AlexNet 分类 FashionMNIST

from mxnet import gluon,init,nd,autograd
from mxnet.gluon import data as gdata,nn
from mxnet.gluon import loss as gloss
import mxnet as mx
import time
import os
import 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*224
def 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_iter

batch_size = 128
train_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 ctx


lr = 0.01
num_epochs = 5
ctx = 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)