[MXNet逐梦之旅]练习一·使用MXNet拟合直线手动实现

#%%from matplotlib import pyplot as pltfrom mxnet import autograd, ndimport random#%%num_inputs = 1num_examples = 100true_w = 1.56true_b = 1.24features = nd.arange(0,10,0.1).reshape((-1, 1))labels = true_w * features + true_blabels += 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 = 10for 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 = 0.05num_epochs = 20net = linregloss = squared_lossfor 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()