Pytorch 实现多层感知机

1 导入实验所需要的包

from torch.autograd import Variable
from torch.utils.data import TensorDataset,DataLoader
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
import torch.nn.functional as F
import pandas as pd
from torch import nn
from sklearn.model_selection import train_test_split
#魔法函数
%matplotlib inline       

2 加载原始数据集

data = pd.read_csv("./dataset/HR.csv")   #加载csv数据
data = data.join(pd.get_dummies(data.salary))  #将salary 转换成类似one-hot形式加到数据列中
del data['salary']   #删除原有的salary这列
data = data.join(pd.get_dummies(data.part)) #将 part 转换成类似one-hot形式加到数据列中
del data['part']   #删除原有的part这列

3 原始数据集转换

Y_data = data.left.values.reshape(-1,1)   #获取标签数据
Y = torch.from_numpy(Y_data).type(torch.FloatTensor).cuda()    #转换到cuda上
#不加.values    X_data 的数据类型是pandas.core.frame.DataFrame；加上变成numpy.ndarray
X_data = data[ [c for c in data.columns if c!='left'] ].values
X= torch.from_numpy(X_data).type(torch.FloatTensor).cuda()

4 定义模型

#模型创建方法
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(20,64)
        self.linear2 = nn.Linear(64,128)
        self.linear3 = nn.Linear(128,1)
    def forward(self,input):
        x = F.relu( self.linear1(input))
        x = F.relu(self.linear2(x))
#         y = F.sigmid(self.linear3(x))
        y = torch.sigmoid(self.linear3(x))
        return y

5 定义优化器和设置模型

def get_model(lr):
    model = Model().cuda()
    optimizer = torch.optim.Adam(model.parameters(),lr=lr)
    return model,optimizer
model,optimizer = get_model(lr=0.0001)

6 定义损失函数

loss_fn = nn.BCELoss()

7 定义超参数

batch_size = 64
num_epoch = 1000

8 划分训练集和测试集

train_x,test_x,train_y,test_y =  train_test_split(X,Y,train_size=0.8,shuffle= True) #划分数据集

hr_train_dataset = TensorDataset(train_x,train_y)     #转换数据集到数据加载器
hr_train_iter = DataLoader(hr_train_dataset,batch_size=64,shuffle = True)
hr_test_dataset = TensorDataset(test_x,test_y)
hr_test_iter = DataLoader(hr_test_dataset,batch_size=64)

9 定义准确率函数

def accurancy(y_pred,y):
    y_pred=(y_pred >0.5).type(torch.int32)
    acc = (y_pred==y).float().mean()
    return acc

10 训练

for epoch in range(num_epoch):
    for x,y in hr_train_iter:
        y_hat = model(x)
        loss = loss_fn(y_hat,y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    with torch.no_grad():
        train_acc = accurancy(model(train_x),train_y)
        test_acc = accurancy(model(test_x),test_y)
        print("epoch : ",epoch,"train_loss:  ",loss_fn(model(train_x),train_y).data.item(),"  train_acc :",train_acc.data.item(),
             "  train_loss:  ",loss_fn(model(test_x),test_y).data.item(),"  test_acc :",test_acc.data.item()
             )