演示了纯手动添加隐藏层，这次使用gluon让代码更精减，代码来自：

from mxnet import gluonfrom mxnet import ndarray as ndimport matplotlib.pyplot as pltimport mxnet as mxfrom mxnet import autograd  def transform(data, label):    return data.astype('float32')/255, label.astype('float32')  mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform)mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform)  def show_images(images):    n = images.shape[0]    _, figs = plt.subplots(1, n, figsize=(15, 15))    for i in range(n):        figs[i].imshow(images[i].reshape((28, 28)).asnumpy())        figs[i].axes.get_xaxis().set_visible(False)        figs[i].axes.get_yaxis().set_visible(False)    plt.show()def get_text_labels(label):    text_labels = [        'T 恤', '长 裤', '套头衫', '裙 子', '外 套',        '凉 鞋', '衬 衣', '运动鞋', '包 包', '短 靴'    ]    return [text_labels[int(i)] for i in label]  data, label = mnist_train[0:10]  print('example shape: ', data.shape, 'label:', label)show_images(data)print(get_text_labels(label))  batch_size = 256train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False)  #计算模型net = gluon.nn.Sequential()with net.name_scope():    net.add(gluon.nn.Flatten())    net.add(gluon.nn.Dense(256, activation="relu"))    net.add(gluon.nn.Dense(10))net.initialize()  softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()#定义训练器trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5}) def accuracy(output, label):    return nd.mean(output.argmax(axis=1) == label).asscalar()  def _get_batch(batch):    if isinstance(batch, mx.io.DataBatch):        data = batch.data[0]        label = batch.label[0]    else:        data, label = batch    return data, label  def evaluate_accuracy(data_iterator, net):    acc = 0.    if isinstance(data_iterator, mx.io.MXDataIter):        data_iterator.reset()    for i, batch in enumerate(data_iterator):        data, label = _get_batch(batch)        output = net(data)        acc += accuracy(output, label)    return acc / (i+1)  for epoch in range(5):    train_loss = 0.    train_acc = 0.    for data, label in train_data:        with autograd.record():            output = net(data)            loss = softmax_cross_entropy(output, label)        loss.backward()        trainer.step(batch_size) #使用训练器，向"前"走一步        train_loss += nd.mean(loss).asscalar()        train_acc += accuracy(output, label)    test_acc = evaluate_accuracy(test_data, net)    print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % (        epoch, train_loss/len(train_data), train_acc/len(train_data), test_acc))data, label = mnist_test[0:10]show_images(data)print('true labels')print(get_text_labels(label))  predicted_labels = net(data).argmax(axis=1)print('predicted labels')print(get_text_labels(predicted_labels.asnumpy()))

 有变化的地方，已经加上了注释。运行效果，跟一篇完全相同，就不重复贴图了