sgd_experimental的使用方法

SGD (Stochastic Gradient Descent)是一种常用的优化方法，用于训练机器学习模型。sgd_experimental是MXNet的一个实验性功能，用于加速SGD的收敛速度和稳定性。

以下是sgd_experimental的使用方法：

1. 导入相关包

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from mxnet import autograd

2. 定义模型

net = nn.Sequential()
net.add(nn.Dense(128, activation='relu'))
net.add(nn.Dense(64, activation='relu'))
net.add(nn.Dense(10))

3. 初始化SGD优化器

optimizer = mx.optimizer.SGD(momentum=0.9, wd=0.001, learning_rate=0.1, rescale_grad=1.0/128)
optimizer = mx.optimizer.sgd_experimental.SGDEx(optimizer)

4. 定义损失函数

loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()

5. 定义训练函数

def train(net, dataloader, loss_fn, optimizer, ctx):
    cumulative_loss = 0.0
    cumulative_accuracy = 0.0
    total_samples = 0
    for data, label in dataloader:
        data = data.as_in_context(ctx)
        label = label.as_in_context(ctx)
        with autograd.record():
            output = net(data)
            loss = loss_fn(output, label)
        loss.backward()
        optimizer.update(data.shape[0])
        cumulative_loss += mx.nd.sum(loss).asscalar()
        cumulative_accuracy += mx.nd.sum(output.argmax(axis=1) == label).asscalar()
        total_samples += label.size
    return cumulative_loss / total_samples, cumulative_accuracy / total_samples

6. 训练模型

ctx = mx.cpu()
epochs = 10
batch_size = 128
train_dataset = gluon.data.vision.datasets.MNIST(train=True)
train_dataloader = gluon.data.DataLoader(train_dataset.transform_first(transformer), batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
    train_loss, train_accuracy = train(net, train_dataloader, loss_fn, optimizer, ctx)
    print('epoch: %d, train_loss: %.4f, train_accuracy: %.4f' % (epoch+1, train_loss, train_accuracy))

以上就是sgd_experimental的使用方法。需要注意的是，由于sgd_experimental是实验性功能，可能在未来版本中发生变化。建议在使用时查看最新的MXNet文档。