基于WIN10的64位系统演示

一、写在前面

（1）SqueezeNet

SqueezeNet是一种轻量级的深度神经网络架构，由Iandola等人在2016年提出。这种模型的最大特点是参数量极少，仅有510千个参数，而且模型大小只有5MB，比许多现有的深度学习模型小得多。尽管模型的参数量和模型大小极小，但其在ImageNet数据集上的精度却可以与AlexNet等更大的模型相媲美。

SqueezeNet的设计主要是通过使用“火焰模块（Fire Module）”来降低参数量。每个火焰模块由一个"squeeze"卷积层（使用1x1的卷积核）和一个"expand"卷积层（使用1x1和3x3的卷积核）组成。"squeeze"卷积层的作用是降低数据的维度，而"expand"卷积层则用于增加数据的宽度。这种设计方式显著减少了模型的参数数量，同时还保持了较好的性能。这种轻量级的设计使得SqueezeNet非常适合在资源受限的设备上运行，例如移动设备和嵌入式设备，在实际应用中具有很高的灵活性。

（2）SqueezeNet的预训练版本

PyTorch提供的ShuffleNet预训练版本主要有以下几种：

SqueezeNet 1.0：这是SqueezeNet的原始版本，结构比较简单，参数数量较少，模型大小约为5MB。

SqueezeNet 1.1：这是对SqueezeNet的改进版本，相比原始版本，在保持模型大小和精度几乎不变的情况下，计算量减少了50%。

这里我就测试原始尺寸的SqueezeNet 1.1。

二、SqueezeNet迁移学习代码实战

我们继续胸片的数据集：肺结核病人和健康人的胸片的识别。其中，肺结核病人700张，健康人900张，分别存入单独的文件夹中。

（a）导入包

import copy
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision import models
from torch.utils.data import DataLoader
from torch import optim, nn
from torch.optim import lr_scheduler
import os
import matplotlib.pyplot as plt
import warnings
import numpy as npwarnings.filterwarnings("ignore")
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False# 设置GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

（b）导入数据集

import torch
from torchvision import datasets, transforms
import os# 数据集路径
data_dir = "./cats_vs_dogs"# 图像的大小
img_height = 100
img_width = 100# 数据预处理
data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(img_height),transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.RandomRotation(0.2),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),'val': transforms.Compose([transforms.Resize((img_height, img_width)),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
}# 加载数据集
full_dataset = datasets.ImageFolder(data_dir)# 获取数据集的大小
full_size = len(full_dataset)
train_size = int(0.7 * full_size)  # 假设训练集占80%
val_size = full_size - train_size  # 验证集的大小# 随机分割数据集
torch.manual_seed(0)  # 设置随机种子以确保结果可重复
train_dataset, val_dataset = torch.utils.data.random_split(full_dataset, [train_size, val_size])# 将数据增强应用到训练集
train_dataset.dataset.transform = data_transforms['train']# 创建数据加载器
batch_size = 32
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=4)dataloaders = {'train': train_dataloader, 'val': val_dataloader}
dataset_sizes = {'train': len(train_dataset), 'val': len(val_dataset)}
class_names = full_dataset.classes

（c）导入SqueezeNet

# 定义SqueezeNet 1.1模型
model = models.squeezenet1_1(pretrained=True)
num_ftrs = model.classifier[1].in_channels
# 根据分类任务修改最后一层
model.classifier[1] = nn.Conv2d(num_ftrs, len(class_names), kernel_size=(1,1))
# 将模型转移到相应的设备
model = model.to(device)
# 打印模型摘要
print(model)

（d）编译模型

# 定义损失函数
criterion = nn.CrossEntropyLoss()# 定义优化器
optimizer = optim.Adam(model.parameters())# 定义学习率调度器
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)# 开始训练模型
num_epochs = 10
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0# 初始化记录器
train_loss_history = []
train_acc_history = []
val_loss_history = []
val_acc_history = []for epoch in range(num_epochs):print('Epoch {}/{}'.format(epoch, num_epochs - 1))print('-' * 10)# 每个epoch都有一个训练和验证阶段for phase in ['train', 'val']:if phase == 'train':model.train()  # Set model to training modeelse:model.eval()   # Set model to evaluate moderunning_loss = 0.0running_corrects = 0# 遍历数据for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)# 零参数梯度optimizer.zero_grad()# 前向with torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)# 只在训练模式下进行反向和优化if phase == 'train':loss.backward()optimizer.step()# 统计running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)epoch_loss = running_loss / dataset_sizes[phase]epoch_acc = (running_corrects.double() / dataset_sizes[phase]).item()# 记录每个epoch的loss和accuracyif phase == 'train':train_loss_history.append(epoch_loss)train_acc_history.append(epoch_acc)else:val_loss_history.append(epoch_loss)val_acc_history.append(epoch_acc)print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))# 深拷贝模型if phase == 'val' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())print()print('Best val Acc: {:4f}'.format(best_acc))

（e）Accuracy和Loss可视化

epoch = range(1, len(train_loss_history)+1)fig, ax = plt.subplots(1, 2, figsize=(10,4))
ax[0].plot(epoch, train_loss_history, label='Train loss')
ax[0].plot(epoch, val_loss_history, label='Validation loss')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('Loss')
ax[0].legend()ax[1].plot(epoch, train_acc_history, label='Train acc')
ax[1].plot(epoch, val_acc_history, label='Validation acc')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Accuracy')
ax[1].legend()#plt.savefig("loss-acc.pdf", dpi=300,format="pdf")

观察模型训练情况：

蓝色为训练集，橙色为验证集。可以只需要10次迭代，性能就无与伦比了。

（f）混淆矩阵可视化以及模型参数

from sklearn.metrics import classification_report, confusion_matrix
import math
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib.pyplot import imshow# 定义一个绘制混淆矩阵图的函数
def plot_cm(labels, predictions):# 生成混淆矩阵conf_numpy = confusion_matrix(labels, predictions)# 将矩阵转化为 DataFrameconf_df = pd.DataFrame(conf_numpy, index=class_names ,columns=class_names)  plt.figure(figsize=(8,7))sns.heatmap(conf_df, annot=True, fmt="d", cmap="BuPu")plt.title('Confusion matrix',fontsize=15)plt.ylabel('Actual value',fontsize=14)plt.xlabel('Predictive value',fontsize=14)def evaluate_model(model, dataloader, device):model.eval()   # 设置模型为评估模式true_labels = []pred_labels = []# 遍历数据for inputs, labels in dataloader:inputs = inputs.to(device)labels = labels.to(device)# 前向with torch.no_grad():outputs = model(inputs)_, preds = torch.max(outputs, 1)true_labels.extend(labels.cpu().numpy())pred_labels.extend(preds.cpu().numpy())return true_labels, pred_labels# 获取预测和真实标签
true_labels, pred_labels = evaluate_model(model, dataloaders['val'], device)# 计算混淆矩阵
cm_val = confusion_matrix(true_labels, pred_labels)
a_val = cm_val[0,0]
b_val = cm_val[0,1]
c_val = cm_val[1,0]
d_val = cm_val[1,1]# 计算各种性能指标
acc_val = (a_val+d_val)/(a_val+b_val+c_val+d_val)  # 准确率
error_rate_val = 1 - acc_val  # 错误率
sen_val = d_val/(d_val+c_val)  # 灵敏度
sep_val = a_val/(a_val+b_val)  # 特异度
precision_val = d_val/(b_val+d_val)  # 精确度
F1_val = (2*precision_val*sen_val)/(precision_val+sen_val)  # F1值
MCC_val = (d_val*a_val-b_val*c_val) / (np.sqrt((d_val+b_val)*(d_val+c_val)*(a_val+b_val)*(a_val+c_val)))  # 马修斯相关系数# 打印出性能指标
print("验证集的灵敏度为：", sen_val, "验证集的特异度为：", sep_val,"验证集的准确率为：", acc_val, "验证集的错误率为：", error_rate_val,"验证集的精确度为：", precision_val, "验证集的F1为：", F1_val,"验证集的MCC为：", MCC_val)# 绘制混淆矩阵
plot_cm(true_labels, pred_labels)# 获取预测和真实标签
train_true_labels, train_pred_labels = evaluate_model(model, dataloaders['train'], device)
# 计算混淆矩阵
cm_train = confusion_matrix(train_true_labels, train_pred_labels)  
a_train = cm_train[0,0]
b_train = cm_train[0,1]
c_train = cm_train[1,0]
d_train = cm_train[1,1]
acc_train = (a_train+d_train)/(a_train+b_train+c_train+d_train)
error_rate_train = 1 - acc_train
sen_train = d_train/(d_train+c_train)
sep_train = a_train/(a_train+b_train)
precision_train = d_train/(b_train+d_train)
F1_train = (2*precision_train*sen_train)/(precision_train+sen_train)
MCC_train = (d_train*a_train-b_train*c_train) / (math.sqrt((d_train+b_train)*(d_train+c_train)*(a_train+b_train)*(a_train+c_train))) 
print("训练集的灵敏度为：",sen_train, "训练集的特异度为：",sep_train,"训练集的准确率为：",acc_train, "训练集的错误率为：",error_rate_train,"训练集的精确度为：",precision_train, "训练集的F1为：",F1_train,"训练集的MCC为：",MCC_train)# 绘制混淆矩阵
plot_cm(train_true_labels, train_pred_labels)

效果非常不错：

（g）AUC曲线绘制

from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns
import pandas as pd
import mathdef plot_roc(name, labels, predictions, **kwargs):fp, tp, _ = metrics.roc_curve(labels, predictions)plt.plot(fp, tp, label=name, linewidth=2, **kwargs)plt.plot([0, 1], [0, 1], color='orange', linestyle='--')plt.xlabel('False positives rate')plt.ylabel('True positives rate')ax = plt.gca()ax.set_aspect('equal')# 确保模型处于评估模式
model.eval()train_ds = dataloaders['train']
val_ds = dataloaders['val']val_pre_auc   = []
val_label_auc = []for images, labels in val_ds:for image, label in zip(images, labels):      img_array = image.unsqueeze(0).to(device)  # 在第0维增加一个维度并将图像转移到适当的设备上prediction_auc = model(img_array)  # 使用模型进行预测val_pre_auc.append(prediction_auc.detach().cpu().numpy()[:,1])val_label_auc.append(label.item())  # 使用Tensor.item()获取Tensor的值
auc_score_val = metrics.roc_auc_score(val_label_auc, val_pre_auc)train_pre_auc   = []
train_label_auc = []for images, labels in train_ds:for image, label in zip(images, labels):img_array_train = image.unsqueeze(0).to(device) prediction_auc = model(img_array_train)train_pre_auc.append(prediction_auc.detach().cpu().numpy()[:,1])  # 输出概率而不是标签！train_label_auc.append(label.item())
auc_score_train = metrics.roc_auc_score(train_label_auc, train_pre_auc)plot_roc('validation AUC: {0:.4f}'.format(auc_score_val), val_label_auc , val_pre_auc , color="red", linestyle='--')
plot_roc('training AUC: {0:.4f}'.format(auc_score_train), train_label_auc, train_pre_auc, color="blue", linestyle='--')
plt.legend(loc='lower right')
#plt.savefig("roc.pdf", dpi=300,format="pdf")print("训练集的AUC值为：",auc_score_train, "验证集的AUC值为：",auc_score_val)

ROC曲线如下：