pytorch实现手写字体识别(Mnist数据集)

1.加载数据集

一个快速体验学习的小tip在google的云jupyter上做实验，速度快的飞起。

import torch
from torch.nn import Linear, ReLU
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
from torchvision import datasets,transforms
from torch.autograd import Variable
import torch.optim as optim
transformation = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])
#data/表示下载数据集到的目录，transformation表示对数据集进行的相关处理
train_dataset = datasets.MNIST('data/',train=True, transform=transformation,download=True)
test_dataset = datasets.MNIST('data/', train=False, transform=transformation,download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=True)

2.显示图片

#将数据加载为一个迭代器，读取其中一个批次
simple_data = next(iter(train_loader))
import matplotlib.pyplot as plt
def plot_img(image):
    image = image.numpy()[0]
    mean = 0.1307
    std = 0.3081
    image = ((mean * image) + std)
    plt.imshow(image,cmap='gray')


plot_img(simple_data[0][3])

 3.构造网络模型

import torch.nn.functional as F
class Mnist_Net(nn.Module):
  def __init__(self):
    super(Mnist_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) #320是根据卷积计算而来4*4*20(4*4表示大小,20表示通道数)
    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,p=0.1, training=self.training)
    x = self.fc2(x)
    return F.log_softmax(x,d