MXNET深度学习框架-12-使用gluon实现LeNet-5

上一章从0开始实现了一个简单的CNN，但是有点麻烦，接下来使用gluon中的api来实现经典的LeNet-5：
代码如下：

import mxnet.ndarray as nd
import mxnet.autograd as ag
import mxnet.gluon as gn
import mxnet as mx
import matplotlib.pyplot as plt
import sys
from mxnet import init
import os
# 继续使用FashionMNIST
mnist_train = gn.data.vision.FashionMNIST(train=True)
mnist_test = gn.data.vision.FashionMNIST(train=False)


def transform(data, label):
    return data.astype("float32") / 255, label.astype("float32")  # 样本归一化

'''----数据读取----'''
batch_size = 256

train_data = gn.data.DataLoader(dataset=mnist_train, batch_size=batch_size, shuffle=True)
test_data = gn.data.DataLoader(dataset=mnist_test, batch_size=batch_size, shuffle=False)
ctx = mx.gpu(0)

# 定义模型
def get_net():
    net = gn.nn.Sequential()
    net.add(gn.nn.Conv2D(channels=6, kernel_size=5, activation='sigmoid'),
        gn.nn.MaxPool2D(pool_size=2, strides=2),
        gn.nn.Conv2D(channels=16, kernel_size=5, activation='sigmoid'),
        gn.nn.MaxPool2D(pool_size=2, strides=2),
        gn.nn.Dense(120, activation='sigmoid'),
        gn.nn.Dense(84, activation='sigmoid'),
        gn.nn.Dense(10))
    net.initialize(ctx=ctx, init=init.Xavier())  # init.Xavier()随机初始化参数
    return net
net=get_net()
# 定义准确率
def accuracy(output,label):
    return nd.mean(output.argmax(axis=1)==label).asscalar()

def evaluate_accuracy(data_iter,net):# 定义测试集准确率
    acc=0
    for data,label in data_iter:
        data, label = data.as_in_context(ctx), label.as_in_context(ctx)
        data,label=transform(data,label)
        output=net(data.reshape(-1,1,28,28))
        acc+=accuracy(output,label)
    return acc/len(data_iter)

# softmax和交叉熵分开的话数值可能会不稳定
cross_loss=gn.loss.SoftmaxCrossEntropyLoss()
# 优化
train_step=gn.Trainer(net.collect_params(),'sgd',{"learning_rate":0.9})
'''---训练---'''
epochs=50
train_avg_acc,train_avg_ls,test_avg_acc=[],[],[]
for epoch in range(epochs):
    train_loss = 0
    train_acc = 0
    for image,y in train_data:
        image, y = image.as_in_context(ctx), y.as_in_context(ctx)
        image, y = transform(image, y)  # 类型转换，数据归一化
        image=image.reshape(-1,1,28,28)
        with ag.record():
            output=net(image)
            loss=cross_loss(output,y)
        loss.backward()
        train_step.step(batch_size)
        train_loss += nd.mean(loss).asscalar()
        train_acc += accuracy(output, y)
    test_acc = evaluate_accuracy(test_data, net)
    print("Epoch %d, Loss:%f, Train acc:%f, Test acc:%f"
          % (epoch, train_loss / len(train_data), train_acc / len(train_data), test_acc))
    train_avg_acc.append(train_acc / len(train_data))
    train_avg_ls.append(train_loss / len(train_data))
    test_avg_acc.append(test_acc)
plt.ylim(0, 1) #设置y轴区间
plt.grid() #网格线
plt.plot(train_avg_acc)
plt.plot(train_avg_ls)
plt.plot(test_avg_acc,linestyle=':') # 虚线
plt.legend(['train acc','train loss','test acc'])
plt.show()

运行结果：