pytorch实现mnist手写彩色数字识别

前言

环境:

  •  语言环境:Python3.6
  • 编译器:jupyter lab
  • 深度学习环境:pytorch1.10

 要求:

  • 学习如何编写一个完整的深度学习程序(✔)
  • 手动推导卷积层与池化层的计算过程(✔)

一 前期工作

环境:python3.6,1080ti,pytorch1.10(实验室服务器的环境)

1.设置GPU或者cpu

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
device

2.导入数据

train_ds = torchvision.datasets.MNIST('data', 
 train=True, 
 transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
 download=True)
 
test_ds = torchvision.datasets.MNIST('data', 
 train=False, 
 transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
 download=True)

二 数据预处理

1.加载数据

设置数据尺寸

batch_size = 32

设置dataset

train_dl = torch.utils.data.DataLoader(train_ds, 
 batch_size=batch_size, 
 shuffle=True)
 
test_dl = torch.utils.data.DataLoader(test_ds, 
 batch_size=batch_size)

2.可视化数据

打印部分图片:

import numpy as np
 
 # 指定图片大小,图像大小为20宽、5高的绘图(单位为英寸inch)
plt.figure(figsize=(20, 5)) 
for i, imgs in enumerate(imgs[:20]):
 # 维度缩减
 npimg = imgs.numpy().transpose((1, 2, 0))
 # 将整个figure分成2行10列,绘制第i+1个子图。
 plt.subplot(2, 10, i+1)
 plt.imshow(npimg, cmap=plt.cm.binary)
 plt.axis('off')

3.再次检查数据

输出数据的尺寸:

# 取一个批次查看数据格式
# 数据的shape为:[batch_size, channel, height, weight]
# 其中batch_size为自己设定,channel,height和weight分别是图片的通道数,高度和宽度。
imgs, labels = next(iter(train_dl))
imgs.shape

三 搭建网络

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential,ReLU
 
num_classes = 10 
 
class Model(nn.Module):
 def __init__(self):
 super(Model,self).__init__()
 # 卷积层
 self.layers = Sequential(
 # 第一层
 Conv2d(3, 64, kernel_size=3),
 MaxPool2d(2),
 ReLU(),
 # 第二层
 Conv2d(64, 64, kernel_size=3),
 MaxPool2d(2),
 ReLU(),
 Conv2d(64, 128, kernel_size=3),
 MaxPool2d(2),
 ReLU(),
 Flatten(),
 Linear(512, 256,bias=True),
 ReLU(),
 Linear(256, 64,bias=True),
 ReLU(),
 Linear(64, num_classes,bias=True)
 )
 def forward(self, x):
 x = self.layers(x)
 return x

 打印网络结构:

vgg16网络搭建:未修改尺寸

from torch import nn
 
vgg16=torchvision.models.vgg16(pretrained=True)#经过训练的
class Model(nn.Module):
 def __init__(self):
 super(Model,self).__init__()
 # 卷积层
 self.layers = Sequential(
 vgg16
 )
 def forward(self, x):
 x = self.layers(x)
 return x

 vgg16网络搭建:修改尺寸

四 训练模型

1.设置学习率

loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-2 # 学习率
opt = torch.optim.SGD(model.parameters(),lr=learn_rate)

2.模型训练

训练函数:

# 训练循环
def train(dataloader, model, loss_fn, optimizer):
 size = len(dataloader.dataset) # 训练集的大小,一共60000张图片
 num_batches = len(dataloader) # 批次数目,1875(60000/32)
 
 train_loss, train_acc = 0, 0 # 初始化训练损失和正确率

 for X, y in dataloader: # 获取图片及其标签
 X, y = X.to(device), y.to(device)

 # 计算预测误差
 pred = model(X) # 网络输出
 loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距,targets为真实值,计算二者差值即为损失

 # 反向传播
 optimizer.zero_grad() # grad属性归零
 loss.backward() # 反向传播
 optimizer.step() # 每一步自动更新

 # 记录acc与loss
 train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
 train_loss += loss.item()

 train_acc /= size
 train_loss /= num_batches
 
 return train_acc, train_loss

测试函数 :

def test (dataloader, model, loss_fn):
 size = len(dataloader.dataset) # 测试集的大小,一共10000张图片
 num_batches = len(dataloader) # 批次数目,313(10000/32=312.5,向上取整)
 test_loss, test_acc = 0, 0

 # 当不进行训练时,停止梯度更新,节省计算内存消耗
 with torch.no_grad():
 for imgs, target in dataloader:
 imgs, target = imgs.to(device), target.to(device)

 # 计算loss
 target_pred = model(imgs)
 loss = loss_fn(target_pred, target)

 test_loss += loss.item()
 test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
 
 test_acc /= size
 test_loss /= num_batches
 
 return test_acc, test_loss

具体训练代码 :

epochs = 20
train_loss = []
train_acc = []
test_loss = []
test_acc = []
 
for epoch in range(epochs):
 model.train()
 epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)

 model.eval()
 epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)

 train_acc.append(epoch_train_acc)
 train_loss.append(epoch_train_loss)
 test_acc.append(epoch_test_acc)
 test_loss.append(epoch_test_loss)

 template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%,Test_loss:{:.3f}')
 print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')

五 模型评估

1.Loss和Accuracy图

 2.总结

  • 1.本文与上篇文章区别在于灰色图像和彩色图像通道数一个为1,一个为3.所以这里的卷积输入都是3.
  • 2.关于各层计算这里简单说一下,我们以范文举例:

卷积层:32->30因为((32-3)/1)+1=30

池化池:30->15因为30÷2=15

具体计算可以参考我题目开头的文章,这里不在赘述

我们可以看到本次训练效果不好,那我们可以利用经典网络vgg16进行修改,准确率提高到了百分之88了。

其代码如上:

作者:重邮研究森原文地址:https://blog.csdn.net/m0_60524373/article/details/127113923

%s 个评论

要回复文章请先登录注册