1105-pytorch实践

pytorch实现单维度线性回归

代码

import torch

x_data=torch.Tensor([[1.0],[2.0],[3.0]])
y_data=torch.Tensor([[2.0],[4.0],[6.0]])

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel,self).__init__()
        self.linear=torch.nn.Linear(1,1)

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

model=LinearModel()

criterion=torch.nn.MSELoss(size_average=False)
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(1000):
    y_pred=model(x_data)
    loss=criterion(y_pred,y_data)
    print(epoch,loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())

x_test=torch.Tensor([[4.0]])
y_test=model(x_test)
print('y_pred=',y_test.data)

结果

logist逻辑回归分类 

代码

import torch
import torch.nn.functional as F

x_data=torch.Tensor([[1.0],[2.0],[3.0]])
y_data=torch.Tensor([[0],[0],[1]])

class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel,self).__init__()
        self.linear=torch.nn.Linear(1,1)

    def forward(self,x):
        y_pred=torch.sigmoid(self.linear(x))
        return y_pred

model=LogisticRegressionModel()

criterion=torch.nn.BCELoss(reduction='sum')
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(1000):
    y_pred=model(x_data)
    loss=criterion(y_pred,y_data)
    print(epoch,loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())

x_test=torch.Tensor([[4.0]])
y_test=model(x_test)
print('y_pred=',y_test.data)

x_test=torch.Tensor([[2.5]])
y_test=model(x_test)
print('y_pred=',y_test.data)

import numpy as np
import matplotlib.pyplot as plt

x=np.linspace(0,10,200)
x_t=torch.Tensor(x).view((200,1))
y_t=model(x_t)
y=y_t.data.numpy()
plt.plot(x,y)
plt.plot([0,10],[0.5,0.5],c='r')
plt.xlabel('Hours')
plt.ylabel('Probablity of Pass')
plt.grid()
plt.show()

结果

 

 

 多维度输入分类

代码

import torch
import numpy as np

xy=np.loadtxt('diabetes.csv.gz',delimiter=',',dtype=np.float32)
x_data=torch.from_numpy(xy[:,:-1])
y_data=torch.from_numpy(xy[:,[-1]])

class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1=torch.nn.Linear(8,6)
        self.linear2 = torch.nn.Linear(6,4)
        self.linear3 = torch.nn.Linear(4,1)
        self.sigmoid=torch.nn.Sigmoid()
        self.activate=torch.nn.ReLU()

    def forward(self,x):
        x=self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x

model=Model()

criterion=torch.nn.BCELoss(reduction='sum')
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(100):
    #forward
    y_pred=model(x_data)
    loss=criterion(y_pred,y_data)
    print(epoch,loss.item())

    #backward
    optimizer.zero_grad()
    loss.backward()

    #update
    optimizer.step()

结果

 

 总结

pytorch相对于tensorflow来说代码更加方便，而且模块化的效果很好，继续学习，另外力推B站pytorch视频：https://www.bilibili.com/video/BV1Y7411d7Ys?p=8&spm_id_from=pageDriver