手写pytorch线性回归

Python下划线的五种用法

手写线性回归
教程地址 未解决的问题：plt.show()会阻塞

import torch
from IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random
from tqdm import tqdm
from multiprocessing import Pool


# generate dataset

true_w = torch.Tensor([2, -3.4])
true_w = torch.unsqueeze(true_w, 1)
true_b = torch.Tensor([4.2])

feature_num = 2
data_num = 10000

features = torch.randn(data_num, feature_num, dtype=torch.float32)

true_labels = features.mm(true_w) + true_b
# add Gaussian noise
noise_labels = true_labels + torch.tensor(np.random.normal(0, 0.01, size=true_labels.size()), dtype=torch.float32)

# visualize
def use_svg_display():
    display.set_matplotlib_formats('svg')


def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    plt.rcParams['figure.figsize'] = figsize

#  手写数据迭代器
def data_iter(batch_size, features, labels):
    data_num = len(features)
    indices = list(range(data_num))
    random.shuffle(indices)  # 将indices的列表顺序打乱
    for i in range(0, data_num, batch_size):  # 从0迭代到data_num-1
        # 构建long类型的张量，and防止最后一次不足一个batch
        j = torch.LongTensor(indices[i: min(i + batch_size, data_num)])
        #  yield相当于return，但在哪里跌倒就在哪里站起来，并且节约空间
        # 0代表按行索引，j代表索引哪些行
        yield features.index_select(0, j), labels.index_select(0, j)


# initialize
w = torch.tensor(np.random.normal(0, 0.01, (feature_num, 1)), dtype=torch.float32)
b = torch.zeros(1, dtype=torch.float32)

w.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True)


# 线性回归计算
def linreg(X, w, b):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    return torch.mm(X, w) + b


def squared_loss(y_hat, y):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    # 注意这里返回的是向量, 另外, pytorch里的MSELoss并没有除以 2
    # size不需要用到
    # return (y_hat - y.view(y_hat.size())) ** 2 / 2
    return (y_hat - y.data) ** 2 / 2


def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    for param in params:
        # 注意这里更改param时用的param.data，这样不会被追踪自动微分
        # 因为微分是根据y的值用反向传播算出来的，如果直接改变x的值，会把改变的范围也算进微分里面
        # 或者会导致梯度算出来是None，因为x不再是叶子节点
        param.data -= lr * param.grad / batch_size


# configuration
batch_size = 100
lr = 0.03
num_epochs = 10
#  函数句柄
net = linreg
loss = squared_loss

for epoch in tqdm(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, noise_labels):
        l = loss(net(X, w, b), y).sum()  # 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

        # 不要忘了梯度清零
        w.grad.data.zero_()
        b.grad.data.zero_()
    train_l = loss(net(features, w, b), noise_labels)
    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))

# 输出运行结果
print(true_w, '\n', w)
print(true_b, '\n', b)


# 画图的放在前面会阻塞后面的进程
set_figsize()
plt.scatter(features[:, 1].numpy(), noise_labels.numpy(), 1, c='r')
plt.scatter(features[:, 1].numpy(), true_labels.numpy(), 1, c='g')
plt.ioff()
plt.show()
plt.ioff()

运行结果

调用nn.linear自己构造类

import torch
import torch.utils.data as Data
import torch.nn as nn
import numpy as np

# generate data
num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2

features = torch.tensor(np.random.normal(1, 1, (num_examples, num_inputs)), dtype=torch.float)
labels = true_w[0] * features[:, 0] + true_w[1] + features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)

batch_size = 10
# 数据和标签组合
dataset = Data.TensorDataset(features, labels)
data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)


class LinearNet(nn.Module):
    def __init__(self, n_feature):
        ##  python2写法，class，self
        # super(LinearNet, self).__init__()
        # python3也可以这么写
        super().__init__()
        self.linear = nn.Linear(in_features=n_feature, out_features=1, bias=True)

    def forward(self, x):
        y = self.linear(x)
        return y


net = LinearNet(num_inputs)
print(net)

nn.init.normal_(net.linear.weight, mean=0, std=0.01)
nn.init.constant_(net.linear.bias, val=0)

loss = nn.MSELoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.03)


num_epochs = 3
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        output = net(X)
        # output是（10,1），所以也要改y
        l = loss(output, y.view(-1, 1))
        optimizer.zero_grad()
        l.backward()
        optimizer.step()
    print('epoch %d, loss:%f' % (epoch, l.item()))

结果：

用sequential构造网络

import torch
import torch.utils.data as Data
import torch.nn as nn
import numpy as np

# generate data
num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2

features = torch.tensor(np.random.normal(1, 1, (num_examples, num_inputs)), dtype=torch.float)
labels = true_w[0] * features[:, 0] + true_w[1] + features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)

batch_size = 10
# 数据和标签组合
dataset = Data.TensorDataset(features, labels)
data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)

net = nn.Sequential()
net.add_module('linear', nn.Linear(num_inputs, 1))

print(net)
# 打印出第0层
print(net[0])

# 打印所有可学习参数
for param in net.parameters():
    print(param)

nn.init.normal_(net[0].weight, mean=0, std=0.01)
nn.init.constant_(net[0].bias, val=0)

loss = nn.MSELoss()

# 不同的网络设置不同的学习率
optimizer = torch.optim.SGD([
    {'params': net.linear.parameters()},  # lr=0.01
], lr=0.03)
print(optimizer)

num_epochs = 3
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        output = net(X)
        # output是（10,1），所以也要改y
        l = loss(output, y.view(-1, 1))
        optimizer.zero_grad()
        l.backward()
        optimizer.step()
    print('epoch %d, loss:%f' % (epoch, l.item()))

运行结果：