pytorch入门（三）——minist手写体数字识别案例

from torch.nn import init
import torch.nn as nn
import math
import time
import torch
from torchvision import transforms
from sklearn.model_selection import train_test_split
import numpy as np
import torch.nn.functional as F
import pandas as pd
import torchvision

第一步：定义好网络

#网络1
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5) 
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50) #4*4*20
        self.fc2 = nn.Linear(50, 10)
    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x,dim=1)

第二步：定义好参数

#定义批次大小（每次传递512条记录给模型训练，充分利用矩阵计算的并行性能）
batch_size=512
#定义训练epoch
epoch=20
#定义模型
model=Net()
#初始化权重参数
for layer in model.modules():
    if isinstance(layer,nn.Linear):
        init.xavier_uniform_(layer.weight)

#定义优化器
optimizer=torch.optim.Adam(model.parameters(),0.001)
#定义损失函数
criterion=nn.CrossEntropyLoss()#创建交叉熵函数
#定义损失函数数组，用于可视化训练过程
loss_holder=[]
#损失值设为无限大，每次迭代若损失值比loss_value小则保存模型，并将最新的损失值赋给loss_value
loss_value=np.inf
step=0

第三步：加载数据集
minist数据集训练集有60000张照片，测试集有10000张照片。
batch_size=512,所以有118组batch_size

train_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST(\'./data/\', train=True, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
  batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
  torchvision.datasets.MNIST(\'./data/\', train=False, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.1307,), (0.3081,))
                             ])),
  batch_size=batch_size, shuffle=False)

第四步：训练

for i in range(epoch):
    for batch_idx, (data,label) in enumerate(train_loader):
        #输出值
        outputs = model(data)
        #损失值
        loss = criterion(outputs, label)
        #反向传播，将所有梯度的参数清0，否则该步的梯度会和前面已经计算的梯度累乘
        optimizer.zero_grad()  
        loss.backward() 
        #更新参数
        optimizer.step()
        #记录误差
        print(\'epoch{},Train loss{:.6f},Dealed/Records:{}/{}\'.format(i,loss/batch_size,(batch_idx+1)*batch_size,60000))
        if batch_idx%20==0:
            step+=1
            loss_holder.append([step,loss/batch_size])
        #模型性能有所提升则保存模型，并更新loss_value
        if batch_idx%20==0 and loss

import matplotlib.pyplot as plt
fig=plt.figure(figsize=(20,15))
#x轴斜体避免重叠
fig.autofmt_xdate()
loss_df=pd.DataFrame(loss_holder,columns=[\'time\',\'loss\'])
x_times=loss_df[\'time\'].values
plt.ylabel(\'loss\')
plt.xlabel(\'times\')
plt.plot(loss_df[\'loss\'].values)
plt.xticks([10,30,50,70,80,100,120,140,160,200])
plt.show()

#读取模型
model_path=\'model.ckpt\'
model=torch.load(model_path)
#转化为测试模式
model.eval()
for layer in model.modules():
    layer.requires_grad=False
    for batch_idx, (data,label) in enumerate(test_loader):
        #只进行前向传播，不进行反向传播
        outputs = model(data)
        loss = criterion(outputs, label)
        print(\'Test loss{:.6f},Dealed/Records:{}/{}\'.format(loss/batch_size,(batch_idx+1)*batch_size,60000))