pytorch K折交叉验证过程说明及实现方式

目录

• K折交叉交叉验证的过程如下
• 交叉验证区分k折代码分析
• 总结

K折交叉交叉验证的过程如下

以200条数据，十折交叉验证为例子，十折也就是将数据分成10组，进行10组训练，每组用于测试的数据为：数据总条数/组数，即每组20条用于valid，180条用于train，每次valid的都是不同的。

（1）将200条数据，分成按照 数据总条数/组数（折数），进行切分。然后取出第i份作为第i次的valid，剩下的作为train

（2）将每组中的train数据利用DataLoader和Dataset，进行封装。

（3）将train数据用于训练，epoch可以自己定义，然后利用valid做验证。得到一次的train_loss和 valid_loss。

（4）重复（2）（3）步骤，得到最终的 averge_train_loss和averge_valid_loss

上述过程如下图所示：

上述的代码如下：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader,Dataset
import torch.nn.functional as F
from torch.autograd import Variable

#####构造的训练集####
x = torch.rand(100,28,28)
y = torch.randn(100,28,28)
x = torch.cat((x,y),dim=0)
label =[1] *100 + [0]*100
label = torch.tensor(label,dtype=torch.long)

######网络结构##########
class Net(nn.Module):
    #定义Net
    def __init__(self):
        super(Net, self).__init__() 

        self.fc1   = nn.Linear(28*28, 120)
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 2)

    def forward(self, x):

        x = x.view(-1, self.num_flat_features(x)) 

        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

##########定义dataset##########
class TraindataSet(Dataset):
    def __init__(self,train_features,train_labels):
        self.x_data = train_features
        self.y_data = train_labels
        self.len = len(train_labels)

    def __getitem__(self,index):
        return self.x_data[index],self.y_data[index]
    def __len__(self):
        return self.len

########k折划分############
def get_k_fold_data(k, i, X, y):  ###此过程主要是步骤（1）
    # 返回第i折交叉验证时所需要的训练和验证数据，分开放，X_train为训练数据，X_valid为验证数据
    assert k > 1
    fold_size = X.shape[0] // k  # 每份的个数:数据总条数/折数（组数）

    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j * fold_size, (j + 1) * fold_size)  #slice(start,end,step)切片函数
        ##idx 为每组 valid
        X_part, y_part = X[idx, :], y[idx]
        if j == i: ###第i折作valid
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        else:
            X_train = torch.cat((X_train, X_part), dim=0) #dim=0增加行数，竖着连接
            y_train = torch.cat((y_train, y_part), dim=0)
    #print(X_train.size(),X_valid.size())
    return X_train, y_train, X_valid,y_valid

def k_fold(k, X_train, y_train, num_epochs=3,learning_rate=0.001, weight_decay=0.1, batch_size=5):
    train_loss_sum, valid_loss_sum = 0, 0
    train_acc_sum ,valid_acc_sum = 0,0

    for i in range(k):
        data = get_k_fold_data(k, i, X_train, y_train) # 获取k折交叉验证的训练和验证数据
        net =  Net()  ### 实例化模型
        ### 每份数据进行训练,体现步骤三####
        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,\
                                   weight_decay, batch_size) 

        print('*'*25,'第',i+1,'折','*'*25)
        print('train_loss:%.6f'%train_ls[-1][0],'train_acc:%.4f\n'%valid_ls[-1][1],\
              'valid loss:%.6f'%valid_ls[-1][0],'valid_acc:%.4f'%valid_ls[-1][1])
        train_loss_sum += train_ls[-1][0]
        valid_loss_sum += valid_ls[-1][0]
        train_acc_sum += train_ls[-1][1]
        valid_acc_sum += valid_ls[-1][1]
    print('#'*10,'最终k折交叉验证结果','#'*10)
    ####体现步骤四#####
    print('train_loss_sum:%.4f'%(train_loss_sum/k),'train_acc_sum:%.4f\n'%(train_acc_sum/k),\
          'valid_loss_sum:%.4f'%(valid_loss_sum/k),'valid_acc_sum:%.4f'%(valid_acc_sum/k))

#########训练函数##########
def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate,weight_decay, batch_size):
    train_ls, test_ls = [], [] ##存储train_loss,test_loss
    dataset = TraindataSet(train_features, train_labels)
    train_iter = DataLoader(dataset, batch_size, shuffle=True)
    ### 将数据封装成 Dataloder 对应步骤（2）

    #这里使用了Adam优化算法
    optimizer = torch.optim.Adam(params=net.parameters(), lr= learning_rate, weight_decay=weight_decay)

    for epoch in range(num_epochs):
        for X, y in train_iter:  ###分批训练
            output  = net(X)
            loss = loss_func(output,y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        ### 得到每个epoch的 loss 和 accuracy
        train_ls.append(log_rmse(0,net, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(1,net, test_features, test_labels))
    #print(train_ls,test_ls)
    return train_ls, test_ls

def log_rmse(flag,net,x,y):
    if flag == 1: ### valid 数据集
        net.eval()
    output = net(x)
    result = torch.max(output,1)[1].view(y.size())
    corrects = (result.data == y.data).sum().item()
    accuracy = corrects*100.0/len(y)  #### 5 是 batch_size
    loss = loss_func(output,y)
    net.train()

    return (loss.data.item(),accuracy)

loss_func = nn.CrossEntropyLoss() ###申明loss函
k_fold(10,x,label) ### k=10,十折交叉验证

上述代码中，直接按照顺序从x中每次截取20条作为valid，也可以先打乱然后在截取，这样效果应该会更好。

如下所示：

import random
import torch

x = torch.rand(100,28,28)
y = torch.randn(100,28,28)
x = torch.cat((x,y),dim=0)
label =[1] *100 + [0]*100
label = torch.tensor(label,dtype=torch.long)

index = [i for i in range(len(x))]
random.shuffle(index)
x = x[index]
label = label[index]

交叉验证区分k折代码分析

from  sklearn.model_selection import GroupKFold
x = np.array([1,2,3,4,5,6,7,8,9,10])
y = np.array([1,2,3,4,5,6,7,8,9,10])
z = np.array(['hello1','hello2','hello3','hello4','hello5','hello6','hello7','hello8','hello9','hello10'])
gkf = GroupKFold(n_splits = 5)
for  i,(train_idx,valid_idx) in enumerate(list(gkf.split(x,y,z))):
#groups:object,Always ignored,exists for compatibility.
    print('train_idx = ')
    print(train_idx)
    print('valid_idx = ')
    print(valid_idx)

可以看出来首先train_idx以及valid_idx的相应值都是从中乱序提取的，其次每个相应值只提取一次，不会重复提取。

注意交叉验证的流程：这里首先放一个对应的交叉验证的图片：

注意这里的训练方式是每个初始化的模型分别训练n折的数值，然后算出对应的权重内容

也就是说这里每一次计算对应的权重内容(1~n)的时候，需要将模型的权重初始化，然后再进行训练，训练最终结束之后，模型的权重为训练完成之后的平均值，多模类似于模型融合

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。