gluon 实现多层感知机MLP分类FashionMNIST

from mxnet import gluon,init
from mxnet.gluon import loss as gloss, nn
from mxnet.gluon import data as gdata
from mxnet import nd,autograd
import gluonbook as gb

import sys

# 读取数据
# 读取数据
mnist_train = gdata.vision.FashionMNIST(train=True)
mnist_test = gdata.vision.FashionMNIST(train=False)

batch_size = 256
transformer = gdata.vision.transforms.ToTensor()
if sys.platform.startswith(‘win‘):
    num_workers = 0
else:
    num_workers = 4

# 小批量数据迭代器
train_iter = gdata.DataLoader(mnist_train.transform_first(transformer),batch_size=batch_size,shuffle=True,num_workers=num_workers)
test_iter = gdata.DataLoader(mnist_test.transform_first(transformer),batch_size=batch_size,shuffle=False,num_workers=num_workers)

# 定义网络
net = nn.Sequential()
net.add(nn.Dense(256,activation=‘relu‘),nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))

# 损失函数
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(),‘sgd‘,{‘learning_rate‘:0.5})


def accuracy(y_hat, y):
    return (y_hat.argmax(axis=1) == y.astype(‘float32‘)).mean().asscalar()

def evaluate_accuracy(data_iter, net):
    acc = 0
    for X, y in data_iter:
        acc += accuracy(net(X), y)
    return acc / len(data_iter)

num_epochs = 5

def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,trainer=None):
    for epoch in range(num_epochs):
        train_l_sum = 0
        train_acc_sum = 0
        for X,y in train_iter:
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat,y)
            l.backward()

            if trainer is None:
                gb.sgd(params,lr,batch_size)
            else:
                trainer.step(batch_size)

            train_l_sum += l.mean().asscalar()


        test_acc = evaluate_accuracy(test_iter,net)
        print(‘epoch %d,loss %.4f,test acc %.3f‘%(epoch+1,train_l_sum / len(train_iter),test_acc))

train(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,trainer)