使用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，要求用到torch来完成代码

首先，需要导入相关的库，读取并处理数据：

import torch
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler

# 读取数据
data = pd.read_csv("iris.csv")

# 将类别数据进行编码
le = LabelEncoder()
data['species'] = le.fit_transform(data['species'])

# 将数据进行归一化处理
sc = StandardScaler()
data.iloc[:, :-1] = sc.fit_transform(data.iloc[:, :-1])

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data['species'], test_size=0.2, random_state=42)

接着，可以使用 PyTorch 搭建神经网络模型：

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(4, 10)
        self.fc2 = torch.nn.Linear(10, 10)
        self.fc3 = torch.nn.Linear(10, 3)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

在训练模型之前，需要定义损失函数和优化器：

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

接下来，可以开始训练模型了：

for epoch in range(1000):
    inputs = torch.tensor(X_train.values, dtype=torch.float32)
    labels = torch.tensor(y_train.values, dtype=torch.long)

    optimizer.zero_grad()

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

    if epoch % 100 == 0:
        print("epoch {} loss: {}".format(epoch, loss.item()))

最后，可以在测试集上进行预测并计算准确率：

with torch.no_grad():
    inputs = torch.tensor(X_test.values, dtype=torch.float32)
    labels = torch.tensor(y_test.values, dtype=torch.long)

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

    total = labels.size(0)
    correct = (predicted == labels).sum().item()
    accuracy = correct / total
    print("Accuracy: {}".format(accuracy))

同时，还可以分别计算不同类别的准确率：

for i in range(3):
    with torch.no_grad():
        inputs = torch.tensor(X_test[y_test == i].values, dtype=torch.float32)
        labels = torch.tensor(y_test[y_test == i].values, dtype=torch.long)

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

        total = labels.size(0)
        correct = (predicted == labels).sum().item()
        accuracy = correct / total
        print("Class {} Accuracy: {}".format(i, accuracy))