给我一个以MXNet为框架的深度学习实例代码

以下是一个使用MXNet框架实现的简单的图像分类模型的示例代码：

import mxnet as mx
from mxnet import gluon, init, nd
from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
from mxnet.gluon.data.vision import CIFAR10

# 定义图像预处理操作
transform_train = transforms.Compose([
    transforms.RandomFlipLeftRight(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
                         std=[0.2023, 0.1994, 0.2010])
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
                         std=[0.2023, 0.1994, 0.2010])
])

# 加载CIFAR-10数据集
train_data = CIFAR10(train=True).transform_first(transform_train)
test_data = CIFAR10(train=False).transform_first(transform_test)

# 定义模型
net = nn.Sequential()
net.add(nn.Conv2D(channels=32, kernel_size=3, padding=1, activation='relu'))
net.add(nn.BatchNorm())
net.add(nn.Conv2D(channels=32, kernel_size=3, padding=1, activation='relu'))
net.add(nn.MaxPool2D(pool_size=2, strides=2))
net.add(nn.Conv2D(channels=64, kernel_size=3, padding=1, activation='relu'))
net.add(nn.BatchNorm())
net.add(nn.Conv2D(channels=64, kernel_size=3, padding=1, activation='relu'))
net.add(nn.MaxPool2D(pool_size=2, strides=2))
net.add(nn.Flatten())
net.add(nn.Dense(units=512, activation='relu'))
net.add(nn.Dropout(rate=0.5))
net.add(nn.Dense(units=10))

# 初始化模型参数
net.initialize(init=init.Xavier())

# 定义损失函数和优化器
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
optimizer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': 0.001})

# 训练模型
batch_size = 128
train_loader = gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = gluon.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)

for epoch in range(10):
    for X, y in train_loader:
        with mx.autograd.record():
            outputs = net(X)
            loss = loss_fn(outputs, y)
        loss.backward()
        optimizer.step(batch_size)

    # 在测试集上评估模型性能
    test_accuracy = mx.metric.Accuracy()
    for X, y in test_loader:
        outputs = net(X)
        predictions = nd.argmax(outputs, axis=1)
        test_accuracy.update(preds=predictions, labels=y)
    print(f"Epoch {epoch}: Test accuracy = {test_accuracy.get()[1]}")

在这个示例代码中，我们首先定义了一个用于对CIFAR-10数据集进行预处理的操作，然后加载了数据集，并使用定义的预处理操作对其进行处理。接着，我们定义了一个简单的卷积神经网络模型，并使用Xavier初始化方法初始化了模型参数。然后，我们定义了一个SoftmaxCross