#%%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()
黄色是原始数据
绿色为拟合数据
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