AlexNet 分类 FashionMNIST

from mxnet import gluon,init,nd,autogradfrom mxnet.gluon import data as gdata,nnfrom mxnet.gluon import loss as glossimport mxnet as mximport timeimport osimport sys# 建立网络net = nn.Sequential()# 使用较大的 11 x 11 窗口来捕获物体。同时使用步幅 4 来较大减小输出高和宽。# 这里使用的输入通道数比 LeNet 中的也要大很多。net.add(nn.Conv2D(96, kernel_size=11, strides=4, activation=‘relu‘), ???????nn.MaxPool2D(pool_size=3, strides=2), ???????# 减小卷积窗口，使用填充为 2 来使得输入输出高宽一致，且增大输出通道数。 ???????nn.Conv2D(256, kernel_size=5, padding=2, activation=‘relu‘), ???????nn.MaxPool2D(pool_size=3, strides=2), ???????# 连续三个卷积层，且使用更小的卷积窗口。除了最后的卷积层外，进一步增大了输出通道数。 ???????# 前两个卷积层后不使用池化层来减小输入的高和宽。 ???????nn.Conv2D(384, kernel_size=3, padding=1, activation=‘relu‘), ???????nn.Conv2D(384, kernel_size=3, padding=1, activation=‘relu‘), ???????nn.Conv2D(256, kernel_size=3, padding=1, activation=‘relu‘), ???????nn.MaxPool2D(pool_size=3, strides=2), ???????# 这里全连接层的输出个数比 LeNet 中的大数倍。使用丢弃层来缓解过拟合。 ???????nn.Dense(4096, activation="relu"), nn.Dropout(0.5), ???????nn.Dense(4096, activation="relu"), nn.Dropout(0.5), ???????# 输出层。由于这里使用 Fashion-MNIST，所以用类别数为 10，而非论文中的 1000。 ???????nn.Dense(10))X = nd.random.uniform(shape=(1,1,224,224))net.initialize()for layer in net: ???X = layer(X) ???print(layer.name,‘output shape:\t‘,X.shape)# 读取数据# fashionMNIST 28*28 转为224*224def load_data_fashion_mnist(batch_size, resize=None, root=os.path.join( ???????‘~‘, ‘.mxnet‘, ‘datasets‘, ‘fashion-mnist‘)): ???root = os.path.expanduser(root) ?# 展开用户路径 ‘~‘。 ???transformer = [] ???if resize: ???????transformer += [gdata.vision.transforms.Resize(resize)] ???transformer += [gdata.vision.transforms.ToTensor()] ???transformer = gdata.vision.transforms.Compose(transformer) ???mnist_train = gdata.vision.FashionMNIST(root=root, train=True) ???mnist_test = gdata.vision.FashionMNIST(root=root, train=False) ???num_workers = 0 if sys.platform.startswith(‘win32‘) else 4 ???train_iter = gdata.DataLoader( ???????mnist_train.transform_first(transformer), batch_size, shuffle=True, ???????num_workers=num_workers) ???test_iter = gdata.DataLoader( ???????mnist_test.transform_first(transformer), batch_size, shuffle=False, ???????num_workers=num_workers) ???return train_iter, test_iterbatch_size = 128train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=224)def accuracy(y_hat,y): ???return (y_hat.argmax(axis=1)==y.astype(‘float32‘)).mean().asscalar()def evaluate_accuracy(data_iter,net,ctx): ???acc = nd.array([0],ctx=ctx) ???for X,y in data_iter: ???????X = X.as_in_context(ctx) ???????y = y.as_in_context(ctx) ???????acc+=accuracy(net(X),y) ???return acc.asscalar() / len(data_iter)# 训练模型def train(net,train_iter,test_iter,batch_size,trainer,ctx,num_epochs): ???print(‘training on‘,ctx) ???loss = gloss.SoftmaxCrossEntropyLoss() ???for epoch in range(num_epochs): ???????train_l_sum = 0 ???????train_acc_sum = 0 ???????start = time.time() ???????for X,y in train_iter: ???????????X = X.as_in_context(ctx) ???????????y = y.as_in_context(ctx) ???????????with autograd.record(): ???????????????y_hat = net(X) ???????????????l = loss(y_hat,y) ???????????l.backward() ???????????trainer.step(batch_size) ???????????train_l_sum += l.mean().asscalar() ???????????train_acc_sum += evaluate_accuracy(test_iter,net,ctx) ???????test_acc = evaluate_accuracy(test_iter,net,ctx) ???????print(‘epoch %d, loss %.4f, train acc %.3f, test acc %.3f, ‘ ?????????????‘time %.1f sec‘ % (epoch+1,train_l_sum/len(train_iter),test_acc,time.time()-start))def try_gpu(): ???try: ???????ctx = mx.gpu() ???????_ = nd.zeros((1,),ctx=ctx) ???except mx.base.MXNetError: ???????ctx = mx.cpu() ???return ctxlr = 0.01num_epochs = 5ctx = try_gpu()net.initialize(force_reinit=True,ctx=ctx,init=init.Xavier())trainer = gluon.Trainer(net.collect_params(),‘sgd‘,{‘learning_rate‘:lr})train(net,train_iter,test_iter,batch_size,trainer,ctx,num_epochs)