在 begin-end 之间补充代码,使用 sklearn 构建感知机模型,利用训练集数据与训练标签对模型进行训练,然后使用训练好的模型对测试集数据进行预测,并将预测结果保存到./step2/result.csv中
时间: 2023-06-25 17:02:46 浏览: 47
```python
from sklearn.linear_model import Perceptron
import pandas as pd
# 读取数据
train_data = pd.read_csv('./step2/train_data.csv')
train_label = pd.read_csv('./step2/train_label.csv')
test_data = pd.read_csv('./step2/test_data.csv')
# 创建模型
model = Perceptron()
# 训练模型
model.fit(train_data, train_label)
# 预测测试集
test_pred = model.predict(test_data)
# 保存预测结果
result = pd.DataFrame({'label': test_pred})
result.to_csv('./step2/result.csv', index=False)
```
相关问题
在Begin-End区域中编写代码完成以下任务:使用pytorch搭建出卷积神经网络模型,再对模型进行训练。
```python
import torch
import torch.nn as nn
import torch.optim as optim
# 定义卷积神经网络模型
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.fc1 = nn.Linear(32 * 8 * 8, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 32 * 8 * 8)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
在右侧编辑器中Begin-End之间补充代码,将读入的测试数据列表List转换为元组并输出。
test_data = input().split(',') # 读入测试数据列表
test_tuple = tuple(test_data) # 将列表转换为元组
print(test_tuple) # 输出元组
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