基于pytorch的RML2016.10a数据集的分类算法

RML2016.10a是一个无线通信数据集，包含了11种不同的调制方式和20个信噪比（SNR）水平。在pytorch中，可以使用卷积神经网络（CNN）来对该数据集进行分类。

下面是一个简单的RML2016.10a数据集的分类算法的示例代码：

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.model_selection import train_test_split
from skrf import data, processing

# 数据准备
data_path = data.data_dir + "/RML2016.10a_dict.pkl"
data_dict = processing.load(data_path)
X = data_dict["X"]
mods = data_dict["modulations"]
snrs = data_dict["snrs"]
num_samples = X.shape[0]

# 数据预处理
X = X.reshape(num_samples, 2, -1)
X = np.transpose(X, (0, 2, 1))
X = (X - np.mean(X)) / np.std(X)

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, mods, test_size=0.2, random_state=42)

# 转换为张量
X_train = torch.from_numpy(X_train).float()
X_test = torch.from_numpy(X_test).float()
y_train = torch.from_numpy(y_train).long()
y_test = torch.from_numpy(y_test).long()

# 构建数据集和数据加载器
train_dataset = TensorDataset(X_train, y_train)
test_dataset = TensorDataset(X_test, y_test)
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义网络结构
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv1d(2, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(256 * 4, 128)
        self.fc2 = nn.Linear(128, 11)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = x.view(-1, 256 * 4)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 初始化模型和优化器
model = CNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 50
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = nn.functional.cross_entropy(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print("Epoch %d, Loss: %.3f" % (epoch+1, running_loss / len(train_loader)))

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for data in test_loader:
        inputs, labels = data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print("Accuracy: %.3f" % (correct / total))

这个示例代码使用了一个简单的卷积神经网络来对RML2016.10a数据集进行分类。该网络包含三个卷积层和两个全连接层。在训练过程中，使用Adam优化器和交叉熵损失函数。在测试过程中，计算模型的准确率。

需要注意的是，这个示例代码只是一个简单的示例，可能需要根据具体情况进行更改和优化。