【神经网络手写数字识别-最全源码（pytorch）】

Torch安装的方法

学习方法

• 1.边用边学，torch只是一个工具，真正用，查的过程才是学习的过程
• 2.直接就上案例就行，先来跑，遇到什么来解决什么

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

读取Mnist数据集

• 会自动进行下载

# 查看自己的torch的版本
import torch
print(torch.__version__)

%matplotlib inline

# 前两步，不用管是在网上下载数据，后续的我们都是在本地的数据进行操作
from pathlib import Path
import requestsDATA_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 gzipwith gzip.open((PATH / FILENAME).as_posix(), "rb") as f:((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1")

784是mnist数据集每个样本的像素点个数

from matplotlib import pyplot
import numpy as nppyplot.imshow(x_train[0].reshape((28, 28)), cmap="gray")
print(x_train.shape)

全连接神经网络的结构
注意数据需转换成tensor才能参与后续建模训练

import torchx_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())

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 Floss_func = F.cross_entropydef 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

• 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 nnclass Mnist_NN(nn.Module):# 构造函数def __init__(self):super().__init__()self.hidden1 = nn.Linear(784, 128)self.hidden2 = nn.Linear(128, 256)self.out  = nn.Linear(256, 10)self.dropout = nn.Dropout(0.5)#前向传播自己定义，反向传播是自动进行的def forward(self, x):x = F.relu(self.hidden1(x))x = self.dropout(x)x = F.relu(self.hidden2(x))x = self.dropout(x)#x = F.relu(self.hidden3(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())

使用TensorDataset和DataLoader来简化

from torch.utils.data import TensorDataset
from torch.utils.data import DataLoadertrain_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)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 npdef fit(steps, model, loss_func, opt, train_dl, valid_dl):for step in range(steps):model.train()  # 训练的时候需要更新权重参数for xb, yb in train_dl:loss_batch(model, loss_func, xb, yb, opt)model.eval() # 验证的时候不需要更新权重参数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))

zip的用法

a = [1,2,3]
b = [4,5,6]
zipped = zip(a,b)
print(list(zipped))
a2,b2 = zip(*zip(a,b))
print(a2)
print(b2)

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！

train_dl,valid_dl = get_data(train_ds, valid_ds, bs)
model, opt = get_model()
fit(100, model, loss_func, opt, train_dl, valid_dl)

correct = 0
total = 0
for xb,yb in valid_dl:outputs = model(xb)_,predicted = torch.max(outputs.data,1)total += yb.size(0)correct += (predicted == yb).sum().item()
print(f"Accuracy of the network the 10000 test imgaes {100*correct/total}")

![在这里插入图片描述](https://img-blog.csdnimg.cn/89e5e749b680426c9700aac9f93bf76a.png

后期有兴趣的小伙伴们可以比较SGD和Adam两种优化器，哪个效果更好一点

-SGD 20epoch 85%
-Adam 20epoch 85%