LeNet

import mxnet as mx
import sys
from mxnet import autograd,nd
from mxnet import gluon,init
from mxnet.gluon import nn,loss as gloss
from mxnet.gluon import data as gdata

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

batch_size = 256

trainsformer = gdata.vision.transforms.ToTensor()

if sys.platform.startswith(‘win‘):
    num_workers = 0
else:
    num_workers = 4

train_iter = gdata.DataLoader(mnist_train.transform_first(trainsformer),batch_size=batch_size,shuffle=True,num_workers=num_workers)
test_iter = gdata.DataLoader(mnist_test.transform_first(trainsformer),batch_size=batch_size,shuffle=False,num_workers=num_workers)

# 使用GPU
def try_gpu():
    try:
        ctx = mx.gpu()
        _ = nd.zeros((1,),ctx=ctx)
    except mx.base.MXNetError:
        ctx = mx.cpu()
    return ctx

# 计算正确率
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)

# LeNet,建立卷积神经网络
net = nn.Sequential()
net.add(nn.Conv2D(channels=6, kernel_size=5, activation=‘sigmoid‘),
        nn.MaxPool2D(pool_size=2, strides=2),
        nn.Conv2D(channels=16, kernel_size=5, activation=‘sigmoid‘),
        nn.MaxPool2D(pool_size=2, strides=2),
        # Dense 会默认将（批量大小，通道，高，宽）形状的输入转换成
        # （批量大小，通道 * 高 * 宽）形状的输入。
        nn.Dense(120, activation=‘sigmoid‘),
        nn.Dense(84, activation=‘sigmoid‘),
        nn.Dense(10))

X = nd.random.uniform(shape=(1,1,28,28))
net.initialize()

for layer in net:
    X = layer(X)
    print(layer.name,‘output shape:\t‘,X.shape)

K = nd.array([[[0, 1], [2, 3]], [[1, 2], [3, 4]]])
K = nd.stack(K, K + 1, K + 2)
print(K)