用iris.csv数据构建神经网络预测花的种类:将所有数据 随机分为训练集和测试集,分别计算测试集上总的准确率和不同类的准确率。torch

首先，我们需要导入必要的库和数据集：

import torch
import torch.nn as nn
import pandas as pd
from sklearn.model_selection import train_test_split

# 读取数据集
iris_df = pd.read_csv('iris.csv')

# 将类别转换为数字
iris_df['species'] = pd.Categorical(iris_df['species']).codes

# 将特征和标签分开
X = iris_df.iloc[:, :-1].values
y = iris_df.iloc[:, -1].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们可以定义一个简单的神经网络模型，包括一个输入层、一个隐藏层和一个输出层：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(4, 16)
        self.fc2 = nn.Linear(16, 3)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

然后，我们可以使用该模型对数据进行训练和测试：

# 定义模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    inputs = torch.tensor(X_train, dtype=torch.float32)
    labels = torch.tensor(y_train, dtype=torch.long)

    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    if epoch % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
with torch.no_grad():
    inputs = torch.tensor(X_test, dtype=torch.float32)
    labels = torch.tensor(y_test, dtype=torch.long)

    outputs = model(inputs)
    _, predicted = torch.max(outputs.data, 1)

    total = labels.size(0)
    correct = (predicted == labels).sum().item()
    accuracy = correct / total
    print('Accuracy of the model on the test set: {:.2f}%'.format(accuracy * 100))

    class_correct = [0 for _ in range(3)]
    class_total = [0 for _ in range(3)]

    for i in range(len(labels)):
        label = labels[i]
        prediction = predicted[i]
        if prediction == label:
            class_correct[label] += 1
        class_total[label] += 1

    for i in range(3):
        print('Accuracy of {} : {:.2f}%'.format(i, 100 * class_correct[i] / class_total[i]))

在上述代码中，我们使用交叉熵损失函数和随机梯度下降优化器进行训练。在测试模型时，我们计算了总的准确率和每个类别的准确率。