gluon 实现多层感知机MLP分类FashionMNIST
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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)
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