• 🍨 本文為🔗365天深度學習訓練營 中的學習記錄博客
• 🍖 原作者：K同學啊

前言

• 如果說最經典的神經網絡，ResNet肯定是一個，從ResNet發布后，很多人做了修改，denseNet網絡無疑是最成功的一個，它采用密集型連接，將通道數連接在一起；
• 本文是基于上一篇復現DenseNet121模型，做一個乳腺癌圖像識別，效果還行，準確率0.9+;
• CNN經典網絡之“DenseNet”簡介，源碼研究與復現(pytorch)： https://blog.csdn.net/weixin_74085818/article/details/146102290?spm=1001.2014.3001.5501
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文章目錄

• • 1、導入數據
  • • 1、導入庫
    • 2、查看數據信息和導入數據
    • 3、展示數據
    • 4、數據導入
    • 5、數據劃分
    • 6、動態加載數據
  • 2、構建DenseNet121網絡
  • 3、模型訓練
  • • 1、構建訓練集
    • 2、構建測試集
    • 3、設置超參數
  • 4、模型訓練
  • 5、結果可視化
  • 6、模型評估

1、導入數據

1、導入庫

import torch  
import torch.nn as nn
import torchvision 
import numpy as np 
import os, PIL, pathlib 
from collections import OrderedDict
import re
from torch.hub import load_state_dict_from_url# 設置設備
device = "cuda" if torch.cuda.is_available() else "cpu"device 

'cuda'

2、查看數據信息和導入數據

data_dir = "./data/"data_dir = pathlib.Path(data_dir)# 類別數量
classnames = [str(path).split("\\")[0] for path in os.listdir(data_dir)]classnames

['0', '1']

3、展示數據

import matplotlib.pylab as plt  
from PIL import Image # 獲取文件名稱
data_path_name = "./data/0/"  # 不患病的
data_path_list = [f for f in os.listdir(data_path_name) if f.endswith(('jpg', 'png'))]# 創建畫板
fig, axes = plt.subplots(2, 8, figsize=(16, 6))for ax, img_file in zip(axes.flat, data_path_list):path_name = os.path.join(data_path_name, img_file)img = Image.open(path_name) # 打開# 顯示ax.imshow(img)ax.axis('off')plt.show()

?

?

4、數據導入

from torchvision import transforms, datasets # 數據統一格式
img_height = 224
img_width = 224 data_tranforms = transforms.Compose([transforms.Resize([img_height, img_width]),transforms.ToTensor(),transforms.Normalize(   # 歸一化mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225] )
])# 加載所有數據
total_data = datasets.ImageFolder(root="./data/", transform=data_tranforms)

5、數據劃分

# 大小 8 : 2
train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_size train_data, test_data = torch.utils.data.random_split(total_data, [train_size, test_size])

6、動態加載數據

batch_size = 64train_dl = torch.utils.data.DataLoader(train_data,batch_size=batch_size,shuffle=True
)test_dl = torch.utils.data.DataLoader(test_data,batch_size=batch_size,shuffle=False
)

# 查看數據維度
for data, labels in train_dl:print("data shape[N, C, H, W]: ", data.shape)print("labels: ", labels)break

data shape[N, C, H, W]:  torch.Size([64, 3, 224, 224])
labels:  tensor([1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1])

2、構建DenseNet121網絡

import torch.nn.functional as F# 實現DenseBlock中的部件：DenseLayer
'''  
1、BN + ReLU: 處理部分，首先進行歸一化，然后在用激活函數ReLU
2、Bottlenck Layer：稱為瓶頸層，這個層在yolov5中常用，但是yolov5中主要用于特征提取+維度降維，這里采用1 * 1卷積核 + 3 * 3的卷積核進行卷積操作，目的：減少輸入輸入特征維度
3、BN + ReLU：對 瓶頸層 數據進行歸一化，ReLU激活函數，歸一化可以確保梯度下降的時候較為平穩
4、3 * 3 生成新的特征圖
'''
class _DenseLayer(nn.Sequential):def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):'''  num_input_features: 輸入特征數，也就是通道數，在DenseNet中，每一層都會接受之前層的輸出作為輸入，故，這個數值通常會隨著網絡深度增加而增加growth_rate: 增長率，這個是 DenseNet的核心概念，決定了每一層為全局狀態貢獻的特征數量，他的用處主要在于決定了中間瓶頸層的輸出通道，需要結合代碼去研究bn_size: 瓶頸層中輸出通道大小，含義：在使用1 * 1卷積核去提取特征數時，目標通道需要擴展到growth_rate的多少倍倍數， bn_size * growth_rate(輸出維度)drop_rate: 使用Dropout的參數'''super(_DenseLayer, self).__init__()self.add_module("norm1", nn.BatchNorm2d(num_input_features))self.add_module("relu1", nn.ReLU(inplace=True))# 輸出維度： bn_size * growth_rate, 1 * 1卷積核，步伐為1，只起到特征提取作用self.add_module("conv1", nn.Conv2d(num_input_features, bn_size * growth_rate, stride=1, kernel_size=1, bias=False))self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate))self.add_module("relu2", nn.ReLU(inplace=True))# 輸出通道：growth_rate, 維度計算：不變self.add_module("conv2", nn.Conv2d(bn_size * growth_rate, growth_rate, stride=1, kernel_size=3, padding=1, bias=False))self.drop_rate = drop_ratedef forward(self, x):new_features = super(_DenseLayer, self).forward(x)  # 傳播if self.drop_rate > 0:new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)  # self.training 繼承nn.Sequential，是否訓練模式# 模型融合，即，特征通道融合，形成新的特征圖return torch.cat([x, new_features], dim=1)  # (N, C, H, W)  # 即 C1 + C2，通道上融合'''  
DenseNet網絡核心由DenseBlock模塊組成，DenseBlock網絡由DenseLayer組成，從 DenseLayer 可以看出，DenseBlock是密集連接，每一層的輸入不僅包含前一層的輸出，還包含網絡中所有之前層的輸出
'''
# 構建DenseBlock模塊, 通過上圖
class _DenseBlock(nn.Sequential):# num_layers 幾層DenseLayer模塊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)# Transition層，用于維度壓縮# 組成：一個卷積層 + 一個池化層
class _Transition(nn.Sequential):def __init__(self, num_init_features, num_out_features):super(_Transition, self).__init__()self.add_module("norm", nn.BatchNorm2d(num_init_features))self.add_module("relu", nn.ReLU(inplace=True))self.add_module("conv", nn.Conv2d(num_init_features, num_out_features, kernel_size=1, stride=1, bias=False))# 降維self.add_module("pool", nn.AvgPool2d(2, stride=2))# 搭建DenseNet網絡
class DenseNet(nn.Module):def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, compression_rate=0.5, drop_rate=0.5, num_classes=1000):'''  growth_rate、num_init_features、num_init_features、drop_rate 和denselayer一樣block_config : 參數在 DenseNet 架構中用于指定每個 Dense Block 中包含的層數, 如：DenseNet-121: block_config=(6, 12, 24, 16) 表示第一個 Dense Block 包含 6 層，第二個包含 12 層，第三個包含 24 層，第四個包含 16 層。DenseNet-169: block_config=(6, 12, 32, 32)DenseNet-201: block_config=(6, 12, 48, 32)DenseNet-264: block_config=(6, 12, 64, 48)compression_rate: 壓縮維度, DenseNet 中用于 Transition Layer（過渡層）的一個重要參數，它控制了從一個 Dense Block 到下一個 Dense Block 之間特征維度的壓縮程度'''super(DenseNet, self).__init__()# 第一層卷積# OrderedDict，讓模型層有序排列self.features = nn.Sequential(OrderedDict([# 輸出維度：((w - k + 2 * p) / s) + 1("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(3, stride=2, padding=1))  # 降維]))# 搭建DenseBlock層num_features = num_init_features# num_layers: 層數for i, num_layers in enumerate(block_config):block = _DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)# nn.Module 中features封裝了nn.Sequentialself.features.add_module("denseblock%d" % (i + 1), block)'''  # 這個計算反映了 DenseNet 中的一個關鍵特性：每一層輸出的特征圖（即新增加的通道數）由 growth_rate 決定，# 并且這些新生成的特征圖會被傳遞給該 Dense Block 中的所有后續層以及下一個 Dense Block。'''num_features += num_layers * growth_rate  # 疊加，每一次疊加# 判斷是否需要使用Transition層if i != len(block_config) - 1:transition = _Transition(num_features, int(num_features*compression_rate)) # compression_rate 作用self.features.add_module("transition%d" % (i + 1), transition)num_features = int(num_features*compression_rate)  # 更新維度# 最后一層self.features.add_module("norm5", nn.BatchNorm2d(num_features))self.features.add_module("relu5", nn.ReLU(inplace=True))# 分類層         self.classifier = nn.Linear(num_features, num_classes)# params initialization         for m in self.modules():             if isinstance(m, nn.Conv2d):         '''如果當前模塊是一個二維卷積層 (nn.Conv2d)，那么它的權重 (m.weight) 將通過 Kaiming 正態分布 (kaiming_normal_) 進行初始化。這種初始化方式特別適合與ReLU激活函數一起使用，有助于緩解深度網絡中的梯度消失問題，促進有效的訓練。  '''       nn.init.kaiming_normal_(m.weight)             elif isinstance(m, nn.BatchNorm2d):      '''  對于二維批歸一化層 (nn.BatchNorm2d)，偏置項 (m.bias) 被初始化為0，而尺度因子 (m.weight) 被初始化為1。這意味著在沒有數據經過的情況下，批歸一化層不會對輸入進行額外的縮放或偏移，保持輸入不變。'''           nn.init.constant_(m.bias, 0)                 nn.init.constant_(m.weight, 1)             elif isinstance(m, nn.Linear):        '''  對于全連接層 (nn.Linear)，只對其偏置項 (m.bias) 進行了初始化，設置為0'''         nn.init.constant_(m.bias, 0)def forward(self, x):features = self.features(x)out = F.avg_pool2d(features, 7, stride=1).view(x.size(0), -1)out = self.classifier(out)return outmodel = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 12, 16))model.to(device)

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)))(transition3): _Transition((norm): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(conv): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)(pool): AvgPool2d(kernel_size=2, stride=2, padding=0))(denseblock4): _DenseBlock((denselayer1): _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))(denselayer2): _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))(denselayer3): _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))(denselayer4): _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))(denselayer5): _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))(denselayer6): _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))(denselayer7): _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))(denselayer8): _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))(denselayer9): _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))(denselayer10): _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))(denselayer11): _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))(denselayer12): _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))(denselayer13): _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))(denselayer14): _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))(denselayer15): _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))(denselayer16): _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)))(norm5): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu5): ReLU(inplace=True))(classifier): Linear(in_features=832, out_features=1000, bias=True)
)

3、模型訓練

1、構建訓練集

def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)batch_size = len(dataloader)train_acc, train_loss = 0, 0 for X, y in dataloader:X, y = X.to(device), y.to(device)# 訓練pred = model(X)loss = loss_fn(pred, y)# 梯度下降法optimizer.zero_grad()loss.backward()optimizer.step()# 記錄train_loss += loss.item()train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_acc /= sizetrain_loss /= batch_sizereturn train_acc, train_loss

2、構建測試集

def test(dataloader, model, loss_fn):size = len(dataloader.dataset)batch_size = len(dataloader)test_acc, test_loss = 0, 0 with torch.no_grad():for X, y in dataloader:X, y = X.to(device), y.to(device)pred = model(X)loss = loss_fn(pred, y)test_loss += loss.item()test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()test_acc /= sizetest_loss /= batch_sizereturn test_acc, test_loss

3、設置超參數

loss_fn = nn.CrossEntropyLoss()  # 損失函數     
learn_lr = 1e-4             # 超參數
optimizer = torch.optim.Adam(model.parameters(), lr=learn_lr)   # 優化器

4、模型訓練

通過實驗發現，還是設置20輪次附件最好

import copytrain_acc = []
train_loss = []
test_acc = []
test_loss = []epoches = 20best_acc = 0    # 設置一個最佳準確率，作為最佳模型的判別指標for i in range(epoches):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到 best_model     if epoch_test_acc > best_acc:         best_acc   = epoch_test_acc         best_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 獲取當前的學習率     lr = optimizer.state_dict()['param_groups'][0]['lr']# 輸出template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}')print(template.format(i + 1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))PATH = './best_model.pth'  # 保存的參數文件名 
torch.save(best_model.state_dict(), PATH)print("Done")

Epoch: 1, Train_acc:79.3%, Train_loss:1.948, Test_acc:84.6%, Test_loss:1.079
Epoch: 2, Train_acc:85.3%, Train_loss:0.395, Test_acc:85.2%, Test_loss:0.721
Epoch: 3, Train_acc:87.3%, Train_loss:0.318, Test_acc:86.5%, Test_loss:0.526
Epoch: 4, Train_acc:89.0%, Train_loss:0.277, Test_acc:86.6%, Test_loss:0.494
Epoch: 5, Train_acc:89.0%, Train_loss:0.266, Test_acc:87.9%, Test_loss:0.400
Epoch: 6, Train_acc:89.6%, Train_loss:0.252, Test_acc:84.6%, Test_loss:0.524
Epoch: 7, Train_acc:90.3%, Train_loss:0.239, Test_acc:85.5%, Test_loss:0.445
Epoch: 8, Train_acc:90.2%, Train_loss:0.235, Test_acc:87.6%, Test_loss:0.359
Epoch: 9, Train_acc:90.0%, Train_loss:0.235, Test_acc:89.3%, Test_loss:0.298
Epoch:10, Train_acc:91.0%, Train_loss:0.220, Test_acc:89.5%, Test_loss:0.307
Epoch:11, Train_acc:90.8%, Train_loss:0.222, Test_acc:88.3%, Test_loss:0.316
Epoch:12, Train_acc:91.4%, Train_loss:0.210, Test_acc:83.3%, Test_loss:0.516
Epoch:13, Train_acc:91.5%, Train_loss:0.208, Test_acc:91.3%, Test_loss:0.247
Epoch:14, Train_acc:91.5%, Train_loss:0.206, Test_acc:90.1%, Test_loss:0.269
Epoch:15, Train_acc:92.0%, Train_loss:0.199, Test_acc:91.1%, Test_loss:0.242
Epoch:16, Train_acc:92.1%, Train_loss:0.194, Test_acc:89.4%, Test_loss:0.285
Epoch:17, Train_acc:92.4%, Train_loss:0.193, Test_acc:91.0%, Test_loss:0.229
Epoch:18, Train_acc:92.4%, Train_loss:0.188, Test_acc:88.0%, Test_loss:0.317
Epoch:19, Train_acc:92.7%, Train_loss:0.182, Test_acc:89.2%, Test_loss:0.285
Epoch:20, Train_acc:92.6%, Train_loss:0.182, Test_acc:78.5%, Test_loss:0.728
Done

5、結果可視化

import matplotlib.pyplot as plt
#隱藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息epochs_range = range(epoches)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 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= Loss')
plt.show()

?

?

在20輪測試集準確率變化比較大，從跑的幾次實驗來看，這次是偶然事件，測試集損失率后面一直穩定在0.3附件，測試準確率一直在0.8、0.89、0.90附件徘徊

6、模型評估

# 將參數加載到model當中 
best_model.load_state_dict(torch.load(PATH, map_location=device)) 
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)print(epoch_test_acc, epoch_test_loss)

0.9134651249533756 0.24670581874393283