《动手学深度学习》 Linear_Regression with gluon

Linear Regression

用mxnet写了最简单的线性回归

代码

from mxnet import autograd, nd
from mxnet.gluon import data as gdata
from mxnet.gluon import nn
from mxnet import init
from mxnet.gluon import loss as gloss
from mxnet import gluon


num_inputs = 2
num_examples = 1000
true_w = [2.1, -5]
true_b = 3.4
samples = nd.random.normal(scale=1, shape=(num_examples, num_inputs))
labels = true_w[0] * samples[:, 0] + true_w[1] * samples[:, 1] + true_b
labels += nd.random.normal(scale=0.01, shape=labels.shape)
batch_size = 10
dataset = gdata.ArrayDataset(samples, labels)
data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)

net = nn.Sequential()
net.add(nn.Dense(1))
net.initialize((init.Normal(sigma=0.01)))
loss = gloss.L2Loss()

trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})

num_epochs = 5
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        with autograd.record():
            l = loss(net(X), y)
        l.backward()
        trainer.step(batch_size)
    l = loss(net(samples), labels)
    print('epoch {}, loss: {}'.format(epoch, l.mean().asnumpy()))

dense = net[0]
print('真实权重:{},训练权重:{}'.format(true_w, dense.weight.data()))
print('真是偏差:{},训练偏差:{}'.format(true_b, dense.bias.data()))

结果

epoch 1, loss: [0.04274421]
epoch 2, loss: [0.00014196]
epoch 3, loss: [4.8662067e-05]
epoch 4, loss: [4.8426606e-05]
epoch 5, loss: [4.8332287e-05]
真实权重:[2.1, -5],训练权重:
[[ 2.099422 -5.0002365]]
<NDArray 1x2 @cpu(0)>
真是偏差:3.4,训练偏差:
[3.4005916]
<NDArray 1 @cpu(0)>