Day16 职训(机器学习与资料分析工程师培训班): MVC & MTV架构

上午: AIoT资料分析应用系统框架设计与实作

今天教学一些Django的一些格式用法，及MTV架构代表对应的python档案

from django.db import modelsfrom django.db.models.base import Model# Create your models here.class registration_info(models.Model):    uid = models.CharField(max_length=20, unique=True, verbose_name='使用者名称')    pwd = models.CharField(max_length=256)    email = models.EmailField()    signup_time = models.DateTimeField(auto_now_add=True)    last_login = models.DateTimeField(auto_now=True)    id_no = models.CharField(max_length=10)    cellphone = models.CharField(max_length=10)    address = models.TextField()    webpage = models.URLField()    dept = models.CharField(max_length=100)    st = models.BooleanField()    def __str__(self):        return self.email

下午: Pytorch与深度学习初探

今日延续上次内容，推演梯度下降法的数学公式，也教了一些pytorch的语法

## python native type to torch.tensor 资料格式转换a=3.0print(type(a))a=torch.tensor(a)print(type(a))a=a.item()print(type(a))## python numpy to torch.tensor 资料格式转换print("============np array===")a=np.array([2,2])print(type(a))a=torch.from_numpy(a)print(type(a))a=a.numpy()print(type(a))

import numpy as npimport torchimport torch.nn as nnimport matplotlib.pyplot as pltimport numpy as np### step 1 load data and plot #############torch.manual_seed(2)X=torch.randn(100,1)*10Y= X+torch.randn(100,1)*3plt.scatter(X,Y)xm=np.array([-30,30])w=2b=10ym=w*xm+bplt.plot(xm,ym,'r')