1d-cnn如何做测试集无标签数据预测，请写出pytorch代码

对于1D-CNN，可以通过以下步骤对测试集无标签数据进行预测：

1. 加载已训练好的模型

import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data

# 定义1D-CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(
                in_channels=1,      # 输入通道数
                out_channels=16,    # 输出通道数
                kernel_size=5,      # 卷积核大小
                stride=1,           # 步长
                padding=2,          # 填充
            ),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv1d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool1d(2),
        )
        self.out = nn.Linear(32 * 25, 2)  # 全连接层

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output

# 加载模型
model = CNN()
model.load_state_dict(torch.load('model.pth'))

2. 加载测试集数据

# 加载测试集数据
test_data = torch.from_numpy(test_data_np).float()
test_loader = Data.DataLoader(test_data, batch_size=batch_size, shuffle=False)

3. 预测无标签数据

# 预测无标签数据
model.eval()
preds = []
with torch.no_grad():
    for x_test in test_loader:
        outputs = model(x_test)
        _, predicted = torch.max(outputs.data, 1)
        preds.extend(predicted.numpy().tolist())

完整代码如下：

import torch.nn as nn
import torch.optim as optim
import torch.utils.data as Data

# 定义1D-CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(
                in_channels=1,      # 输入通道数
                out_channels=16,    # 输出通道数
                kernel_size=5,      # 卷积核大小
                stride=1,           # 步长
                padding=2,          # 填充
            ),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv1d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool1d(2),
        )
        self.out = nn.Linear(32 * 25, 2)  # 全连接层

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output

# 加载模型
model = CNN()
model.load_state_dict(torch.load('model.pth'))

# 加载测试集数据
test_data = torch.from_numpy(test_data_np).float()
test_loader = Data.DataLoader(test_data, batch_size=batch_size, shuffle=False)

# 预测无标签数据
model.eval()
preds = []
with torch.no_grad():
    for x_test in test_loader:
        outputs = model(x_test)
        _, predicted = torch.max(outputs.data, 1)
        preds.extend(predicted.numpy().tolist())