Pytorch框架实现mnist手写库识别（与tensorflow对比）
前言最近在学习过程中需要用到pytorch框架，简单学习了一下，写了一个简单的案例，记录一下pytorch中搭建一个识别网络基础的东西。对应一位博主写的tensorflow的识别mnist数据集，将其改为pytorch框架，也可以详细看到两个框架大体的区别。
Tensorflow版本转载来源（CSDN博主「兔八哥1024」）：https://www.jb51.net/article/191157.htm
Pytorch实战mnist手写数字识别
#需要导入的包
import torch
import torch.nn as nn#用于构建网络层
import torch.optim as optim#导入优化器
from torch.utils.data import DataLoader#加载数据集的迭代器
from torchvision import datasets, transforms#用于加载mnsit数据集
#下载数据集
train_set = datasets.MNIST('./data', train=True, download=True,transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1037,), (0.3081,))
]))
test_set = datasets.MNIST('./data', train=False, download=True,transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1037,), (0.3081,))
]))
#构建网络（网络结构对应tensorflow的那一篇文章）
class Net(nn.Module):
def __init__(self, num_classes=10):
super(Net, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=2,stride=2),
nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=2,stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(3136, 7*7*64),
nn.Linear(3136, num_classes),
)
def forward(self,x):
x = self.features(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
net=Net()
net.cuda()#用GPU运行
#计算误差，使用adam优化器优化误差
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), 1e-2)
train_data = DataLoader(train_set, batch_size=128, shuffle=True)
test_data = DataLoader(test_set, batch_size=128, shuffle=False)
#训练过程
for epoch in range(1):
net.train() ##在进行训练时加上train()，测试时加上eval()
batch = 0
for batch_images, batch_labels in train_data:
average_loss = 0
train_acc = 0
##在pytorch0.4之后将Variable 与tensor进行合并，所以这里不需要进行Variable封装
if torch.cuda.is_available():
batch_images, batch_labels = batch_images.cuda(),batch_labels.cuda()
#前向传播
out = net(batch_images)
loss = criterion(out,batch_labels)
average_loss = loss
prediction = torch.max(out,1)[1]
# print(prediction)
train_correct = (prediction == batch_labels).sum()
##这里得到的train_correct是一个longtensor型，需要转换为float
train_acc = (train_correct.float()) / 128
optimizer.zero_grad() #清空梯度信息，否则在每次进行反向传播时都会累加
loss.backward() #loss反向传播
optimizer.step() ##梯度更新
batch+=1
print("Epoch: %d/%d || batch:%d/%d average_loss: %.3f || train_acc: %.2f"
%(epoch, 20, batch, float(int(50000/128)), average_loss, train_acc))
# 在测试集上检验效果
net.eval() # 将模型改为预测模式
for idx,(im1, label1) in enumerate(test_data):
if torch.cuda.is_available():
im, label = im1.cuda(),label1.cuda()
out = net(im)
loss = criterion(out, label)
eval_loss = loss
pred = torch.max(out,1)[1]
num_correct = (pred == label).sum()
acc = (num_correct.float())/ 128
eval_acc = acc
print('EVA_Batch:{}, Eval Loss: {:.6f}, Eval Acc: {:.6f}'
.format(idx,eval_loss , eval_acc))
运行结果：
到此这篇关于Pytorch框架实现mnist手写库识别（与tensorflow对比）的文章就介绍到这了,更多相关Pytorch框架实现mnist手写库识别（与tensorflow对比）内容请搜索软件开发网以前的文章或继续浏览下面的相关文章希望大家以后多多支持软件开发网！
您可能感兴趣的文章:pytorch实现MNIST手写体识别使用PyTorch实现MNIST手写体识别代码pytorch 利用lstm做mnist手写数字识别分类的实例Pytorch实现的手写数字mnist识别功能完整示例详解PyTorch手写数字识别(MNIST数据集)PyTorch CNN实战之MNIST手写数字识别示例
