请根据一下内容完善一下代码：使用 6 个卷积层，卷积层的 kernel size=3、没经过两个卷积层，特征的 空间分辨率降低两倍，特征的 channel 数量提升两倍；每个卷积层后跟一个 Batch Normalization、一个 ReLU 层；在最后的输出层，使用 Global Average Pooling 操作，把特征层转换为 一个特征向量；使用全连接神经网络完成最终的预测；

以下是使用 PyTorch 实现该卷积神经网络的代码：

import torch
import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=True)
        
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=True)
        
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        self.relu4 = nn.ReLU(inplace=True)
        
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.bn5 = nn.BatchNorm2d(256)
        self.relu5 = nn.ReLU(inplace=True)
        
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.bn6 = nn.BatchNorm2d(256)
        self.relu6 = nn.ReLU(inplace=True)
        
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        
        self.fc = nn.Linear(256, num_classes)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        
        x = self.pool1(x)
        
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        
        x = self.pool2(x)
        
        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu5(x)
        
        x = self.conv6(x)
        x = self.bn6(x)
        x = self.relu6(x)
        
        x = self.pool3(x)
        
        x = self.avgpool(x)
        
        x = x.view(x.size(0), -1)
        
        x = self.fc(x)
        
        return x

该网络实现了6个卷积层，每个卷积层后跟一个 Batch Normalization 和一个 ReLU 层，经过两个卷积层后，特征的空间分辨率降低两倍，特征的 channel 数量提升两倍。在最后的输出层，使用 Global Average Pooling 操作，把特征层转换为一个特征向量，然后使用全连接神经网络完成最终的预测。