1 匯入實驗需要的包

import numpy as np
import pandas as pd 
import torch
from torch import nn
import matplotlib.pyplot as plt
from torch.autograd import Variable
from torch.utils.data import DataLoader,TensorDataset
import os
os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE"

2 載入資料集

data =pd.read_csv("./dataset/credit-a.csv
 
")
X = data.iloc[:,:-1]  
Y = data.iloc[:,-1]  

3 資料轉換

Y = Y.replace(-1,0)  #替換標籤 -1 ，改成 0 
Y.unique() #檢視有幾種標籤資料
#資料轉換為Tensor
X = torch.from_numpy(X.values).type(torch.float32)
Y = torch.from_numpy(Y.values.reshape(-1,1)).type(torch.float32)

4 設定迭代器，將資料轉換在cuda下

def load_data(X,Y,batch_size):
    X 
 
= torch.autograd.Variable(X).cuda()
    Y = torch.autograd.Variable(Y).cuda()
    print("type x",X)
    data = TensorDataset(X,Y)
    data_loader = DataLoader(data,batch_size,shuffle=True)
#     data_loader = DataLoader(data,batch_size,shuffle=False)
    return data_loader

5 引數初始化及讀取資料

batchs_size = 16
data_iter 
 
= load_data(X,Y,batchs_size)
#最後的程式碼用到了，cpu（）那行
X = torch.autograd.Variable(X).cuda()
Y = torch.autograd.Variable(Y).cuda()

6 設定模型

model = nn.Sequential()
model.add_module('mylinear1',nn.Linear(15,1))
model.add_module('mysigmoid',nn.Sigmoid())
if torch.cuda.is_available():
    model.cuda()

7 設定損失函式

loss_fn = nn.BCELoss()

8 設定優化器

optimizer = torch.optim.Adam(model.parameters(),lr=0.0001)
# optimizer = torch.optim.SGD(model.parameters(),lr=0.001)

9 訓練模型

epochs = 150
for epoch in range(epochs):
    correct = 0
    for x,y in data_iter:
        y_hat = model(x)
        loss = loss_fn(y_hat,y)
#         print(y_hat.ge(0.5))
        out = y_hat.ge(0.5).float()  # 以0.5為閾值進行分類
        correct += (out == y).sum()  # 計算正確預測的樣本個數
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print("epoch  %d ,current acc = %.4f"%(epoch+1,correct/Y.shape[0]))
    print("epoch  %d ,current loss = %.4f"%(epoch+1,loss))

10 其他

# ((model(X).data.numpy()> 0.5).astype('int')==Y.numpy()).mean()
((model(X).data.cpu().numpy()> 0.5).astype('int')==Y.cpu().numpy()).mean()

model(X).data.cpu()

因上求緣，果上努力~~~~ 作者：希望每天漲粉，轉載請註明原文連結：https://www.cnblogs.com/BlairGrowing/p/15432982.html