python和pytorch关系_PyTorch线性回归和逻辑回归实战示例

线性回归实战

使用PyTorch定义线性回归模型一般分以下几步：

1.设计网络架构

2.构建损失函数(loss)和优化器(optimizer)

3.训练(包括前馈(forward)、反向传播(backward)、更新模型参数(update))

#author:yuquanle

#data:2018.2.5

#Study of LinearRegression use PyTorch

import torch

from torch.autograd import Variable

# train data

x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))

y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))

class Model(torch.nn.Module):

def __init__(self):

super(Model, self).__init__()

self.linear = torch.nn.Linear(1, 1) # One in and one out

def forward(self, x):

y_pred = self.linear(x)

return y_pred

# our model

model = Model()

criterion = torch.nn.MSELoss(size_average=False) # Defined loss function

optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # Defined optimizer

# Training: forward, loss, backward, step

# Training loop

for epoch in range(50):

# Forward pass

y_pred = model(x_data)

# Compute loss

loss = criterion(y_pred, y_data)

print(epoch, loss.data[0])

# Zero gradients

optimizer.zero_grad()

# perform backward pass

loss.backward()

# update weights

optimizer.step()

# After training

hour_var = Variable(torch.Tensor([[4.0]]))

print("predict (after training)", 4, model.forward(hour_var).data[0][0])

迭代十次打印结果：

0 123.87958526611328

1 55.19491195678711

2 24.61777114868164

3 11.005026817321777

4 4.944361686706543

5 2.2456750869750977

6 1.0436556339263916

7 0.5079189538955688

8 0.2688019871711731

9 0.16174012422561646

predict (after training) 4 7.487752914428711

loss还在继续下降，此时输入4得到的结果还不是预测的很准

当迭代次数设置为50时：

0 35.38422393798828

5 0.6207122802734375

10 0.012768605723977089

15 0.0020055510103702545

20 0.0016929294215515256

25 0.0015717096393927932

30 0.0014619173016399145

35 0.0013598509831354022

40 0.0012649153359234333

45 0.00117658288218081

50 0.001094428705982864

predict (after training) 4 8.038028717041016

此时，函数已经拟合比较好了

再运行一次：

0 159.48605346679688

5 2.827991485595703

10 0.08624256402254105

15 0.03573693335056305

20 0.032463930547237396

25 0.030183646827936172

30 0.02807590737938881

35 0.026115568354725838

40 0.02429218217730522

45 0.022596003487706184

50 0.0210183784365654

predict (after training) 4 7.833342552185059

发现同为迭代50次，但是当输入为4时，结果不同，感觉应该是使用pytorch定义线性回归模型时:

torch.nn.Linear(1, 1)，只需要知道输入和输出维度，里面的参数矩阵是随机初始化的(具体是不是随机的还是按照一定约束条件初始化的我不确定)，所有每次计算loss会下降到不同的位置(模型的参数更新从而也不同)，导致结果不一样。

逻辑回归实战

线性回归是解决回归问题的，逻辑回归和线性回归很像，但是它是解决分类问题的(一般二分类问题:0 or 1)。也可以多分类问题(用softmax可以实现)。

使用pytorch实现逻辑回归的基本过程和线性回归差不多，但是有以下几个区别：

下面为sigmoid函数：

在逻辑回归中，我们预测如果 当输出大于0.5时，y=1;否则y=0。

损失函数一般采用交叉熵loss:

# date:2018.2.6

# LogisticRegression

import torch

from torch.autograd import Variable

x_data = Variable(torch.Tensor([[0.6], [1.0], [3.5], [4.0]]))

y_data = Variable(torch.Tensor([[0.], [0.], [1.], [1.]]))

class Model(torch.nn.Module):

def __init__(self):

super(Model, self).__init__()

self.linear = torch.nn.Linear(1, 1) # One in one out

self.sigmoid = torch.nn.Sigmoid()

def forward(self, x):

y_pred = self.sigmoid(self.linear(x))

return y_pred

# Our model

model = Model()

# Construct loss function and optimizer

criterion = torch.nn.BCELoss(size_average=True)

optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training loop

for epoch in range(500):

# Forward pass

y_pred = model(x_data)

# Compute loss

loss = criterion(y_pred, y_data)

if epoch % 20 == 0:

print(epoch, loss.data[0])

# Zero gradients

optimizer.zero_grad()

# Backward pass

loss.backward()

# update weights

optimizer.step()

# After training

hour_var = Variable(torch.Tensor([[0.5]]))

print("predict (after training)", 0.5, model.forward(hour_var).data[0][0])

hour_var = Variable(torch.Tensor([[7.0]]))

print("predict (after training)", 7.0, model.forward(hour_var).data[0][0])

输入结果：

0 0.9983477592468262

20 0.850886881351471

40 0.7772406339645386

60 0.7362991571426392

80 0.7096697092056274

100 0.6896909475326538

120 0.6730546355247498

140 0.658246636390686

160 0.644534170627594

180 0.6315458416938782

200 0.6190851330757141

220 0.607043981552124

240 0.5953611731529236

260 0.5840001106262207

280 0.5729377269744873

300 0.5621585845947266

320 0.5516515970230103

340 0.5414079427719116

360 0.5314203500747681

380 0.5216821432113647

400 0.512187123298645

420 0.5029295086860657

440 0.49390339851379395

460 0.4851033389568329

480 0.47652381658554077

predict (after training) 0.5 0.49599987268447876

predict (after training) 7.0 0.9687209129333496

Process finished with exit code 0

训练完模型之后，输入新的数据0.5，此时输出小于0.5，则为0类别，输入7输出大于0.5，则为1类别。使用softmax做多分类时，那个维度的数值大，则为那个数值所对应位置的类别。

更深更宽的网络

前面的例子都是浅层输入为一维的网络，如果需要更深更宽的网络，使用pytorch也可以很好的实现，以逻辑回归为例：

当输入x的维度很大时，需要更宽的网络:

更深的网络：

输入维度为八。

#author:yuquanle

#date:2018.2.7

#Deep and Wide

import torch

from torch.autograd import Variable

import numpy as np

xy = np.loadtxt('./data/diabetes.csv', delimiter=',', dtype=np.float32)

x_data = Variable(torch.from_numpy(xy[:, 0:-1]))

y_data = Variable(torch.from_numpy(xy[:, [-1]]))

#print(x_data.data.shape)

#print(y_data.data.shape)

class Model(torch.nn.Module):

def __init__(self):

super(Model, self).__init__()

self.l1 = torch.nn.Linear(8, 6)

self.l2 = torch.nn.Linear(6, 4)

self.l3 = torch.nn.Linear(4, 1)

self.sigmoid = torch.nn.Sigmoid()

def forward(self, x):

x = self.sigmoid(self.l1(x))

x = self.sigmoid(self.l2(x))

y_pred = self.sigmoid(self.l3(x))

return y_pred

# our model

model = Model()

cirterion = torch.nn.BCELoss(size_average=True)

optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

hour_var = Variable(torch.Tensor([[-0.294118,0.487437,0.180328,-0.292929,0,0.00149028,-0.53117,-0.0333333]]))

print("(Before training)", model.forward(hour_var).data[0][0])

# Training loop

for epoch in range(1000):

y_pred = model(x_data)

# y_pred,y_data不能写反(因为损失函数为交叉熵loss)

loss = cirterion(y_pred, y_data)

optimizer.zero_grad()

loss.backward()

optimizer.step()

if epoch % 50 == 0:

print(epoch, loss.data[0])

# After training

hour_var = Variable(torch.Tensor([[-0.294118,0.487437,0.180328,-0.292929,0,0.00149028,-0.53117,-0.0333333]]))

print("predict (after training)", model.forward(hour_var).data[0][0])

结果：

(Before training) 0.5091859698295593

0 0.6876295208930969

50 0.6857835650444031

100 0.6840178370475769

150 0.6823290586471558

200 0.6807141900062561

250 0.6791688203811646

300 0.6776910424232483

350 0.6762782335281372

400 0.6749269366264343

450 0.6736343502998352

500 0.6723981499671936

550 0.6712161302566528

600 0.6700847744941711

650 0.6690039038658142

700 0.667969822883606

750 0.666980504989624

800 0.6660353541374207

850 0.6651310324668884

900 0.664265513420105

950 0.6634389758110046

predict (after training) 0.5618339776992798

Process finished with exit code 0

参考:

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