首先,导入需要用到的包。
            
            
              Python
              
              
            
          
          import numpy as np
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import mnist
from torch import nn
from torch.autograd import Variable
import matplotlib.pyplot as plt然后构建MNIST数据集数据转换函数,将图像转换为Pytorch能处理的张量。
            
            
              Python
              
              
            
          
          def data_transform(img):
    img= np.array(img, dtype="float") / 255
    img= (img- 0.5) / 0.5
    img= img.reshape((-1))
    img= torch.Tensor(img)
    return img通过Python下载MNIST数据,构建训练集与测试集,此处"./data2"为数据的存放位置。
            
            
              Python
              
              
            
          
          train_dataset = mnist.MNIST("./data2", train=True, transform=data_transform, download=True)
test_dataset = mnist.MNIST("./data2", train=False, transform=data_transform, download=True)构建神经网络。由于所采用的MNIST数据集一张图像的大小为28*28,所以设置输入数据时设置28*28=784个输入值,一共有0-9十个数字,所以最终的输出为10个输出值。通过ReLU函数设置如下。
            
            
              Python
              
              
            
          
          net = nn.Sequential(
    nn.Linear(784, 400),
    nn.ReLU(),
    nn.Linear(400, 200),
    nn.ReLU(),
    nn.Linear(200, 100),
    nn.ReLU(),
    nn.Linear(100, 10)
)然后,构建损失函数与优化器。这里使用交叉熵设置损失函数。
            
            
              Python
              
              
            
          
          criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), 1e-1)创建四个数组用于存放每次处理后的损失值与准确度,以便于图像显示。
            
            
              Python
              
              
            
          
          losses = []
acces = []
eval_losses = []
eval_acces = []开始训练模型并测试。设置训练准确度与训练损失参数,通过循环遍历每一批数据。在处理一批数据时,首先将图像与标签数据类型转换为张量,然后通过建立的神经网络训练数据并通过损失函数获取损失。接着,将参数的梯度归零,对损失求导并更新参数。其次,将该批次的损失汇总并计算准确度。最后,在完成内循环后将损失与准确度添加到相关数组中用于图像显示。而测试过程与训练过程类似,只是没有求梯度的过程。
            
            
              Python
              
              
            
          
          for e in range(20):
    train_loss = 0
    train_acc = 0
    for im, label in train_dataset:
        im = Variable(im)
        label = Variable(label)
 
        out = net(im)
        loss = criterion(out, label)
 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        train_loss += loss
        _, pred = out.max(1)
        num_correct = (pred == label).sum()
        acc = num_correct / im.shape[0]
        train_acc += acc
 
    losses.append(train_loss / len(train_dataset))
    acces.append(train_acc / len(train_dataset))
 
    eval_loss = 0
    eval_acc = 0
    for im, label in test_dataset:
        im = Variable(im)
        label = Variable(label)
        out = net(im)
        loss = criterion(out, label)
        eval_loss += loss
        _, pred = out.max(1)
        num_correct = (pred == label).sum()
        acc = num_correct / im.shape[0]
        eval_acc += acc
    eval_losses.append(eval_loss / len(test_dataset))
    eval_acces.append(eval_acc / len(test_dataset))
print("epoch:{},Train Loss:{:.6f},Train acc:{:.6f},Eval Loss:{:.6f},Eval acc:{:.6f}".format(e + 1, train_loss / len(train_dataset),train_acc / len(train_dataset),eval_loss / len(test_dataset),eval_acc / len(test_dataset)))接下来,将数组中的数据类型从张量转换为可以处理的numpy数据格式。
            
            
              Python
              
              
            
          
          losses = [item.detach().numpy() for item in losses]
acces = [item.detach().numpy() for item in acces]
eval_acces = [item.detach().numpy() for item in eval_acces]
eval_losses = [item.detach().numpy() for item in eval_losses]最后构建绘图函数并完成绘图。
            
            
              Python
              
              
            
          
          def make_plt(title, list):
    plt.title(title)
    plt.plot(np.arange(len(list)), list)
    plt.show()
 
make_plt("train loss", losses)
make_plt("tain acc", acces)
make_plt("eval loss", eval_losses)
make_plt("eval acc", eval_acces)最终得到以下四个图像与输出。
            
            
              Console
              
              
            
          
          ......
epoch:14,Train Loss:0.019388,Train acc:0.993687,Eval Loss:0.073528,Eval acc:0.980716
epoch:15,Train Loss:0.018121,Train acc:0.994170,Eval Loss:0.075069,Eval acc:0.979727
epoch:16,Train Loss:0.013503,Train acc:0.995436,Eval Loss:0.078196,Eval acc:0.980617
epoch:17,Train Loss:0.012675,Train acc:0.995919,Eval Loss:0.070875,Eval acc:0.981309
epoch:18,Train Loss:0.014213,Train acc:0.995769,Eval Loss:0.076365,Eval acc:0.981408
epoch:19,Train Loss:0.011078,Train acc:0.996335,Eval Loss:0.068176,Eval acc:0.983683
epoch:20,Train Loss:0.006785,Train acc:0.998118,Eval Loss:0.114188,Eval acc:0.974684