pytorch深度学习入门与实战 孙玉林 第六章卷积神经网络识别Fashion-MNIST

import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import copy
import time
import torch
import torch.nn as nn
from torch.optim import SGD,Adam
import torch.utils.data as Data
from torchvision import transforms
from torchvision.datasets import FashionMNIST
#使用FashionMNIST数据，准备训练数据集
train_data = FashionMNIST(root="../resourses/datasets/",train=True,transform=transforms.ToTensor(),download=False)
#定义一个数据加载器
train_loader = Data.DataLoader(dataset=train_data,batch_size=64,shuffle=False,num_workers=0)
#计算train_loader有多少个batch
print("train_loader的batch数量为：",len(train_loader))
#获取一个batch的数据
for step,(b_x,b_y) in enumerate(train_loader):
    if step > 0:
        break
#可视化一个batch的图像
print("b_x:",b_x.shape) #torch.Size([64, 1, 28, 28])
batch_x = b_x.squeeze().numpy()
batch_y = b_y.numpy()
print("batch_x:",batch_x.shape,"batch_y:",batch_y.shape) #batch_x: (64, 28, 28) batch_y: (64,)
class_label = train_data.classes
class_label[0] = "T-shirt"
plt.figure(figsize=(12,5))
for ii in np.arange(len(batch_y)):
    plt.subplot(4,16,ii+1) # 4行16列
    plt.imshow(batch_x[ii,:,:],cmap=plt.cm.gray) #batch_x是三维的，顾要加两个分号
    plt.title(class_label[batch_y[ii]],size=9)
    plt.axis("off")
    plt.subplots_adjust(wspace=0.5) # subplot的间距
plt.show()
#对测试集进行处理
test_data = FashionMNIST(root="../resourses/datasets/",train=False,download=False)
#为数据添加一个通道维度，并且取值范围缩放到0-1之间
test_data_x = test_data.data.type(torch.FloatTensor) / 255.0
test_data_x = torch.unsqueeze(test_data_x,dim=1)
test_data_y = test_data.targets #测试集的标签
print("test_data_x.shape",test_data_x.shape) #test_data_x.shape torch.Size([10000, 1, 28, 28])
print("test_data_y.shape",test_data_y.shape) #test_data_y.shape torch.Size([10000])
#搭建卷积神经网络
class MyConvNet(nn.Module):
    def __init__(self):
        super(MyConvNet, self).__init__()
        #定义第一个卷积层
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1,out_channels=16,kernel_size=3,stride=1,padding=1),#卷积后：(1*28*28)->(16*28*28)
            nn.ReLU(),
            nn.AvgPool2d(kernel_size=2,stride=2) #池化后：(16*28*28)->(16*14*14)
        )
        #定义第二个卷积层
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=16,out_channels=32,kernel_size=3,stride=1,padding=0),#卷积后：(16*14*14)->(32*12*12)
            nn.ReLU(),
            nn.AvgPool2d(kernel_size=2,stride=2) #池化后：(32*12*12)->(32*6*6)
        )
        self.classifier = nn.Sequential(
            nn.Linear(32*6*6,256), #长方体变平面
            nn.ReLU(),
            nn.Linear(256,128),
            nn.ReLU(),
            nn.Linear(128,10)
        )
    #定义网络的前向传播路径
    def forward(self,x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0),-1) #展平多维的卷积图层
        output = self.classifier(x)
        return output
#输出网络结构
myconvnet = MyConvNet()
print(myconvnet)
#定义网络的训练过程函数
def train_model(model,traindataloader,train_rate,criterion,optimizer,num_epochs=25):
    #train_rate：训练集batchsize百分比
    #计算训练使用的batch数量
    batch_num = len(traindataloader)
    train_batch_num = round(batch_num * train_rate) #前train_rate的batch进行训练
    #复制模型的参数
    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    train_loss_all = []
    train_acc_all = []
    val_loss_all = []
    val_acc_all = []
    since = time.time()
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch,num_epochs-1))
        print('-' * 10)
        #每个epoch有两个训练阶段
        train_loss = 0.0
        train_corrects = 0
        train_num = 0
        val_loss = 0.0
        val_corrects = 0
        val_num = 0
        for step,(b_x,b_y) in enumerate(traindataloader):
            if step < train_batch_num: #前train_rate的batch进行训练
                model.train() #设置模型为训练模式
                output = model(b_x)
                pre_lab = torch.argmax(output,1)
                loss = criterion(output,b_y) #loss是一个batch的loss，每个样本的loss均值，
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                train_loss += loss.item() * b_x.size(0) #此处的b_x.size(0)=batch_size。此处相当于一个batch的loss
                train_corrects += torch.sum(pre_lab == b_y.data)
                train_num += b_x.size(0)
            else:
                model.eval() #设置模型为评估模式
                output = model(b_x)
                pre_lab = torch.argmax(output,1)
                loss = criterion(output,b_y)
                val_loss += loss.item() * b_x.size(0)
                val_corrects += torch.sum(pre_lab == b_y.data)
                val_num += b_x.size(0)
        #计算一个epoch在训练集和验证集上的损失和精度
        train_loss_all.append(train_loss / train_num)
        train_acc_all.append(train_corrects.double().item() / train_num)
        val_loss_all.append(val_loss / val_num)
        val_acc_all.append(val_corrects.double().item() / val_num)
        print('{} Train Loss: {:.4f} Train Acc: {:.4f}'.format(epoch,train_loss_all[-1],train_acc_all[-1])) #此处-1没搞明白
        print('{} Val Loss: {:.4f} Val Acc: {:.4f}'.format(epoch,val_loss_all[-1],val_acc_all[-1]))
        #拷贝模型最高精度下的参数
        if val_acc_all[-1] > best_acc:
            best_acc = val_acc_all[-1]
            best_model_wts = copy.deepcopy(model.state_dict())
        time_use = time.time() - since
        print("Train and val complete in {:.0f}m {:.0f}s".format(time_use // 60,time_use % 60))
    #使用最好模型的参数
    model.load_state_dict(best_model_wts)
    #组成数据表格train_process输出
    train_process = pd.DataFrame(data={"epoch":range(num_epochs),
                                       "train_loss_all":train_loss_all,
                                       "val_loss_all":val_loss_all,
                                       "train_acc_all":train_acc_all,
                                       "val_acc_all":val_acc_all})
    return model,train_process
#对模型进行训练
optimizer = Adam(myconvnet.parameters(),lr=0.0003)
criterion = nn.CrossEntropyLoss()
myconvnet,train_process = train_model(myconvnet,train_loader,0.8,criterion,optimizer,num_epochs=25)
#可视化模型训练过程中
plt.figure(figsize=(12,4))
plt.subplot(1,2,1)
plt.plot(train_process.epoch,train_process.train_loss_all,"ro-",label="Train loss")
plt.plot(train_process.epoch,train_process.val_loss_all,"bs-",label="Val loss")
plt.legend()
plt.xlabel("epoch")
plt.ylabel("Loss")
plt.subplot(1,2,2)
plt.plot(train_process.epoch,train_process.train_acc_all,"ro-",label="Train acc")
plt.plot(train_process.epoch,train_process.val_acc_all,"bs-",label="Val acc")
plt.xlabel("epoch")
plt.ylabel("acc")
plt.legend()
plt.show()
#对测试集进行预测，并可视化预测结果。计算模型的泛化能力
myconvnet.eval()
output = myconvnet(test_data_x)
pre_lab = torch.argmax(output,1)
acc = accuracy_score(test_data_y,pre_lab)
print("在测试集上的预测精度为：",acc)
#计算混淆矩阵并可视化
conf_mat = confusion_matrix(test_data_y,pre_lab)
df_cm = pd.DataFrame(conf_mat,index=class_label,columns=class_label)
heatmap = sns.heatmap(df_cm,annot=True,fmt="d",cmap="YlGnBu")
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(),rotation=0,ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(),rotation=45,ha='right')
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()

空洞卷积部分：

#空洞卷积部分
class MyConvdilaNet(nn.Module):
    def __init__(self):
        super(MyConvdilaNet, self).__init__()
        #定义第一个卷积层
        self.conv1 = nn.Sequential(
            #卷积后：(1*28*28)->(16*26*26)
            nn.Conv2d(in_channels=1,out_channels=16,kernel_size=3,stride=1,padding=1,dilation=2),
            nn.ReLU(),
            nn.AvgPool2d(2,2) #(16*26*26)->(16*13*13)
        )
        #定义第二个卷积层
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=16,out_channels=32,kernel_size=3,stride=1,padding=0,dilation=2),
            #卷积操作：(16*13*13)->(32*9*9)
            nn.ReLU(),
            nn.AvgPool2d(2,2) #(32*9*9)->(32*4*4)
        )
        self.classifier = nn.Sequential(
            nn.Linear(32*4*4,256),
            nn.ReLU(),
            nn.Linear(256,128),
            nn.ReLU(),
            nn.Linear(128,10)
        )
    #定义网络的前向传播路径
    def forward(self,x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0),-1) #展平多维的卷积图层
        output = self.classifier(x)
        return output
#输出网络结构
myconvdilanet = MyConvdilaNet()
print(myconvdilanet)
#对模型进行训练
optimizer = Adam(myconvdilanet.parameters(),lr=0.0003)
criterion = nn.CrossEntropyLoss()
myconvdilanet,train_process = train_model(myconvdilanet,train_loader,0.8,criterion,optimizer,num_epochs=25)
#可视化模型训练过程中的精度和损失函数
plt.figure(figsize=(12,4))
plt.subplot(1,2,1)
plt.plot(train_process.epoch,train_process.train_loss_all,"ro-",label="Train loss")
plt.plot(train_process.epoch,train_process.val_loss_all,"bs-",label="Val loss")
plt.legend()
plt.xlabel("epoch")
plt.ylabel("loss")
plt.subplot(1,2,2)
plt.plot(train_process.epoch,train_process.train_acc_all,"ro-",label="Train acc")
plt.plot(train_process.epoch,train_process.val_acc_all,"bs-",label="Val acc")
plt.xlabel("epoch")
plt.ylabel("acc")
plt.legend()
plt.show()
#对测试集进行预测，并可视化预测效果
myconvdilanet.eval()
output = myconvdilanet(test_data_x)
pre_lab = torch.argmax(output,1)
acc = accuracy_score(test_data_y,pre_lab)
print("在测试集上的预测精度为：",acc)
#计算混淆矩阵并可视化
conf_mat = confusion_matrix(test_data_y,pre_lab)
df_cm = pd.DataFrame(conf_mat,index=class_label,columns=class_label)
heatmap = sns.heatmap(df_cm,annot=True,fmt="d",cmap="YlGnBu")
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(),rotation=0,ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(),rotation=45,ha='right')
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()