[MXNet逐梦之旅]练习一·使用MXNet拟合直线手动实现

code
#%%
from matplotlib import pyplot as plt
from mxnet import autograd, nd
import random

#%%
num_inputs = 1
num_examples = 100
true_w = 1.56
true_b = 1.24
features = nd.arange(0,10,0.1).reshape((-1, 1))
labels = true_w * features + true_b
labels += nd.random.normal(scale=0.2, shape=labels.shape)

features[0], labels[0]



#%%
# 本函数已保存在d2lzh包中方便以后使用
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)  # 样本的读取顺序是随机的
    for i in range(0, num_examples, batch_size):
        j = nd.array(indices[i: min(i + batch_size, num_examples)])
        yield features.take(j), labels.take(j)  # take函数根据索引返回对应元素


#%%
batch_size = 10

for X, y in data_iter(batch_size, features, labels):
    print(X, y)
    break


#%%
w = nd.random.normal(scale=0.01, shape=(num_inputs, 1))
b = nd.zeros(shape=(1,))


#%%

w.attach_grad()
b.attach_grad()


#%%
def linreg(X, w, b):  # 本函数已保存在d2lzh包中方便以后使用
    return nd.dot(X, w) + b

#%%


def squared_loss(y_hat, y):  # 本函数已保存在d2lzh包中方便以后使用
    return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2


#%%

def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh包中方便以后使用
    for param in params:
        param[:] = param - lr * param.grad / batch_size


#%%

lr = 0.05
num_epochs = 20
net = linreg
loss = squared_loss

for epoch in range(num_epochs):  # 训练模型一共需要num_epochs个迭代周期
    # 在每一个迭代周期中，会使用训练数据集中所有样本一次（假设样本数能够被批量大小整除）。X
    # 和y分别是小批量样本的特征和标签
    for X, y in data_iter(batch_size, features, labels):
        with autograd.record():
            l = loss(net(X, w, b), y)  # l是有关小批量X和y的损失
        l.backward()  # 小批量的损失对模型参数求梯度
        sgd([w, b], lr, batch_size)  # 使用小批量随机梯度下降迭代模型参数
    train_l = loss(net(features, w, b), labels)
    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().asnumpy()))



#%%
true_w, w


#%%
true_b, b


#%%
plt.scatter(features.asnumpy(), labels.asnumpy(), 1)

labels1 = linreg(features,w,b)
plt.scatter(features.asnumpy(), labels1.asnumpy(), 1)
plt.show()
黄色是原始数据
绿色为拟合数据