1.Mnist分类任务

网络基本构建与训练方法，常用函数解析
torch.nn.functional模块
nn.Module模块

学习方法：边用边查，多打印，duo'gua

使用jupyter的优点，可以打印出每一个步骤。

2.读取数据集

自动下载

%matplotlib inline
#查看本机torch的版本
import torch
print(torch.__version__)#打印torch的版本

加载并读取数据集

from pathlib import Path
import requests

DATA_PATH = Path("data")
PATH = DATA_PATH / "mnist"

PATH.mkdir(parents=True, exist_ok=True)

URL = "http://deeplearning.net/data/mnist/"
FILENAME = "mnist.pkl.gz"

if not (PATH / FILENAME).exists():
        content = requests.get(URL + FILENAME).content
        (PATH / FILENAME).open("wb").write(content)


import pickle
import gzip

with gzip.open((PATH / FILENAME).as_posix(), "rb") as f:
        ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1")

观察数据的结构

784是mnist数据集每个样本的像素点个数

print(x_train[0].shape)
print(x_train.shape)
y_train

显示一个记录的灰度图

from matplotlib import pyplot
import numpy as np

pyplot.imshow(x_train[0].reshape((28, 28)), cmap="gray")
print(x_train.shape)

numpy和torch的区别：
torch->gpu->tensor
numpy->cpu->ndarray

数据转化：

import torch

x_train, y_train, x_valid, y_valid = map(
    torch.tensor, (x_train, y_train, x_valid, y_valid)
)
n, c = x_train.shape
x_train, x_train.shape, y_train.min(), y_train.max()
print(x_train, y_train)
print(x_train.shape)
print(y_train.min(), y_train.max())

3 torch.nn.functional 很多层和函数在这里都会见到

torch.nn.functional中有很多功能，后续会常用的。那什么时候使用nn.Module，什么时候使用nn.functional呢？一般情况下，如果模型有可学习的参数，最好用nn.Module，其他情nn.functional相对更简单一些

import torch.nn.functional as F

loss_func = F.cross_entropy

def model(xb):
    return xb.mm(weights) + bias


bs = 64
xb = x_train[0:bs]  # a mini-batch from x
yb = y_train[0:bs]
weights = torch.randn([784, 10], dtype = torch.float,  requires_grad = True) 
#线性代数的相关知识，weights与输入想乘之后，需要输出的格式为10分类，所以的weights的矩阵为(784,10)
bs = 64
bias = torch.zeros(10, requires_grad=True)#偏执的设置，常数值作为初始化，因为这个东西对模型的影###响不是很大。

print(loss_func(model(xb), yb))：#计算真实值和预测值之间的误差

4 创建一个model来更简化代码

必须继承nn.Module且在其构造函数中需调用nn.Module的构造函数
无需写反向传播函数，nn.Module能够利用autograd自动实现反向传播
Module中的可学习参数可以通过named_parameters()或者parameters()返回迭代器

from torch import nn

class Mnist_NN(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden1 = nn.Linear(784, 128)
        self.hidden2 = nn.Linear(128, 256)
        self.out  = nn.Linear(256, 10)
   #定义前向传播，torch有一个优点：前向传播自己定义，反向传播自动实现。
    def forward(self, x):
        x = F.relu(self.hidden1(x))
        x = F.relu(self.hidden2(x))
        x = self.out(x)
        return x

net = Mnist_NN()
print(net)

可以打印我们定义好名字里的权重和偏置项

for name, parameter in net.named_parameters():
    print(name, parameter,parameter.size())

5 使用TensorDataset和DataLoader来简化

from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader

train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)#shuffle=True：洗牌的操作。
valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs * 2)

def get_data(train_ds, valid_ds, bs):
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs * 2),
    )