动手学深度学习PyTorch版---day01

 

目录

Day01

1.线性回归

方法1---手动实现

方法2---pytorch实现

2.Softmax

1.相关概念

2.交叉熵损失函数

3.从零实现softmax回归

4.pytorch实现softmax回归

3.多层感知机

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Day01

线性回归，softmax回归与分类模型，多层感知机

参考：https://tangshusen.me/Dive-into-DL-PyTorch/#/

《动手学深度学习》组队学习 学员学习手册  https://shimo.im/docs/pdr3wkyHKrxJYdyT/read

1.线性回归

方法1---手动实现

import torch
#import IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random

num_inputs = 2
num_examples = 1000

true_w = [2, -3,4]
true_b = 4.2

features = torch.randn(num_examples,num_inputs,
                       dtype=torch.float32) #标准正态分布
labels = true_w[0]*features[:,0]+true_w[1]*features[:,1]+true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()),
                      dtype=torch.float32)
# plt.figure()
# #画散点图
# plt.scatter(features[:,1].numpy(),labels.numpy(),1)
# plt.show()

#读取数据
def data_iter(batch_size,features,labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        j = torch.LongTensor(indices[i:min(i+batch_size, num_examples)])
        yield features.index_select(0, j), labels.index_select(0, j)

batch_size = 10
for X,y in data_iter(batch_size, features, labels):
    print(X,'\n', y)
    break

# 初始化模型参数
w = torch.tensor(np.random.normal(0,0.01,(num_inputs,1)),dtype=torch.float32)
b = torch.zeros(1,dtype=torch.float32)

w.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True)

#定义模型
de