线性回归——PyTorch版代码

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导入PyTorch包

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import torch
from torch import nn
import numpy as np

torch.manual_seed(1)  #设置随机数种子

print(torch.__version__)   #查看PyTorch版本
torch.set_default_tensor_type('torch.FloatTensor')#设置默认的向量类型

生成数据集

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#生成数据集

num_inputs = 2
num_examples = 1000

true_w = [2, -3.4]
true_b = 4.2

features = torch.tensor(np.random.normal(0, 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)

读取数据集

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#读取数据集
import torch.utils.data as Data

batch_size = 10

# combine featues and labels of dataset
dataset = Data.TensorDataset(features, labels)

# put dataset into DataLoader
data_iter = Data.DataLoader(
    dataset=dataset,  # torch TensorDataset format
    batch_size=batch_size,  # mini batch size
    shuffle=True,  # whether shuffle the data or not
    num_workers=2,  # read data in multithreading
)
for X, y in data_iter:
    print(X, '\n', y)
    break

定义模型

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#定义模型
class LinearNet(nn.Module):
    def __init__(self, n_feature):
        super(LinearNet, self).__init__()  # call father function to init
        self.linear = nn.Linear(n_feature,
                                1)  # function prototype: `torch.nn.Linear(in_features, out_features, bias=True)`

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

net = LinearNet(num_inputs)
print(net)
# ways to init a multilayer network
#添加网络层的三种方法
# method one     #
net = nn.Sequential(
    nn.Linear(num_inputs, 1)
    # other layers can be added here
)

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

# method three
from collections import OrderedDict

net = nn.Sequential(OrderedDict([
    ('linear', nn.Linear(num_inputs, 1))
    # ......
]))

print(net)
print(net[0])

初始化模型参数

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#初始化模型参数
from torch.nn import init

init.normal_(net[0].weight, mean=0.0, std=0.01)
init.constant_(net[0].bias, val=0.0)  # or you can use `net[0].bias.data.fill_(0)` to modify it directly
for param in net.parameters():
    print(param)

定义损失函数

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#定义损失函数
loss = nn.MSELoss()  # nn built-in squared loss function
# function prototype: `torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')`

定义优化函数

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#定义优化函数
import torch.optim as optim

optimizer = optim.SGD(net.parameters(), lr=0.03)  # built-in random gradient descent function
print(optimizer)  # function prototype: `torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)`

训练

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#训练
num_epochs = 3
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        output = net(X)
        l = loss(output, y.view(-1, 1))
        optimizer.zero_grad()  # reset gradient, equal to net.zero_grad()
        l.backward()
        optimizer.step()
    print('epoch %d, loss: %f' % (epoch, l.item()))
# result comparision
dense = net[0]
print(true_w, dense.weight.data)
print(true_b, dense.bias.data)

总结

看着浅显易懂，并且没有TensorFlow那么复杂。

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