手动实现前馈神经网络解决二分类任务

2022-03-06 12:04:09 阅读：252 来源： 互联网

标签：loss torch num 手动前馈神经网络 train test size

1 导入实验需要的包

import numpy as np
import torch
from torch import nn
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt 
from torch.utils.data import TensorDataset,DataLoader

2 创建数据

num_example,num_input = 10000,200
x1 = torch.normal(-2,1,size = (num_example,num_input))
y1 = torch.ones((num_example,1))
x2 = torch.normal(2,1,size = (num_example,num_input))
y2 = torch.zeros((num_example,1))
x = torch.cat((x1,x2),dim = 0)
y = torch.cat((y1,y2),dim = 0)
train_x,test_x,train_y,test_y =train_test_split(x,y,shuffle = True,stratify = y,random_state= 1,test_size=0.3)
print(train_x.shape)
print(test_x.shape)

3 加载数据

batch_size = 50
train_dataset = TensorDataset(train_x,train_y)
train_iter = DataLoader(
    dataset = train_dataset,
    shuffle = True,
    batch_size = batch_size,
    num_workers = 0
)

test_dataset = TensorDataset(test_x,test_y)
test_iter = DataLoader(
    dataset = test_dataset,
    shuffle = True,
    batch_size = batch_size,
    num_workers = 0
)

# print(np.unique(y))
# np.sum([ele.data==1  for ele in train_y.flatten() ])
# np.sum([ele.data==0  for ele in train_y.flatten() ])

4 初始化参数

num_hidden,num_output = 256,1
torch.set_default_tensor_type = torch.float32
# w1 = torch.tensor(torch.normal(0,0.001,size = (num_hidden,num_input))).type(torch.float32)
w1 = torch.tensor(torch.normal(0,0.001,size = (num_hidden,num_input))).type(torch.FloatTensor)
b1 = torch.ones(1)
# b1 = torch.ones(1,dtype = torch.float32)
print(w1.dtype)
print(b1.dtype)
      
w2 = torch.tensor(torch.normal(0,0.001,size = (num_output,num_hidden)),dtype = torch.float32)
b2 = torch.ones(1)    
params = [w1,w2,b1,b2]

for param in params:  
    param.requires_grad_(requires_grad=True)

5 定义激活函数、模型、优化器、损失函数

def ReLU(x):
    return torch.max(x,other = torch.tensor(0.0))

loss = nn.BCEWithLogitsLoss()
# loss = nn.BCELoss()
def net(x):
    H1 = ReLU(torch.matmul(x,w1.t())+b1)
    H2 = torch.matmul(H1,w2.t())+b2
    return H2

def SGD(params,lr):
    param.data -= lr*param.grad/batch_size

6 定义训练模型

def train(net ,train_iter,test_iter,lr,num_epochs,params):
    train_l,test_l = [],[]
    for epoch in range(num_epochs):
        train_l_sum,n = 0,0
        for x,y in train_iter:
            n += y.shape[0] 
            y_pred = net(x)
            l = loss(y_pred,y)
            train_l_sum += l.item()
            if params is not None and params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()

            l.backward()
            SGD(params,lr)
        train_l.append(train_l_sum/n)
        
        test_l_sum,n = 0,0
        for x,y in test_iter:
            n += y.shape[0] 
            y_pred = net(x)
            l = loss(y_pred,y)
            test_l_sum += l.item()
        test_l.append(test_l_sum/n)
        print('epoch %d, train_loss %.6f,test_loss %.6f'%(epoch+1, train_l[epoch],test_l[epoch]))
    return train_l,test_l

7 训练

lr,num_epochs = 0.01,100
train_loss,test_loss =train(net ,train_iter,test_iter,lr,num_epochs,params)

8 可视化

x = np.linspace(0,len(train_loss),len(train_loss))  
plt.plot(x,train_loss,label="train_loss",linewidth=1.5)  
plt.plot(x,test_loss,label="test_loss",linewidth=1.5)  
plt.xlabel("epoch")  
plt.ylabel("loss")  
plt.legend()  
plt.show()

标签：loss,torch,num,手动,前馈,神经网络,train,test,size
来源： https://www.cnblogs.com/BlairGrowing/p/15971254.html

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。

ICode9

手动实现前馈神经网络解决 二分类 任务