Pytorch实现mnist

读取Mnist数据集

from pathlib import Path   # python3中 取代os.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():    #mnist文件未下载时，requests去get URL下载文件
        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")
```![在这里插入图片描述](https://www.icode9.com/i/ll/?i=20210304210945960.png?,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTA3NDU2OA==,size_16,color_FFFFFF,t_70#pic_center)
#### 注意数据需转换成tensor才能参与后续建模训练

```python
import torch

x_train, y_train, x_valid, y_valid = map(
    torch.tensor, (x_train, y_train, x_valid, y_valid)  # map(function,iter)   iter中的元素调用function
)
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())

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，其他情况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) 
bs = 64
bias = torch.zeros(10, requires_grad=True)

print(loss_func(model(xb), yb))

创建一个model来更简化代码

(1)必须继承nn.Module且在其构造函数中需调用nn.Module的构造函数
(2)无需写反向传播函数，nn.Module能够利用autograd自动实现反向传播
(3)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)

    def forward(self, x):
        x = F.relu(self.hidden1(x))
        x = F.relu(self.hidden2(x))
        x = self.out(x)
        return x
        

使用TensorDataset和DataLoader来简化

这里TensorDataset将x_train和y_train进行绑定，DataLoader类似python的生成器，在做数据增强的时候使用的。

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

train_ds = TensorDataset(x_train, y_train)  #类似python中zip   
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)   #一般与数据增强搭配使用，类似python中生成器

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),
    )

一般在训练模型时加上model.train()，这样会正常使用Batch Normalization和 Dropout

测试的时候一般选择model.eval()，这样就不会使用Batch Normalization和 Dropout

import numpy as np

def fit(steps, model, loss_func, opt, train_dl, valid_dl):
    for step in range(steps):
        model.train()  # 在训练的时候调用，因为train的时候用的数据都是不同的
        for xb, yb in train_dl:
            loss_batch(model, loss_func, xb, yb, opt)

        model.eval()#  在预测的时候调用，因为val中 用的数据集是整体的
        with torch.no_grad():
            losses, nums = zip(
                *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]
            )
        val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
        print('当前step:'+str(step), '验证集损失：'+str(val_loss))

from torch import optim
def get_model():
    model = Mnist_NN()
    return model, optim.SGD(model.parameters(), lr=0.001)

def loss_batch(model, loss_func, xb, yb, opt=None):
    loss = loss_func(model(xb), yb)

    if opt is not None:
        loss.backward()
        opt.step()
        opt.zero_grad()

    return loss.item(), len(xb)

三行搞定测试！！！

train_dl, valid_dl = get_data(train_ds, valid_ds, bs)  # 获取batch数据，跟python生成器类似
model, opt = get_model()# 获得model，以及优化器
fit(100, model, loss_func, opt, train_dl, valid_dl)# 将参数传入fit