How to construct the network architecture in MXNet?

In MXNet, we can build network structures using the gluon.nn module. Here is a simple example.

from mxnet import gluon
from mxnet.gluon import nn

# 定义一个网络类
class MyNetwork(nn.Block):
    def __init__(self, **kwargs):
        super(MyNetwork, self).__init__(**kwargs)
        
        # 定义网络的层
        self.conv = nn.Conv2D(channels=32, kernel_size=3, activation='relu')
        self.pool = nn.MaxPool2D(pool_size=2, strides=2)
        self.fc = nn.Dense(units=10)
        
    # 定义前向传播函数
    def forward(self, x):
        x = self.conv(x)
        x = self.pool(x)
        x = x.flatten()
        x = self.fc(x)
        return x

# 创建网络实例
net = MyNetwork()

# 打印网络结构
print(net)

In this example, we created a network class named MyNetwork that inherits from the nn.Block class. In the __init__ function, we defined the layers of the network, including a convolutional layer, a pooling layer, and a fully connected layer. The forward function defines the operations for forward propagation, which processes the input data through the convolutional layer, pooling layer, and fully connected layer, and finally returns the output of the network.

By calling the net.initialize() method, the parameters of the network can be initialized. After that, forward propagation calculations can be performed using net(X).

This is just a simple example, in actuality more complex network structures can be set up as needed. MXNet offers a variety of different types of layers and activation functions that can be chosen and combined according to specific requirements.