python 训练鸢尾花损失函数代码

以下是使用PyTorch库训练鸢尾花数据集的代码示例，包括定义损失函数和训练过程中的代码：

import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np

# Load Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define custom dataset
class IrisDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.tensor(X, dtype=torch.float32)
        self.y = torch.tensor(y, dtype=torch.long)
    
    def __len__(self):
        return len(self.X)
    
    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

# Initialize dataset and dataloader
iris_train = IrisDataset(X_train, y_train)
train_loader = DataLoader(iris_train, batch_size=16, shuffle=True)

# Define model architecture
class IrisNet(nn.Module):
    def __init__(self):
        super(IrisNet, self).__init__()
        self.fc1 = nn.Linear(4, 16)
        self.fc2 = nn.Linear(16, 3)
        
    def forward(self, x):
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# Initialize model and optimizer
model = IrisNet()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Define loss function
criterion = nn.CrossEntropyLoss()

# Train model
num_epochs = 50
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    # Print training loss after each epoch
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

在上述代码中，我们使用了PyTorch库来定义模型、损失函数和优化器，以及进行训练。我们还使用了sklearn库来加载鸢尾花数据集并将其拆分为训练和测试集。损失函数使用交叉熵损失函数。在训练过程中，我们通过迭代训练数据集中的每个批次来更新模型的权重，并在每个epoch结束后打印训练损失。