pytorch 卷积神经网络识别Fashion-MNIST

一、导包

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
import seaborn as sns
import copy
import time
import torch
import torch.nn as nn
from torch.optim import Adam
import torch.utils.data as Data
from torchvision import transforms
from torchvision.datasets import FashionMNIST

二、训练集图像数据准备

##图像数据准备

#使用FasnionMNIST数据，准备训练数据集
train_data = FashionMNIST(
    root = "./data/FashionMNIST",
    train = True,
    transform = transforms.ToTensor(),
    download = True
)
#定义一个数据加载器
train_loader = Data.DataLoader(
    dataset = train_data,
    batch_size = 64,
    shuffle = False,
    num_workers = 2,
)
#计算train_loader有多少个batch
print("train_loader的batch数量为：",len(train_loader))
# train_loader的batch数量为： 938

三、获取一个batch的图像，将其可视化

#获得一个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
batch_x = b_x.squeeze().numpy()
batch_y = b_y.numpy()
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)
    plt.imshow(batch_x[ii,:,:],cmap = plt.cm.gray)
    plt.title(class_label[batch_y[ii]],size = 9)
    plt.axis("off")
    plt.subplots_adjust(wspace = 0.05)

四、测试数据集处理

#对测试集进行处理
test_data = FashionMNIST(
    root = "./data/FashionMNIST",
    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
print("test_data_x.shape:",test_data_x.shape)
print("test_data_y.shape:",test_data_y.shape)
# test_data_x.shape: torch.Size([10000, 1, 28, 28])
# test_data_y.shape: torch.Size([10000])

五、卷积神经网络的搭建

##卷积神经网络搭建
class MyConvNet(nn.Module):
    def __init__(self):
        super(MyConvNet,self).__init__()
        ##定义一个卷积层
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels = 1, ##输入的feature map
                out_channels = 16, ##输出的feature map
                kernel_size = 3,  ##卷积核尺寸
                stride = 1,  ##卷积核步长
                padding = 1,  ##进行填充
            ),   ##卷积后
            nn.ReLU(),
            nn.AvgPool2d(
                kernel_size = 2,
                stride = 2,
            ),
        )
        ##定义第二个卷积层
        self.conv2 = nn.Sequential(
            nn.Conv2d(16,32,3,1,0),
            nn.ReLU(),
            nn.AvgPool2d(2,2)
        )
        self.classifier = nn.Sequential(
            nn.Linear(32*6*6,256),
            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)
        output = self.classifier(x)
        return output
##输出我们的网络结构
myconvnet = MyConvNet()
print(myconvnet)
# MyConvNet(
#   (conv1): Sequential(
#     (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (1): ReLU()
#     (2): AvgPool2d(kernel_size=2, stride=2, padding=0)
#   )
#   (conv2): Sequential(
#     (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))
#     (1): ReLU()
#     (2): AvgPool2d(kernel_size=2, stride=2, padding=0)
#   )
#   (classifier): Sequential(
#     (0): Linear(in_features=1152, out_features=256, bias=True)
#     (1): ReLU()
#     (2): Linear(in_features=256, out_features=128, bias=True)
#     (3): ReLU()
#     (4): Linear(in_features=128, out_features=10, bias=True)
#   )
# )

六、卷积神经网络预测与训练—定义网络的训练过程函数

##定义网络的训练过程函数
def train_model(model,traindataloader,train_rate,criterion,optimizer,num_epochs=25):
    #model:网络模型
    #trainloader:训练数据集，会切分为训练集和验证集
    #train_rate:训练集batchsize百分比
    #criterion:损失函数
    #optimizer:优化方法
    #num_epochs:训练的轮数
    ##计算训练使用的batch数量
    batch_num = len(traindataloader)
    train_batch_num = round(batch_num * train_rate)
    ##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:
                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)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                train_loss += loss.item() * b_x.size(0)
                train_corrects += torch.sum(pre_lab == b_y.data)
                train_num += b_x.size(0)
            else:
                model.eval() #3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().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]))
        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)
    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

七、对指定的模型和优化器进行训练

##对模型进行训练,train_model()函数
optimizer = torch.optim.Adam(myconvnet.parameters(),lr = 0.0003)
criterion = nn.CrossEntropyLoss() ##损失函数
myconvnet,train_process = train_model(
    myconvnet,train_loader,0.8,criterion,optimizer,num_epochs = 25
)
# Epoch 0/24
# ----------
# 0 Train Loss:0.7892  Train Acc: 0.7123
# 0 Val Loss:0.5722  Val Acc:0.7743
# Train and val complete in 0m 17s
# Epoch 1/24
# ----------
# 1 Train Loss:0.5273  Train Acc: 0.8047
# 1 Val Loss:0.4755  Val Acc:0.8224
# Train and val complete in 0m 35s
# ......
# Epoch 23/24
# ----------
# 23 Train Loss:0.1942  Train Acc: 0.9283
# 23 Val Loss:0.2933  Val Acc:0.8967
# Train and val complete in 7m 11s
# Epoch 24/24
# ----------
# 24 Train Loss:0.1883  Train Acc: 0.9308
# 24 Val Loss:0.2943  Val Acc:0.8974
# Train and val complete in 7m 29s

八、可视化损失函数训练过程

##可视化模型训练过程
plt.figure(figsize=(12,4))
##损失函数
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")
##精度
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()

九、测试集预测，并使用混淆矩阵热力图可视化

##对测试集进行预测，并可视化预测结果
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)
# 在测试集上的预测精度为： 0.8897

##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()