, 128) 24576 [activation_8[0][0]] batch_normalization_10 (BatchN (None, 56, 56, 128) 512 [conv2d_9[0][0]] ormalization) activation_9 (Activation) (None, 56, 56, 128) 0 [batch_normalization_10[0][0]] conv2d_10 (Conv2D) (None, 56, 56, 32) 36864 [activation_9[0][0]] concatenate_4 (Concatenate) (None, 56, 56, 224) 0 [concatenate_3[0][0], conv2d_10[0][0]] batch_normalization_11 (BatchN (None, 56, 56, 224) 896 [concatenate_4[0][0]] ormalization) activation_10 (Activation) (None, 56, 56, 224) 0 [batch_normalization_11[0][0]] conv2d_11 (Conv2D) (None, 56, 56, 128) 28672 [activation_10[0][0]] batch_normalization_12 (BatchN (None, 56, 56, 128) 512 [conv2d_11[0][0]] ormalization) activation_11 (Activation) (None, 56, 56, 128) 0 [batch_normalization_12[0][0]] conv2d_12 (Conv2D) (None, 56, 56, 32) 36864 [activation_11[0][0]] concatenate_5 (Concatenate) (None, 56, 56, 256) 0 [concatenate_4[0][0], conv2d_12[0][0]] batch_normalization_13 (BatchN (None, 56, 56, 256) 1024 [concatenate_5[0][0]] ormalization) activation_12 (Activation) (None, 56, 56, 256) 0 [batch_normalization_13[0][0]] conv2d_13 (Conv2D) (None, 56, 56, 128) 32768 [activation_12[0][0]] average_pooling2d (AveragePool (None, 28, 28, 128) 0 [conv2d_13[0][0]] ing2D) batch_normalization_14 (BatchN (None, 28, 28, 128) 512 [average_pooling2d[0][0]] ormalization) activation_13 (Activation) (None, 28, 28, 128) 0 [batch_normalization_14[0][0]] conv2d_14 (Conv2D) (None, 28, 28, 128) 16384 [activation_13[0][0]] batch_normalization_15 (BatchN (None, 28, 28, 128) 512 [conv2d_14[0][0]] ormalization) activation_14 (Activation) (None, 28, 28, 128) 0 [batch_normalization_15[0][0]] conv2d_15 (Conv2D) (None, 28, 28, 32) 36864 [activation_14[0][0]] concatenate_6 (Concatenate) (None, 28, 28, 160) 0 [average_pooling2d[0][0], conv2d_15[0][0]] batch_normalization_16 (BatchN (None, 28, 28, 160) 640 [concatenate_6[0][0]] ormalization) activation_15 (Activation) (None, 28, 28, 160) 0 [batch_normalization_16[0][0]] conv2d_16 (Conv2D) (None, 28, 28, 128) 20480 [activation_15[0][0]] batch_normalization_17 (BatchN (None, 28, 28, 128) 512 [conv2d_16[0][0]] ormalization) activation_16 (Activation) (None, 28, 28, 128) 0 [batch_normalization_17[0][0]] conv2d_17 (Conv2D) (None, 28, 28, 32) 36864 [activation_16[0][0]] concatenate_7 (Concatenate) (None, 28, 28, 192) 0 [concatenate_6[0][0], conv2d_17[0][0]] batch_normalization_18 (BatchN (None, 28, 28, 192) 768 [concatenate_7[0][0]] ormalization) activation_17 (Activation) (None, 28, 28, 192) 0 [batch_normalization_18[0][0]] conv2d_18 (Conv2D) (None, 28, 28, 128) 24576 [activation_17[0][0]] batch_normalization_19 (BatchN (None, 28, 28, 128) 512 [conv2d_18[0][0]] ormalization) activation_18 (Activation) (None, 28, 28, 128) 0 [batch_normalization_19[0][0]] conv2d_19 (Conv2D) (None, 28, 28, 32) 36864 [activation_18[0][0]] concatenate_8 (Concatenate) (None, 28, 28, 224) 0 [concatenate_7[0][0], conv2d_19[0][0]] batch_normalization_20 (BatchN (None, 28, 28, 224) 896 [concatenate_8[0][0]] ormalization) activation_19 (Activation) (None, 28, 28, 224) 0 [batch_normalization_20[0][0]] conv2d_20 (Conv2D) (None, 28, 28, 128) 28672 [activation_19[0][0]] batch_normalization_21 (BatchN (None, 28, 28, 128) 512 [conv2d_20[0][0]] ormalization) activation_20 (Activation) (None, 28, 28, 128) 0 [batch_normalization_21[0][0]] conv2d_21 (Conv2D) (None, 28, 28, 32) 36864 [activation_20[0][0]] concatenate_9 (Concatenate) (None, 28, 28, 256) 0 [concatenate_8[0][0], conv2d_21[0][0]] batch_normalization_22 (BatchN (None, 28, 28, 256) 1024 [concatenate_9[0][0]] ormalization) activation_21 (Activation) (None, 28, 28, 256) 0 [batch_normalization_22[0][0]] conv2d_22 (Conv2D) (None, 28, 28, 128) 32768 [activation_21[0][0]] batch_normalization_23 (BatchN (None, 28, 28, 128) 512 [conv2d_22[0][0]] ormalization) activation_22 (Activation) (None, 28, 28, 128) 0 [batch_normalization_23[0][0]] conv2d_23 (Conv2D) (None, 28, 28, 32) 36864 [activation_22[0][0]] concatenate_10 (Concatenate) (None, 28, 28, 288) 0 [concatenate_9[0][0], conv2d_23[0][0]] batch_normalization_24 (BatchN (None, 28, 28, 288) 1152 [concatenate_10[0][0]] ormalization) activation_23 (Activation) (None, 28, 28, 288) 0 [batch_normalization_24[0][0]] conv2d_24 (Conv2D) (None, 28, 28, 128) 36864 [activation_23[0][0]] batch_normalization_25 (BatchN (None, 28, 28, 128) 512 [conv2d_24[0][0]] ormalization) activation_24 (Activation) (None, 28, 28, 128) 0 [batch_normalization_25[0][0]] conv2d_25 (Conv2D) (None, 28, 28, 32) 36864 [activation_24[0][0]] concatenate_11 (Concatenate) (None, 28, 28, 320) 0 [concatenate_10[0][0], conv2d_25[0][0]] batch_normalization_26 (BatchN (None, 28, 28, 320) 1280 [concatenate_11[0][0]] ormalization) activation_25 (Activation) (None, 28, 28, 320) 0 [batch_normalization_26[0][0]] conv2d_26 (Conv2D) (None, 28, 28, 128) 40960 [activation_25[0][0]] batch_normalization_27 (BatchN (None, 28, 28, 128) 512 [conv2d_26[0][0]] ormalization) activation_26 (Activation) (None, 28, 28, 128) 0 [batch_normalization_27[0][0]] conv2d_27 (Conv2D) (None, 28, 28, 32) 36864 [activation_26[0][0]] concatenate_12 (Concatenate) (None, 28, 28, 352) 0 [concatenate_11[0][0], conv2d_27[0][0]] batch_normalization_28 (BatchN (None, 28, 28, 352) 1408 [concatenate_12[0][0]] ormalization) activation_27 (Activation) (None, 28, 28, 352) 0 [batch_normalization_28[0][0]] conv2d_28 (Conv2D) (None, 28, 28, 128) 45056 [activation_27[0][0]] batch_normalization_29 (BatchN (None, 28, 28, 128) 512 [conv2d_28[0][0]] ormalization) activation_28 (Activation) (None, 28, 28, 128) 0 [batch_normalization_29[0][0]] conv2d_29 (Conv2D) (None, 28, 28, 32) 36864 [activation_28[0][0]] concatenate_13 (Concatenate) (None, 28, 28, 384) 0 [concatenate_12[0][0], conv2d_29[0][0]] batch_normalization_30 (BatchN (None, 28, 28, 384) 1536 [concatenate_13[0][0]] ormalization) activation_29 (Activation) (None, 28, 28, 384) 0 [batch_normalization_30[0][0]] conv2d_30 (Conv2D) (None, 28, 28, 128) 49152 [activation_29[0][0]] batch_normalization_31 (BatchN (None, 28, 28, 128) 512 [conv2d_30[0][0]] ormalization) activation_30 (Activation) (None, 28, 28, 128) 0 [batch_normalization_31[0][0]] conv2d_31 (Conv2D) (None, 28, 28, 32) 36864 [activation_30[0][0]] concatenate_14 (Concatenate) (None, 28, 28, 416) 0 [concatenate_13[0][0], conv2d_31[0][0]] batch_normalization_32 (BatchN (None, 28, 28, 416) 1664 [concatenate_14[0][0]] ormalization) activation_31 (Activation) (None, 28, 28, 416) 0 [batch_normalization_32[0][0]] conv2d_32 (Conv2D) (None, 28, 28, 128) 53248 [activation_31[0][0]] batch_normalization_33 (BatchN (None, 28, 28, 128) 512 [conv2d_32[0][0]] ormalization) activation_32 (Activation) (None, 28, 28, 128) 0 [batch_normalization_33[0][0]] conv2d_33 (Conv2D) (None, 28, 28, 32) 36864 [activation_32[0][0]] concatenate_15 (Concatenate) (None, 28, 28, 448) 0 [concatenate_14[0][0], conv2d_33[0][0]] batch_normalization_34 (BatchN (None, 28, 28, 448) 1792 [concatenate_15[0][0]] ormalization) activation_33 (Activation) (None, 28, 28, 448) 0 [batch_normalization_34[0][0]] conv2d_34 (Conv2D) (None, 28, 28, 128) 57344 [activation_33[0][0]] batch_normalization_35 (BatchN (None, 28, 28, 128) 512 [conv2d_34[0][0]] ormalization) activation_34 (Activation) (None, 28, 28, 128) 0 [batch_normalization_35[0][0]] conv2d_35 (Conv2D) (None, 28, 28, 32) 36864 [activation_34[0][0]] concatenate_16 (Concatenate) (None, 28, 28, 480) 0 [concatenate_15[0][0], conv2d_35[0][0]] batch_normalization_36 (BatchN (None, 28, 28, 480) 1920 [concatenate_16[0][0]] ormalization) activation_35 (Activation) (None, 28, 28, 480) 0 [batch_normalization_36[0][0]] conv2d_36 (Conv2D) (None, 28, 28, 128) 61440 [activation_35[0][0]] batch_normalization_37 (BatchN (None, 28, 28, 128) 512 [conv2d_36[0][0]] ormalization) activation_36 (Activation) (None, 28, 28, 128) 0 [batch_normalization_37[0][0]] conv2d_37 (Conv2D) (None, 28, 28, 32) 36864 [activation_36[0][0]] concatenate_17 (Concatenate) (None, 28, 28, 512) 0 [concatenate_16[0][0], conv2d_37[0][0]] batch_normalization_38 (BatchN (None, 28, 28, 512) 2048 [concatenate_17[0][0]] ormalization) activation_37 (Activation) (None, 28, 28, 512) 0 [batch_normalization_38[0][0]] conv2d_38 (Conv2D) (None, 28, 28, 256) 131072 [activation_37[0][0]] average_pooling2d_1 (AveragePo (None, 14, 14, 256) 0 [conv2d_38[0][0]] oling2D) batch_normalization_39 (BatchN (None, 14, 14, 256) 1024 [average_pooling2d_1[0][0]] ormalization) activation_38 (Activation) (None, 14, 14, 256) 0 [batch_normalization_39[0][0]] conv2d_39 (Conv2D) (None, 14, 14, 128) 32768 [activation_38[0][0]] batch_normalization_40 (BatchN (None, 14, 14, 128) 512 [conv2d_39[0][0]] ormalization) activation_39 (Activation) (None, 14, 14, 128) 0 [batch_normalization_40[0][0]] conv2d_40 (Conv2D) (None, 14, 14, 32) 36864 [activation_39[0][0]] concatenate_18 (Concatenate) (None, 14, 14, 288) 0 [average_pooling2d_1[0][0], conv2d_40[0][0]] batch_normalization_41 (BatchN (None, 14, 14, 288) 1152 [concatenate_18[0][0]] ormalization) activation_40 (Activation) (None, 14, 14, 288) 0 [batch_normalization_41[0][0]] conv2d_41 (Conv2D) (None, 14, 14, 128) 36864 [activation_40[0][0]] batch_normalization_42 (BatchN (None, 14, 14, 128) 512 [conv2d_41[0][0]] ormalization) activation_41 (Activation) (None, 14, 14, 128) 0 [batch_normalization_42[0][0]] conv2d_42 (Conv2D) (None, 14, 14, 32) 36864 [activation_41[0][0]] concatenate_19 (Concatenate) (None, 14, 14, 320) 0 [concatenate_18[0][0], conv2d_42[0][0]] batch_normalization_43 (BatchN (None, 14, 14, 320) 1280 [concatenate_19[0][0]] ormalization) activation_42 (Activation) (None, 14, 14, 320) 0 [batch_normalization_43[0][0]] conv2d_43 (Conv2D) (None, 14, 14, 128) 40960 [activation_42[0][0]] batch_normalization_44 (BatchN (None, 14, 14, 128) 512 [conv2d_43[0][0]] ormalization) activation_43 (Activation) (None, 14, 14, 128) 0 [batch_normalization_44[0][0]] conv2d_44 (Conv2D) (None, 14, 14, 32) 36864 [activation_43[0][0]] concatenate_20 (Concatenate) (None, 14, 14, 352) 0 [concatenate_19[0][0], conv2d_44[0][0]] batch_normalization_45 (BatchN (None, 14, 14, 352) 1408 [concatenate_20[0][0]] ormalization) activation_44 (Activation) (None, 14, 14, 352) 0 [batch_normalization_45[0][0]] conv2d_45 (Conv2D) (None, 14, 14, 128) 45056 [activation_44[0][0]] batch_normalization_46 (BatchN (None, 14, 14, 128) 512 [conv2d_45[0][0]] ormalization) activation_45 (Activation) (None, 14, 14, 128) 0 [batch_normalization_46[0][0]] conv2d_46 (Conv2D) (None, 14, 14, 32) 36864 [activation_45[0][0]] concatenate_21 (Concatenate) (None, 14, 14, 384) 0 [concatenate_20[0][0], conv2d_46[0][0]] batch_normalization_47 (BatchN (None, 14, 14, 384) 1536 [concatenate_21[0][0]] ormalization) activation_46 (Activation) (None, 14, 14, 384) 0 [batch_normalization_47[0][0]] conv2d_47 (Conv2D) (None, 14, 14, 128) 49152 [activation_46[0][0]] batch_normalization_48 (BatchN (None, 14, 14, 128) 512 [conv2d_47[0][0]] ormalization) activation_47 (Activation) (None, 14, 14, 128) 0 [batch_normalization_48[0][0]] conv2d_48 (Conv2D) (None, 14, 14, 32) 36864 [activation_47[0][0]] concatenate_22 (Concatenate) (None, 14, 14, 416) 0 [concatenate_21[0][0], conv2d_48[0][0]] batch_normalization_49 (BatchN (None, 14, 14, 416) 1664 [concatenate_22[0][0]] ormalization) activation_48 (Activation) (None, 14, 14, 416) 0 [batch_normalization_49[0][0]] conv2d_49 (Conv2D) (None, 14, 14, 128) 53248 [activation_48[0][0]] batch_normalization_50 (BatchN (None, 14, 14, 128) 512 [conv2d_49[0][0]] ormalization) activation_49 (Activation) (None, 14, 14, 128) 0 [batch_normalization_50[0][0]] conv2d_50 (Conv2D) (None, 14, 14, 32) 36864 [activation_49[0][0]] concatenate_23 (Concatenate) (None, 14, 14, 448) 0 [concatenate_22[0][0], conv2d_50[0][0]] batch_normalization_51 (BatchN (None, 14, 14, 448) 1792 [concatenate_23[0][0]] ormalization) activation_50 (Activation) (None, 14, 14, 448) 0 [batch_normalization_51[0][0]] conv2d_51 (Conv2D) (None, 14, 14, 128) 57344 [activation_50[0][0]] batch_normalization_52 (BatchN (None, 14, 14, 128) 512 [conv2d_51[0][0]] ormalization) activation_51 (Activation) (None, 14, 14, 128) 0 [batch_normalization_52[0][0]] conv2d_52 (Conv2D) (None, 14, 14, 32) 36864 [activation_51[0][0]] concatenate_24 (Concatenate) (None, 14, 14, 480) 0 [concatenate_23[0][0], conv2d_52[0][0]] batch_normalization_53 (BatchN (None, 14, 14, 480) 1920 [concatenate_24[0][0]] ormalization) activation_52 (Activation) (None, 14, 14, 480) 0 [batch_normalization_53[0][0]] conv2d_53 (Conv2D) (None, 14, 14, 128) 61440 [activation_52[0][0]] batch_normalization_54 (BatchN (None, 14, 14, 128) 512 [conv2d_53[0][0]] ormalization) activation_53 (Activation) (None, 14, 14, 128) 0 [batch_normalization_54[0][0]] conv2d_54 (Conv2D) (None, 14, 14, 32) 36864 [activation_53[0][0]] concatenate_25 (Concatenate) (None, 14, 14, 512) 0 [concatenate_24[0][0], conv2d_54[0][0]] batch_normalization_55 (BatchN (None, 14, 14, 512) 2048 [concatenate_25[0][0]] ormalization) activation_54 (Activation) (None, 14, 14, 512) 0 [batch_normalization_55[0][0]] conv2d_55 (Conv2D) (None, 14, 14, 128) 65536 [activation_54[0][0]] batch_normalization_56 (BatchN (None, 14, 14, 128) 512 [conv2d_55[0][0]] ormalization) activation_55 (Activation) (None, 14, 14, 128) 0 [batch_normalization_56[0][0]] conv2d_56 (Conv2D) (None, 14, 14, 32) 36864 [activation_55[0][0]] concatenate_26 (Concatenate) (None, 14, 14, 544) 0 [concatenate_25[0][0], conv2d_56[0][0]] batch_normalization_57 (BatchN (None, 14, 14, 544) 2176 [concatenate_26[0][0]] ormalization) activation_56 (Activation) (None, 14, 14, 544) 0 [batch_normalization_57[0][0]] conv2d_57 (Conv2D) (None, 14, 14, 128) 69632 [activation_56[0][0]] batch_normalization_58 (BatchN (None, 14, 14, 128) 512 [conv2d_57[0][0]] ormalization) activation_57 (Activation) (None, 14, 14, 128) 0 [batch_normalization_58[0][0]] conv2d_58 (Conv2D) (None, 14, 14, 32) 36864 [activation_57[0][0]] concatenate_27 (Concatenate) (None, 14, 14, 576) 0 [concatenate_26[0][0], conv2d_58[0][0]] batch_normalization_59 (BatchN (None, 14, 14, 576) 2304 [concatenate_27[0][0]] ormalization) activation_58 (Activation) (None, 14, 14, 576) 0 [batch_normalization_59[0][0]] conv2d_59 (Conv2D) (None, 14, 14, 128) 73728 [activation_58[0][0]] batch_normalization_60 (BatchN (None, 14, 14, 128) 512 [conv2d_59[0][0]] ormalization) activation_59 (Activation) (None, 14, 14, 128) 0 [batch_normalization_60[0][0]] conv2d_60 (Conv2D) (None, 14, 14, 32) 36864 [activation_59[0][0]] concatenate_28 (Concatenate) (None, 14, 14, 608) 0 [concatenate_27[0][0], conv2d_60[0][0]] batch_normalization_61 (BatchN (None, 14, 14, 608) 2432 [concatenate_28[0][0]] ormalization) activation_60 (Activation) (None, 14, 14, 608) 0 [batch_normalization_61[0][0]] conv2d_61 (Conv2D) (None, 14, 14, 128) 77824 [activation_60[0][0]] batch_normalization_62 (BatchN (None, 14, 14, 128) 512 [conv2d_61[0][0]] ormalization) activation_61 (Activation) (None, 14, 14, 128) 0 [batch_normalization_62[0][0]] conv2d_62 (Conv2D) (None, 14, 14, 32) 36864 [activation_61[0][0]] concatenate_29 (Concatenate) (None, 14, 14, 640) 0 [concatenate_28[0][0], conv2d_62[0][0]] batch_normalization_63 (BatchN (None, 14, 14, 640) 2560 [concatenate_29[0][0]] ormalization) activation_62 (Activation) (None, 14, 14, 640) 0 [batch_normalization_63[0][0]] conv2d_63 (Conv2D) (None, 14, 14, 128) 81920 [activation_62[0][0]] batch_normalization_64 (BatchN (None, 14, 14, 128) 512 [conv2d_63[0][0]] ormalization) activation_63 (Activation) (None, 14, 14, 128) 0 [batch_normalization_64[0][0]] conv2d_64 (Conv2D) (None, 14, 14, 32) 36864 [activation_63[0][0]] concatenate_30 (Concatenate) (None, 14, 14, 672) 0 [concatenate_29[0][0], conv2d_64[0][0]] batch_normalization_65 (BatchN (None, 14, 14, 672) 2688 [concatenate_30[0][0]] ormalization) activation_64 (Activation) (None, 14, 14, 672) 0 [batch_normalization_65[0][0]] conv2d_65 (Conv2D) (None, 14, 14, 128) 86016 [activation_64[0][0]] batch_normalization_66 (BatchN (None, 14, 14, 128) 512 [conv2d_65[0][0]] ormalization) activation_65 (Activation) (None, 14, 14, 128) 0 [batch_normalization_66[0][0]] conv2d_66 (Conv2D) (None, 14, 14, 32) 36864 [activation_65[0][0]] concatenate_31 (Concatenate) (None, 14, 14, 704) 0 [concatenate_30[0][0], conv2d_66[0][0]] batch_normalization_67 (BatchN (None, 14, 14, 704) 2816 [concatenate_31[0][0]] ormalization) activation_66 (Activation) (None, 14, 14, 704) 0 [batch_normalization_67[0][0]] conv2d_67 (Conv2D) (None, 14, 14, 128) 90112 [activation_66[0][0]] batch_normalization_68 (BatchN (None, 14, 14, 128) 512 [conv2d_67[0][0]] ormalization) activation_67 (Activation) (None, 14, 14, 128) 0 [batch_normalization_68[0][0]] conv2d_68 (Conv2D) (None, 14, 14, 32) 36864 [activation_67[0][0]] concatenate_32 (Concatenate) (None, 14, 14, 736) 0 [concatenate_31[0][0], conv2d_68[0][0]] batch_normalization_69 (BatchN (None, 14, 14, 736) 2944 [concatenate_32[0][0]] ormalization) activation_68 (Activation) (None, 14, 14, 736) 0 [batch_normalization_69[0][0]] conv2d_69 (Conv2D) (None, 14, 14, 128) 94208 [activation_68[0][0]] batch_normalization_70 (BatchN (None, 14, 14, 128) 512 [conv2d_69[0][0]] ormalization) activation_69 (Activation) (None, 14, 14, 128) 0 [batch_normalization_70[0][0]] conv2d_70 (Conv2D) (None, 14, 14, 32) 36864 [activation_69[0][0]] concatenate_33 (Concatenate) (None, 14, 14, 768) 0 [concatenate_32[0][0], conv2d_70[0][0]] batch_normalization_71 (BatchN (None, 14, 14, 768) 3072 [concatenate_33[0][0]] ormalization) activation_70 (Activation) (None, 14, 14, 768) 0 [batch_normalization_71[0][0]] conv2d_71 (Conv2D) (None, 14, 14, 128) 98304 [activation_70[0][0]] batch_normalization_72 (BatchN (None, 14, 14, 128) 512 [conv2d_71[0][0]] ormalization) activation_71 (Activation) (None, 14, 14, 128) 0 [batch_normalization_72[0][0]] conv2d_72 (Conv2D) (None, 14, 14, 32) 36864 [activation_71[0][0]] concatenate_34 (Concatenate) (None, 14, 14, 800) 0 [concatenate_33[0][0], conv2d_72[0][0]] batch_normalization_73 (BatchN (None, 14, 14, 800) 3200 [concatenate_34[0][0]] ormalization) activation_72 (Activation) (None, 14, 14, 800) 0 [batch_normalization_73[0][0]] conv2d_73 (Conv2D) (None, 14, 14, 128) 102400 [activation_72[0][0]] batch_normalization_74 (BatchN (None, 14, 14, 128) 512 [conv2d_73[0][0]] ormalization) activation_73 (Activation) (None, 14, 14, 128) 0 [batch_normalization_74[0][0]] conv2d_74 (Conv2D) (None, 14, 14, 32) 36864 [activation_73[0][0]] concatenate_35 (Concatenate) (None, 14, 14, 832) 0 [concatenate_34[0][0], conv2d_74[0][0]] batch_normalization_75 (BatchN (None, 14, 14, 832) 3328 [concatenate_35[0][0]] ormalization) activation_74 (Activation) (None, 14, 14, 832) 0 [batch_normalization_75[0][0]] conv2d_75 (Conv2D) (None, 14, 14, 128) 106496 [activation_74[0][0]] batch_normalization_76 (BatchN (None, 14, 14, 128) 512 [conv2d_75[0][0]] ormalization) activation_75 (Activation) (None, 14, 14, 128) 0 [batch_normalization_76[0][0]] conv2d_76 (Conv2D) (None, 14, 14, 32) 36864 [activation_75[0][0]] concatenate_36 (Concatenate) (None, 14, 14, 864) 0 [concatenate_35[0][0], conv2d_76[0][0]] batch_normalization_77 (BatchN (None, 14, 14, 864) 3456 [concatenate_36[0][0]] ormalization) activation_76 (Activation) (None, 14, 14, 864) 0 [batch_normalization_77[0][0]] conv2d_77 (Conv2D) (None, 14, 14, 128) 110592 [activation_76[0][0]] batch_normalization_78 (BatchN (None, 14, 14, 128) 512 [conv2d_77[0][0]] ormalization) activation_77 (Activation) (None, 14, 14, 128) 0 [batch_normalization_78[0][0]] conv2d_78 (Conv2D) (None, 14, 14, 32) 36864 [activation_77[0][0]] concatenate_37 (Concatenate) (None, 14, 14, 896) 0 [concatenate_36[0][0], conv2d_78[0][0]] batch_normalization_79 (BatchN (None, 14, 14, 896) 3584 [concatenate_37[0][0]] ormalization) activation_78 (Activation) (None, 14, 14, 896) 0 [batch_normalization_79[0][0]] conv2d_79 (Conv2D) (None, 14, 14, 128) 114688 [activation_78[0][0]] batch_normalization_80 (BatchN (None, 14, 14, 128) 512 [conv2d_79[0][0]] ormalization) activation_79 (Activation) (None, 14, 14, 128) 0 [batch_normalization_80[0][0]] conv2d_80 (Conv2D) (None, 14, 14, 32) 36864 [activation_79[0][0]] concatenate_38 (Concatenate) (None, 14, 14, 928) 0 [concatenate_37[0][0], conv2d_80[0][0]] batch_normalization_81 (BatchN (None, 14, 14, 928) 3712 [concatenate_38[0][0]] ormalization) activation_80 (Activation) (None, 14, 14, 928) 0 [batch_normalization_81[0][0]] conv2d_81 (Conv2D) (None, 14, 14, 128) 118784 [activation_80[0][0]] batch_normalization_82 (BatchN (None, 14, 14, 128) 512 [conv2d_81[0][0]] ormalization) activation_81 (Activation) (None, 14, 14, 128) 0 [batch_normalization_82[0][0]] conv2d_82 (Conv2D) (None, 14, 14, 32) 36864 [activation_81[0][0]] concatenate_39 (Concatenate) (None, 14, 14, 960) 0 [concatenate_38[0][0], conv2d_82[0][0]] batch_normalization_83 (BatchN (None, 14, 14, 960) 3840 [concatenate_39[0][0]] ormalization) activation_82 (Activation) (None, 14, 14, 960) 0 [batch_normalization_83[0][0]] conv2d_83 (Conv2D) (None, 14, 14, 128) 122880 [activation_82[0][0]] batch_normalization_84 (BatchN (None, 14, 14, 128) 512 [conv2d_83[0][0]] ormalization) activation_83 (Activation) (None, 14, 14, 128) 0 [batch_normalization_84[0][0]] conv2d_84 (Conv2D) (None, 14, 14, 32) 36864 [activation_83[0][0]] concatenate_40 (Concatenate) (None, 14, 14, 992) 0 [concatenate_39[0][0], conv2d_84[0][0]] batch_normalization_85 (BatchN (None, 14, 14, 992) 3968 [concatenate_40[0][0]] ormalization) activation_84 (Activation) (None, 14, 14, 992) 0 [batch_normalization_85[0][0]] conv2d_85 (Conv2D) (None, 14, 14, 128) 126976 [activation_84[0][0]] batch_normalization_86 (BatchN (None, 14, 14, 128) 512 [conv2d_85[0][0]] ormalization) activation_85 (Activation) (None, 14, 14, 128) 0 [batch_normalization_86[0][0]] conv2d_86 (Conv2D) (None, 14, 14, 32) 36864 [activation_85[0][0]] concatenate_41 (Concatenate) (None, 14, 14, 1024 0 [concatenate_40[0][0], ) conv2d_86[0][0]] batch_normalization_87 (BatchN (None, 14, 14, 1024 4096 [concatenate_41[0][0]] ormalization) ) activation_86 (Activation) (None, 14, 14, 1024 0 [batch_normalization_87[0][0]] ) conv2d_87 (Conv2D) (None, 14, 14, 512) 524288 [activation_86[0][0]] average_pooling2d_2 (AveragePo (None, 7, 7, 512) 0 [conv2d_87[0][0]] oling2D) batch_normalization_88 (BatchN (None, 7, 7, 512) 2048 [average_pooling2d_2[0][0]] ormalization) activation_87 (Activation) (None, 7, 7, 512) 0 [batch_normalization_88[0][0]] conv2d_88 (Conv2D) (None, 7, 7, 128) 65536 [activation_87[0][0]] batch_normalization_89 (BatchN (None, 7, 7, 128) 512 [conv2d_88[0][0]] ormalization) activation_88 (Activation) (None, 7, 7, 128) 0 [batch_normalization_89[0][0]] conv2d_89 (Conv2D) (None, 7, 7, 32) 36864 [activation_88[0][0]] concatenate_42 (Concatenate) (None, 7, 7, 544) 0 [average_pooling2d_2[0][0], conv2d_89[0][0]] batch_normalization_90 (BatchN (None, 7, 7, 544) 2176 [concatenate_42[0][0]] ormalization) activation_89 (Activation) (None, 7, 7, 544) 0 [batch_normalization_90[0][0]] conv2d_90 (Conv2D) (None, 7, 7, 128) 69632 [activation_89[0][0]] batch_normalization_91 (BatchN (None, 7, 7, 128) 512 [conv2d_90[0][0]] ormalization) activation_90 (Activation) (None, 7, 7, 128) 0 [batch_normalization_91[0][0]] conv2d_91 (Conv2D) (None, 7, 7, 32) 36864 [activation_90[0][0]] concatenate_43 (Concatenate) (None, 7, 7, 576) 0 [concatenate_42[0][0], conv2d_91[0][0]] batch_normalization_92 (BatchN (None, 7, 7, 576) 2304 [concatenate_43[0][0]] ormalization) activation_91 (Activation) (None, 7, 7, 576) 0 [batch_normalization_92[0][0]] conv2d_92 (Conv2D) (None, 7, 7, 128) 73728 [activation_91[0][0]] batch_normalization_93 (BatchN (None, 7, 7, 128) 512 [conv2d_92[0][0]] ormalization) activation_92 (Activation) (None, 7, 7, 128) 0 [batch_normalization_93[0][0]] conv2d_93 (Conv2D) (None, 7, 7, 32) 36864 [activation_92[0][0]] concatenate_44 (Concatenate) (None, 7, 7, 608) 0 [concatenate_43[0][0], conv2d_93[0][0]] batch_normalization_94 (BatchN (None, 7, 7, 608) 2432 [concatenate_44[0][0]] ormalization) activation_93 (Activation) (None, 7, 7, 608) 0 [batch_normalization_94[0][0]] conv2d_94 (Conv2D) (None, 7, 7, 128) 77824 [activation_93[0][0]] batch_normalization_95 (BatchN (None, 7, 7, 128) 512 [conv2d_94[0][0]] ormalization) activation_94 (Activation) (None, 7, 7, 128) 0 [batch_normalization_95[0][0]] conv2d_95 (Conv2D) (None, 7, 7, 32) 36864 [activation_94[0][0]] concatenate_45 (Concatenate) (None, 7, 7, 640) 0 [concatenate_44[0][0], conv2d_95[0][0]] batch_normalization_96 (BatchN (None, 7, 7, 640) 2560 [concatenate_45[0][0]] ormalization) activation_95 (Activation) (None, 7, 7, 640) 0 [batch_normalization_96[0][0]] conv2d_96 (Conv2D) (None, 7, 7, 128) 81920 [activation_95[0][0]] batch_normalization_97 (BatchN (None, 7, 7, 128) 512 [conv2d_96[0][0]] ormalization) activation_96 (Activation) (None, 7, 7, 128) 0 [batch_normalization_97[0][0]] conv2d_97 (Conv2D) (None, 7, 7, 32) 36864 [activation_96[0][0]] concatenate_46 (Concatenate) (None, 7, 7, 672) 0 [concatenate_45[0][0], conv2d_97[0][0]] batch_normalization_98 (BatchN (None, 7, 7, 672) 2688 [concatenate_46[0][0]] ormalization) activation_97 (Activation) (None, 7, 7, 672) 0 [batch_normalization_98[0][0]] conv2d_98 (Conv2D) (None, 7, 7, 128) 86016 [activation_97[0][0]] batch_normalization_99 (BatchN (None, 7, 7, 128) 512 [conv2d_98[0][0]] ormalization) activation_98 (Activation) (None, 7, 7, 128) 0 [batch_normalization_99[0][0]] conv2d_99 (Conv2D) (None, 7, 7, 32) 36864 [activation_98[0][0]] concatenate_47 (Concatenate) (None, 7, 7, 704) 0 [concatenate_46[0][0], conv2d_99[0][0]] batch_normalization_100 (Batch (None, 7, 7, 704) 2816 [concatenate_47[0][0]] Normalization) activation_99 (Activation) (None, 7, 7, 704) 0 [batch_normalization_100[0][0]]conv2d_100 (Conv2D) (None, 7, 7, 128) 90112 [activation_99[0][0]] batch_normalization_101 (Batch (None, 7, 7, 128) 512 [conv2d_100[0][0]] Normalization) activation_100 (Activation) (None, 7, 7, 128) 0 [batch_normalization_101[0][0]]conv2d_101 (Conv2D) (None, 7, 7, 32) 36864 [activation_100[0][0]] concatenate_48 (Concatenate) (None, 7, 7, 736) 0 [concatenate_47[0][0], conv2d_101[0][0]] batch_normalization_102 (Batch (None, 7, 7, 736) 2944 [concatenate_48[0][0]] Normalization) activation_101 (Activation) (None, 7, 7, 736) 0 [batch_normalization_102[0][0]]conv2d_102 (Conv2D) (None, 7, 7, 128) 94208 [activation_101[0][0]] batch_normalization_103 (Batch (None, 7, 7, 128) 512 [conv2d_102[0][0]] Normalization) activation_102 (Activation) (None, 7, 7, 128) 0 [batch_normalization_103[0][0]]conv2d_103 (Conv2D) (None, 7, 7, 32) 36864 [activation_102[0][0]] concatenate_49 (Concatenate) (None, 7, 7, 768) 0 [concatenate_48[0][0], conv2d_103[0][0]] batch_normalization_104 (Batch (None, 7, 7, 768) 3072 [concatenate_49[0][0]] Normalization) activation_103 (Activation) (None, 7, 7, 768) 0 [batch_normalization_104[0][0]]conv2d_104 (Conv2D) (None, 7, 7, 128) 98304 [activation_103[0][0]] batch_normalization_105 (Batch (None, 7, 7, 128) 512 [conv2d_104[0][0]] Normalization) activation_104 (Activation) (None, 7, 7, 128) 0 [batch_normalization_105[0][0]]conv2d_105 (Conv2D) (None, 7, 7, 32) 36864 [activation_104[0][0]] concatenate_50 (Concatenate) (None, 7, 7, 800) 0 [concatenate_49[0][0], conv2d_105[0][0]] batch_normalization_106 (Batch (None, 7, 7, 800) 3200 [concatenate_50[0][0]] Normalization) activation_105 (Activation) (None, 7, 7, 800) 0 [batch_normalization_106[0][0]]conv2d_106 (Conv2D) (None, 7, 7, 128) 102400 [activation_105[0][0]] batch_normalization_107 (Batch (None, 7, 7, 128) 512 [conv2d_106[0][0]] Normalization) activation_106 (Activation) (None, 7, 7, 128) 0 [batch_normalization_107[0][0]]conv2d_107 (Conv2D) (None, 7, 7, 32) 36864 [activation_106[0][0]] concatenate_51 (Concatenate) (None, 7, 7, 832) 0 [concatenate_50[0][0], conv2d_107[0][0]] batch_normalization_108 (Batch (None, 7, 7, 832) 3328 [concatenate_51[0][0]] Normalization) activation_107 (Activation) (None, 7, 7, 832) 0 [batch_normalization_108[0][0]]conv2d_108 (Conv2D) (None, 7, 7, 128) 106496 [activation_107[0][0]] batch_normalization_109 (Batch (None, 7, 7, 128) 512 [conv2d_108[0][0]] Normalization) activation_108 (Activation) (None, 7, 7, 128) 0 [batch_normalization_109[0][0]]conv2d_109 (Conv2D) (None, 7, 7, 32) 36864 [activation_108[0][0]] concatenate_52 (Concatenate) (None, 7, 7, 864) 0 [concatenate_51[0][0], conv2d_109[0][0]] batch_normalization_110 (Batch (None, 7, 7, 864) 3456 [concatenate_52[0][0]] Normalization) activation_109 (Activation) (None, 7, 7, 864) 0 [batch_normalization_110[0][0]]conv2d_110 (Conv2D) (None, 7, 7, 128) 110592 [activation_109[0][0]] batch_normalization_111 (Batch (None, 7, 7, 128) 512 [conv2d_110[0][0]] Normalization) activation_110 (Activation) (None, 7, 7, 128) 0 [batch_normalization_111[0][0]]conv2d_111 (Conv2D) (None, 7, 7, 32) 36864 [activation_110[0][0]] concatenate_53 (Concatenate) (None, 7, 7, 896) 0 [concatenate_52[0][0], conv2d_111[0][0]] batch_normalization_112 (Batch (None, 7, 7, 896) 3584 [concatenate_53[0][0]] Normalization) activation_111 (Activation) (None, 7, 7, 896) 0 [batch_normalization_112[0][0]]conv2d_112 (Conv2D) (None, 7, 7, 128) 114688 [activation_111[0][0]] batch_normalization_113 (Batch (None, 7, 7, 128) 512 [conv2d_112[0][0]] Normalization) activation_112 (Activation) (None, 7, 7, 128) 0 [batch_normalization_113[0][0]]conv2d_113 (Conv2D) (None, 7, 7, 32) 36864 [activation_112[0][0]] concatenate_54 (Concatenate) (None, 7, 7, 928) 0 [concatenate_53[0][0], conv2d_113[0][0]] batch_normalization_114 (Batch (None, 7, 7, 928) 3712 [concatenate_54[0][0]] Normalization) activation_113 (Activation) (None, 7, 7, 928) 0 [batch_normalization_114[0][0]]conv2d_114 (Conv2D) (None, 7, 7, 128) 118784 [activation_113[0][0]] batch_normalization_115 (Batch (None, 7, 7, 128) 512 [conv2d_114[0][0]] Normalization) activation_114 (Activation) (None, 7, 7, 128) 0 [batch_normalization_115[0][0]]conv2d_115 (Conv2D) (None, 7, 7, 32) 36864 [activation_114[0][0]] concatenate_55 (Concatenate) (None, 7, 7, 960) 0 [concatenate_54[0][0], conv2d_115[0][0]] batch_normalization_116 (Batch (None, 7, 7, 960) 3840 [concatenate_55[0][0]] Normalization) activation_115 (Activation) (None, 7, 7, 960) 0 [batch_normalization_116[0][0]]conv2d_116 (Conv2D) (None, 7, 7, 128) 122880 [activation_115[0][0]] batch_normalization_117 (Batch (None, 7, 7, 128) 512 [conv2d_116[0][0]] Normalization) activation_116 (Activation) (None, 7, 7, 128) 0 [batch_normalization_117[0][0]]conv2d_117 (Conv2D) (None, 7, 7, 32) 36864 [activation_116[0][0]] concatenate_56 (Concatenate) (None, 7, 7, 992) 0 [concatenate_55[0][0], conv2d_117[0][0]] batch_normalization_118 (Batch (None, 7, 7, 992) 3968 [concatenate_56[0][0]] Normalization) activation_117 (Activation) (None, 7, 7, 992) 0 [batch_normalization_118[0][0]]conv2d_118 (Conv2D) (None, 7, 7, 128) 126976 [activation_117[0][0]] batch_normalization_119 (Batch (None, 7, 7, 128) 512 [conv2d_118[0][0]] Normalization) activation_118 (Activation) (None, 7, 7, 128) 0 [batch_normalization_119[0][0]]conv2d_119 (Conv2D) (None, 7, 7, 32) 36864 [activation_118[0][0]] concatenate_57 (Concatenate) (None, 7, 7, 1024) 0 [concatenate_56[0][0], conv2d_119[0][0]] global_average_pooling2d (Glob (None, 1024) 0 [concatenate_57[0][0]] alAveragePooling2D) dense (Dense) (None, 16) 16400 [global_average_pooling2d[0][0]] activation_119 (Activation) (None, 16) 0 [dense[0][0]] dense_1 (Dense) (None, 1024) 17408 [activation_119[0][0]] activation_120 (Activation) (None, 1024) 0 [dense_1[0][0]] reshape (Reshape) (None, 1, 1, 1024) 0 [activation_120[0][0]] tf.math.multiply (TFOpLambda) (None, 7, 7, 1024) 0 [concatenate_57[0][0], reshape[0][0]] batch_normalization_120 (Batch (None, 7, 7, 1024) 4096 [tf.math.multiply[0][0]] Normalization) activation_121 (Activation) (None, 7, 7, 1024) 0 [batch_normalization_120[0][0]]global_average_pooling2d_1 (Gl (None, 1024) 0 [activation_121[0][0]] obalAveragePooling2D) dense_2 (Dense) (None, 1000) 1025000 [global_average_pooling2d_1[0][0]]
Total params: 8,096,312
Trainable params: 8,012,664
Non-trainable params: 83,648
__________________________________________________________________________________________________3.10.编译模型
#设置初始学习率
initial_learning_rate 1e-4
opt tf.keras.optimizers.Adam(learning_rateinitial_learning_rate)
model.compile(optimizeropt,losssparse_categorical_crossentropy,metrics[accuracy])
3.11.训练模型
训练模型
epochs 20
history model.fit(train_ds,validation_dataval_ds,epochsepochs
)训练记录如下
Epoch 1/20
54/54 [] - ETA: 0s - loss: 4.1244 - accuracy: 0.5560
Epoch 1: val_accuracy improved from -inf to 0.07818, saving model to best_model.h5
54/54 [] - 25s 236ms/step - loss: 4.1244 - accuracy: 0.5560 - val_loss: 8.7794 - val_accuracy: 0.0782
Epoch 2/20
54/54 [] - ETA: 0s - loss: 1.3264 - accuracy: 0.6972
Epoch 2: val_accuracy improved from 0.07818 to 0.63477, saving model to best_model.h5
54/54 [] - 12s 214ms/step - loss: 1.3264 - accuracy: 0.6972 - val_loss: 4.7183 - val_accuracy: 0.6348
Epoch 3/20
54/54 [] - ETA: 0s - loss: 0.6500 - accuracy: 0.7515
Epoch 3: val_accuracy did not improve from 0.63477
54/54 [] - 11s 210ms/step - loss: 0.6500 - accuracy: 0.7515 - val_loss: 3.0509 - val_accuracy: 0.5828
Epoch 4/20
54/54 [] - ETA: 0s - loss: 0.4991 - accuracy: 0.8028
Epoch 4: val_accuracy improved from 0.63477 to 0.65811, saving model to best_model.h5
54/54 [] - 12s 217ms/step - loss: 0.4991 - accuracy: 0.8028 - val_loss: 1.5565 - val_accuracy: 0.6581
Epoch 5/20
54/54 [] - ETA: 0s - loss: 0.3937 - accuracy: 0.8448
Epoch 5: val_accuracy did not improve from 0.65811
54/54 [] - 11s 211ms/step - loss: 0.3937 - accuracy: 0.8448 - val_loss: 1.0164 - val_accuracy: 0.6540
Epoch 6/20
54/54 [] - ETA: 0s - loss: 0.3300 - accuracy: 0.8716
Epoch 6: val_accuracy did not improve from 0.65811
54/54 [] - 11s 210ms/step - loss: 0.3300 - accuracy: 0.8716 - val_loss: 0.8846 - val_accuracy: 0.5799
Epoch 7/20
54/54 [] - ETA: 0s - loss: 0.2872 - accuracy: 0.8839
Epoch 7: val_accuracy improved from 0.65811 to 0.66219, saving model to best_model.h5
54/54 [] - 12s 221ms/step - loss: 0.2872 - accuracy: 0.8839 - val_loss: 0.8020 - val_accuracy: 0.6622
Epoch 8/20
54/54 [] - ETA: 0s - loss: 0.2339 - accuracy: 0.9090
Epoch 8: val_accuracy improved from 0.66219 to 0.81855, saving model to best_model.h5
54/54 [] - 12s 220ms/step - loss: 0.2339 - accuracy: 0.9090 - val_loss: 0.4418 - val_accuracy: 0.8186
Epoch 9/20
54/54 [] - ETA: 0s - loss: 0.2030 - accuracy: 0.9247
Epoch 9: val_accuracy improved from 0.81855 to 0.82555, saving model to best_model.h5
54/54 [] - 12s 222ms/step - loss: 0.2030 - accuracy: 0.9247 - val_loss: 0.4440 - val_accuracy: 0.8256
Epoch 10/20
54/54 [] - ETA: 0s - loss: 0.1891 - accuracy: 0.9259
Epoch 10: val_accuracy did not improve from 0.82555
54/54 [] - 12s 215ms/step - loss: 0.1891 - accuracy: 0.9259 - val_loss: 1.0064 - val_accuracy: 0.7421
Epoch 11/20
54/54 [] - ETA: 0s - loss: 0.1565 - accuracy: 0.9440
Epoch 11: val_accuracy did not improve from 0.82555
54/54 [] - 11s 211ms/step - loss: 0.1565 - accuracy: 0.9440 - val_loss: 0.5438 - val_accuracy: 0.8121
Epoch 12/20
54/54 [] - ETA: 0s - loss: 0.1580 - accuracy: 0.9422
Epoch 12: val_accuracy did not improve from 0.82555
54/54 [] - 11s 210ms/step - loss: 0.1580 - accuracy: 0.9422 - val_loss: 1.1685 - val_accuracy: 0.7100
Epoch 13/20
54/54 [] - ETA: 0s - loss: 0.1100 - accuracy: 0.9650
Epoch 13: val_accuracy improved from 0.82555 to 0.89148, saving model to best_model.h5
54/54 [] - 12s 218ms/step - loss: 0.1100 - accuracy: 0.9650 - val_loss: 0.3890 - val_accuracy: 0.8915
Epoch 14/20
54/54 [] - ETA: 0s - loss: 0.0560 - accuracy: 0.9848
Epoch 14: val_accuracy did not improve from 0.89148
54/54 [] - 11s 213ms/step - loss: 0.0560 - accuracy: 0.9848 - val_loss: 0.4445 - val_accuracy: 0.8676
Epoch 15/20
54/54 [] - ETA: 0s - loss: 0.0700 - accuracy: 0.9772
Epoch 15: val_accuracy did not improve from 0.89148
54/54 [] - 11s 212ms/step - loss: 0.0700 - accuracy: 0.9772 - val_loss: 0.4124 - val_accuracy: 0.8839
Epoch 16/20
54/54 [] - ETA: 0s - loss: 0.0994 - accuracy: 0.9638
Epoch 16: val_accuracy did not improve from 0.89148
54/54 [] - 11s 211ms/step - loss: 0.0994 - accuracy: 0.9638 - val_loss: 0.5568 - val_accuracy: 0.8261
Epoch 17/20
54/54 [] - ETA: 0s - loss: 0.0689 - accuracy: 0.9743
Epoch 17: val_accuracy did not improve from 0.89148
54/54 [] - 11s 214ms/step - loss: 0.0689 - accuracy: 0.9743 - val_loss: 0.5721 - val_accuracy: 0.8436
Epoch 18/20
54/54 [] - ETA: 0s - loss: 0.0355 - accuracy: 0.9924
Epoch 18: val_accuracy improved from 0.89148 to 0.91832, saving model to best_model.h5
54/54 [] - 12s 219ms/step - loss: 0.0355 - accuracy: 0.9924 - val_loss: 0.3478 - val_accuracy: 0.9183
Epoch 19/20
54/54 [] - ETA: 0s - loss: 0.0100 - accuracy: 0.9994
Epoch 19: val_accuracy improved from 0.91832 to 0.94516, saving model to best_model.h5
54/54 [] - 12s 217ms/step - loss: 0.0100 - accuracy: 0.9994 - val_loss: 0.1933 - val_accuracy: 0.9452
Epoch 20/20
54/54 [] - ETA: 0s - loss: 0.0208 - accuracy: 0.9947
Epoch 20: val_accuracy did not improve from 0.94516
54/54 [] - 11s 211ms/step - loss: 0.0208 - accuracy: 0.9947 - val_loss: 0.6098 - val_accuracy: 0.8460
3.12.模型评估
模型评估
acc history.history[accuracy]
val_acc history.history[val_accuracy]
loss history.history[loss]
val_loss history.history[val_loss]
epochs_range range(len(loss))
plt.figure(figsize(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, labelTraining Accuracy)
plt.plot(epochs_range, val_acc, labelValidation Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, labelTraining Loss)
plt.plot(epochs_range, val_loss, labelValidation Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show() 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, fontsize10)
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)], fontsize10)plt.axis(off)
plt.show()4 知识点详解
4.1ResNeXt50详解
论文Aggregated Residual Transformations for Deep Neural Networks.pdf
ResNeXt是由何凯明团队在2017年CVPR会议上提出来的新型图像分类网络。ResNeXt是ResNet的升级版在ResNet的基础上引入了cardinality的概念类似于ResNetResNeXt也有ResNeXt-50ResNeXt-101的版本。
这篇文章介绍了一种用于图像分类的简单而有效的网络架构称为Aggregated Residual Transformations for Deep Neural Networks。该网络采用了VGG/ResNets的策略通过重复层来增加深度和宽度并利用分裂-变换-合并策略以易于扩展的方式进行转换。文章还提出了一个新的维度——“基数”它是指转换集合的大小可以在保持复杂性不变的情况下提高分类准确性。作者在ImageNet-1K数据集上进行了实证研究证明了这种方法的有效性。
下图是ResNet左与ResNeXt右block的差异。在ResNet中输入的具有256个通道的特征经过1×1卷积压缩4倍到64个通道之后3×3的卷积核用于处理特征经1×1卷积扩大通道数与原特征残差连接后输出。 ResNeXt也是相同的处理策略但在ResNeXt中输入的具有256个通道的特征被分为32个组每组被压缩64倍到4个通道后进行处理。32个组相加后与原特征残差连接后输出。这里cardinatity指的是一个block中所具有的相同分支的数目。下图为等效模型。 下图为ResNet50和ResNeXt50(32x4d)的结构对比图。
分组卷积 ResNeXt中采用的分组卷机简单来说就是将特征图分为不同的组再对每组特征图分别进行卷积这个操作可以有效的降低计算量。 在分组卷积中每个卷积核只处理部分通道比如下图中红色卷积核只处理红色的通道绿色卷积核只处理绿色通道黄色卷积核只处理黄色通道。此时每个卷积核有2个通道每个卷积核生成一张特征图。
4.2 ResNeXt50对比ResNet50V2、DenseNet
4.2.1 网络结构 ResNet-50v2是ResNet系列中的一个经典模型由50层卷积层、批量归一化、激活函数和池化层构成。它引入了一种全新的残差块结构即bottleneck结构使得网络参数量大幅度降低同时精度也有所提升。 DenseNet是一种全新的网络结构其特点是不同于传统的网络结构DenseNet中每一层的输出不仅和前一层的输出有关还和之前所有层的输出有关这种密集连接的结构可以有效地缓解梯度消失和参数稀疏问题提高了模型的泛化能力和精度。 AggResNetResNeXt50则是基于ResNet结构改进而来的新型深度神经网络结构其特点是采用了聚合残差结构和局部连接结构同时引入了Random Erasing和Mixup等数据增强和正则化方法可以进一步提高网络的精度和鲁棒性。
4.2.2 精度和计算量 在ImageNet数据集上ResNet-50v2和DenseNet在Top-1和Top-5指标上都取得了优异的性能。与之相比AggResNet在相同的深度下具有更高的精度并且在参数量和计算量上都显著降低。同时在较深的网络结构下AggResNet的优势更加明显可以达到更高的精度而ResNet-50v2和DenseNet则难以继续提高精度。
4.2.3 适用范围 ResNet-50v2适用于各种图像分类任务但在一些特定的视觉任务如目标检测、语义分割等方面的表现可能不如其他模型。 DenseNet则在各种任务中都具有优异的性能尤其在目标检测和语义分割等像素级别的任务中表现突出。 AggResNet则不仅适用于图像分类任务同时也可以应用于目标检测、语义分割和行人重识别等视觉任务中并且在这些任务中具有优异的性能。
4 总结 ResNet-50v2、DenseNet和AggResNet都是非常优秀的深度神经网络结构它们在不同的任务和场景中都具有不同的优势和适用性。
