Pytorch deep learning practice lesson 8 importing data

b Station Liu Er video , Address ：

https://www.bilibili.com/video/BV1Y7411d7Ys?p=9&vd_source=79d752a233297190ff0b01ca81ccd878

Code （ Homework in class ） 

Or the binary classification of diabetes in last class , Four step construction

import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

#-------------------------------------------step1  prepare data----------------------------------------
class Data(Dataset): # Construct your own class , Inherited from Dataset class 
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
                        # File path     The separator in the data     data type 
        self.len = xy.shape[0] # Return several rows and columns （ matrix ）
        self.x_data = torch.from_numpy(xy[:, :-1])# Take each line , Get to the last column 
        self.y_data = torch.from_numpy(xy[:, [-1]])# Take each line , Take the last column 

    def __getitem__(self, item): # Get a line of elements 
        return self.x_data[item], self.y_data[item]

    def __len__(self):
        return self.len

dataset = Data('diabetes.csv')
dataloader = DataLoader(dataset=dataset, batch_size=32, shuffle=True)

#------------------------------------------setp2 design model--------------------------------------------

class Modle(torch.nn.Module):
    def __init__(self):
        super(Modle, 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()

    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 = Modle()

#---------------------------------------step3    constuct loss and optimizer-----------------------------

criteration = torch.nn.BCELoss(reduction='mean')#mean Calculating mean 
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) # To update is model Parameters in ,  Learning rate 

#---------------------------------------step4 traning cycle------------------------------------------

if __name__ == '__main__': # I want to write this 

    loss_lst = []

    for epoch in range(1000):# Outer layer epoch It is a training that all data sets have been traversed 
        sum = 0#
        for i, data in enumerate(dataloader, 0):# One at a time batch, From a part of the whole data set 
            inputs, lables = data#x y
            y_pred = model(inputs)
            loss = criteration(y_pred, lables)
            sum += loss.item()

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

        loss_lst.append(sum / dataloader.batch_size)
    # visualization 
    num_lst = [i for i in range(len(loss_lst))]
    plt.plot(num_lst, loss_lst)
    plt.xlabel("epoch")
    plt.ylabel("loss")
    plt.show()

Running results

MINIST Data import