%matplotlib inlinefrom mxnet import ndimport numpy as npfrom mxnet import autograd,gluon,init,ndfrom mxnet.gluon import nn,data as gdata,loss as glossimport timedef get_data():    data = np.genfromtxt('./data/airfoil_self_noise.dat', delimiter='\t')    data = (data - data.mean(axis=0)) / data.std(axis=0)    return nd.array(data[:1500, :-1]), nd.array(data[:1500, -1])features, labels = get_data()features[0]labels[0]# 定义网络def linreg(X,w,b):    return nd.dot(X,w) + b# 平方损失def squared_loss(y_hat,y):    return (y_hat - y.reshape(y_hat.shape))**2/2# 初始化参数def init_momentum_states():    v_w = nd.zeros((features.shape[1], 1))    v_b = nd.zeros(1)    return (v_w, v_b)# params [w,b]# states [v_w,v_b] 初始化状态# hyperparams {'lr':0.02,'momentum':0.5}def sgd_momentum(params, states, hyperparams):    for p, v in zip(params, states):        v[:] = hyperparams['momentum'] * v + hyperparams['lr'] * p.grad        p[:] -= vdef train(trainer_fn, states, hyperparams, features, labels,              batch_size=10, num_epochs=2):    # 初始化模型。    net, loss = gb.linreg, gb.squared_loss    w = nd.random.normal(scale=0.01, shape=(features.shape[1], 1))    b = nd.zeros(1)    w.attach_grad()    b.attach_grad()    def eval_loss():        return loss(net(features, w, b), labels).mean().asscalar()    ls = [eval_loss()]    data_iter = gdata.DataLoader(        gdata.ArrayDataset(features, labels), batch_size, shuffle=True)    for _ in range(num_epochs):        start = time.time()        for batch_i, (X, y) in enumerate(data_iter):            with autograd.record():                l = loss(net(X, w, b), y).mean()  # 使用平均损失。            l.backward()            trainer_fn([w, b], states, hyperparams)  # 迭代模型参数。            if (batch_i + 1) * batch_size % 100 == 0:                ls.append(eval_loss())  # 每 100 个样本记录下当前训练误差。    # 打印结果和作图。    print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start))    gb.set_figsize()    gb.plt.plot(np.linspace(0, num_epochs, len(ls)), ls)    gb.plt.xlabel('epoch')    gb.plt.ylabel('loss')train(trainer_fn=sgd_momentum,states= init_momentum_states(),hyperparams={
   'lr': 0.02, 'momentum': 0.5}, features=features, labels=labels)train(sgd_momentum,init_momentum_states(),{
    'lr':0.02,'momentum':0.9},features,labels)train(sgd_momentum,init_momentum_states(),{
    'lr':0.004,'momentum':0.9},features,labels)

gluon 版：

 

def train_gluon(trainer_name,trainer_hyperparams,features,labels,batch_size=10,num_epochs=2):    # 初始化模型    net = nn.Sequential()    net.add(nn.Dense(1))    net.initialize(init.Normal(sigma=0.01))    loss = gloss.L2Loss()        def eval_loss():        return loss(net(features),labels).mean().asscalar()        ls = [eval_loss()]    data_iter = gdata.DataLoader(gdata.ArrayDataset(features,labels),batch_size,shuffle=True)        # 创建 Trainer 实例迭代模型参数    trainer = gluon.Trainer(net.collect_params(),trainer_name,trainer_hyperparams)        for _ in range(num_epochs):        start = time.time()        for batch_i, (X,y) in enumerate(data_iter):            with autograd.record():                l = loss(net(X),y)            l.backward()            trainer.step(batch_size)            if (batch_i + 1) * batch_size % 100 ==0:                ls.append(eval_loss())                    # 打印结果和作图。    print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start))    gb.set_figsize()    gb.plt.plot(np.linspace(0, num_epochs, len(ls)), ls)    gb.plt.xlabel('epoch')    gb.plt.ylabel('loss')train_gluon('sgd',{
   'learning_rate':0.004,'momentum':0.9},features,labels)