文章作者：Tyan
博客：noahsnail.com | CSDN | 简书
注：本文为李沐大神的《动手学深度学习》的课程笔记！
创建数据集
# 导入mxnet
import random
import mxnet as mx
# 导入mxnet的gluon, ndarray, autograd
from mxnet import gluon
from mxnet import autograd
from mxnet import ndarray as nd
# 设置随机种子
mx.random.seed(1)
random.seed(1)
# 训练数据的维度
num_inputs = 2
# 训练数据的样本数量
num_examples = 1000
# 实际的权重w
true_w = [2, -3.4]
# 实际的偏置b
true_b = 4.2
# 随机生成均值为0, 方差为1, 服从正态分布的训练数据X,
X = nd.random_normal(shape=(num_examples, num_inputs))
# 根据X, w, b生成对应的输出y
y = true_w[0] * X[:, 0] + true_w[1] * X[:, 1] + true_b
# 给y加上随机噪声
y += 0.01 * nd.random_normal(shape=y.shape)
数据展示
%matplotlib inline
import matplotlib.pyplot as plt
# 绘制数据的散点图
plt.scatter(X[:, 1].asnumpy(), y.asnumpy())
plt.show()
数据展示
数据读取
# 训练时的批数据大小
batch_size = 10
# 创建数据集
dataset = gluon.data.ArrayDataset(X, y)
# 读取数据
data_iter = gluon.data.DataLoader(dataset, batch_size, shuffle=True)
# 查看数据
for data, label in data_iter:
print data, label
break
[[-2.11255503 0.61242002]
[ 2.18546367 -0.48856559]
[ 0.91085583 0.38985687]
[-0.56097323 1.44421673]
[ 0.31765923 -1.75729597]
[-0.57738042 2.03963804]
[-0.91808975 0.64181799]
[-0.20269176 0.21012937]
[-0.22549874 0.19895147]
[ 1.42844415 0.06982213]]
<NDArray 10x2 @cpu(0)>
[ -2.11691356 10.22533131 4.70613146 -1.82755637 10.82125568
-3.88111711 0.17608714 3.07074499 3.06542921 6.82972908]
<NDArray 10 @cpu(0)>
定义模型
# 定义一个空的模型
net = gluon.nn.Sequential()
# 加入一个Dense层
net.add(gluon.nn.Dense(1))
初始化模型参数
net.initialize()
定义损失函数
square_loss = gluon.loss.L2Loss()
优化
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
训练
# 定义训练的迭代周期
epochs = 5
# 训练
for epoch in xrange(epochs):
# 总的loss
total_loss = 0
for data, label in data_iter:
# 记录梯度
with autograd.record():
# 计算预测值
output = net(data)
# 计算loss
loss = square_loss(output, label)
# 根据loss进行反向传播计算梯度
loss.backward()
# 更新权重, batch_size用来进行梯度平均
trainer.step(batch_size)
# 计算总的loss
total_loss += nd.sum(loss).asscalar()
print "Epoch %d, average loss: %f" % (epoch, total_loss/num_examples)
Epoch 0, average loss: 7.403182
Epoch 1, average loss: 0.854247
Epoch 2, average loss: 0.099864
Epoch 3, average loss: 0.011887
Epoch 4, average loss: 0.001479
代码地址
https://github.com/SnailTyan/gluon-practice-code
参考资料
ArrayDataset
https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.ArrayDatasetTrainer
https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=trainer#mxnet.gluon.Trainer
