对多分类采用10折交叉验证评估实验结果

1 导入实验所需要的包

import torch
import torch.nn as nn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import random
from pandas import *
%matplotlib inline

2 加载数据

train_dataset = torchvision.datasets.MNIST(root='./Datasets/MNIST', train=True, transform=transforms.ToTensor(),download=True)
test_dataset = torchvision.datasets.MNIST(root='./Datasets/MNIST', train=False, transform = transforms.ToTensor(),download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

mnist_features = torch.cat((train_loader.dataset.data,test_loader.dataset.data),dim=0)
mnist_labels = torch.cat((train_loader.dataset.train_labels,test_loader.dataset.test_labels))
mnist_features = mnist_features.float()
mnist_labels = mnist_labels.long()

3 读取数据

def get_data_iter(X_train, y_train, X_valid, y_valid,batch_size):
    train_dataset = torch.utils.data.TensorDataset(X_train.cuda(),y_train.cuda())
    test_dataset = torch.utils.data.TensorDataset(X_valid.cuda(),y_valid.cuda())
    train_iter = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True)
    test_iter = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=False)
    return train_iter, test_iter

4 定义模型

#定义网络
class LinearNet(nn.Module):
    def __init__(self,num_inputs, num_outputs, num_hiddens):
        super(LinearNet,self).__init__()
        self.linear1 = nn.Linear(num_inputs,num_hiddens)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(num_hiddens,num_outputs)
    
    def forward(self,x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        y = self.relu(x)
        return y

5 定义训练模型

#模型训练
def train(train_iter,test_iter,if_reshape,num_epochs,num_inputs,net,loss):
    optimizer = torch.optim.SGD(net.parameters(),lr=0.001)
    train_ls, test_ls = [], []
    for epoch in range(num_epochs):
        ls, count = 0, 0
        if if_reshape ==False:
            for X,y in train_iter:
                l=loss(net(X),y.view(-1,1))
                optimizer.zero_grad()
                l.backward()
                optimizer.step()
                ls += l.item()
                count += y.shape[0]
            train_ls.append(ls/count)
            ls, count = 0, 0
            for X,y in test_iter:
                l=loss(net(X),y.view(-1,1))
                ls += l.item()
                count += y.shape[0]
        else:
            for X,y in train_iter:
                X = X.reshape(-1,num_inputs)
                l=loss(net(X),y).sum()
                optimizer.zero_grad()
                l.backward()
                optimizer.step()
                ls += l.item()
                count += y.shape[0]
            train_ls.append(ls/count)
            ls, count = 0, 0
            for X,y in test_iter:
                X = X.reshape(-1,num_inputs)
                l=loss(net(X),y).sum()
                ls += l.item()
                count += y.shape[0]
        test_ls.append(ls/count)
        if(epoch+1)%5==0:
            print('epoch: %d, train loss: %f, valid loss: %f'%(epoch+1,train_ls[-1],test_ls[-1]))
    return train_ls,test_ls

6 获取k折交叉验证某一折的训练集和验证集

def get_kfold_data(k, i, X, y):
    fold_size = X.shape[0]//k
    val_start = i * fold_size
    if i  != k - 1:
        val_end = (i + 1) * fold_size
        X_valid, y_valid = X[val_start:val_end],y[val_start:val_end]
        X_train = torch.cat((X[0:val_start],X[val_end:]),dim=0)
        y_train = torch.cat((y[0:val_start],y[val_end:]),dim=0)
    else:
        X_valid,y_valid = X[val_start:], y[val_start:]
        X_train = X[0:val_start]
        y_train = y[0:val_start]
    
    return X_train, y_train, X_valid, y_valid

7 K折交叉验证

def k_fold(k, X_train, y_train,if_reshape,num_epochs,num_inputs,net,loss):
    my_k_train_ls, my_k_valid_ls = [], []
    train_loss_sum, valid_loss_sum = 0, 0
    for i in range(k):
        print('第', i+1, '折验证结果')
        X_train, y_train, X_valid, y_valid = get_kfold_data(k, i, X_train, y_train)
        train_iter, valid_iter = gen_data_iter(X_train, y_train, X_valid, y_valid,batch_size=100)
        train_loss, val_loss = train(train_iter,valid_iter,if_reshape,num_epochs,num_inputs,net,loss)
        
        my_k_train_ls.append(train_loss)
        my_k_valid_ls.append(val_loss)
        train_loss_sum += train_loss[-1]
        valid_loss_sum += val_loss[-1]
    
    print("最终平均k折交叉验证结果")
    
    print(f'average train loss: {train_loss_sum/k}')
    print(f'average valid loss: {valid_loss_sum/k}')
    
    return my_k_train_ls, my_k_valid_ls

8 训练模型

k=10
mynum_epochs= 20
mynet=LinearNet(784, 10, 100).cuda()
my_k_train_ls, my_k_valid_ls = k_fold(k, mnist_features, mnist_labels,if_reshape=True, num_epochs=mynum_epochs, num_inputs = 784, net =mynet ,loss=nn.CrossEntropyLoss())

9 绘制损失函数图

# 绘图
train_loss, valid_loss = [], []
for i in range(len(my_k_train_ls)):
    train_loss.append(my_k_train_ls[i][-1])
    valid_loss.append(my_k_valid_ls[i][-1])
    
x = np.linspace(0,len(my_k_train_ls),len(my_k_train_ls))
plt.plot(x,train_loss,'o-',label='train_loss',linewidth=1.5)
plt.plot(x,valid_loss,'o-',label='valid_loss',linewidth=1.5)
plt.xlabel('K value')
plt.ylabel('loss')
plt.legend()
plt.show()

10 绘制损失函数图表

# 绘制表格
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
randn = np.random.randn
idx = []
for i in range(1,21):
    idx.append(f'epoch {i}')

data_train, data_valid = np.zeros((10,20)),np.zeros((10,20))
for i in range(10):
    for j in range(20):
        data_train[i,j], data_valid[i,j] = my_k_train_ls[i][j], my_k_valid_ls[i][j] 
       
    
df = DataFrame(data_train.T, index=idx, columns=['第1折', '第2折', '第3折', '第4折', '第5折',
                                                '第6折', '第7折', '第8折', '第9折', '第10折'])

vals = np.around(df.values,7)
fig = plt.figure(figsize=(8,3))
ax = fig.add_subplot(111, frameon=False, xticks=[], yticks=[])
the_table=plt.table(cellText=vals, rowLabels=df.index, colLabels=df.columns,
                    colWidths = [0.1]*vals.shape[1], loc='center',cellLoc='center')
the_table.set_fontsize(20)

the_table.scale(2.5,2.58)