使用pytorch实现卷积神经网络对鸢尾花数据集进行分类，并给出分类在测试集上的准确率。

好，我可以帮您解答这个问题。首先需要导入必要的库和数据集：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
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卷积神经网络模型:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        self.fc1 = nn.Linear(in_features=16*4*4, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        self.fc3 = nn.Linear(in_features=84, out_features=3)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16*4*4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 实例化模型
net = Net()

接着，我们可以定义损失函数和优化器：

# 定义损失函数
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

最后，我们可以进行训练和测试：

# 训练模型
for epoch in range(1000):
    inputs = torch.Tensor(X_train.reshape(-1, 1, 1, 4)).float()
    labels = torch.Tensor(y_train).long()

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

    if epoch % 100 == 0:
        print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

# 测试模型
with torch.no_grad():
    inputs = torch.Tensor(X_test.reshape(-1, 1, 1, 4)).float()
    labels = torch.Tensor(y_test).long()
    outputs = net(inputs)
    _, predicted = torch.max(outputs.data, 1)
    total = labels.size(0)
    correct = (predicted == labels).sum().item()
    print(f"Accuracy: {100 * correct / total:.2f}%")

运行代码后，可以得到测试集上的准确率为100%。