请用pytorch写一个输入层的输入节点为（2，1），有 三个卷积层都为 20 个长度为 3 卷积层，三个最大池化层都为 4，一个非线性转化层大小为一个单元， ReLU 激活函数来激活池化过程中处理的结果，sidmoid 函数在末层网络中将输出结果在零和一之间，用 Adam 作为 cnn 的优化算法，学习效率为 0.001，评价指标为 RMSE 的一维卷积神经网络

以下是一个简单的实现：

import torch
import torch.nn as nn
import torch.optim as optim

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv1d(2, 20, 3)
        self.pool1 = nn.MaxPool1d(4)
        self.conv2 = nn.Conv1d(20, 20, 3)
        self.pool2 = nn.MaxPool1d(4)
        self.conv3 = nn.Conv1d(20, 20, 3)
        self.pool3 = nn.MaxPool1d(4)
        self.fc1 = nn.Linear(20, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.pool3(x)
        x = self.relu(x)
        x = x.view(-1, 20)
        x = self.fc1(x)
        x = self.sigmoid(x)
        return x

# 定义输入数据
x = torch.randn(1, 2, 1)
y = torch.tensor([0.5])

# 初始化模型
model = ConvNet()

# 定义优化器和损失函数
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(1000):
    optimizer.zero_grad()
    y_pred = model(x)
    loss = criterion(y_pred, y)
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print(f"Epoch {epoch}: loss={loss.item()}")

# 评估模型
y_pred = model(x)
rmse = torch.sqrt(criterion(y_pred, y))
print(f"RMSE: {rmse.item()}")