PyTorch处理多维特征输入

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PyTorch处理多维特征输入

使用sklearn包中的糖尿病数据集
路径为：conda\\lib\\site-packages\\sklearn\\datasets\\data

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# -*- coding: UTF-8 -*-
import torch.nn as nn
import torch.optim as optim
import torch
import numpy as np
import matplotlib.pyplot as plt

# gz压缩包的文件名一样，可以使用loadtxt把数据读出来
# delimiter=','，使用逗号作为分隔符
# dtype=np.float32，数据类型为32位的浮点型
xy = np.loadtxt('./diabetes_data/diabetes.csv.gz',delimiter=',',dtype=np.float32)

#
x_data = torch.from_numpy(xy[:,:-1]) # 所有行，除去最后一列
y_data = torch.from_numpy(xy[:,[-1]]) #所有行，最后一列转为矩阵

class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1 = nn.Linear(8,16)
        self.linear2 = nn.Linear(16,32)
        self.linear3 = nn.Linear(32,64)
        self.linear4 = nn.Linear(64,32)
        self.linear5 = nn.Linear(32,1)
        self.sigmoid = nn.Sigmoid()

    def forward(self,x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        x = self.sigmoid(self.linear4(x))
        x = self.sigmoid(self.linear5(x))
        return x

model = Model()

loss_function = nn.BCELoss(reduction='mean')
optimizer = optim.Adam(model.parameters(),lr=0.055)

epoch_list = []
loss_list = []

for epoch in range(1000):
    y_pred = model(x_data)
    loss = loss_function(y_pred,y_data)

    epoch_list.append(epoch)
    loss_list.append(loss.data.item())

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

plt.plot(epoch_list, loss_list)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

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